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/dev/null and b/.doctrees/tutorials/logging.doctree differ diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 000000000..e69de29bb diff --git a/_images/ReAct.jpg b/_images/ReAct.jpg new file mode 100644 index 000000000..b062d2582 Binary files /dev/null and b/_images/ReAct.jpg differ diff --git a/_sources/apis/components/agent_prompt.rst.txt b/_sources/apis/components/agent_prompt.rst.txt new file mode 100644 index 000000000..5432c9121 --- /dev/null +++ b/_sources/apis/components/agent_prompt.rst.txt @@ -0,0 +1,71 @@ +.. _agent_prompt: + +.. _DEFAULT_REACT_AGENT_SYSTEM_PROMPT: + +DEFAULT_REACT_AGENT_SYSTEM_PROMPT +---------------------------------- + +This is the default prompt used by the system to interact with the agents. It contains the following structure: + +.. code-block:: python + + DEFAULT_REACT_AGENT_SYSTEM_PROMPT = r""" + {# role/task description #} + You task is to answer user's query with minimum steps and maximum accuracy using the tools provided. + {# REACT instructions #} + Each step you will read the previous Thought, Action, and Observation(execution result of the action)steps and then provide the next Thought and Action. + + You only have access to the following tools: + {# tools #} + {% for tool in tools %} + {{ loop.index }}. ToolName: {{ tool.metadata.name }} + Tool Description: {{ tool.metadata.description }} + Tool Parameters: {{ tool.metadata.fn_schema_str }} {#tool args can be misleading, especially if we already have type hints and docstring in the function#} + {% endfor %} + {# output is always more robust to use json than string #} + --- + Your output must be in valid JSON format(raw Python string format) with two keys: + { + "thought": "", + "action": "ToolName(, )" + } + - Must double quote the JSON str. + - Inside of the JSON str, Must use escape double quote and escape backslash for string. + For example: + "action": "finish(\"John's.\")" + --- + {# Specifications TODO: preference between the usage of llm tool vs the other tool #} + Process: + - Step 1: Read the user query and potentially divide it into subqueries. And get started with the first subquery. + - Call one available tool at a time to solve each subquery/subquestion. \ + - At step 'finish', join all subqueries answers and finish the task. + Remember: + - Action must call one of the above tools with Took Name. It can not be empty. + - Read the Tool Description and ensure your args and kwarg follow what each tool expects in types. e.g. (a=1, b=2) if it is keyword argument or (1, 2) if it is positional. + - You will always end with 'finish' action to finish the task. The answer can be the final answer or failure message. + - When the initial query is simple, use minimum steps to answer the query. + {#Examples can be here#} + {# Check if there are any examples #} + {% if examples %} + + {% for example in examples %} + {{ example }} + {% endfor %} + + {% endif %} + <> + ----------------- + {# History #} + {% for history in step_history %} + Step {{history.step}}: + { + "thought": "{{history.thought}}", + "action": "{{history.action}}", + } + "observation": "{{history.observation}}" + {% endfor %} + {% if input_str %} + User query: + {{ input_str }} + {% endif %} + """ \ No newline at end of file diff --git a/_sources/apis/components/index.rst.txt b/_sources/apis/components/index.rst.txt new file mode 100644 index 000000000..3a3116172 --- /dev/null +++ b/_sources/apis/components/index.rst.txt @@ -0,0 +1,68 @@ +Components +============== + +The components section of the LightRAG API documentation outlines the detailed specifications and functionalities of various API components. Each component plays a crucial role in the LightRAG framework, providing specialized capabilities and interactions. + +Overview +---------- +.. autosummary:: + + components.agent + components.model_client + components.data_process + + .. components.reasoning + + components.retriever + components.output_parsers + + +Output Parsers +-------------- +.. toctree:: + :maxdepth: 1 + + components.output_parsers + +Agents +------ +.. toctree:: + :maxdepth: 1 + + components.agent + +Model Clients +----------------- +.. toctree:: + :maxdepth: 1 + + components.model_client + +Data Process +---------------- +.. toctree:: + :maxdepth: 1 + + components.data_process + +.. Embedders +.. --------- +.. .. toctree:: +.. :maxdepth: 1 + +.. components.embedder + +.. Reasoners +.. --------- +.. .. toctree:: +.. :maxdepth: 1 + +.. components.reasoning + +Retrievers +---------- +.. toctree:: + :maxdepth: 1 + + components.retriever + diff --git a/_sources/apis/core/index.rst.txt b/_sources/apis/core/index.rst.txt new file mode 100644 index 000000000..dc5dc1945 --- /dev/null +++ b/_sources/apis/core/index.rst.txt @@ -0,0 +1,99 @@ +Core +=================== + +The core section of the LightRAG API documentation provides detailed information about the foundational components of the LightRAG system. These components are essential for the basic operations and serve as the building blocks for higher-level functionalities. + +Overview +---------- +.. autosummary:: + + core.base_data_class + core.component + core.db + core.default_prompt_template + core.embedder + core.functional + core.generator + core.memory + core.model_client + core.parameter + core.prompt_builder + core.retriever + core.string_parser + core.tokenizer + core.func_tool + core.tool_manager + core.types + + +Model Client +--------------- +.. toctree:: + :maxdepth: 1 + + core.model_client + +Component +-------------- +.. toctree:: + :maxdepth: 1 + + core.component + +Data Handling +------------- +.. toctree:: + :maxdepth: 1 + + core.base_data_class + core.types + + core.db + +Prompts and Templates +--------------------- +.. toctree:: + :maxdepth: 1 + + core.default_prompt_template + core.prompt_builder + +.. Document Processing +.. ------------------- +.. .. toctree:: +.. :maxdepth: 1 + + .. core.document_splitter + core.text_splitter + +Embedding and Retrieval +----------------------- +.. toctree:: + :maxdepth: 1 + + core.embedder + core.retriever + +Generation and Utilities +------------------------ +.. toctree:: + :maxdepth: 1 + + core.generator + core.functional + core.memory + +------------------------ +.. toctree:: + :maxdepth: 1 + + core.string_parser + core.tokenizer + core.func_tool + +Parameters +------------------------ +.. toctree:: + :maxdepth: 1 + + core.parameter diff --git a/_sources/apis/eval/index.rst.txt b/_sources/apis/eval/index.rst.txt new file mode 100644 index 000000000..966c01a10 --- /dev/null +++ b/_sources/apis/eval/index.rst.txt @@ -0,0 +1,21 @@ +Evaluation +============== + +Overview +---------- +.. autosummary:: + + eval.answer_match_acc + eval.retriever_recall + eval.retriever_relevance + eval.llm_as_judge + +Evaluator +---------- +.. toctree:: + :maxdepth: 1 + + eval.answer_match_acc + eval.retriever_recall + eval.retriever_relevance + eval.llm_as_judge diff --git a/_sources/apis/index.rst.txt b/_sources/apis/index.rst.txt new file mode 100644 index 000000000..6b4af1d24 --- /dev/null +++ b/_sources/apis/index.rst.txt @@ -0,0 +1,128 @@ +API Reference +============= + +Welcome to the LightRAG API reference! This section provides detailed documentation of the internal APIs that make up the LightRAG framework. Explore the APIs to understand how to effectively utilize and integrate LightRAG components into your projects. + + +Core +---------- + +The core section of the LightRAG API documentation provides detailed information about the foundational components of the LightRAG system. These components are essential for the basic operations and serve as the building blocks for higher-level functionalities. + +.. autosummary:: + + core.base_data_class + core.model_client + core.component + core.data_components + core.db + core.default_prompt_template + core.embedder + core.functional + core.generator + core.memory + core.parameter + core.prompt_builder + core.retriever + core.string_parser + core.tokenizer + core.func_tool + core.tool_manager + core.types + + +Components +----------- + +The components section of the LightRAG API documentation outlines the detailed specifications and functionalities of various API components. Each component plays a crucial role in the LightRAG framework, providing specialized capabilities and interactions. + +.. autosummary:: + + components.agent + components.model_client + componnets.data_process + .. components.reasoning + + components.retriever + components.output_parsers + + +Evaluation +---------- +.. autosummary:: + + eval.answer_match_acc + eval.retriever_recall + eval.retriever_relevance + eval.llm_as_judge + + +Optimizer +---------- +.. autosummary:: + :maxdepth: 2 + + optim.optimizer + optim.sampler + optim.few_shot_optimizer + optim.llm_augment + optim.llm_optimizer + + +Tracing +---------- +.. autosummary:: + + tracing.decorators + tracing.generator_state_logger + tracing.generator_call_logger + + +Utils +---------- +.. autosummary:: + + utils.logger + utils.serialization + utils.config + utils.registry + utils.setup_env + + +.. toctree:: + :maxdepth: 2 + :hidden: + + core/index + + +.. toctree:: + :maxdepth: 2 + :hidden: + + components/index + + +.. toctree:: + :maxdepth: 2 + :hidden: + + optim/index + +.. toctree:: + :maxdepth: 2 + :hidden: + + tracing/index + +.. toctree:: + :maxdepth: 2 + :hidden: + + eval/index + +.. toctree:: + :maxdepth: 2 + :hidden: + + utils/index diff --git a/_sources/apis/optim/index.rst.txt b/_sources/apis/optim/index.rst.txt new file mode 100644 index 000000000..748943494 --- /dev/null +++ b/_sources/apis/optim/index.rst.txt @@ -0,0 +1,25 @@ +.. Optimizer +.. ============== + +Optimizer +---------- +.. autosummary:: + :maxdepth: 2 + + optim.optimizer + optim.sampler + optim.few_shot_optimizer + optim.llm_augment + optim.llm_optimizer + +.. Optimizer +.. ---------- +.. toctree:: + :maxdepth: 1 + :hidden: + + optim.optimizer + optim.sampler + optim.few_shot_optimizer + optim.llm_augment + optim.llm_optimizer \ No newline at end of file diff --git a/_sources/apis/tracing/index.rst.txt b/_sources/apis/tracing/index.rst.txt new file mode 100644 index 000000000..66ffceefa --- /dev/null +++ b/_sources/apis/tracing/index.rst.txt @@ -0,0 +1,25 @@ +Tracing +============== + +Overview +---------- +.. autosummary:: + + tracing.decorators + tracing.generator_state_logger + tracing.generator_call_logger + +Decorators +-------------- +.. toctree:: + :maxdepth: 1 + + tracing.decorators + +Loggers +-------------- +.. toctree:: + :maxdepth: 1 + + tracing.generator_state_logger + tracing.generator_call_logger \ No newline at end of file diff --git a/_sources/apis/utils/index.rst.txt b/_sources/apis/utils/index.rst.txt new file mode 100644 index 000000000..4f13a5c5c --- /dev/null +++ b/_sources/apis/utils/index.rst.txt @@ -0,0 +1,34 @@ +Utils +============================= + +Overview +---------- +.. autosummary:: + + utils.logger + utils.serialization + utils.config + utils.registry + utils.setup_env + +Logger +-------- +.. toctree:: + :maxdepth: 1 + + utils.logger + +Serialization +---------------- +.. toctree:: + :maxdepth: 1 + + utils.serialization + +Setup_env +-------------- +.. toctree:: + :maxdepth: 1 + + utils.setup_env + diff --git a/_sources/contributor/contribute_to_document.rst.txt b/_sources/contributor/contribute_to_document.rst.txt new file mode 100644 index 000000000..2f33a24de --- /dev/null +++ b/_sources/contributor/contribute_to_document.rst.txt @@ -0,0 +1,115 @@ +Contribute to Document +=============================================== + +.. contents:: + :local: + :depth: 2 + +.. _Writing documentation: + +Writing Documentation +--------------------------- + +- **User-Facing Documentation**: Found on the main docs site. These include tutorials, guides, and usage documentation meant for end users. +- **Developer Documentation**: Located within the repository's READMEs and the ``docs/`` directory. These documents are more technical and intended for contributors and maintainers. + +This section is about user-facing documentation. + +LightRAG uses `Sphinx `_ for documentation, leveraging both `reStructuredText `_ and Sphinx's `autodoc `_ feature to pull docstrings from code and organize them through ``.rst`` files. Our documentation is split into: + +Souce Code Docstring Standard +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Sphinx automatically pulls docstrings from source code and uses them as the docs in API reference. For clarity and consistency, we have a standard for all the code contributors. + +Aligning with Pytorch, LightRAG uses the `Google style with Sphinx `_ for formatting docstrings `(detailed styles) `_, emphasizing **docstring** and **type control** to guarantee the document and code quality. + + +Setup & Build Documentation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**1. Clone the GitHub Project** + +.. code-block:: bash + + git clone https://github.com/SylphAI-Inc/LightRAG.git + +**2. Install Necessary Packages** + +LightRAG's documentation style is `pydata_sphinx_theme `_ (version: 0.15.3). + +Install by ``pip``: + +.. code-block:: bash + + cd docs + pip install -r requirements.txt + +Install by ``poetry`` along with all other dependencies for LightRAG: + +.. code-block:: bash + + poetry install + +**3. Build the Documentation** + +.. code-block:: bash + + cd docs + make html + + +**conf.py** + +This file (``docs/source/conf.py``) contains configurations used by Sphinx, including extensions, templates, HTML theme, and language settings. + +**Source Code Doc-string** + +Follow `Google style docstrings `_ to update your source code docstrings. Limit lines to **80** characters for better readability in various environments. + +**RST Files**: Directly edit ``.rst`` files for broader changes or new sections. Use the ``.. toctree::`` directive to link documents. + +The ``.rst`` files are in the ``docs/source``. The majority of ``.rst`` files in the ``docs/source/apis`` are generated automatically from the Python code docstrings using ``sphinx-apidoc``. + +To shorten the doc generating process, please remove the files that is not included in your project. + +The Sphinx build will show warnings but the docs will still be completed. + +If you have a module folder containing code, for example, ``components/``, please add the following line to the ``docs/Makefile`` in the ``apidoc:`` section. + +.. code-block:: bash + + @sphinx-apidoc -o $(APIDOCOUTDIR)/components ../components --separate --force + + +**4. View the Documentation Locally** + +After building, open ``docs/build/html/index.html`` in a web browser. If you face issues with local resources, such as the browser prohibits loading the web page correctly, run a local server: + +.. code-block:: bash + + cd docs/build + python -m http.server 8000 + +Then navigate to the corresbonding site in your browser. E.g. it can be `http://127.0.0.1:8000/`. + + + +Adding Documentation Tests +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To ensure the documentation remains up-to-date, LightRAG uses Sphinx's Doctest extension. Add ``.. testcode::`` to your ``.rst`` files or docstrings and run ``make doctest`` to test your documentation snippets. + +To manually run these tests, run: + +.. code-block:: bash + + cd docs + make doctest + + + +Commit Changes +~~~~~~~~~~~~~~~~~~~~~~~~~ + +After making changes, commit the ``.rst`` and source files, avoiding the ``docs/build`` directory, and push them to your GitHub fork for review. + diff --git a/_sources/contributor/contribution_guide.rst.txt b/_sources/contributor/contribution_guide.rst.txt new file mode 100644 index 000000000..bf6df9504 --- /dev/null +++ b/_sources/contributor/contribution_guide.rst.txt @@ -0,0 +1,6 @@ +LightRAG Contribution Guide +====================================== + +#. Writing Documentation + +Please see our `documentation contribution guidelines <./contribute_to_document.html>`_ for more details. \ No newline at end of file diff --git a/_sources/contributor/index.rst.txt b/_sources/contributor/index.rst.txt new file mode 100644 index 000000000..2b2306e7a --- /dev/null +++ b/_sources/contributor/index.rst.txt @@ -0,0 +1,9 @@ +For Contributors +============================= + + +.. toctree:: + :maxdepth: 2 + + contribution_guide + contribute_to_document \ No newline at end of file diff --git a/_sources/developer_notes/agent.rst.txt b/_sources/developer_notes/agent.rst.txt new file mode 100644 index 000000000..0c9b6834c --- /dev/null +++ b/_sources/developer_notes/agent.rst.txt @@ -0,0 +1,26 @@ +Agent +==================== + +.. epigraph:: + + “An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future.” + + -- Franklin and Graesser (1997) + +Agents are LLM-based and themselves belong to another popular family of LLM applications besides of the well-known RAGs. +The key on Agents are their ability to reasoning, plannning, and acting via accessible tools. +In LightRAG, agents are simply a generator which can use tools, take multiple steps(sequential or parallel ) to complete a user query. + + + +.. admonition:: References + :class: highlight + + 1. A survey on large language model based autonomous agents: https://github.com/Paitesanshi/LLM-Agent-Survey + 2. ReAct: https://arxiv.org/abs/2210.03629 + + +.. admonition:: API References + :class: highlight + + - :class:`components.agent.react.ReactAgent` diff --git a/_sources/developer_notes/base_data_class.rst.txt b/_sources/developer_notes/base_data_class.rst.txt new file mode 100644 index 000000000..8ba75bfd0 --- /dev/null +++ b/_sources/developer_notes/base_data_class.rst.txt @@ -0,0 +1,273 @@ +.. _core-base_data_class_note: +DataClass +============ + +.. admonition:: Author + :class: highlight + + `Li Yin `_ + +In PyTorch, ``Tensor`` is the data type used in ``Module`` and ``Optimizer`` across the library. +The data in particular is a multi-dimensional matrix such as such as weights, biases, and even inputs and predictions. +In LLM applications, you can think of the data as a freeform data class with various fields and types of data. +For instance: + +.. code-block:: python + + from dataclasses import dataclass + + @dataclass + class TrecData: + question: str + label: int + +It is exactly a single input data item in a typical PyTorch ``Dataset`` or a `HuggingFace` ``Dataset``. +The unique thing is all data or tools interact with LLMs via prompt and text prediction, which is a single ``str``. + +Most existing libraries use `Pydantic` to handle the serialization(convert to string) and deserialization(convert from string) of the data. +But, in LightRAG, we in particular designed :class:`core.base_data_class.DataClass` using native `dataclasses` module. +The reasons are: + +1. ``dataclasses`` module's `dataclass` decorator, along with `field` (`metadata`, `default`) can be especially helpful to describe the data format to LLMs. `dataclass` also saves users time on writing the boilerplate code such as `__init__`, `__repr__`, `__str__` etc. + +2. `dataclasses` native module is more lightweight, flexible, and user-friendly than `Pydantic`. + +3. Though we need more customization on ``BaseClass`` compared with directly using `Pydantic`, we will enjoy more transparency and control over the data format. + +Here is how users can define a data class with our customized methods in LightRAG: + +.. code-block:: python + + from lightrag.core.base_data_class import ( + DataClass, + required_field, + ) + from dataclasses import field + + + class MyOutputs(DataClass): + name: str = field( + default="John Doe", # Optional field + metadata={"desc": "The name of the person", "prefix": "Name:"}, + ) + age: int = field( + default_factory=required_field, # Required field + metadata={"desc": "The age of the person", "prefix": "Age:"}, + ) + +.. note:: + + `required_field` is a helper function to mark the field as required. Otherwise, using either `default` or `default_factory` will make the field optional. + +.. Now, let's see how we design class and instance methods to describe the data format and the data instance to LLMs. + + +Describe data to LLMs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Data Format +^^^^^^^^^^^^^^^^^^^^^^^^^ + +We need to describe either the input/output data format to give LLMs context on how to understand the input data and to generate the output data. + +What we want to let LLM know about our input/output data format: +In particular, it is important for LLMs to know these five things about the data format: + +1. **Description** of what this field is for. We use `desc` key in the `metadata` of `field` to describe this field. Example: + +.. code-block:: python + + thought: str = field( + metadata={"desc": "The reasoning or thought behind the question."} + ) + +2. **Required/Optional**. We use either `default` or `default_factory` to mark the field as optional except when our specialized function :func:`core.base_data_class.required_field` is used in `default_factory`, which marks the field as required. +3. **Field Data Type** such as `str`, `int`, `float`, `bool`, `List`, `Dict`, etc. +4. **Order of the fields** matter as in a typical Chain of Thought, we want the reasoning/thought field to be in the output ahead of the answer. +5. The ablility to **exclude** some fields from the output. + +We provide two ways: (1) ``schema`` and (2) ``signature`` to describe the data format in particular. + +**Schema** + +``schema`` will be a dict or json string and it is more verbose compared with ``signature``. +``signature`` imitates the exact data format (`yaml` or `json`) that you want LLMs to generate. + +Here is a quick example on our ``schema`` for the ``MyOutputs`` data class using the `to_data_class_schema` method: + +.. code-block:: python + + MyOutputs.to_data_class_schema() + +The output will be a dict: + +.. code-block:: json + + { + "name": { + "type": "str", + "desc": "The name of the person", + "required": false + }, + "age": { + "type": "int", + "desc": "The age of the person", + "required": true + } + } + +You can use `to_data_class_schema_str` to have the json string output. + +In comparison with the schema used in other libraries: + +.. code-block:: json + + { + "properties": { + "name": { + "title": "Name", + "description": "The name of the user", + "default": "John Doe", + "type": "string", + }, + "age": { + "title": "Age", + "description": "The age of the user", + "type": "integer", + }, + }, + "required": ["age"], + } + +Even our ``schema`` is more token efficient as you can see. We opted out of the `default` field as it is more of a fallback value in the program +rather than a description of the data format to LLMs. + +.. note:: + + If you use ``schema`` (json string) to instruct LLMs to output `yaml` data, the LLMs might get confused and can potentially output `json` data instead. + + +**Signature** + +``signature`` is a string that imitates the exact data format (here we support `yaml` or `json`) that you want LLMs to generate. + +Let's use class methods ``to_json_signature`` and ``to_yaml_signature`` to generate the signature for the ``MyOutputs`` data class: + +.. code-block:: python + + print(MyOutputs.to_json_signature()) + print(MyOutputs.to_yaml_signature()) + +The json signature output will be: + +.. code-block:: json + + { + "name": "The name of the person (str) (optional)", + "age": "The age of the person (int) (required)" + } + +The yaml signature output will be: + +.. code-block:: yaml + + name: The name of the person (str) (optional) + age: The age of the person (int) (required) + +All of the above methods support `exclude` parameter to exclude some fields from the output. + +Data Instance or say Example +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +To better demonstrate either the data format or provide examples seen in few-shot In-context learning, +we provide two methods: `to_json` and `to_yaml` to convert the data instance to json or yaml string. + +First, let's create an instance of the `MyOutputs` and get the json and yaml string of the instance: + +.. code-block:: python + + instance = MyOutputs(name="Jane Doe", age=25) + print(instance.to_json()) + print(instance.to_yaml()) + +The json output will be: + +.. code-block:: json + + { + "name": "Jane Doe", + "age": 25 + } +You can use `json.loads` to convert the json string back to a dictionary. + +The yaml output will be: + +.. code-block:: yaml + + name: "John Doe" + age: 25 + +You can use `yaml.safe_load` to convert the yaml string back to a dictionary. + + + + +Load data from dataset as example +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +As we need to load or create an instance from a dataset, which is typically from Pytorch dataset or huggingface dataset and each data point is in +the form of a dictionary. + +Let's create an instance of the `MyOutputs` from a dictionary: + +.. code-block:: python + + data = {"name": "Jane Doe", "age": 25} + print(MyOutputs.from_dict(data)) + + # Output + # MyOutputs(name='Jane Doe', age=25) + +In most cases, your dataset's key and the field name might not directly match. +Instead of providing a mapping argument in the library, we suggest users to customize `from_dict` method for more **control** and **flexibility**. + +Here is a real-world example: + +.. code-block:: python + + class OutputFormat(DataClass): + thought: str = field( + metadata={ + "desc": "Your reasoning to classify the question to class_name", + } + ) + class_name: str = field(metadata={"desc": "class_name"}) + class_index: int = field(metadata={"desc": "class_index in range[0, 5]"}) + + @classmethod + def from_dict(cls, data: Dict[str, Any]): + _COARSE_LABELS_DESC = [ + "Abbreviation", + "Entity", + "Description and abstract concept", + "Human being", + "Location", + "Numeric value", + ] + data = { + "thought": None, + "class_index": data["coarse_label"], + "class_name": _COARSE_LABELS_DESC[data["coarse_label"]], + } + return super().from_dict(data) + +.. note:: + + If you are looking for data types we used to support each component or any other class like `Optimizer`, you can check out the :ref:`core.types` file. + + + +.. Document +.. ------------ +.. We defined `Document` to function as a `string` container, and it can be used for any kind of text data along its `metadata` and relations +.. such as `parent_doc_id` if you have ever splitted the documents into chunks, and `embedding` if you have ever computed the embeddings for the document. + +.. It functions as the data input type for some `string`-based components, such as `DocumentSplitter`, `Retriever`. \ No newline at end of file diff --git a/_sources/developer_notes/component.rst.txt b/_sources/developer_notes/component.rst.txt new file mode 100644 index 000000000..f06706af5 --- /dev/null +++ b/_sources/developer_notes/component.rst.txt @@ -0,0 +1,259 @@ +Component +============ +What you will learn? + +1. What is ``Component`` and why is it designed this way? +2. How to use ``Component`` along with helper classes like ``FunComponent`` and ``Sequential``? + +Component +--------------------------------------- + :ref:`Component` is to LLM task pipelines what ``nn.Module`` is to PyTorch models. + +It is the base class for components, such as ``Prompt``, ``ModelClient``, ``Generator``, ``Retriever`` in LightRAG. +Your task pipeline should subclass from ``Component`` too. Instead of working with ``Tensor`` and ``Parameter`` to train models with weights and biases, our component works with any data, ``Parameter`` that can be any data type for LLM in-context learning, from manual to auto prompt engineering. +We name it differently to avoid confusion and also for better compatibility with `PyTorch`. + + + +Here is the comparison of writing a PyTorch model and a LightRAG task component. + +.. grid:: 2 + :gutter: 1 + + .. grid-item-card:: PyTorch + + .. code-block:: python + + import torch.nn as nn + import torch.nn.functional as F + + class Model(nn.Module): + def __init__(self): + super().__init__() + self.conv1 = nn.Conv2d(1, 20, 5) + self.conv2 = nn.Conv2d(20, 20, 5) + + def forward(self, x): + x = F.relu(self.conv1(x)) + return F.relu(self.conv2(x)) + + .. grid-item-card:: LightRAG + + .. code-block:: python + + from lightrag.core import Component, Generator + from lightrag.components.model_client import OpenAIClient + + + template_doc = r""" You are a doctor User: {{input_str}}""" + + class DocQA(Component): + def __init__(self): + super().__init__() + self.doc = Generator( + template=template_doc, + model_client=OpenAIClient(), + model_kwargs={"model": "gpt-3.5-turbo"}, + ) + + def call(self, query: str) -> str: + return self.doc(prompt_kwargs={"input_str": query}).data + +In this note, we are creating an AI doctor to answer medical questions. +Run the ``DocQA`` on a query: + +.. code-block:: python + + doc = DocQA() + print(doc("What is the best treatment for headache?")) + +The response is: + +.. code-block:: + + As a doctor, the best treatment for a headache would depend on the underlying cause of the headache. Typically, over-the-counter pain relievers such as acetaminophen, ibuprofen, or aspirin can help to alleviate the pain. However, if the headache is severe or persistent, it is important to see a doctor for further evaluation and to determine the most appropriate treatment option. Other treatment options may include prescription medications, lifestyle modifications, stress management techniques, and relaxation techniques. + +As the foundamental building block in LLM task pipeline, the component is designed to serve four main purposes: + +1. **Standarize the interface for all components.** This includes the `__init__` method, the `call` method for synchronous call, the `acall` method for asynchronous call, and the `__call__` which in default calls the `call` method. +2. **Provide a unified way to visualize the structure of the task pipeline** via `__repr__` method. And subclass can additional add `_extra_repr` method to add more information than the default `__repr__` method. +3. **Tracks, adds all subcomponents and parameters automatically and recursively** to assistant the building and optimizing process of the task pipeline. +4. **Manages the states and serialization**, with `state_dict` and `load_state_dict` methods in particular for parameters and `to_dict` method for serialization of all the states fall into the component's attributes, from subcomponents to parameters, to any other attributes of various data type. + + +Here are the benefits of using the Component class: + +- Transparency. +- Flexibility. +- Searialization and deserialization. + +.. Transparency +.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We can easily visualize the structure via `print`: + +.. code-block:: python + + doc = DocQA() + print(doc) + +The printout: + +.. code-block:: + + + DocQA( + (doc): Generator( + model_kwargs={'model': 'gpt-3.5-turbo'}, model_type=ModelType.LLM + (prompt): Prompt(template: You are a doctor User: {{input_str}}, prompt_variables: ['input_str']) + (model_client): OpenAIClient() + ) + ) + + + + + + +.. Flexibility +.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +As the above example shows, we added subcomponent via attributes. +We can also use methods to add more subcomponnents or parameters. + +.. code-block:: python + + from lightrag.core.parameter import Parameter + + doc.register_parameter("demo", param=Parameter(data="demo")) + # list all parameters + for param in doc.named_parameters(): + print(param) + # output + # ('demo', Parameter: demo) + +You can easily save the detailed states: + +.. code-block:: python + + from utils.file_io import save_json + + save_json(doc.to_dict(), "doc.json") + + +To adds even more flexibility, we provide :class:`core.component.FunComponent` and :class:`core.component.Sequential` for more advanced use cases. + + +**Searalization and deserialization** + +We provide ``is_pickable`` method to check if the component is pickable. +And any of your component, it is a good practise to ensure it is pickable. + +FunComponent +-------------- + Use :func:`core.component.fun_to_component` as a decorator to convert any function to a Component with its unique class name. + +:class:`core.component.FunComponent` is a subclass of :class:`core.component.Component` that allows you to define a component with a function. +You can directly use this class as: + +.. code-block:: python + + from lightrag.core.component import FunComponent + + def add_one(x): + return x + 1 + + fun_component = FunComponent(add_one) + print(fun_component(1)) + print(type(fun_component)) + + # output: + # 2 + # + + +We also have :func:`core.component.fun_to_component` to convert a function to a FunComponent via decorator or directly call the function. +This approach gives you a unique component converted from the function name. + +Via direct call: + +.. code-block:: python + + from lightrag.core.component import fun_to_component + + fun_component = fun_to_component(add_one) + print(fun_component(1)) + print(type(fun_component)) + + # output: + # 2 + # + + +Via decorator will be even more convenient to have a component from a function: + +.. code-block:: python + + .. @fun_to_component + def add_one(x): + return x + 1 + + print(add_one(1)) + print(type(add_one)) + + # output: + # 2 + # + +Sequential +-------------- +We have :class:`core.component.Sequential` class to PyTorch's ``nn.Sequential`` class. This is especially useful to chain together components in a sequence. Much like the concept of ``chain`` or ``pipeline`` in other LLM libraries. +Let's put the FunComponent and DocQA together in a sequence: + +.. code-block:: python + + from lightrag.core.component import Sequential + + @fun_to_component + def enhance_query(query:str) -> str: + return query + "Please be concise and only list the top treatments." + + seq = Sequential(enhance_query, doc) + + query = "What is the best treatment for headache?" + print(seq(query)) + +We automatically enhance users' queries before passing them to the DocQA component. +The output is: + +.. code-block:: + + 1. Over-the-counter pain relievers like acetaminophen, ibuprofen, or aspirin + 2. Rest and relaxation + 3. Stay hydrated and drink plenty of water + +The structure of the sequence using ``print(seq)``: + +.. code-block:: + + Sequential( + (0): EnhanceQueryComponent() + (1): DocQA( + (doc): Generator( + model_kwargs={'model': 'gpt-3.5-turbo'}, model_type=ModelType.LLM + (prompt): Prompt(template: You are a doctor User: {{input_str}}, prompt_variables: ['input_str']) + (model_client): OpenAIClient() + ) + ) + ) + +.. admonition:: API reference + :class: highlight + + - :class:`core.component.Component` + - :class:`core.component.FunComponent` + - :class:`core.component.Sequential` + - :func:`core.component.fun_to_component` + + +We will have more advanced use cases in the upcoming tutorials. diff --git a/_sources/developer_notes/data_pipeline.rst.txt b/_sources/developer_notes/data_pipeline.rst.txt new file mode 100644 index 000000000..ab4b2bd2b --- /dev/null +++ b/_sources/developer_notes/data_pipeline.rst.txt @@ -0,0 +1,2 @@ +Data Pipeline +================ diff --git a/_sources/developer_notes/db.rst.txt b/_sources/developer_notes/db.rst.txt new file mode 100644 index 000000000..a1a754bd6 --- /dev/null +++ b/_sources/developer_notes/db.rst.txt @@ -0,0 +1,522 @@ +Data & RAG +==================== + + The purpose of this note is to provide an overview on data, data modeling, and data storage in LLM applications along with how LightRAG works with data. +We will conver: + +* Data models on how to represent important data. +* Data pipeline to process data in LLM applications. +* Local database providing in-memory data management and storage. +* Examples on working with Cloud database using `fsspec` and `SQLAlchemy`. + +.. note :: + This note focus more on dealing with data needed to perform the LLM task. + Datasets, including the input and ground truth output loading and dataset will be covered in the optimizing section. + +So far, we have seen how our core components like ``Generator``, ``Embedder``, and ``Retriever`` work without any data cache/database and enforced data format to read data from and to write data to. +However, in real-world LLM applications, we can not avoid to deal with data storage: + +1. Our documents to retrieve context from can be large and be stored in a file system or in a database in forms of tables or graphs. +2. We often need to pre-process a large amount of data (like text splitting and embedding and idf in BM25) in a datapipline into a cloud database. +3. We need to write records, logs to files or databases for monitoring and debugging, such as the failed llm calls. +4. When it comes to applications where states matter, like games and chatbots, we need to store the states and conversational history. + + +.. figure:: /_static/database.png + :align: center + :alt: Data model and database + :width: 620px + + Data model and database + + + +Data Models +-------------------- + +Besides of having a unified `GeneratorOutput`, `EmbedderOutput`, and `RetrieverOutput` data format, +we provide mainly :class:`core.types.Document` and :class:`core.types.DialogTurn` to help with text document and conversational histor processing and data storage. + +We will explain the design of Document and DialogTurn. In this note, we will continue use these simple documents we used in the previous notes: + +.. code-block:: python + + org_documents =[ + { + "title": "The Impact of Renewable Energy on the Economy", + "content": "Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure." + }, + { + "title": "Understanding Solar Panels", + "content": "Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels." + }, + { + "title": "Pros and Cons of Solar Energy", + "content": "While solar energy offers substantial environmental benefits, such as reducing carbon footprints and pollution, it also has downsides. The production of solar panels can lead to hazardous waste, and large solar farms require significant land, which can disrupt local ecosystems." + }, + { + "title": "Renewable Energy and Its Effects", + "content": "Renewable energy sources like wind, solar, and hydro power play a crucial role in combating climate change. They do not produce greenhouse gases during operation, making them essential for sustainable development. However, the initial setup and material sourcing for these technologies can still have environmental impacts." + } + ] + + turns = [ + { + "user": "What are the benefits of renewable energy?", + "system": "I can see you are interested in renewable energy. Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure.", + "user_time": "2021-09-01T12:00:00Z", + "system_time": "2021-09-01T12:00:01Z" + }, + { + "user": "How do solar panels impact the environment?", + "system": "Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels.", + "user_time": "2021-09-01T12:00:02Z", + "system_time": "2021-09-01T12:00:03Z" + } + ] + + +Document +~~~~~~~~~~~~~~~ +The :class:`core.types.Document` is used as Document data structure and to assist text processing in LLM applications. + +1. A general document/text container with fields ``text``, ``meta_data``, and ``id``. +2. Assist text splitting with fields ``parent_doc_id`` and ``order``. +3. Assist embedding with fields ``vector``. +4. Assist using it as a prompt for LLM with fields ``estimated_num_tokens``. + +This is why data processing components like ``TextSplitter`` and ``ToEmbeddings`` requires ``Document`` as input of each data item. + +**Create a Document** + +.. code-block:: python + + from lightrag.core.types import Document + + documents = [Document(text=doc['content'], meta_data={'title': doc['title']}) for doc in org_documents] + print(documents) + +The printout will be: + +.. code-block:: + + [Document(id=73c12be3-7844-435b-8678-2e8e63041698, text='Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute...', meta_data={'title': 'The Impact of Renewable Energy on the Economy'}, vector=[], parent_doc_id=None, order=None, score=None), Document(id=7a17ed45-569a-4206-9670-5316efd58d58, text='Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock ele...', meta_data={'title': 'Understanding Solar Panels'}, vector=[], parent_doc_id=None, order=None, score=None), Document(id=bcbc6ff9-518a-405a-8b0d-840021aa1953, text='While solar energy offers substantial environmental benefits, such as reducing carbon footprints and...', meta_data={'title': 'Pros and Cons of Solar Energy'}, vector=[], parent_doc_id=None, order=None, score=None), Document(id=ec910402-f98f-4077-a958-7335e34ee0c6, text='Renewable energy sources like wind, solar, and hydro power play a crucial role in combating climate ...', meta_data={'title': 'Renewable Energy and Its Effects'}, vector=[], parent_doc_id=None, order=None, score=None)] + + +DialogTurn +~~~~~~~~~~~~~~~~~~ +The :class:`core.types.DialogTurn` is only used as a data structure to a user-assistant conversation turn in LLM applications. +**If we need to apply a text processing pipeline to a conversational history, we will use our text container``Document`` to store the text we need to use.** + +.. note :: + For both ``Document`` and ``DialogTurn``, we have an equivalent class in :doc:`database.sqlalchemy.model`(:class:`database.sqlalchemy.modoel.Document`) to handle the persitence of data in a SQL database. + +Here is how to get a list of ``DialogTurn`` from the ``turns``: + +.. code-block:: python + + from lightrag.core.types import DialogTurn, UserQuery, AssistantResponse + + dialog_turns = [ + DialogTurn( + user_query=UserQuery(query_str=turn["user"]), + assistant_response=AssistantResponse(response_str=turn["system"]), + user_query_timestamp=turn["user_time"], + assistant_response_timestamp=turn["system_time"], + ) + for turn in turns + ] + print(dialog_turns) + +The printout will be: + +.. code-block:: + + [DialogTurn(id='f2eddc77-4667-43f5-87e0-fd11f12958b3', user_id=None, session_id=None, order=None, user_query=UserQuery(query_str='What are the benefits of renewable energy?', metadata=None), assistant_response=AssistantResponse(response_str='I can see you are interested in renewable energy. Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure.', metadata=None), user_query_timestamp='2021-09-01T12:00:00Z', assistant_response_timestamp='2021-09-01T12:00:01Z', metadata=None, vector=None), DialogTurn(id='b2dbdf2f-f513-493d-aaa8-c77c98ac260f', user_id=None, session_id=None, order=None, user_query=UserQuery(query_str='How do solar panels impact the environment?', metadata=None), assistant_response=AssistantResponse(response_str='Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels.', metadata=None), user_query_timestamp='2021-09-01T12:00:02Z', assistant_response_timestamp='2021-09-01T12:00:03Z', metadata=None, vector=None)] + + +Data Pipeline +-------------------- +Let's see how to can write a data pipeline that can process any form of text data by using intermediate data model-``Document``. +Currently, we have two data processing components: ``TextSplitter`` and ``ToEmbeddings`` in the ``components.data_process`` module. + +We will use ``ord_documents`` and a list of ``DialogTurn`` as examples. As our data pipelines are designed to work with ``Document`` structure, +we simplify just need to add a mapping function to convert the original data to ``Document``. + +.. code-block:: python + + # mapping function for org_documents + def map_to_document(doc: Dict) -> Document: + return Document(text=doc['content'], meta_data={'title': doc['title']}) + + def map_dialogturn_to_document(turn: DialogTurn) -> Document: + # it can be important to keep the original data's id + return Document(id=turn.id, text=turn.user_query.query_str + ' ' + turn.assistant_response.response_str) + +You can refer to :doc:`text_splitter` for more details on how to use ``TextSplitter``. +``ToEmbeddings`` is an orchestrator on ``BatchEmbedder`` and it will generate embeddings for a list of ``Document`` and store the embeddings as ``List[Float]`` in the ``vector`` field of each ``Document``. + +``Sequential`` can be easily used to chain multiple data processing components together. +Here is the code to form a data pipeline: + +.. code-block:: python + + from lightrag.core.embedder import Embedder + from lightrag.core.types import ModelClientType + from lightrag.components.data_process import DocumentSplitter, ToEmbeddings + from lightrag.core.component import Sequential + + + model_kwargs = { + "model": "text-embedding-3-small", + "dimensions": 256, + "encoding_format": "float", + } + + splitter_config = { + "split_by": "word", + "split_length": 50, + "split_overlap": 10 + } + + splitter = DocumentSplitter(**splitter_config) + embedder = Embedder(model_client =ModelClientType.OPENAI(), model_kwargs=model_kwargs) + embedder_transformer = ToEmbeddings(embedder, batch_size=2) + data_transformer = Sequential(splitter, embedder_transformer) + print(data_transformer) + +The printout will be: + +.. code-block:: + + Sequential( + (0): DocumentSplitter(split_by=word, split_length=50, split_overlap=10) + (1): ToEmbeddings( + batch_size=2 + (embedder): Embedder( + model_kwargs={'model': 'text-embedding-3-small', 'dimensions': 256, 'encoding_format': 'float'}, + (model_client): OpenAIClient() + ) + (batch_embedder): BatchEmbedder( + (embedder): Embedder( + model_kwargs={'model': 'text-embedding-3-small', 'dimensions': 256, 'encoding_format': 'float'}, + (model_client): OpenAIClient() + ) + ) + ) + ) + +Now, apply the data pipeline to the ``dialog_turns``: + +.. code-block:: python + + dialog_turns_as_documents = [map_dialogturn_to_document(turn) for turn in dialog_turns] + print(dialog_turns_as_documents) + + # apply data transformation to the documents + output = data_transformer(dialog_turns_as_documents) + print(output) + +The printout will be: + +.. code-block:: + + [Document(id=e3b48bcc-df68-43a4-aa81-93922b619293, text='What are the benefits of renewable energy? I can see you are interested in renewable energy. Renewab...', meta_data=None, vector=[], parent_doc_id=None, order=None, score=None), Document(id=21f0385d-d19a-442f-ae99-910e984cdb65, text='How do solar panels impact the environment? Solar panels convert sunlight into electricity by allowi...', meta_data=None, vector=[], parent_doc_id=None, order=None, score=None)] + Splitting documents: 100%|██████████| 2/2 [00:00<00:00, 609.37it/s] + Batch embedding documents: 100%|██████████| 2/2 [00:00<00:00, 3.79it/s] + Adding embeddings to documents from batch: 2it [00:00, 10205.12it/s] + [Document(id=e636facc-8bc3-483b-afbd-37e1d8ff0526, text='What are the benefits of renewable energy? I can see you are interested in renewable energy. Renewab...', meta_data=None, vector='len: 256', parent_doc_id=e3b48bcc-df68-43a4-aa81-93922b619293, order=0, score=None), Document(id=06ea7cea-c4e4-4f5f-b3e9-2e6f4452827b, text='and installation sectors. The growth in renewable energy usage boosts local economies through increa...', meta_data=None, vector='len: 256', parent_doc_id=e3b48bcc-df68-43a4-aa81-93922b619293, order=1, score=None), Document(id=0018af12-c8fc-49ff-ab64-a2acf8ba4c27, text='How do solar panels impact the environment? Solar panels convert sunlight into electricity by allowi...', meta_data=None, vector='len: 256', parent_doc_id=21f0385d-d19a-442f-ae99-910e984cdb65, order=0, score=None), Document(id=c5431397-2a78-4870-abce-353b738c1b71, text='has been found to have a significant positive effect on the environment by reducing the reliance on ...', meta_data=None, vector='len: 256', parent_doc_id=21f0385d-d19a-442f-ae99-910e984cdb65, order=1, score=None)] + + + +Local database +-------------------- + +**LocalDB class** + +:class:`core.db.LocalDB` is a powerful data management class: + +1. It manages a sequence of data items of any data type with CRUD operations. +2. Keep track and apply data transfomation/processing pipelines to its items. +3. Save and load the state of the items to/from a file, including all data and data transformer records. + +This table lists its attributes and important methods: + +.. list-table:: + :header-rows: 1 + + * - + - Attribute/Method + - Description + * - Attributes + - ``name`` + - The name of the database. + * - + - ``items`` + - A list of items in the database. + * - + - ``transformed_items`` + - A dictionary to store the transformed items. + * - + - ``transformer_setups`` + - A dictionary to store the transformer setups. + * - + - ``mapper_setups`` + - A dictionary to store the mapping functions used together with transformer. + * - Data CRUD Operations + - ``load(items: List[Any])`` + - Load a list of items to the database ``items``. + * - + - ``extend(items, List[Any], apply_transformer: bool = True)`` + - Add items to the end of ``items``. Optionally apply transformer from ``transformer_setups``. + * - + - ``add(item: Any, index: Optional[int] = None, apply_transformer: bool = True)`` + - Add a single item by index or append to the end. Optionally apply the transformer. + * - + - ``delete(index: Optional[int] = None, remove_transformed: bool = True)`` + - Remove items by index or pop the last item. Optionally remove the transformed data as well. Assume the transformed item has the same index as the original item. Might not always be the case. + * - + - ``reset()`` + - Reset all attributes to the initial state. + * - Data Processing + - ``register_transformer(transformer: Component, key: Optional[str], map_fn: Optional[Callable])`` + - Register a data transformation to the database to be used later. + * - + - ``transform(key: str)`` + - Apply a transformer by key to the data. + * - + - ``transform(transformer: Component, map_fn: Callable, key: str)`` + - Register and apply a transformer to the data. + * - Data Persistence + - ``save_state(filepath: str)`` + - Save the state of the database to a pickle file. + * - + - ``load_state(filepath: str)`` + - A class method to load the state of the database from a pickle file. + +Now, finally, we have a good way to organize important data along its pipeline like ``Document`` and ``DialogTurn`` in a database. + + +**Data Loading and CRUD Operations** + +Let's create a ``LocalDB`` to manage the ``dialog_turns`` and its data processing pipeline: + +.. code-block:: python + + from lightrag.core.db import LocalDB + + dialog_turn_db = LocalDB('dialog_turns') + print(dialog_turn_db) + + dialog_turn_db.load(dialog_turns) + print(dialog_turn_db) + +The printout will be: + +.. code-block:: + + LocalDB(name='dialog_turns', items=[], transformed_items={}, transformer_setups={}, mapper_setups={}) + LocalDB(name='dialog_turns', items=[DialogTurn(id='f2eddc77-4667-43f5-87e0-fd11f12958b3', user_id=None, session_id=None, order=None, user_query=UserQuery(query_str='What are the benefits of renewable energy?', metadata=None), assistant_response=AssistantResponse(response_str='I can see you are interested in renewable energy. Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure.', metadata=None), user_query_timestamp='2021-09-01T12:00:00Z', assistant_response_timestamp='2021-09-01T12:00:01Z', metadata=None, vector=None), DialogTurn(id='b2dbdf2f-f513-493d-aaa8-c77c98ac260f', user_id=None, session_id=None, order=None, user_query=UserQuery(query_str='How do solar panels impact the environment?', metadata=None), assistant_response=AssistantResponse(response_str='Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels.', metadata=None), user_query_timestamp='2021-09-01T12:00:02Z', assistant_response_timestamp='2021-09-01T12:00:03Z', metadata=None, vector=None)], transformed_items={}, transformer_setups={}, mapper_setups={}) + + +**Data Processing/Transformation Pipeline(such as TextSplitter and Embedder)** + +We register and apply the transformer from the last section to the data stored in the ``dialog_turn_db``: + +.. code-block:: python + + key = "split_and_embed" + dialog_turn_db.transform(data_transformer, map_fn=map_dialogturn_to_document, key=key) + + print(dialog_turn_db.transformed_items[key]) + print(dialog_turn_db.transformer_setups[key]) + print(dialog_turn_db.mapper_setups[key]) + +The printout will be: + +.. code-block:: + + Splitting documents: 100%|██████████| 2/2 [00:00<00:00, 2167.04it/s] + Batch embedding documents: 100%|██████████| 2/2 [00:00<00:00, 5.46it/s] + Adding embeddings to documents from batch: 2it [00:00, 63072.24it/s] + [Document(id=64987b2b-b6c6-4eb4-9122-02448e3fd394, text='What are the benefits of renewable energy? I can see you are interested in renewable energy. Renewab...', meta_data=None, vector='len: 256', parent_doc_id=f2eddc77-4667-43f5-87e0-fd11f12958b3, order=0, score=None), Document(id=9a424d4c-4bd0-48ce-aba9-7a4f86892556, text='and installation sectors. The growth in renewable energy usage boosts local economies through increa...', meta_data=None, vector='len: 256', parent_doc_id=f2eddc77-4667-43f5-87e0-fd11f12958b3, order=1, score=None), Document(id=45efa517-8e52-4780-bdbd-2329ffa8d4b6, text='How do solar panels impact the environment? Solar panels convert sunlight into electricity by allowi...', meta_data=None, vector='len: 256', parent_doc_id=b2dbdf2f-f513-493d-aaa8-c77c98ac260f, order=0, score=None), Document(id=bc0ff7f6-27cc-4e24-8c3e-9435ed755e20, text='has been found to have a significant positive effect on the environment by reducing the reliance on ...', meta_data=None, vector='len: 256', parent_doc_id=b2dbdf2f-f513-493d-aaa8-c77c98ac260f, order=1, score=None)] + Sequential( + (0): DocumentSplitter(split_by=word, split_length=50, split_overlap=10) + (1): ToEmbeddings( + batch_size=2 + (embedder): Embedder( + model_kwargs={'model': 'text-embedding-3-small', 'dimensions': 256, 'encoding_format': 'float'}, + (model_client): OpenAIClient() + ) + (batch_embedder): BatchEmbedder( + (embedder): Embedder( + model_kwargs={'model': 'text-embedding-3-small', 'dimensions': 256, 'encoding_format': 'float'}, + (model_client): OpenAIClient() + ) + ) + ) + ) + + +**Save/Reload Data** + +.. code-block:: python + + dialog_turn_db.save_state(filepath='.storage/dialog_turns.pkl') + reloaded_dialog_turn_db = LocalDB.load_state(filepath='.storage/dialog_turns.pkl') + print(str(dialog_turn_db.__dict__) == str(restored_dialog_turn_db.__dict__)) + +This will print ``True`` if the two databases are the same. We can use the reloaded db class to continue to work with the data. +This data class can be really helpful for researchers and developers to run and track local experiments to optimize the data processing pipelines + +**CRUD Operations using with Generator for a conversation** + +We will have a chatbot and add new conversation turns to the database. When the conversation is too long to fit into token limit of your LLM model, you can easily +use a retriever to control the conversation history length. + +First, let us prepare the generator. We will use ``input_str`` and ``chat_history_str`` from our default prompt. +This will also leverage ``DialogTurn`` 's inheritant ability from ``DataClass`` to quickly form the ``chat_history_str``. + +.. code-block:: python + + from lightrag.core import Generator + + llm_kwargs = { + "model": "gpt-3.5-turbo" + } + + generator = Generator(model_client = ModelClientType.OPENAI(), model_kwargs=llm_kwargs) + +Here is the code to form the prompt and we will use ``generator.print_prompt()`` to check how the prompt will look like: + +.. code-block:: python + + from typing import List + + input_str = "What are the benefits of renewable energy? Did I ask this before?" + + def format_chat_history_str(turns: List[DialogTurn]) -> str: + chat_history_str = [] + for turn in turns: + chat_history_str.append( + turn.to_yaml( + exclude=[ + "id", + "user_id", + "session_id", + "user_query_timestamp", + "assistant_response_timestamp", + "order", + "metadata", + "vector", + ], + ) + ) + chat_history_str = '\n_________\n'.join(chat_history_str) + return chat_history_str + + chat_history_str = format_chat_history_str(dialog_turn_db.items[0:1]) + print(generator.print_prompt(input_str=input_str, chat_history_str=chat_history_str)) + +The printout will be: + +.. code-block:: + + Prompt: + + + + user_query: + metadata: null + query_str: What are the benefits of renewable energy? + assistant_response: + metadata: null + response_str: I can see you are interested in renewable energy. Renewable energy technologies + not only help in reducing greenhouse gas emissions but also contribute significantly + to the economy by creating jobs in the manufacturing and installation sectors. The + growth in renewable energy usage boosts local economies through increased investment + in technology and infrastructure + + + + What are the benefits of renewable energy? Did I ask this before? + + You: + +Now, let us chat with the generator and add the conversation turns to the database: + + +.. code-block:: python + + response = generator(prompt_kwargs={"input_str": input_str, "chat_history_str": chat_history_str}) + print(response) + + # add the turn and apply the transformer + new_turn = DialogTurn( + user_query=UserQuery(query_str=input_str), + assistant_response=AssistantResponse(response_str=response.data), + ) + dialog_turn_db.add(new_turn, apply_transformer=True) + + print(dialog_turn_db.length, len(dialog_turn_db.transformed_items[key])) + + # 3 6 + + + +**Use With Retriever** + +Assume our history is getting too long to fit into the token limit. +We will use a semantic retriever to fetch relevant chunked documents from the database. +Then, instead of directly using the documents, we will find its relevant dialog turns by comparing the ``parent_doc_id`` with the ``id`` of the document. +Here is the code to prepare the relevant dialog turns. + +.. code-block:: python + + from lightrag.components.retriever.faiss_retriever import FAISSRetriever + + retriever = FAISSRetriever(top_k=3, embedder=embedder) + embeddings = [item.vector for item in dialog_turn_db.transformed_items[key]] + retriever.build_index_from_documents(documents=embeddings) + + # get the relevant documents + top_k_documents = retriever(input=input_str) + + # get the relevant dialog turns + parent_doc_ids = set( + [ + dialog_turn_db.transformed_items[key][doc_index].parent_doc_id + for doc_index in top_k_documents[0].doc_indices + ] + ) + + condition_fn = lambda item: item.id in parent_doc_ids + fetched_dialog_turns = [item for item in dialog_turn_db.items if condition_fn(item)] + + +Now, we can use the ``fetched_dialog_turns`` to continue the conversation with the generator. + + + + +Cloud database +-------------------- + +Suggestion on File reading and writing +------------------------------------------ +We dont provide integration on using ``fsspec``, but here we can give you some suggestions on how to use it. + + + +Graph database +-------------------- + + +.. admonition:: API References + :class: highlight + + - :class:`core.types.Document` + - :class:`core.types.DialogTurn` + - :class:`core.db.LocalDB` + - :class:`components.data_process.DocumentSplitter` + - :class:`components.data_process.ToEmbeddings` diff --git a/_sources/developer_notes/embedder.rst.txt b/_sources/developer_notes/embedder.rst.txt new file mode 100644 index 000000000..4e71c74bf --- /dev/null +++ b/_sources/developer_notes/embedder.rst.txt @@ -0,0 +1,219 @@ +Embedder +============ +What you will learn? + +1. What is ``Embedder`` and why is it designed this way? +2. When to use ``Embedder`` and how to use it? +3. How to batch processing with ``BatchEmbedder``? + +:class:`core.embedder.Embedder` class is similar to ``Generator``, it is a user-facing component that orchestrates embedding models via ``ModelClient`` and ``output_processors``. +Compared with using ``ModelClient`` directly, ``Embedder`` further simplify the interface and output a standard ``EmbedderOutput`` format. + +By switching the ``ModelClient``, you can use different embedding models in your task pipeline easily, or even embedd different data such as text, image, etc. + +EmbedderOutput +-------------- + +:class:`core.types.EmbedderOutput` is a standard output format of ``Embedder``. It is a subclass of `DataClass` and it contains the following core fields: + +- ``data``: a list of embeddings, each embedding if of type :class:`core.types.Embedding`. +- ``error``: Error message if any error occurs during the model inference stage. Failure in the output processing stage will raise an exception instead of setting this field. +- ``raw_response``: Used for failed model inference. + +Additionally, we add three properties to the ``EmbedderOutput``: + +- ``length``: The number of embeddings in the ``data``. +- ``embedding_dim``: The dimension of the embeddings in the ``data``. +- ``is_normalized``: Whether the embeddings are normalized to unit vector or not using ``numpy``. + + +Embedder in Action +------------------- +We currently support `all embedding models from OpenAI `_ and `'thenlper/gte-base' `_ from HuggingFace `transformers `_. +We will use these two to demonstrate how to use ``Embedder``, one from the API provider and the other using local model. For the local model, you might need to ensure ``transformers`` is installed. + +.. note :: + The ``output_processors`` can be a component or a ``Sequential`` container to chain together multiple components. The output processors are applied in order and is adapted only on the ``data`` field of the ``EmbedderOutput``. + +Use OpenAI API +^^^^^^^^^^^^^^^ +Before you start ensure you config the API key either in the environment variable or `.env` file, or directly pass it to the ``OpenAIClient``. + +.. code-block:: python + + from lightrag.core.embedder import Embedder + from lightrag.components.model_client import OpenAIClient + from lightrag.utils import setup_env # ensure you setup OPENAI_API_KEY in your project .env file + + model_kwargs = { + "model": "text-embedding-3-small", + "dimensions": 256, + "encoding_format": "float", + } + + query = "What is the capital of China?" + + queries = [query] * 100 + + + embedder = Embedder(model_client=OpenAIClient(), model_kwargs=model_kwargs) + +We find the ``model_kwargs`` from the OpenAI API documentation. We setup `query` to demonstrate call on a single query and `queries` to demonstrate batch call. + +**Visualize structure**: we use ``print(embedder)``. The output will be: + +.. code-block:: + + Embedder( + model_kwargs={'model': 'text-embedding-3-small', 'dimensions': 256, 'encoding_format': 'float'}, + (model_client): OpenAIClient() + ) + +**Embed single query**: +Run the embedder and print the length and embedding dimension of the output. + +.. code-block:: python + + output = embedder(query) + print(output.length, output.embedding_dim, output.is_normalized) + # 1 256 True + + +**Embed batch queries**: + +.. code-block:: python + + output = embedder(queries) + print(output.length, output.embedding_dim) + # 100 256 + +Use Local Model +^^^^^^^^^^^^^^^ +Set up the embedder with the local model. + +.. code-block:: python + + from lightrag.core.embedder import Embedder + from lightrag.components.model_client import TransformersClient + + model_kwargs = {"model": "thenlper/gte-base"} + local_embedder = Embedder(model_client=TransformersClient(), model_kwargs=model_kwargs) + +Now, call the embedder with the same query and queries. + +.. code-block:: python + + output = local_embedder(query) + print(output.length, output.embedding_dim, output.is_normalized) + # 1 768 True + + output = local_embedder(queries) + print(output.length, output.embedding_dim, output.is_normalized) + # 100 768 True + +Use Output Processors +^^^^^^^^^^^^^^^^^^^^^^^ +If we want to decreate the embedding dimension to only 256 to save memory, we can customize an additional output processing step and pass it to embedder via the ``output_processors`` argument. + +.. code-block:: python + + from lightrag.core.types import Embedding + from lightrag.core.functional import normalize_vector + from typing import List + from lightrag.core.component import Component + from copy import deepcopy + + class DecreaseEmbeddingDim(Component): + def __init__(self, old_dim: int, new_dim: int, normalize: bool = True): + super().__init__() + self.old_dim = old_dim + self.new_dim = new_dim + self.normalize = normalize + assert self.new_dim < self.old_dim, "new_dim should be less than old_dim" + + def call(self, input: List[Embedding]) -> List[Embedding]: + output: List[Embedding] = deepcopy(input) + for embedding in output: + old_embedding = embedding.embedding + new_embedding = old_embedding[: self.new_dim] + if self.normalize: + new_embedding = normalize_vector(new_embedding) + embedding.embedding = new_embedding + return output + + def _extra_repr(self) -> str: + repr_str = f"old_dim={self.old_dim}, new_dim={self.new_dim}, normalize={self.normalize}" + return repr_str + +This output procesor will process on the ``data`` field of the ``EmbedderOutput``, which is of type ``List[Embedding]``. Thus we have ``input: List[Embedding] -> output: List[Embedding]`` in the ``call`` method. +Putting it all together, we can create a new embedder with the output processor. + +.. code-block:: python + + local_embedder_256 = Embedder( + model_client=TransformersClient(), + model_kwargs=model_kwargs, + output_processors=DecreaseEmbeddingDim(768, 256), + ) + print(local_embedder_256) + +The structure looks like: + +.. code-block:: + + Embedder( + model_kwargs={'model': 'thenlper/gte-base'}, + (model_client): TransformersClient() + (output_processors): DecreaseEmbeddingDim(old_dim=768, new_dim=256, normalize=True) + ) + +Run a query: + +.. code-block:: python + + output = local_embedder_256(query) + print(output.length, output.embedding_dim, output.is_normalized) + # 1 256 True + +.. note:: + Please find relevant research on how directly decreasing the embedding dimension affects the performance of your downstream tasks. We simply used this as an example to demonstrate the output processor. + +BatchEmbedder +-------------- +Especially in data processing pipelines, you can often have more than 1000 queries to embed. We need to chunk our queries into smaller batches to avoid memory overflow. +:class:`core.embedder.BatchEmbedder` is designed to handle this situation. For now, the code is rather simple, but in the future it can be extended to support multi-processing when you use LightRAG in production data pipeline. + +The BatchEmbedder orchestrates the ``Embedder`` and handles the batching process. To use it, you need to pass the ``Embedder`` and the batch size to the constructor. + +.. code-block:: python + + from lightrag.core.embedder import BatchEmbedder + + batch_embedder = BatchEmbedder(embedder=local_embedder, batch_size=100) + + queries = [query] * 1000 + + response = batch_embedder(queries) + # 100%|██████████| 11/11 [00:04<00:00, 2.59it/s] + + +.. note:: + To integrate your own embedding model or from API providers, you need to implement your own subclass of ``ModelClient``. + +.. admonition:: References + :class: highlight + + - transformers: https://huggingface.co/docs/transformers/en/index + - thenlper/gte-base model: https://huggingface.co/thenlper/gte-base + + +.. admonition:: API reference + :class: highlight + + - :class:`core.embedder.Embedder` + - :class:`core.embedder.BatchEmbedder` + - :class:`core.types.EmbedderOutput` + - :class:`core.types.Embedding` + - :class:`components.model_client.openai_client.OpenAIClient` + - :class:`components.model_client.transformers_client.TransformersClient` + - :class:`core.functional.normalize_vector` \ No newline at end of file diff --git a/_sources/developer_notes/embedder_xy.rst.txt b/_sources/developer_notes/embedder_xy.rst.txt new file mode 100644 index 000000000..e714a1533 --- /dev/null +++ b/_sources/developer_notes/embedder_xy.rst.txt @@ -0,0 +1,105 @@ +Embedder +============ + +A commonly applied approach in the NLP field to transform texts to numeric representation is Embedding. Embedding turns the texts data to the vector space. +This enables us to calculate numbers such as similarity score. With embedding, during retrieval, we can do semantic search to find similar documents. + +In ``LightRAG``, text embedding is managed through three main components: ``Embedder``, ``ToEmbedderResponse``, and ``ToEmbeddings``. +These components work together to transform text into embeddings using external model APIs from providers like `OpenAI`` and `Google`. + +* **Embedder:** It acts as the interface for external embedding models. It orchestrates embedding models via ``ModeClient`` and ``output_processors`` that can be used to process output from ``ModeClient``. Ensure that your model's API key is stored in the ``.env`` file. This setup prepares the Embedder to generate embeddings effectively. +.. * **ToEmbedderResponse:** It is designed to convert raw model outputs into structured ``EmbedderResponse`` formats, typically lists of float values representing embeddings. It should be used as the ``output_processors`` in the ``Embedder`` setup. +* **ToEmbeddings:** It takes documents from ``Embedder``, processes documents in batches and uses an instance of ``Embedder`` as the ``vectorizer`` to obtain embeddings. After receiving the embeddings, ``ToEmbeddings`` assigns them back to the respective Document objects. This component ensures that the input data is copied and the original data remains unmodified. + +With these components, the embeddings will be created when you run ``ToEmbeddings``. + +Next, we will use an example combining the document splitter and embedder to demonstrate the embedder usage. + +First, import the packages and set up the environment. + +.. code-block:: python + + from lightrag.core.embedder import Embedder + from lightrag.components.model_client import OpenAIClient + from lightrag.core.data_components import ToEmbedderResponse, ToEmbeddings + from lightrag.core.types import Document + from lightrag.components.data_process import DocumentSplitter + + import dotenv + dotenv.load_dotenv(dotenv_path=".env", override=True) + +Then, set up the splitter with a simple example. + +.. code-block:: python + + # combine splitter and embedding + # configure the splitter setting + text_splitter_settings = { + "split_by": "word", + "split_length": 15, + "split_overlap": 2, + } + + # set up the document splitter + text_splitter = DocumentSplitter( + split_by=text_splitter_settings["split_by"], + split_length=text_splitter_settings["split_length"], + split_overlap=text_splitter_settings["split_overlap"], + ) + + # create examples + example1 = Document( + text="Review: What a fantastic movie! Had a great time and would watch it again! Sentiment: Positive", + ) + documents = [example1] + + # split the documents + splitted_docs = (text_splitter.call(documents=documents)) + print(splitted_docs) + + # Document(id=78917586-c4e5-4ce3-ac78-196ec25d39c4, meta_data=None, text=Review: What a fantastic movie! Had a great time and would watch it again! Sentiment: , estimated_num_tokens=None), Document(id=fdbc447d-5ba2-4917-9c14-0d95cc65e6ef, meta_data=None, text=again! Sentiment: Positive, estimated_num_tokens=None)] + +Now, create the embedder. ``LightRAG`` uses ``Embedder`` to configure the embedding model, process output. Developers can set up the ``batch_size`` in ``ToEmbeddings``. + +.. code-block:: python + + # configure the vectorizer(embedding) setting + vectorizer_settings = { + "model_kwargs": { + "model": "text-embedding-3-small", + "dimensions": 256, + "encoding_format": "float", + }, + "batch_size": 100 + } + + # set up the embedder using openai model + vectorizer = Embedder( + model_client=OpenAIClient, + model_kwargs=vectorizer_settings["model_kwargs"], # set up model arguments + output_processors=ToEmbedderResponse(), # convert the model output to EmbedderResponse + ) + + # create embedder + # create embeddings + to_embeddings = ToEmbeddings( + embedder=vectorizer, + batch_size=vectorizer_settings["batch_size"], + ) + +Finally, check the results. + +.. code-block:: python + + # show the embedding for each splitted doc + embeddings = to_embeddings(splitted_docs) + for embedding in embeddings: + print(f"the splitted doc: {embedding.text}") + print(f"the embedding of the doc: {embedding.vector}") + + # the splitted doc: Review: What a fantastic movie! Had a great time and would watch it again! Sentiment: + # the embedding of the doc: [-0.06596588, 0.10054244, -0.19306852, 0.045200635, -0.015742956, 0.017384859, 0.0625372, 0.13164201, 0.020113317, 0.0060756463, 0.056549083, -0.08199859, 0.0073946016, 0.0445487, 0.041820247, 0.046842538, 0.04500747, 0.015549791, 0.11802388, 0.11155285, -0.029578406, 0.08112934, -0.11686489, -0.0076722763, -0.0027390209, -0.024447458, -0.060026057, 0.03940568, 0.06881507, -0.035276778, 0.07113305, -0.022370934, 0.04363117, -0.07881136, -0.05220287, 0.025159756, -0.022491662, -0.07407882, -0.012519513, -0.0354458, 0.059977766, 0.056500793, 0.081225924, -0.006953944, -0.04090271, 0.01965455, -0.010159277, 0.02449575, -0.02094634, 0.034190223, -0.18089913, 0.08962861, -0.028274542, 0.18447268, -0.03841571, -0.030737398, -0.013799232, 0.03575969, 0.047301304, -0.122853, -0.029819863, 0.055824716, 0.055390093, 0.028250396, 0.03648406, -0.074030526, -0.049257103, -0.025304629, 0.0935402, -0.020777322, 0.04964343, 0.03438339, 0.015791247, -0.027646756, 0.06244062, -0.016853655, 0.078569904, -0.05133363, -0.017179621, -0.07456173, -0.115995646, -0.025763396, 0.00016600126, 0.0072255824, 0.039792012, -0.08397853, -0.024821715, 0.07794212, -0.09793471, 0.08277125, 0.13125569, 0.11618881, -0.028419416, -0.013642286, -0.0092658885, 0.0708433, -0.062150873, 0.085717015, 0.014752985, 0.065724425, 0.09793471, 0.0017067948, -0.0780387, -0.00018637415, 0.0841717, 0.07654167, 0.016503545, 0.049112227, 0.052106287, -0.037787925, -0.09213976, -0.020318555, -0.08658626, 0.06978089, -0.012314276, 0.028588437, 0.08494435, -0.09199488, -0.08861449, 0.06437227, 0.00093111617, -0.07185742, -0.010340369, -0.03414193, -0.041868538, -0.0442831, -0.009410762, -0.038294982, -0.015851611, -0.05331357, -0.0009763892, 0.07736263, -0.032958794, -0.033320982, -0.11396741, -0.025811687, -0.054617435, -0.044596992, 0.0003912348, 0.05408623, -0.03288636, -0.10875195, -0.05606617, -0.031992972, 0.13985154, 0.004110795, -0.052057996, 0.06842874, -0.04732545, 0.0098876385, 0.083737075, 0.037280865, -0.14545332, -0.02072903, 0.009845384, -0.046214752, -0.078763075, 0.033200253, -0.034673136, 0.04988489, -0.028709164, 0.01721584, -0.04882248, 0.0017369769, 0.029023057, 0.045755986, -0.048170548, -0.037715487, -0.007086745, -0.04882248, 0.055341803, -0.0048683644, -0.05877048, -0.050126344, 0.007424784, -0.0249183, -0.0016585035, 0.038898624, 0.055631552, 0.0035554452, -0.024773424, -0.0059066266, -0.054762308, 0.01102852, 0.040178344, -0.013376684, -0.016201723, 0.025449503, 0.042810217, 0.100156106, 0.06335816, 0.20417552, -0.016310379, -0.027719192, -0.032282718, -0.004391488, 0.062199164, 0.14796448, -0.045128196, -0.070553556, 0.08103276, 0.057031997, 0.028274542, -0.0946509, 0.03250003, -0.001274437, -0.06615905, -0.043776043, -0.0841717, -0.06799412, 0.17761531, -0.025014881, -0.029095493, -0.11164943, 0.019038836, -0.03530092, 0.017252058, 0.06205429, -0.040274926, 0.00063306844, -0.033031233, 0.010648226, 0.104888655, -0.07296812, 0.027526028, 0.08880766, 0.009380581, -0.01047317, -0.05794953, 0.08088789, -0.048242986, -0.0024688914, 0.027888212, -0.0708916, -0.026560202, -0.0055172783, 0.044017497, 0.01541699, -0.030351067, -0.077507496, 0.09614793, 0.09503723, 0.03520434, 0.017819481, -0.11174601, -0.0015121206, 0.04744618, 0.08962861, -0.014813349, 0.059543144, 0.060750425, -0.02170693, 0.0032596611, -0.09382995, 0.011336377, -0.088952534] + # the splitted doc: again! Sentiment: Positive + # the embedding of the doc: [0.067159414, -0.054115806, -0.057209868, 0.083539754, 0.016137673, 0.10210415, -0.035945754, 0.02686073, 0.04844335, -0.042224888, -0.011026398, -0.06033427, -0.028711103, -0.08426777, 0.13516818, 0.077776305, -0.024570517, 0.03940383, 0.002749016, 0.042285554, -0.008243256, 0.0042391727, 0.011329738, -0.029393617, -0.035399742, 0.02642089, -0.057968218, -0.028999276, 0.1156331, -0.07298353, 0.04865569, -0.09925275, 0.09172993, -0.01283127, -0.062427312, 0.10064811, -0.03773546, -0.00045548353, -0.04735133, -0.042649563, 0.025829377, -0.10046611, 0.0144541375, -0.022795979, -0.050688066, 0.046380643, -0.051385745, -0.09433865, -0.00888027, 0.062609315, -0.090455905, 0.043802254, 0.06351934, 0.17933443, -0.017199362, -0.022022463, 0.036582768, 0.025237864, -0.010723059, -0.031425994, 0.053175453, -0.11423773, 0.08256907, -0.06691674, -0.024995193, -0.0099192085, -0.083600424, 0.121821225, 0.09634069, 0.045500956, 0.02479802, -0.02781625, 0.048018675, 0.013225611, 0.06430802, 0.020702936, 0.1029535, -0.02787692, 0.028392596, -0.103074834, -0.091183916, 0.025344033, -0.001270235, -0.106168896, -0.02047543, -0.102468155, -0.034489725, 0.0025802834, -0.03148666, 0.11532976, 0.043104574, 0.091244586, -0.034338057, -0.0118985, -0.016319677, -0.048534352, -0.03567275, 0.10519821, 0.001149847, 0.08669449, 0.01600117, 0.029454285, -0.059393916, 0.09100191, 0.015500659, 0.039555497, 0.018579558, -0.039828505, -0.019186236, -0.008804435, -0.106957585, -0.0491107, -0.02959079, 0.07595626, -0.02435818, -0.030515974, -0.042922568, 0.06527871, -0.08432844, 0.0253592, -0.026527058, -0.051749755, -0.02435818, -0.031122655, -0.08275107, -0.11496575, -0.049474705, 0.035794087, -0.052417103, -0.099495426, 0.07753363, 0.012596182, 0.05202276, 0.10762493, -0.083054416, -0.035460413, -0.0013953627, -0.042952903, -0.05369113, -0.084389105, 0.09081991, -0.102407485, -0.021855626, -0.030561475, -0.00427709, 0.011663412, 0.0027831418, 0.01821555, 0.022917315, 0.09354997, -0.012414178, 0.13395482, -0.07989968, 0.015417241, 0.039919507, -0.10289283, 0.0032514224, -0.026845565, -0.04352925, 0.0065862634, -0.014256966, 0.03254835, -0.06109262, -0.045318954, 0.090455905, 0.05584484, -0.10204348, -0.10580489, -0.122670576, -0.1124177, 0.05338779, 0.020733269, -0.060758945, -0.03828147, -0.0045538875, -0.02349366, -0.063458666, -0.002240922, -0.019337907, 0.060455605, 0.018185215, 0.090455905, 0.07158817, -0.04537962, 0.03934316, -0.022083132, -0.017942544, 0.13577485, -0.00918361, 0.042497892, 0.014401053, 0.111386344, 0.12218524, 0.065521374, 0.026026547, -0.082872406, 0.08523846, 0.092639945, -0.03718945, -0.07510691, 0.02646639, 0.024691852, 0.07261953, -0.020202424, -0.043893255, -0.044408932, -0.036249094, 0.07249819, -0.08317575, 0.038463477, 0.17654371, -0.060940947, 0.07486424, -0.012740267, -0.08651248, 0.045409955, -0.057118867, 0.069404125, 0.079110995, 0.066492066, -0.024479514, 0.022583641, 0.08851453, 0.023175154, -0.024919357, 0.06424735, 0.09209394, -0.025283365, -0.0428619, -0.16574481, 0.025237864, -0.034793064, 0.076138265, -0.09148726, -0.005429781, 0.0035945757, 0.10950564, 0.0175027, -0.04537962, -0.036188427, -0.014545139, 0.085905805, 0.06358001, 0.07061748, -0.06995014, 0.017123526, 0.06782676, 0.005190901, -0.034398723, 0.05011172, 0.035096403, 0.0023736332, -0.039161157, -0.04874669, -0.012846436, -0.011178068] + +As you can see in the output, each splitted doc will have a vector representation. diff --git a/_sources/developer_notes/evaluation.rst.txt b/_sources/developer_notes/evaluation.rst.txt new file mode 100644 index 000000000..a5f563ead --- /dev/null +++ b/_sources/developer_notes/evaluation.rst.txt @@ -0,0 +1,105 @@ +A Guideline on LLM Evaluation +==================================== + +As the saying goes, "You cannot improve what you cannot measure". This is especially true in the context of LLMs, which have become increasingly popular due to their impressive performance on a wide range of tasks. Evaluating LLMs and their applications is crucial in both research and production to understand their capabilities and limitations. +Overall, such evaluation is a complex and multifaceted process. Below, we provide a guideline for evaluating LLMs and their applications, incorporating aspects outlined by *Chang et al.* [1]_: + +* **What to evaluate**: the tasks and capabilities that LLMs are evaluated on. +* **Where to evaluate**: the datasets and benchmarks that are used for evaluation. +* **How to evaluate**: the protocols and metrics that are used for evaluation. + + +What to evaluate? +------------------------------------------ +When we are considering the LLM evaluation, the first question that arises is what to evaluate. Deciding what tasks to evaluate or which capabilities to assess is crucial, as it influences both the selection of appropriate benchmarks (where to evaluate) and the choice of evaluation methods (how to evaluate). Below are some commonly evaluated tasks and capabilities of LLMs: + +* *Natural language understanding* (NLU) tasks, such as text classification and sentiment analysis, which evaluate the LLM's ability to understand natural language. +* *Natural language generation* (NLG) tasks, such as text summarization, translation, and question answering, which evaluate the LLM's ability to generate natural language. +* *Reasoning* tasks, such as mathematical, logic, and common-sense reasoning, which evaluate the LLM's ability to perform reasoning and inference to obtain the correct answer. +* *Robustness*, which evaluate the LLM's ability to generalize to unexpected inputs. +* *Fairness*, which evaluate the LLM's ability to make unbiased decisions. +* *Domain adaptation*, which evaluate the LLM's ability to adapt from general language to specific new domains, such as medical or legal texts, coding, etc. +* *Agent applications*, which evaluate the LLM's ability to use external tools and APIs to perform tasks, such as web search. + +For a more detailed and comprehensive description of the tasks and capabilities that LLMs are evaluated on, please refer to the review papers by *Chang et al.* [1]_ and *Guo et al.* [2]_. + +Where to evaluate? +------------------------------------------ +Once we have decided what to evaluate, the next question is where to evaluate. The selection of datasets and benchmarks is important, as it determines the quality and relevance of the evaluation. + +To comprehensively assess the capabilities of LLMs, researchers typically utilize benchmarks and datasets that span a broad spectrum of tasks. For example, in the GPT-4 technical report [3]_, the authors employed a variety of general language benchmarks, such as MMLU [4]_, and academic exams, such as the SAT, GRE, and AP courses, to evaluate the diverse capabilities of GPT-4. Below are some commonly used datasets and benchmarks for evaluating LLMs. + +* *MMLU* [4]_, which evaluates the LLM's ability to perform a wide range of language understanding tasks. +* *HumanEval* [5]_, which measures the LLM's capability in writing Python code. +* `HELM `_, which evaluates LLMs across diverse aspects such as language understanding, generation, common-sense reasoning, and domain adaptation. +* `Chatbot Arena `_, which is an open platform to evaluate LLMs through human voting. +* `API-Bank `_, which evaluates LLMs' ability to use external tools and APIs to perform tasks. + +Please refer to the review papers (*Chang et al.* [1]_, *Guo et al.* [2]_, and *Liu et al.* [6]_) for a more comprehensive overview of the datasets and benchmarks used in LLM evaluations. Additionally, a lot of datasets are readily accessible via the `Hugging Face Datasets `_ library. For instance, the MMLU dataset can be easily loaded from the Hub using the following code snippet. + +.. code-block:: python + :linenos: + + from datasets import load_dataset + dataset = load_dataset(path="cais/mmlu", name='abstract_algebra') + print(dataset["test"]) + # Dataset({ + # features: ['question', 'subject', 'choices', 'answer'], + # num_rows: 100 + # }) + +How to evaluate? +------------------------------------------ + +The final question is how to evaluate. Evaluation methods can be divided into *automated evaluation* and *human evaluation* (*Chang et al.* [1]_ and *Liu et al.* [6]_). Automated evaluation typically involves using metrics such as accuracy and BERTScore or employing an LLM as the judge, to quantitatively assess the performance of LLMs on specific tasks. Human evaluation, on the other hand, involves human in the loop to evaluate the quality of the generated text or the performance of the LLM. Here, we recommend a few automated evaluation methods that can be used to evaluate LLMs and their applications. + +If you are interested in computing metrics such as accuracy, F1-score, ROUGE, BERTScore, perplexity, etc for LLMs and LLM applications, you can check out the metrics provided by `Hugging Face Metrics `_ or `TorchMetrics `_. For instance, to compute the BERTScore, you can use the corresponding metric function provided by Hugging Face, which uses the pre-trained contextual embeddings from BERT and matched words in generated text and reference text by cosine similarity. + +.. code-block:: python + :linenos: + + from datasets import load_metric + bertscore = load_metric("bertscore") + generated_text = ["life is good", "aim for the stars"] + reference_text = ["life is great", "make it to the moon"] + results = bertscore.compute(predictions=generated_text, references=reference_text, model_type="distilbert-base-uncased") + print(results) + # {'precision': [0.9419728517532349, 0.7959791421890259], 'recall': [0.9419728517532349, 0.7749403119087219], 'f1': [0.9419728517532349, 0.7853187918663025], 'hashcode': 'distilbert-base-uncased_L5_no-idf_version=0.3.12(hug_trans=4.38.2)'} + +If you are particulay interested in evaluating RAG (Retrieval-Augmented Generation) pipelines, we have several metrics available in LightRAG to assess both the quality of the retrieved context and the quality of the final generated answer. + +- :class:`RetrieverEvaluator `: This evaluator is used to evaluate the performance of the retriever component of the RAG pipeline. It has metric functions to compute the recall and context relevance of the retriever. +- :class:`AnswerMacthEvaluator `: This evaluator is used to evaluate the performance of the generator component of the RAG pipeline. It has metric functions to compute the exact match and fuzzy match accuracy of the generated answer. +- :class:`LLMasJudge `: This evaluator uses an LLM to get the judgement of the predicted answer for a list of questions. The task description and the judgement query of the LLM judge can be customized. It has a metric function to compute the judgement score, which is the number of generated answers that are judged as correct by the LLM divided by the total number of generated answers. + +For example, you can use the following code snippet to compute the recall and relevance of the retriever component of the RAG pipeline for a single query. + +.. code-block:: python + :linenos: + + from eval.evaluators import RetrieverEvaluator + retrieved_context = "Apple is founded before Google." # Retrieved context + gt_context = ["Apple is founded in 1976.", + "Google is founded in 1998.", + "Apple is founded before Google."] # Ground truth context + retriever_evaluator = RetrieverEvaluator() # Initialize the RetrieverEvaluator + recall = retriever_evaluator.compute_recall_single_query( + retrieved_context, gt_context + ) # Compute the recall of the retriever + relevance = retriever_evaluator.compute_context_relevance_single_query( + retrieved_context, gt_context + ) # Compute the relevance of the retriever + print(f"Recall: {recall}, Relevance: {relevance}") + # Recall: 0.3333333333333333, Relevance: 1.0 + +For a more detailed instructions on how to use these evaluators to evaluate RAG pipelines, you can refer to the tutorial on :doc:`Evaluating a RAG Pipeline <../tutorials/eval_a_rag>`, where we provide a step-by-step guide on how to use these evaluators to evaluate a RAG pipeline on HotpotQA dataset. + +If you intent to use metrics that are not available in the LightRAG library, you can also implement your own custom metric functions or use other libraries such as `RAGAS `_ to compute the desired metrics for evaluating RAG pipelines. + + +.. [1] Chang, Yupeng, et al. "A survey on evaluation of large language models." ACM Transactions on Intelligent Systems and Technology 15.3 (2024): 1-45. +.. [2] Guo, Zishan, et al. "Evaluating large language models: A comprehensive survey." arXiv preprint arXiv:2310.19736 (2023). +.. [3] Achiam, Josh, et al. "GPT-4 technical report." arXiv preprint arXiv:2303.08774 (2023). +.. [4] Hendrycks, Dan, et al. "Measuring massive multitask language understanding." International Conference on Learning Representations. 2020. +.. [5] Chen, Mark, et al. "Evaluating large language models trained on code." arXiv preprint arXiv:2107.03374 (2021). +.. [6] Liu, Yang, et al. "Datasets for Large Language Models: A Comprehensive Survey." arXiv preprint arXiv:2402.18041 (2024). diff --git a/_sources/developer_notes/generator.rst.txt b/_sources/developer_notes/generator.rst.txt new file mode 100644 index 000000000..00625a47e --- /dev/null +++ b/_sources/developer_notes/generator.rst.txt @@ -0,0 +1,215 @@ +.. _generator: + +Generator +========= +*The Center of it All* + +Generator is the most essential functional component in LightRAG. +It is a user-facing orchestration component with a simple and unified interface for LLM prediction. +It orchestrates the following components along with their required arguments: + +- ``Prompt`` + +- ``ModelClient`` + +- Output processors to process the raw string response to desired format. + +By switching out the model client, you can call any LLM model on your prompt, either API or local. + +GeneratorOutput +^^^^^^^^^^^^^^^ +Different from all other components, we can not alway enforce LLM to output the right format. +We in particular created a :class:`core.types.GeneratorOutput` (a subclass of ``DataClass``) to store `data` (parsed response), `error` (error message if either the model inference SDKs fail or the output parsing fail) and `raw_response` (raw string response for reference) for any LLM predictions. +It is in developers' hands to process the output accordingly. + +GeneratorInAction +^^^^^^^^^^^^^^^^^ +Beside of these examples, LLM is like water, even in our library, we have components that have adpated Generator to other various functionalities. + +- :class:`components.retriever.llm_retriever.LLMRetriever` is a retriever that uses Generator to call LLM to retrieve the most relevant documents. +- :class:`eval.llm_as_judge.DefaultLLMJudge` is a judge that uses Generator to call LLM to evaluate the quality of the response. +- :class:`optim.llm_optimizer.LLMOptimizer` is an optimizer that uses Generator to call LLM to optimize the prompt. + +Tracing +^^^^^^^^^^^ +In particular, we provide two tracing methods to help you develop and improve the Generator: + +1. Trace the history change(states) on prompt during your development process. Developers typically go through a long process of prompt optimization and it is frustrating +to lose track of the prompt changes when your current change actually makes the performance much worse. + +We created a `GeneratorStateLogger` to handle the logging and saving into json files. To further simplify developers's process, +we provides a class decorator `trace_generator_states` where a single line of code can be added to any of your task component. +It will automatically track any attributes of type `Generator`. + +.. code-block:: python + + from tracing import trace_generator_states + from core import Component, Generator + + @trace_generator_states() + class SimpleQA(Component): + def __init__(self): + super().__init__() + self.generator = Generator(...) + self.generator_2 = Generator(...) + def call(...): + +In default, a dir from the current working directory will be created to store the log files. +The project name in defaul is `SimpleQA` and the log file will be named as `generator_state_trace.json` +where both the `generator` and `generator_2` will be logged. +The structure of log directory is as follows: + +.. code-block:: bash + + . + ├── traces + │ ├── SimpleQA + │ │ ├── generator_state_trace.json + + + +Here is an example log file: + +.. code-block:: json + + { + "generator": [ + { + "prompt_states": { + "_components": {}, + "_parameters": {}, + "training": false, + "_template_string": "{# task desc #}\n{% if task_desc_str %}\n{{task_desc_str}}\n{% else %}\nAnswer user query.\n{% endif %}\n{# output format #}\n{% if output_format_str %}\n\n{{output_format_str}}\n\n{% endif %}\n{# tools #}\n{% if tools_str %}\n\n{{tools_str}}\n\n{% endif %}\n{# example #}\n{% if examples_str %}\n\n{{examples_str}}\n\n{% endif %}\n{# chat history #}\n{% if chat_history_str %}\n\n{{chat_history_str}}\n\n{% endif %}\n{#contex#}\n{% if context_str %}\n\n{{context_str}}\n\n{% endif %}\n{# steps #}\n{% if steps_str %}\n\n{{steps_str}}\n\n{% endif %}\n{% if input_str %}\n\n{{input_str}}\n\n{% endif %}\n{% if output_str %}\n\n{{output_str}}\n\n{% endif %}\n", + "prompt_variables": [ + "chat_history_str", + "context_str", + "examples_str", + "input_str", + "output_format_str", + "output_str", + "steps_str", + "task_desc_str", + "tools_str" + ], + "preset_prompt_kwargs": { + "task_desc_str": "You are a helpful assistant and with a great sense of humor." + } + }, + "time_stamp": "2024-06-02T15:55:21.765794" + }, + { + "prompt_states": { + "_components": {}, + "_parameters": {}, + "training": false, + "_template_string": "{# task desc #}\n{% if task_desc_str %}\n{{task_desc_str}}\n{% else %}\nAnswer user query.\n{% endif %}\n{# output format #}\n{% if output_format_str %}\n\n{{output_format_str}}\n\n{% endif %}\n{# tools #}\n{% if tools_str %}\n\n{{tools_str}}\n\n{% endif %}\n{# example #}\n{% if examples_str %}\n\n{{examples_str}}\n\n{% endif %}\n{# chat history #}\n{% if chat_history_str %}\n\n{{chat_history_str}}\n\n{% endif %}\n{#contex#}\n{% if context_str %}\n\n{{context_str}}\n\n{% endif %}\n{# steps #}\n{% if steps_str %}\n\n{{steps_str}}\n\n{% endif %}\n{% if input_str %}\n\n{{input_str}}\n\n{% endif %}\n{% if output_str %}\n\n{{output_str}}\n\n{% endif %}\n", + "prompt_variables": [ + "chat_history_str", + "context_str", + "examples_str", + "input_str", + "output_format_str", + "output_str", + "steps_str", + "task_desc_str", + "tools_str" + ], + "preset_prompt_kwargs": { + "task_desc_str": "You are a helpful assistant and with a great sense of humor. Second edition." + } + }, + "time_stamp": "2024-06-02T15:56:37.756148" + } + ], + "generator2": [ + { + "prompt_states": { + "_components": {}, + "_parameters": {}, + "training": false, + "_template_string": "{# task desc #}\n{% if task_desc_str %}\n{{task_desc_str}}\n{% else %}\nAnswer user query.\n{% endif %}\n{# output format #}\n{% if output_format_str %}\n\n{{output_format_str}}\n\n{% endif %}\n{# tools #}\n{% if tools_str %}\n\n{{tools_str}}\n\n{% endif %}\n{# example #}\n{% if examples_str %}\n\n{{examples_str}}\n\n{% endif %}\n{# chat history #}\n{% if chat_history_str %}\n\n{{chat_history_str}}\n\n{% endif %}\n{#contex#}\n{% if context_str %}\n\n{{context_str}}\n\n{% endif %}\n{# steps #}\n{% if steps_str %}\n\n{{steps_str}}\n\n{% endif %}\n{% if input_str %}\n\n{{input_str}}\n\n{% endif %}\n{% if output_str %}\n\n{{output_str}}\n\n{% endif %}\n", + "prompt_variables": [ + "chat_history_str", + "context_str", + "examples_str", + "input_str", + "output_format_str", + "output_str", + "steps_str", + "task_desc_str", + "tools_str" + ], + "preset_prompt_kwargs": { + "task_desc_str": "You are the second generator." + } + }, + "time_stamp": "2024-06-03T16:44:45.223220" + } + ] + } + +2. Trace all failed LLM predictions for further improvement. + +Similarly, :class:`tracing.generator_call_logger.GeneratorCallLogger` is created to log generator call input arguments and output results. +`trace_generator_call` decorator is provided to provide one-line setup to trace calls, which in default will log only failed predictions. + +Adding the second decorator to the above example: + +.. code-block:: python + + from tracing import trace_generator_errors + + @trace_generator_call() + @trace_generator_states() + class SimpleQA(Component): + def __init__(self): + super().__init__() + self.generator = Generator(...) + self.generator_2 = Generator(...) + def call(...): + +Now, three more files will be created in the log directory: + +.. code-block:: bash + + . + ├── traces + │ ├── SimpleQA + │ │ ├── logger_metadata.json + │ │ ├── generator_call.jsonl + │ │ ├── generator_2_call.jsonl + +The `logger_metadata.json` file contains the metadata of the logger, it looks like this: + +.. code-block:: json + + { + "generator": "./traces/SimpleQA/generator_call.jsonl", + "generator2": "./traces/SimpleQA/generator2_call.jsonl" + } + +The `generator_call.jsonl` file contains the log of all calls to the generator, it looks like this: + +.. code-block:: json + + {"prompt_kwargs": {"input_str": "What is the capital of France?"}, "model_kwargs": {}, "output": {"data": "Bonjour!\n\nThe capital of France is Paris, of course! But did you know that the Eiffel Tower in Paris is actually the most-visited paid monument in the world? Mind-blowing, right?\n\nNow, would you like to know some more fun facts or perhaps ask another question? I'm all ears (or should I say, all eyes?)", "error_message": null, "raw_response": "Bonjour!\n\nThe capital of France is Paris, of course! But did you know that the Eiffel Tower in Paris is actually the most-visited paid monument in the world? Mind-blowing, right?\n\nNow, would you like to know some more fun facts or perhaps ask another question? I'm all ears (or should I say, all eyes?)"}, "time_stamp": "2024-06-03T16:44:45.582859"} + +.. note :: + + Usually, let the evaluation run on evaluation to collect as much as failed predictions can be highly helpful for either manual prompting or auto-prompt engineering (APE). + +Training [Experimental] +^^^^^^^^^^^^^^^^^^^^^^^ + +.. A Note on Tokenization# +.. By default, LlamaIndex uses a global tokenizer for all token counting. This defaults to cl100k from tiktoken, which is the tokenizer to match the default LLM gpt-3.5-turbo. + +.. If you change the LLM, you may need to update this tokenizer to ensure accurate token counts, chunking, and prompting. + +.. admonition:: API reference + :class: highlight + + - :class:`core.generator.Generator` + - :class:`core.types.GeneratorOutput` + - :class:`tracing.generator_call_logger.GeneratorCallLogger` + - :class:`tracing.generator_state_logger.GeneratorStateLogger` diff --git a/_sources/developer_notes/index.rst.txt b/_sources/developer_notes/index.rst.txt new file mode 100644 index 000000000..28cb8c412 --- /dev/null +++ b/_sources/developer_notes/index.rst.txt @@ -0,0 +1,236 @@ +.. _developer_notes: + + +Developer Notes +============================= + +*Why and How each part works* + +Learn LightRAG design phisolophy and the `why` and `how-to` (customize and integrate) behind each core part within the LightRAG library. +This is our tutorials before you move ahead to build use cases (LLM applications) end to end. + +.. note:: + + You can read interchangably between :ref:`Use Cases `. + + + +.. figure:: /_static/images/LLM_arch.png + :alt: LLM application is no different from a mode training/eval workflow + :align: center + :width: 600px + + LLM application is no different from a mode training/eval workflow + + .. :height: 100px + .. :width: 200px + +LightRAG library focus on providing building blocks for developers to **build** and **optimize** the `task pipeline`. +We have clear design phisolophy: + + + +.. toctree:: + :maxdepth: 1 + :caption: Introduction + + lightrag_design_philosophy + + llm_intro + + + + +Building +============================= + +Base classes +--------------- +Code path: ``lightrag.core``. + +.. list-table:: + :widths: 20 80 + :header-rows: 1 + + * - Base Class + - Description + * - :doc:`component` + - Similar to ``Module`` in `PyTorch`, it standardizes the interface of all components with `call`, `acall`, and `__call__` methods, handles states, and serialization. Components can be easily chained togehter via `Sequential` for now. + * - :doc:`base_data_class` + - Leverages the ``dataclasses`` module in Python to ease the data interaction with prompt and serialization. + + + + +.. create side bar navigation + +.. toctree:: + :maxdepth: 1 + :caption: Base Classes + :hidden: + + component + base_data_class + +RAG Essentials +------------------- +RAG components +~~~~~~~~~~~~~~~~~~~~~~ + +Code path: ``lightrag.core``. For abstract classes: + +- ``ModelClient``: the functional subclass is in ``lightrag.components.model_client``. +- ``Retriever``: the functional subclass is in ``lightrag.components.retriever``. It works hand-in-hand with the ``LocalDB`` and Cloud DB in ``lightrag.database``. + + +.. list-table:: + :widths: 20 80 + :header-rows: 1 + + * - Part + - Description + * - :doc:`prompt` + - Built on ``jinja2``, it programmablly and flexibly format prompt(text) as **input to the generator**. + * - :doc:`model_client` + - ``ModelClient`` is the protocol and base class for LightRAG to **integrate all models**, either APIs or local, LLMs or Embedding models or any others. + * - :doc:`generator` + - The **center component** that orchestrates the model client(LLMs in particular), prompt, and output processors for format parsing or any post processing. + * - :doc:`embedder` + - The component that orchestrates model client (Embedding models in particular) and output processors. + * - :doc:`retriever` + - The base class for all retrievers who in particular retrieve relevant documents from a given database to add **context** to the generator. + +Data, Processing, and storage +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Data Processing: including transformer, pipeline, and storage. Code path: ``lightrag.components.data_process``, ``lightrag.core.db``, and ``lightrag.database``. +Components work on a sequence of ``Document`` and return a sequence of ``Document``. + +.. list-table:: + :widths: 20 80 + :header-rows: 1 + + * - Part + - Description + * - :doc:`text_splitter` + - To split long text into smaller chunks to fit into the token limits of embedder and generator or to ensure more relevant context while being used in RAG. + * - :doc:`db` + - Understanding the **data modeling, processing, and storage** as a whole. We will build a chatbot with enhanced memory and memoy retrieval in this note (RAG). + + +.. * - :doc:`data_pipeline` +.. - The pipeline to process data, including text splitting, embedding, and retrieval. + +.. Let us put all of these components together to build a :doc:`rag` (Retrieval Augmented Generation), which requires data processing pipeline along with a task pipeline to run user queries. + +.. toctree:: + :maxdepth: 1 + :caption: RAG Essentials + :hidden: + + prompt + model_client + generator + embedder + retriever + text_splitter + db + + + +Agent Essentials +----------------------------- +Agent in ``components.agent`` is LLM great with reasoning, planning, and using tools to interact and accomplish tasks. + +.. list-table:: + :widths: 30 70 + :header-rows: 1 + + * - Part + - Description + * - :doc:`tool_helper` + - Provide tools (function calls) to interact with the generator. + * - :doc:`agent` + - The ReactAgent. + +.. toctree:: + :maxdepth: 1 + :caption: Agent Essentials + :hidden: + + tool_helper + agent + +.. Core functionals +.. ------------------- +.. Code path: ``lightrag.core`` + +.. :widths: 20 80 +.. :header-rows: 1 + +.. * - Functional +.. - Description +.. * - :doc:`string_parser` +.. - Parse the output string to structured data. +.. * - :doc:`tool_helper` +.. - Provide tools to interact with the generator. + +.. * - :doc:`memory` +.. - Store the history of the conversation. + + + + + + + + +Optimizing +============================= + +Datasets and Evaulation + +.. toctree:: + :maxdepth: 1 + :caption: Datasets and Evaulation + + + datasets + + evaluation + + +Optimizer & Trainer + +.. toctree:: + :maxdepth: 1 + :caption: Optimizer & Trainer + + parameter + + optimizer + trainer + + +Logging & Tracing & Configurations +============================= +Code path: ``lightrag.utils``. + +.. list-table:: + :widths: 20 80 + :header-rows: 1 + + * - Part + - Description + * - :doc:`logging` + - LightRAG uses ``logging`` module as the first defense line to help users debug the code. We made the effort to help you set it up easily. + +.. toctree:: + :maxdepth: 1 + :caption: Logging & Tracing & Configurations + :hidden: + + + logging + logging_tracing + configs diff --git a/_sources/developer_notes/lightrag_design_philosophy.rst.txt b/_sources/developer_notes/lightrag_design_philosophy.rst.txt new file mode 100644 index 000000000..bad3a501f --- /dev/null +++ b/_sources/developer_notes/lightrag_design_philosophy.rst.txt @@ -0,0 +1,62 @@ +LightRAG Design Philosophy +==================================== + +.. Deep understanding of the LLM workflow +.. --------------------------------------- + +LLMs are like `water`, it is all up to users to shape it into different use cases. In `PyTorch`, most likely users do not need to build their +own ``conv`` or ``linear`` module, or their own ``Adam`` optimizer. Their building blocks can meet > 90% of their user's needs on `building` and +`optimizing` (training) their models, leaving less than 10% of users, mostly contributors and researchers to build their own ``Module``, ``Tensor``, +``Optimizer``, etc. Libraries like `PyTorch`, `numpy`, `scipy`, `sklearn`, `pandas`, etc. are all doing the heavy lifting on the computation optimization. +However, for developers to write their own LLM task pipeline, calling apis or using local LLMs to shape the LLMs via prompt into any use case is not a hard feat. +The hard part is on `evaluating` and `optimizing` their task pipeline. + +Optimizing over Building +----------------------------------------------------------------------- + + We help users to build the task pipeline, but we want to help on optimizing even more so. + +In fact, building the task pipeline accounts for only **10%** of users' development process, the other **90%** is on optimtizing and iterating. +The most popular libraries like ``Langchain`` and ``LlamaIndex`` are mainly focusing on `building` the task pipeline, prioritizing integrations and coveraging on different type of tasks, resulting large amounts of classes, each +with many layers of class inheritance. With the existing libraries, users get stuck on just following the examples, and it requires more time for them to figure out customization than writing their +own code. + +How to `build` the task pipeline has starting to mature: `prompt`, `retriever`, `generator`, `RAG`, `Agent` has becoming well-known concepts. +How to `optimize` the task pipeline is still a mystery to most users. And most are still doing `manual` prompt engineering without good +`observability` (or `debugging` ) tools. And these existing `observability` tools are mostly commercialized, prioritizing the `fancy` looks without +real deep understanding of the LLM workflow. + +The existing optimization process of LLM applications are full of frustrations. + +Quality over Quantity +----------------------------------------------------------------------- + + The Quality of core building blocks over the Quantity of integrations. + +The whole `PyTorch` library is built on a few core and base classes: ``Module``, ``Tensor``, ``Parameter``, and ``Optimizer``, +and various ``nn`` modules for users to build a model, along with ``functionals``. +This maps to ``Component``, ``DataClass``, ``Parameter``, and ``Optimizer`` in LightRAG, and various subcomponents +like ``Generator``, ``Retriever``, ``Prompt``, ``Embedder``, ``ModelClient``, along with ``functionals`` to process string, +interprect tool from the string. + +We recognize developers who are building real-world Large Language Model (LLM) applications are the real heroes, doing the hard +work. They need well-designed core building blocks: **easy** to understand, **transparent** to debug, **flexible** enough to customize their own +``ModelClient``, their own ``Prompt``, their own ``Generator`` and even their own ``Optimizer``, ``Trainer``. The need to build their own component is even more so than using `PyTorch.` +LightRAG aggressively focus on the quality and clarity of the core building blocks over the quantity of integrations. + + + +Practicality over Showmanship +----------------------------------------------------------------------- +We put these three hard rules while designing LightRAG: + +- Every layer of abstraction needs to be adjusted and overall we do not allow more than 3 layers of abstraction. +- We minimize the lines of code instead of maximizing the lines of code. +- Go `deep` and `wide` in order to `simplify`. The clarity we achieve is not the result of being easy, but the result of being deep. + + + + + +[Optional] Side story: How `LightRAG` is born +---------------------------------------------- \ No newline at end of file diff --git a/_sources/developer_notes/llm_intro.rst.txt b/_sources/developer_notes/llm_intro.rst.txt new file mode 100644 index 000000000..7612093bd --- /dev/null +++ b/_sources/developer_notes/llm_intro.rst.txt @@ -0,0 +1,84 @@ +Introduction +==================================== + +Introduction to LLM applations +------------------------------------- +All language models are doing is text completion, which means you pass a input text(called "prompt") to the model, and the model will generate the rest of the text for you. The model is trained to predict the next word given the previous words, so it can generate text that is coherent and relevant to the prompt. +However, most API providers such as OpenAI, Anthropic provide the model in Chat Completions API, which take a list of messages instead of a `single text` as input. Why is that? We will have our assumptions in the next section. + +Here is one example from OpenAI: + +.. code-block:: python + :linenos: + + from openai import OpenAI + client = OpenAI() + + response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who won the world series in 2020?"}, + {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."}, + {"role": "user", "content": "Where was it played?"} + ] + ) + +You can notice the first is the "system" message, and with interleave of "user" and "assistant" messages after that. +Different API providers may have different input format, e.g. Anthropic's system is a field itself and requires a string. + +.. code-block:: python + :linenos: + + import anthropic + + anthropic.Anthropic().messages.create( + model="claude-3-opus-20240229", + system="You are a helpful assistant.", + max_tokens=1024, + messages=[ + {"role": "user", "content": "Hello, world"} + ] + ) + +Why do we need chat completion instead of the previous text completion? +----------------------------------------------------------------------- +There are generally three roles("system", "user", "assistant") in chat, and potentially more especially in multi-agent. +Behind the scene, when the API provider receives the messages, they will compose them into a single text using special tokens to denote different messages. +If we follow `Llama3 special tokens `_, the above anthropic example will be converted into text: + +.. code-block:: + :linenos: + + <|begin_of_text|><|start_header_id|>system<|end_header_id|> + You are a helpful assistant. <|eot_id> + <|start_header_id|>user<|end_header_id|> + Hello, world <|eot_id> + <|start_header_id>assistant<|end_header_id> + +And the model will generate the text response with `<|end_of_text|>` token to denote the end of the text. + +The special tokens vary from different language models, and with propetriary models, most of them dont disclose the special tokens. Why? + +1. They keep more secrets about their model. + +2. Without knowing the special tokens, developers will be more dependant on their API, especially for advanced features like `Tools`, `Special output format`. + +But, you don't have to use their advanced features, our library in default will support those advanced features, ensuring developers to have `maximum control` and be almost model-agnostic. + + +Data Flow & LightRAG structure +----------------------------------- + +TODO: Add our data flow diagram here. + + +**Read on to see how.** + +Resources +--------------------- +[1] `OpenAI text generation `_ + +[2] `Anthropic text generation `_ + +[3] `Meta Llama3 `_ \ No newline at end of file diff --git a/_sources/developer_notes/logging.rst.txt b/_sources/developer_notes/logging.rst.txt new file mode 100644 index 000000000..ef950da97 --- /dev/null +++ b/_sources/developer_notes/logging.rst.txt @@ -0,0 +1,2 @@ +Logging +==================== diff --git a/_sources/developer_notes/logging_tracing.rst.txt b/_sources/developer_notes/logging_tracing.rst.txt new file mode 100644 index 000000000..5ea0af875 --- /dev/null +++ b/_sources/developer_notes/logging_tracing.rst.txt @@ -0,0 +1,2 @@ +Logging & Tracing +================== \ No newline at end of file diff --git a/_sources/developer_notes/model_client.rst.txt b/_sources/developer_notes/model_client.rst.txt new file mode 100644 index 000000000..98b21bea9 --- /dev/null +++ b/_sources/developer_notes/model_client.rst.txt @@ -0,0 +1,366 @@ +ModelClient +============ +What you will learn? + +1. What is ``ModelClient`` and why is it designed this way? +2. How to intergrate your own ``ModelClient``? +3. How to use ``ModelClient`` directly? + +:ref:`ModelClient` is the standardized protocol and base class for all model inference SDKs (either via APIs or local) to communicate with LightRAG internal components/classes. +Because so, by switching off ``ModelClient`` in a ``Generator`` or ``Embedder`` component, you can make your prompt or ``Retriever`` model-agnostic. + + +.. figure:: /_static/images/model_client.png + :align: center + :alt: ModelClient + :width: 400px + + The interface to internal components in LightRAG + +.. note:: + + All users are encouraged to customize your own ``ModelClient`` whenever you need to do so. You can refer our code in ``components.model_client`` dir. + +Model Inference SDKs +------------------------ +With cloud API providers like OpenAI, Groq, Anthropic, it often comes with a `sync` and an `async` client via their SDKs. +For example: + +.. code-block:: python + + from openai import OpenAI, AsyncOpenAI + + sync_client = OpenAI() + async_client = AsyncOpenAI() + + # sync call using APIs + response = sync_client.chat.completions.create(...) + +For local models, such as using `huggingface transformers`, you need to create this model inference SDKs yourself. +How you do this is highly flexible. Here is an example to use local embedding model (e.g. ``thenlper/gte-base``) as a model (Refer :class:`components.model_client.transformers_client.TransformerEmbedder` for details). +It really is just normal model inference code. + +.. code-block:: python + + from transformers import AutoTokenizer, AutoModel + + class TransformerEmbedder: + models: Dict[str, type] = {} + + def __init__(self, model_name: Optional[str] = "thenlper/gte-base"): + super().__init__() + + if model_name is not None: + self.init_model(model_name=model_name) + + @lru_cache(None) + def init_model(self, model_name: str): + try: + self.tokenizer = AutoTokenizer.from_pretrained(model_name) + self.model = AutoModel.from_pretrained(model_name) + # register the model + self.models[model_name] = self.model + + except Exception as e: + log.error(f"Error loading model {model_name}: {e}") + raise e + + def infer_gte_base_embedding( + self, + input=Union[str, List[str]], + tolist: bool = True, + ): + model = self.models.get("thenlper/gte-base", None) + if model is None: + # initialize the model + self.init_model("thenlper/gte-base") + + if isinstance(input, str): + input = [input] + # Tokenize the input texts + batch_dict = self.tokenizer( + input, max_length=512, padding=True, truncation=True, return_tensors="pt" + ) + outputs = model(**batch_dict) + embeddings = average_pool( + outputs.last_hidden_state, batch_dict["attention_mask"] + ) + # (Optionally) normalize embeddings + embeddings = F.normalize(embeddings, p=2, dim=1) + if tolist: + embeddings = embeddings.tolist() + return embeddings + + def __call__(self, **kwargs): + if "model" not in kwargs: + raise ValueError("model is required") + # load files and models, cache it for the next inference + model_name = kwargs["model"] + # inference the model + if model_name == "thenlper/gte-base": + return self.infer_gte_base_embedding(kwargs["input"]) + else: + raise ValueError(f"model {model_name} is not supported") + + + + + +ModelClient Protocol +----------------------------------------------------------------------------------------------------------- +A model client can be used to manage different types of models, we defined a ``ModelType`` to categorize the model type. + +.. code-block:: python + + class ModelType(Enum): + EMBEDDER = auto() + LLM = auto() + UNDEFINED = auto() + +We designed 6 abstract methods in the ``ModelClient`` class to be implemented by the subclass model type. +We will use :class:`components.model_client.OpenAIClient` along with the above ``TransformerEmbedder`` as examples. + +First, we offer two methods to initialize the model SDKs: + +.. code-block:: python + + def init_sync_client(self): + raise NotImplementedError( + f"{type(self).__name__} must implement _init_sync_client method" + ) + + def init_async_client(self): + raise NotImplementedError( + f"{type(self).__name__} must implement _init_async_client method" + ) + +This is how `OpenAIClient` implements these methods along with ``__init__`` method: + +.. code-block:: python + + class OpenAIClient(ModelClient): + + def __init__(self, api_key: Optional[str] = None): + + super().__init__() + self._api_key = api_key + self.sync_client = self.init_sync_client() + self.async_client = None # only initialize if the async call is called + + def init_sync_client(self): + api_key = self._api_key or os.getenv("OPENAI_API_KEY") + if not api_key: + raise ValueError("Environment variable OPENAI_API_KEY must be set") + return OpenAI(api_key=api_key) + + def init_async_client(self): + api_key = self._api_key or os.getenv("OPENAI_API_KEY") + if not api_key: + raise ValueError("Environment variable OPENAI_API_KEY must be set") + return AsyncOpenAI(api_key=api_key) + +This is how ``TransformerClient`` does the same thing: + +.. code-block:: python + + class TransformersClient(ModelClient): + def __init__(self) -> None: + super().__init__() + self.sync_client = self.init_sync_client() + self.async_client = None + support_model_list = { + "thenlper/gte-base": { + "type": ModelType.EMBEDDER, + } + } + + def init_sync_client(self): + return TransformerEmbedder() + + +Second. we use `convert_inputs_to_api_kwargs` for subclass to convert LightRAG inputs into the `api_kwargs` (SDKs arguments). + +.. code-block:: python + + def convert_inputs_to_api_kwargs( + self, + input: Optional[Any] = None, + model_kwargs: Dict = {}, + model_type: ModelType = ModelType.UNDEFINED, + ) -> Dict: + raise NotImplementedError( + f"{type(self).__name__} must implement _combine_input_and_model_kwargs method" + ) + +This is how `OpenAIClient` implements this method: + +.. code-block:: python + + def convert_inputs_to_api_kwargs( + self, + input: Optional[Any] = None, + model_kwargs: Dict = {}, + model_type: ModelType = ModelType.UNDEFINED, + ) -> Dict: + + final_model_kwargs = model_kwargs.copy() + if model_type == ModelType.EMBEDDER: + if isinstance(input, str): + input = [input] + # convert input to input + assert isinstance(input, Sequence), "input must be a sequence of text" + final_model_kwargs["input"] = input + elif model_type == ModelType.LLM: + messages: List[Dict[str, str]] = [] + if input is not None and input != "": + messages.append({"role": "system", "content": input}) + assert isinstance( + messages, Sequence + ), "input must be a sequence of messages" + final_model_kwargs["messages"] = messages + else: + raise ValueError(f"model_type {model_type} is not supported") + return final_model_kwargs + +This is how ``TransformerClient`` does the same thing: + +.. code-block:: python + + def convert_inputs_to_api_kwargs( + self, + input: Any, + model_kwargs: dict = {}, + model_type: ModelType = ModelType.UNDEFINED, + ) -> dict: + final_model_kwargs = model_kwargs.copy() + if model_type == ModelType.EMBEDDER: + final_model_kwargs["input"] = input + return final_model_kwargs + else: + raise ValueError(f"model_type {model_type} is not supported") + +In addition, you can add any method that parse the SDK specific output to a format compatible with LightRAG components. +Typically an LLM needs to use `parse_chat_completion` to parse the completion to texts and `parse_embedding_response` to parse the embedding response to a structure LightRAG components can understand. + + +.. code-block:: python + + def parse_chat_completion(self, completion: Any) -> str: + raise NotImplementedError( + f"{type(self).__name__} must implement parse_chat_completion method" + ) + + def parse_embedding_response(self, response: Any) -> EmbedderOutput: + r"""Parse the embedding response to a structure LightRAG components can understand.""" + raise NotImplementedError( + f"{type(self).__name__} must implement parse_embedding_response method" + ) + +You can refer to :class:`components.model_client.openai_client.OpenAIClient` for API embedding model integration and :class:`components.model_client.transformers_client.TransformersClient` for local embedding model integration. + +Then `call` and `acall` methods to call Model inference via their own arguments. +We encourage the subclass provides error handling and retry mechanism in these methods. + +.. code-block:: python + + def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): + raise NotImplementedError(f"{type(self).__name__} must implement _call method") + + async def acall( + self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED + ): + pass + +The `OpenAIClient` example: + +.. code-block:: python + + def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): + if model_type == ModelType.EMBEDDER: + return self.sync_client.embeddings.create(**api_kwargs) + elif model_type == ModelType.LLM: + return self.sync_client.chat.completions.create(**api_kwargs) + else: + raise ValueError(f"model_type {model_type} is not supported") + +The `TransformerClient` example: + +.. code-block:: python + + def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): + return self.sync_client(**api_kwargs) + + +Our library currently integrated with 5 providers: OpenAI, Groq, Anthropic, Huggingface, and Google. +Please check out :ref:`ModelClient Integration`. + +Use ModelClient directly +----------------------------------------------------------------------------------------------------------- +Though ``ModelClient`` is often managed in a ``Generator`` or ``Embedder`` component, you can use it directly if you ever plan to write your own component. +Here is an example to use ``OpenAIClient`` directly, first on LLM model: + +.. code-block:: python + + from lightrag.components.model_client import OpenAIClient + from lightrag.core.types import ModelType + from lightrag.utils import setup_env + + openai_client = OpenAIClient() + + query = "What is the capital of France?" + + # try LLM model + model_type = ModelType.LLM + + prompt = f"User: {query}\n" + model_kwargs = {"model": "gpt-3.5-turbo", "temperature": 0.5, "max_tokens": 100} + api_kwargs = openai_client.convert_inputs_to_api_kwargs(input=prompt, + model_kwargs=model_kwargs, + model_type=model_type) + print(f"api_kwargs: {api_kwargs}") + + response = openai_client.call(api_kwargs=api_kwargs, model_type=model_type) + response_text = openai_client.parse_chat_completion(response) + print(f"response_text: {response_text}") + +The output will be: + +.. code-block:: + + api_kwargs: {'model': 'gpt-3.5-turbo', 'temperature': 0.5, 'max_tokens': 100, 'messages': [{'role': 'system', 'content': 'User: What is the capital of France?\n'}]} + response_text: The capital of France is Paris. + +Then on Embedder model: + +.. code-block:: python + + # try embedding model + model_type = ModelType.EMBEDDER + # do batch embedding + input = [query] * 2 + model_kwargs = {"model": "text-embedding-3-small", "dimensions": 8, "encoding_format": "float"} + api_kwargs = openai_client.convert_inputs_to_api_kwargs(input=input, model_kwargs=model_kwargs, model_type=model_type) + print(f"api_kwargs: {api_kwargs}") + + + + response = openai_client.call(api_kwargs=api_kwargs, model_type=model_type) + reponse_embedder_output = openai_client.parse_embedding_response(response) + print(f"reponse_embedder_output: {reponse_embedder_output}") + +The output will be: + +.. code-block:: + + api_kwargs: {'model': 'text-embedding-3-small', 'dimensions': 8, 'encoding_format': 'float', 'input': ['What is the capital of France?', 'What is the capital of France?']} + reponse_embedder_output: EmbedderOutput(data=[Embedding(embedding=[0.6175549, 0.24047995, 0.4509756, 0.37041178, -0.33437008, -0.050995983, -0.24366009, 0.21549304], index=0), Embedding(embedding=[0.6175549, 0.24047995, 0.4509756, 0.37041178, -0.33437008, -0.050995983, -0.24366009, 0.21549304], index=1)], model='text-embedding-3-small', usage=Usage(prompt_tokens=14, total_tokens=14), error=None, raw_response=None) + +.. admonition:: API reference + :class: highlight + + - :class:`core.model_client.ModelClient` + - :class:`components.model_client.openai_client.OpenAIClient` + - :class:`components.model_client.transformers_client.TransformersClient` + - :class:`components.model_client.groq_client.GroqAPIClient` + - :class:`components.model_client.anthropic_client.AnthropicAPIClient` + - :class:`components.model_client.google_client.GoogleGenAIClient` diff --git a/_sources/developer_notes/optimizer.rst.txt b/_sources/developer_notes/optimizer.rst.txt new file mode 100644 index 000000000..e6201a16b --- /dev/null +++ b/_sources/developer_notes/optimizer.rst.txt @@ -0,0 +1,2 @@ +Optimizer - The Future of LLM applications +========================================================== \ No newline at end of file diff --git a/_sources/developer_notes/parameter.rst.txt b/_sources/developer_notes/parameter.rst.txt new file mode 100644 index 000000000..514e5db9d --- /dev/null +++ b/_sources/developer_notes/parameter.rst.txt @@ -0,0 +1,2 @@ +Parameter +==================== \ No newline at end of file diff --git a/_sources/developer_notes/prompt.rst.txt b/_sources/developer_notes/prompt.rst.txt new file mode 100644 index 000000000..d5624f4e1 --- /dev/null +++ b/_sources/developer_notes/prompt.rst.txt @@ -0,0 +1,169 @@ +Prompt +============ +We strick to maximize developers' control towards the final experience and performance, simplify the development process, and minimize the token consumption. + +For the major chat models, we eventually will only send two messages to the model: the system message and the user message. The user message is simple, +often you have a message `{'role': 'user', 'content': 'Hello, how are you?'}`. The system message is more complex, it contains the task description, tools, examples, chat history, context, and +intermediate step history from agents. + +Prompt template +--------------------- + +Our `DEFAULT_LIGHTRAG_SYSTEM_PROMPT` templates the system prompt with 7 important sections. We leverage `jinjia2` template for **programmable prompt** right along with string. + +The default template comes with 7 variables: `task_desc_str`, `output_format_str`, `tools_str`, `examples_str`, `chat_history_str`, `context_str`, and `steps_str`. + +A jinjia2 template will rendered with :ref:`Prompt` class. If some fields being empty, that section will be empty in the final prompt string. + +.. code-block:: python + :linenos: + + DEFAULT_LIGHTRAG_SYSTEM_PROMPT = r"""{# task desc #} + {% if task_desc_str %} + {{task_desc_str}} + {% endif %} + {# tools #} + {% if tools_str %} + + {{tools_str}} + + {% endif %} + {# example #} + {% if examples_str %} + + {{examples_str}} + + {% endif %} + {# chat history #} + {% if chat_history_str %} + + {{chat_history_str}} + + {% endif %} + {#contex#} + {% if context_str %} + + {{context_str}} + + {% endif %} + {# steps #} + {% if steps_str %} + + {{steps_str}} + + {% endif %} + """ + +Across our library, here our advanced features: + +- Various output formats where the `output_format_str` variable is used to pass the output format to the model. + +- Few-shot and Many-shots In-context Learning (ICL) where the `examples_str` variable is used to pass the examples to the model. + +- Tools/Function Calls where the `tools_str` variable is used to pass the tools to the model. + +- Memory where the `chat_history_str` variable is used to pass the memory to the model. + +- Retrieval augmented generation(RAG) where the `context_str`` variable is used to pass the retrieved context. + +- Agent with multiple step planning and replanning capabilities, where the `steps_str` variable is used to pass the previous steps to the model. + +**Note: this means in default our out-of-box components would not support API providers's tools/function calls as we only send the system and user messages to the model. +But it should not stop you from implementing them yourself.** + +Prompt class +--------------------- +We designed a :ref:`Prompt` class to render the `template` with the variables to string as the final system prompt. In the simplest case, the string is empty and we will only send +a user message to the model. And in most cases, you want to add at least the `task_desc_str` to the system message. + +The cool thing about our `Prompt` system is how flexible it can be. If you need to put another `template` for say `task_desc_str`, you can do that using the `Prompt` class. +For example, your task is to instruct the llm to choose `top_k` from the given choices, you can define a new template like this: + +.. code-block:: python + :linenos: + + from core.prompt_builder import Prompt + + task_desc_template = r""" + Choose the top {{top_k}} from the following choices: {{choices}} + """ + top_k = 3 + choices = ['apple', 'banana', 'orange', 'grape'] + task_desc_prompt = Prompt(template=task_desc_template, preset_prompt_kwargs={'top_k': top_k, 'choices': choices}) + task_desc_str = task_desc_prompt.call() + prompt = Prompt(preset_prompt_kwargs={'task_desc_str': task_desc_str}) + prompt.print_prompt() + +The output would be: + +.. code-block:: xml + :linenos: + + Choose the top 3 from the following choices: ['apple', 'banana', 'orange', 'grape'] + + + + +Prompt and Special Tokens context +---------------------------------- + + +Each section other than `task_desc_str` is encapulated in a special token. Different model can have different special tokens. +Here is one example of `Llama3 Documentation `_ prompts formatted with special tokens: + +input string to the LLM model and minimize the token consumption. +We enable advanced features without relying on API provider's prompt manipulation such as `OpenAI`'s tools or assistant APIs. + +.. code-block:: + :linenos: + + <|begin_of_text|><|start_header_id|>system<|end_header_id|> + + You are a helpful AI assistant for travel tips and recommendations<|eot_id|> + + <|start_header_id|>user<|end_header_id|> + What can you help me with?<|eot_id|> + + <|start_header_id|>assistant<|end_header_id|> + + + + + +Here is how you customize a new prompt: + +.. code-block:: python + :linenos: + + from core.prompt_builder import Prompt + + new_template = r""" + <|begin_of_text|><|start_header_id|>system<|end_header_id|> + {{task_desc_str}} + Your context: {{context_str}} <|eot_id|> + + <|start_header_id|>user<|end_header_id|> + {{query_str}}<|eot_id|> + + <|start_header_id|>assistant<|end_header_id|> + """ + + + prompt = Prompt(template=new_template) + + +Prompt Engineering experience +------------------------------- +There is not robust prompt, and it is one of the most sensitive creatures in the AI world. +Here are some tips: +- Even the output format matters, the order of your output fields, the formating. +Output yaml or json format can lead to different performance. We have better luck with yaml format. +- Few-shot works so well in some case, but it can lead to regression in some cases. +- It is not fun to be a prompt engineer! But what can we do for now. + + +Resources: +1. `Jinja2`: + + + diff --git a/_sources/developer_notes/rag.rst.txt b/_sources/developer_notes/rag.rst.txt new file mode 100644 index 000000000..b1d74de1b --- /dev/null +++ b/_sources/developer_notes/rag.rst.txt @@ -0,0 +1,2 @@ +RAG application +=================== \ No newline at end of file diff --git a/_sources/developer_notes/react_agent_xy.rst.txt b/_sources/developer_notes/react_agent_xy.rst.txt new file mode 100644 index 000000000..c5440dca2 --- /dev/null +++ b/_sources/developer_notes/react_agent_xy.rst.txt @@ -0,0 +1,231 @@ +ReAct Agent +================= + +Check out `react agent notebook <../notebooks/react_agent.ipynb>`_ + +The goal of this tutorial is to: + +1. Demonstrate how ``LightRAG`` implements the ReAct agent +2. Provide a Deep Dive on ReAct Agent(Reference) + +What is an agent and why you need it? +------------------------------------------------ + +An agent, is better defined as a system that strategically uses LLM models and various tools to plan and execute steps. +Although LLMs and RAGs can generate text response with conversation history and internal knowledge, +they are unable to plan sequentially and decide which resource to use. + +Introduction +----------------------- +Before explaining ``LightRAG Agent`` implementation, here is a quick introduction of ReAct Agent. + +To solve a query, the `ReAct Agent `_, like its name(``Re``- Reason; ``Act`` - Act), +first uses LLM to analyze the context and plan actions to answer the query(reasoning). +Then it takes actions to utilize external resources(action). For more details, please see the :ref:`deep-dive`. + +LightRAG's Implementation +----------------------------------------------------- +Next, let's look at how ``LightRAG`` makes the implementation convenient. In ``LightRAG``, the ReAct agent is a type of :ref:`generator` that runs multiple sequential steps to generate the final response, with designed prompt, external functions(named as ``tools``) and ``JsonParser output_processors``. + +1. **Prompt:** We have a easy-to-customizable prompt template designed for ReAct agent that takes in +``tools``, few shot ``examples``, ``history``, and ``user query``. +The ``history`` will be automatically managed by the agent. ``user query`` will be handled in each single turn. +Hence when initializing an agent, we only need to set up the ``tools`` and the ``examples`` in the ``preset_prompt_kwargs`` for the system prompt and +use ``user query`` in each agent call. :ref:`Prompt `. + +2. **Tools:** ReAct Agent needs to plan the tool to use, which means it needs to access the tools' descriptions. +``LightRAG`` provides dynamic tool handling, using ``FunctionTool`` to encapsulate tool functionalities. The metadata(function name, description, and parameters) will be extracted and passed to the prompt automatically. This process not only makes tool integration more seamless but also enhances developer efficiency by allowing straightforward definition and management of tools. + +Here is the example to illustrate the usage of ``FunctionTool``. It's easy to set up using ``from_defaults``. + +.. code-block:: python + + from lightrag.core.tool_helper import FunctionTool + + # define the tools + def multiply(a: int, b: int) -> int: + '''Multiply two numbers.''' + return a * b + def add(a: int, b: int) -> int: + '''Add two numbers.''' + return a + b + + tools = [ + FunctionTool.from_defaults(fn=multiply), + FunctionTool.from_defaults(fn=add), + ] + + for tool in tools: + name = tool.metadata.name + description = tool.metadata.description + parameter = tool.metadata.fn_schema_str + print(f"Function name: {name}") + print(f"Function description: {description}") + print(f"Function parameter: {parameter}") + + # Function name: multiply + # Function description: multiply(a: int, b: int) -> int + # Multiply two numbers. + # Function parameter: {"type": "object", "properties": {"a": {"type": "int"}, "b": {"type": "int"}}, "required": ["a", "b"]} + # Function name: add + # Function description: add(a: int, b: int) -> int + # Add two numbers. + # Function parameter: {"type": "object", "properties": {"a": {"type": "int"}, "b": {"type": "int"}}, "required": ["a", "b"]} + +The agent will then call these external functions based on the function descriptions. +In addition to user-defined tools, the :class:`ReActAgent ` built-in ``llm_tool`` +for leveraging LLM's internal knowledge, and ``finish`` for completing processes. ``llm_tool`` uses the same model with the agent. Developers have the flexibility to enable or disable these as needed. + +3. **Output Parser:** ``LightRAG`` requests the model to output intermediate Thought and Action as JSON, which facilitates better error handling and easier data manipulation than strings. For example, + +.. code-block:: json + + { + "thought": "", + "action": "ToolName(, )" + } + +This format allows the ``LightRAG`` JSON parser to efficiently decode the model's output and extract arguments. +The parsed data is then utilized by the ``StepOutput`` class to manage the flow of thought, action and observation. + +4. **Example:** Let's see a Q&A agent example: + +.. code-block:: python + + from lightrag.core.tool_helper import FunctionTool + from lightrag.components.agent.react import ReActAgent + from lightrag.components.model_client import OpenAIClient + from lightrag.components.model_client import GroqAPIClient + + import dotenv + # load evironment + dotenv.load_dotenv(dotenv_path=".env", override=True) + + # define the tools + def multiply(a: int, b: int) -> int: + '''Multiply two numbers.''' + return a * b + def add(a: int, b: int) -> int: + '''Add two numbers.''' + return a + b + + tools = [ + FunctionTool.from_defaults(fn=multiply), + FunctionTool.from_defaults(fn=add), + ] + + # for tool in tools: + # name = tool.metadata.name + # description = tool.metadata.description + # parameter = tool.metadata.fn_schema_str + # print(f"Function name: {name}") + # print(f"Function description: {description}") + # print(f"Function parameter: {parameter}") + + + examples = [ + """ + User: What is 9 - 3? + You: { + "thought": "I need to subtract 3 from 9, but there is no subtraction tool, so I ask llm_tool to answer the query.", + "action": "llm_tool('What is 9 - 3?')" + } + """ + ] + + preset_prompt_kwargs = {"example": examples} + llm_model_kwargs = { + "model": "llama3-70b-8192", + "temperature": 0.0 + } + + agent = ReActAgent( + tools=tools, + model_client=GroqAPIClient(), + model_kwargs=llm_model_kwargs, + max_steps=3, + preset_prompt_kwargs=preset_prompt_kwargs + ) + + import time + queries = ["What is 3 add 4?", "3*9=?"] + average_time = 0 + for query in queries: + t0 = time.time() + answer = agent(query) + + # Answer: The answer is 7. + # Answer: The answer is 27. + +5. **Subquery and History:** Moreover, in our design, the agent will potentially divide a query into subqueries, join all subqueries answers and finish the task. Developers can customize the prompt depending on the use cases. +The intermediate step history is managed. The agent will visit its previous reasoning, action and observations before making decisions. + +.. _deep-dive: + +ReAct Agent Deep Dive +--------------------------- +Please read this section if you need more information on ReAct agent. + +`ReAct Agent `_, like its name(``Re``- Reason; ``Act`` - Act), is a framework generating reasoning and taking actions in an interleaved manner. The reasoning step guides the model to action plans and the action step allows the agent to interact with external sources such as knowledge bases. + +The paper shows: +1. ReAct with few-shot prompt and Wikipedia API interaction outperforms chain-of-thought on `HotpotQA `_ (Question and Answering) and `Fever `_ (Fact Verification). +2. ReAct performs well on two interactive decision making benchmarks. + +**1. Overall Workflow** + +Unlike the reasoning only and acting only approaches, given a query, the ReAct agent will go through a sequence of steps to solve the problem. (`Source `_) + +Here is an example from the paper that demonstrates the workflow. + +.. image:: ../_static/images/ReAct.jpg + +The environment contains user query, step histories, observations, and external sources. + +At each step, the agent: + +- **[Thought]** In response to the environment and user query, the agent uses its LLM to generate a strategic thought that outlines a plan or hypothesis guiding the subsequent action. + +- **[Action]** The agent executes the action. + +The environment will be updated: + +- **[Observation]** The observation is created after the action is done. + +Then the agent iteratively generates thoughts based on latest observation and context(previous steps), takes actions and gets new observations. + +The termination condition is: + +* The agent finds the answer and takes "finish" action. + +* The agent fails to get the answer when the defined max steps is reached. Return nothing. + +**2. Action Space** + +Now we understand the 3 different stages: Thought, Action, Observation. Let's focus on Action, one of agents' uniqueness. + +Actions refer to the tools the agent uses to interact with the environment and creates observations. +Note: the paper defines Thought(or reasoning trace) as a *language level action* but it is not included in the action space because it doesn't impact the environment. + +Use ``HotpotQA`` dataset as an example, what external source do we need to answer questions? + +`HotpotQA `_ contains Wikipedia-based questions that require multi-hop reasoning. Therefore, the agent will need to query the Wikipedia API. + +In the `ReAct paper `_, researchers include 3 actions in the "action space" (simplified version here): + +* search[entity], returns the first 5 sentences from the corresponding entity wiki page if it exists, or else suggests top-5 similar entities. + +* lookup[string], simulating Ctrl+F functionality on the browser. + +* finish[answer], which would finish the current task with answer. + +**3. Components** + +With the workflow and action space, next, let's focus on the components needed to implement the agent. + +* **prompt:** Besides the role and task-specific description, the key in ReAct prompting is to define the tools to use in the prompt. + +* **function call:** In the implementation, each action is essentially a function to call. Clear functionality definition is important for the agent to determine which action to take next. + +* **parser:** The agent is built on LLMs. It takes in the prompt with context, generates thought and determine the action to take in text response. +To really call functions, we need to parse the text response to get the parameters for the determined function. diff --git a/_sources/developer_notes/retriever.rst.txt b/_sources/developer_notes/retriever.rst.txt new file mode 100644 index 000000000..5ef085604 --- /dev/null +++ b/_sources/developer_notes/retriever.rst.txt @@ -0,0 +1,618 @@ +Retriever +============ + +Context +------------------ + +**Why Retriever for LLM?** + +LLMs halluciate and also has knowledge cut-off. External and relevant context is needed to increase the factuality, relevancy, and freshness on the LLM answers. +Due to LLM's context window limit(can only take so much tokens each time), the ``lost-in-the-middle`` problem[6], and the high cost on speed and resources using large context, +it is practical to use a retriever to retrieve the most relevant information to get the best performance. Retrieval Augemented Generation (RAG)[7] applications become one of main applications in LLMs. + +.. It is easy to build a demo, but hard to excel due to the many different parts in the pipeline that made it difficult to evaluate and to optimize. + +**What is a retriever?** + +Though the definition is simple - "Retrieve relevant information for a given query from a given database", retriever can go as wide as the entire search and information retriever field. +No doubt the retriever part is the one of the most diversity and have the longest history in LLM application landscapes. + + +There are numerous search techniques long existing before the vector/semantic search and reranking models, such as keyword search, fuzzy search, proximity search, phrase search, boolean search, facet search, full-text search, +and can be applied on various data types, such as text, time-sensitive data, locations, sensor data, and images, videos, audios, etc, and stored in various types of databases, such as relational databases, NoSQL databases, and vector databases. + +.. In LightRAG +.. There are also dense and sparse retrieval methods. +.. - Keyword search +.. - Full-text search: Here is one example: https://www.postgresql.org/docs/current/textsearch.html +.. > TF-IDF (Term Frequency-Inverse Document Frequency) +.. > BM25 (Best Matching 25) +.. - Wildcard search, Fuzzy search, Proximity search, Phrase search, Boolean search, facet search etc +.. - Semantic search using embedding models +.. - Reranking using ranking models. + +.. Second, there are numerous data types: Text, Time-sensitive data, Locations, Sensor data, and Images, Videos, Audios etc + +.. Third, the data can be stored anywhere: In-memory data, Local and Disk-based data, and Cloud DBs such as relational databases, NoSQL databases, vector databases etc + +**Retrieval in Production** + +In real production, retrieval is often of multiple-stages, from the cheapest to the most expensive and most accurate, from millions of candidates to a few hundreds or even less. +For example, when you want to search for candidates from a pool of profiles stored in realtional db say Postgres, you can search by name simply using keyword, or check if the name equals to the query. +You also want to search by their profession, which you have already categorized, either by model or human labeling, this makes it a filter search. +Or if the search query requires more semantic understanding, we will leverage semantic search using embedding models. +If we want it to be more accurate, we move up to more expensive and more accurate methods such as reranking models and LLM-based retrieval methods. + + + + +Design pattern +------------------ + +.. figure:: /_static/images/retriever.png + :align: center + :alt: Retriever design + :width: 620px + + LightRAG retriever covers high-precision retriever methods and make them work locally and in-memory, this will help researchers and developers build and test. + We also showcase how it is like to work with cloud database for **large-scale data** along with its built-in search& filter methods. + + +LightRAG library does not prioritize the coverage of integration, the reasons are: + +1. It is literally too-wide to cover them all. +2. The challenges with RAG application lies more in evaluation and optimization due to many different moving parts and many hyperparmeters, and less in implementing or integrating a 3rd party retriever. + +We instead want to provide a design pattern where users can + +1. Learn our existing coverage and implementation and use them out-of-the-box as building blocks. +2. Easily integrate their own retriever and make them work seamlessly with the remaining part of the LLM application so that they can evaluate and optimize the whole task pipeline. +3. Easily combine different retriever methods to form a multiple-stage retrieval pipeline. + +A retriever will work hand in hand with the ``database`` and the ``data model``. +We will have a local database :class:`core.db.LocalDB` and a cloud sql-based database (using ``SQLAlchemy``) that can work with any data class, and espeically with the :class:`core.types.Document` and :class:`core.types.DialogTurn` +which provides ``context`` and ``conversation_history`` and is key to the LLM application. +As for the retriever methods, we cover the most representative methods: LLMAsRetriever, Reranker(Cross-encoder), Semantic Search(Bi-encoder), BM25, database's built-in search such as the full-text search/sql based search using Postgres and the semantic search using ``PgVector``. + +.. so that users can clearly and easily integrate their own retriever, either to work withh local files or to work with cloud databases with the remaining part of an LLM application. +.. Our goal is for doing so, users get to better evaluate and optimize the whole task pipeline as a whole. + +.. A retriever in our library is a component that potentially retrieves relevant ``context`` and pass it to the ``prompt`` of a ``generator``. +.. If your data is big, we assume it is users' responsibility to do fuzzy and cheap filter and search that gives high recall even though low precision till to have a manageable set of candidates (fit into local memory or a latency limit) to optimize for high precision. +.. To optimize recall, often BM25, TF-IDF, and semantic search using embedding models are used. And lastly, reranking models are used for the final precision optimization. +.. As the layer close to deliver the final user experience, we try to provide a great design pattern so that: + + + + +.. A retriever will work hand in hand with a ``database``: the retriever will be responsible for building and querying the index and work with a database, either local or cloud to save and load index. + + +.. A retriever will retrieve the `ids` of the ``top_k`` most relevant documents given a query. The user can then use these `ids` to retrieve the actual documents from the database. +.. The most effective approch would be ``LLMasRetriever``, ``Reranker``, ``Embedding`` + ``BM25``. + +Retriever Data Types +^^^^^^^^^^^^^^^^^^^^^^^^ +In most cases, the query is string. But there are cases we might need both text and images as a query, such as "find me a cloth that looks like this". +We defined the query type as: + +.. code-block:: python + + RetrieverQueryType = TypeVar("RetrieverQueryType", contravariant=True) + RetrieverStrQueryType = str + RetrieverQueriesType = Union[RetrieverQueryType, Sequence[RetrieverQueryType]] + RetrieverStrQueriesType = Union[str, Sequence[RetrieverStrQueryType]] + +As we see, our retriever should be able to handle both single query and multiple queries at once. + +The documents are a sequence of document of any type that will be later specified by the subclass: + +.. code-block:: python + + RetrieverDocumentType = TypeVar("RetrieverDocumentType", contravariant=True) # a single document + RetrieverDocumentsType = Sequence[RetrieverDocumentType] # The final documents types retriever can use + + +We further define the same output format so that we can easily switch between different retrievers in our task pipeline. +Here is our output format: + + +.. code-block:: python + + class RetrieverOutput(DataClass): + __doc__ = r"""Save the output of a single query in retrievers. + + It is up to the subclass of Retriever to specify the type of query and document. + """ + + doc_indices: List[int] = field(metadata={"desc": "List of document indices"}) + doc_scores: Optional[List[float]] = field( + default=None, metadata={"desc": "List of document scores"} + ) + query: Optional[RetrieverQueryType] = field( + default=None, metadata={"desc": "The query used to retrieve the documents"} + ) + documents: Optional[List[RetrieverDocumentType]] = field( + default=None, metadata={"desc": "List of retrieved documents"} + ) + + + RetrieverOutputType = List[RetrieverOutput] # so to support multiple queries at once + +You can find the types in :ref:`core.types`. The list of queries and `RetrieverOutput` can be helpful for: + +(1) Batch-processing: especially for semantic search where multiple queries can be represented as numpy array and be computed all at once with faster speed than doing one by one. +(2) For `query expansion` where to increase the recall, users often generate multiple queries from the original query. + + +.. code-block:: python + + class Retriever(Component, Generic[RetrieverDocumentType, RetrieverQueryType]): + + ... + + def call( + self, + input: RetrieverQueriesType, + top_k: Optional[int] = None, + **kwargs, + ) -> RetrieverOutputType: + raise NotImplementedError(f"retrieve is not implemented") + + async def acall( + self, + input: RetrieverQueriesType, + top_k: Optional[int] = None, + **kwargs, + ) -> RetrieverOutputType: + raise NotImplementedError(f"Async retrieve is not implemented") + + +**Document and TextSplitter** + +If your documents(text format) are too large and it is a common practise to first use ``TextSplitter`` to split them into smaller chunks. +Please refer to :doc:`text_splitter` and our provided notebook on how to use it. + + + +Retriever Base Class +^^^^^^^^^^^^^^^^^^^^^^^^ + +Functionally, the base retriever :class:`core.retriever.Retriever` defines another required method ``build_index_from_documents`` where the subclass will prepare the retriever for the actual retrieval calls. +Optionally, the subclass can implement ``save_to_file`` and ``load_from_file`` to save and load the retriever to/from disk. +As the retriever is a subclass of component, you already inherited powerful serialization and deserialization methods such as ``to_dict``, ``from_dict``, and ``from_config`` to help +with the saving and loading process. As for helper attributes, we have ``indexed`` and ``index_keys`` to differentiate if the retriever is ready for retrieval and the attributes that are key to restore the functionality/states of the retriever. +It is up the subclass to decide how to decide the storage of the index, it can be in-memory, local disk, or cloud storage, or save as json or pickle file or even a db table. +As an example, :class:`components.retriever.bm25_retriever.BM25Retriever` has the following key attributes to index. + +.. code:: python + + self.index_keys = ["nd", "t2d", "idf","doc_len","avgdl","total_documents","top_k","k1","b","epsilon","indexed"] + + +Retriever in Action +-------------------- +All of our retrievers are subclassed from the base retriever, and they are located in the ``components.retriever`` module. +You can skim through their implementations here: :ref:`retriever`. +Currently only :class:`components.retriever.faiss_retriever.BM25Retriever` needs to have its own ``save_to_file`` and ``load_from_file`` to avoid recomputation again. +The ``FAISSRetriever`` will work with a database instead to store the embeddings and it alleviates the need for the retriever to deal with states saving. + +In this note, we will use the following documents and queries for demonstration: + +.. code-block:: python + + query_1 = "What are the benefits of renewable energy?" # gt is [0, 3] + query_2 = "How do solar panels impact the environment?" # gt is [1, 2] + + documents =[ + { + "title": "The Impact of Renewable Energy on the Economy", + "content": "Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure." + }, + { + "title": "Understanding Solar Panels", + "content": "Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels." + }, + { + "title": "Pros and Cons of Solar Energy", + "content": "While solar energy offers substantial environmental benefits, such as reducing carbon footprints and pollution, it also has downsides. The production of solar panels can lead to hazardous waste, and large solar farms require significant land, which can disrupt local ecosystems." + }, + { + "title": "Renewable Energy and Its Effects", + "content": "Renewable energy sources like wind, solar, and hydro power play a crucial role in combating climate change. They do not produce greenhouse gases during operation, making them essential for sustainable development. However, the initial setup and material sourcing for these technologies can still have environmental impacts." + } + ] + +The first query should retrieve the first and the last document, and the second query should retrieve the second and the third document. + +In-memory FAISSRetriever +^^^^^^^^^^^^^^^^^^^^^^^^ +First, let's do semantic search, here we will use in-memory :class:`components.retriever.faiss_retriever.InMemoryFAISSRetriever`. +FAISS retriever takes embeddings which can be ``List[float]`` or ``np.ndarray`` and build an index using FAISS library. +The query can take both embeddings and str formats. + +.. note :: + ``faiss`` package is optional in our library. When you want to use it, ensure you have it installed in your env. + +We will quickly prepare the embeddings of the above documents using `content` field. + +.. code-block:: python + + from lightrag.core.embedder import Embedder + from lightrag.core.types import ModelClientType + + + model_kwargs = { + "model": "text-embedding-3-small", + "dimensions": 256, + "encoding_format": "float", + } + + embedder = Embedder(model_client =ModelClientType.OPENAI(), model_kwargs=model_kwargs) + output = embedder(input=[doc["content"] for doc in documents]) + documents_embeddings = [x.embedding for x in output.data] + + +For the initialization, a retriever can take both its required documents along with hyperparmeters including ``top_k``. +The ``documents`` field is optional. Let's pass it all from ``__init__`` first: + +.. code-block:: python + + from lightrag.components.retriever import FAISSRetriever + retriever = FAISSRetriever(top_k=2, embedder=embedder, documents=documents_embeddings) + + print(retriever) + +The printout: + +.. code-block:: + + FAISSRetriever( + top_k=2, metric=prob, dimensions=256, total_documents=4 + (embedder): Embedder( + model_kwargs={'model': 'text-embedding-3-small', 'dimensions': 256, 'encoding_format': 'float'}, + (model_client): OpenAIClient() + ) + ) + +We can also pass the documents using :meth:`components.retriever.faiss_retriever.InMemoryFAISSRetriever.build_index_from_documents` method after the initialization. +This is helpful when your retriever would need to work with different pool of documents each time. + +.. code-block:: python + + retriever_1 = FAISSRetriever(top_k=2, embedder=embedder) + retriever_1.build_index_from_documents(documents=documents_embeddings) + +Now, we will do the retriever, the input can either be a single query or a list of queries: + +.. code-block:: python + + output_1 = retriever(input=query_1) + output_2 = retriever(input=query_2) + output_3 = retriever(input = [query_1, query_2]) + print(output_1) + print(output_2) + print(output_3) + +The printout is: + +.. code-block:: + + [RetrieverOutput(doc_indices=[0, 3], doc_scores=[0.8119999766349792, 0.7749999761581421], query='What are the benefits of renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[2, 1], doc_scores=[0.8169999718666077, 0.8109999895095825], query='How do solar panels impact the environment?', documents=None)] + [RetrieverOutput(doc_indices=[0, 3], doc_scores=[0.8119999766349792, 0.7749999761581421], query='What are the benefits of renewable energy?', documents=None), RetrieverOutput(doc_indices=[2, 1], doc_scores=[0.8169999718666077, 0.8109999895095825], query='How do solar panels impact the environment?', documents=None)] + +In default, the score is a simulated probabity in range ``[0, 1]`` using consine similarity. The higher the score, the more relevant the document is to the query. +You can check the retriever for more type of scores. + +BM25Retriever +^^^^^^^^^^^^^^^^^^^^^^^^ +So the semantic search works pretty well. We will see how :class:`components.retriever.bm25_retriever.BM25Retriever` works in comparison. +We reimplemented the code in [9]_ with one improvement: instead of using ``text.split(" ")``, we use tokenizer to split the text. Here is a comparison of how they different: + +.. code-block:: python + + from lightrag.components.retriever.bm25_retriever import split_text_by_word_fn_then_lower_tokenized, split_text_by_word_fn + + query_1_words = split_text_by_word_fn(query_1) + query_1_tokens = split_text_by_word_fn_then_lower_tokenized(query_1) + +Output: + +.. code-block:: + + ['what', 'are', 'the', 'benefits', 'of', 'renewable', 'energy?'] + ['what', 'are', 'the', 'benef', 'its', 'of', 're', 'new', 'able', 'energy', '?'] + +We prepare the retriever: + +.. code-block:: python + + from lightrag.components.retriever import BM25Retriever + + document_map_func = lambda x: x["content"] + + bm25_retriever = BM25Retriever(top_k=2, documents=documents, document_map_func=document_map_func) + print(bm25_retriever) + +It takes ``document_map_func`` to map the documents to the text format the retriever can work with. +The output is: + +.. code-block:: + + BM25Retriever(top_k=2, k1=1.5, b=0.75, epsilon=0.25, use_tokenizer=True, total_documents=4) + +Now we call the retriever exactly the same way as we did with the FAISS retriever: + +.. code-block:: python + + output_1 = bm25_retriever(input=query_1) + output_2 = bm25_retriever(input=query_2) + output_3 = bm25_retriever(input = [query_1, query_2]) + print(output_1) + print(output_2) + print(output_3) + +The printout is: + +.. code-block:: + + [RetrieverOutput(doc_indices=[2, 1], doc_scores=[2.151683837681807, 1.6294762236217233], query='What are the benefits of renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[3, 2], doc_scores=[1.5166601493236314, 0.7790170272403408], query='How do solar panels impact the environment?', documents=None)] + [RetrieverOutput(doc_indices=[2, 1], doc_scores=[2.151683837681807, 1.6294762236217233], query='What are the benefits of renewable energy?', documents=None), RetrieverOutput(doc_indices=[3, 2], doc_scores=[1.5166601493236314, 0.7790170272403408], query='How do solar panels impact the environment?', documents=None)] + +Here we see the first query returns ``[2, 1]`` while the ground truth is ``[0, 3]``. The second query returns ``[3, 2]`` while the ground truth is ``[1, 2]``. +The performance is quite disappointing. BM25 is known for lack of semantic understanding and does not consider context. +We tested on the shorter and almost key-word like version of our queries and use both the `title` and `content`, and it gives the right response using the tokenized split. + +.. code-block:: python + + query_1_short = "renewable energy?" # gt is [0, 3] + query_2_short = "solar panels?" # gt is [1, 2] + document_map_func = lambda x: x["title"] + " " + x["content"] + bm25_retriever.build_index_from_documents(documents=documents, document_map_func=document_map_func) + +This time the retrieval gives us the right answer. + +.. code-block:: + + [RetrieverOutput(doc_indices=[0, 3], doc_scores=[0.9498793313012154, 0.8031794089550072], query='renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[2, 1], doc_scores=[0.5343238380789569, 0.4568096570283078], query='solar panels?', documents=None)] + +Reranker as Retriever +^^^^^^^^^^^^^^^^^^^^^^^^ +Semantic search works well, and reranker basd on mostly `cross-encoder` model is supposed to work even better. +We have integrated two rerankers: ``BAAI/bge-reranker-base`` [10]_ hosted on ``transformers`` and rerankers provided by ``Cohere`` [11]_. +These models follow the ``ModelClient`` protocol and are directly accessible as retriever from :class:`components.retriever.reranker_retriever.RerankerRetriever`. + + + + +**Reranker ModelClient Integration** + +A reranker will take ``ModelType.RERANKER`` and the standard LightRAG library requires it to have four arguments in the ``model_kwargs``: +``['model', 'top_k', 'documents', 'query']``. It is in the ModelClient which converts LightRAG's standard arguments to the model's specific arguments. +If you want to intergrate your reranker, either locally or using APIs, check out :class:`components.model_client.transformers_client.TransformersClient` and +:class:`components.model_client.cohere_client.CohereAPIClient` for how to do it. + + +To use it from the ``RerankerRetriever``, we only need to pass the ``model`` along with other arguments who does not +require conversion in the ``model_kwargs``. Here is how we use model `rerank-english-v3.0` from Cohere(Make sure you have the cohere sdk installed and prepared your api key): + +.. code-block:: python + + from lightrag.components.retriever import RerankerRetriever + + model_client = ModelClientType.COHERE() + model_kwargs = {"model": "rerank-english-v3.0"} + + + reranker = RerankerRetriever( + top_k=2, model_client=model_client, model_kwargs=model_kwargs + ) + print(reranker) + +The printout: + +.. code-block:: + + RerankerRetriever( + top_k=2, model_kwargs={'model': 'rerank-english-v3.0'}, model_client=CohereAPIClient(), total_documents=0 + (model_client): CohereAPIClient() + ) + +Now we build the index and do the retrieval: + + +.. code-block:: python + + document_map_func = lambda x: x["content"] + reranker.build_index_from_documents(documents=documents, document_map_func=document_map_func) + + output_1 = reranker(input=query_1) + output_2 = reranker(input=query_2) + output_3 = reranker(input = [query_1, query_2]) + +From the structure after adding documents we see the reranker has passed the documents to the ``model_kwargs`` so that it can send it all to the ``ModelClient``. + +.. code-block:: + + RerankerRetriever( + top_k=2, model_kwargs={'model': 'rerank-english-v3.0', 'documents': ['Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure.', 'Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels.', 'While solar energy offers substantial environmental benefits, such as reducing carbon footprints and pollution, it also has downsides. The production of solar panels can lead to hazardous waste, and large solar farms require significant land, which can disrupt local ecosystems.', 'Renewable energy sources like wind, solar, and hydro power play a crucial role in combating climate change. They do not produce greenhouse gases during operation, making them essential for sustainable development. However, the initial setup and material sourcing for these technologies can still have environmental impacts.']}, model_client=CohereAPIClient(), total_documents=4 + (model_client): CohereAPIClient() + ) + +From the results we see it gets the right answer and has a close to 1 score. + +.. code-block:: + + [RetrieverOutput(doc_indices=[0, 3], doc_scores=[0.99520767, 0.9696708], query='What are the benefits of renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[1, 2], doc_scores=[0.98742366, 0.9701269], query='How do solar panels impact the environment?', documents=None)] + [RetrieverOutput(doc_indices=[0, 3], doc_scores=[0.99520767, 0.9696708], query='What are the benefits of renewable energy?', documents=None), RetrieverOutput(doc_indices=[1, 2], doc_scores=[0.98742366, 0.9701269], query='How do solar panels impact the environment?', documents=None)] + +Now let us see how the ``BAAI/bge-reranker-base` from local transformers model works: + +.. code-block:: python + + model_client = ModelClientType.TRANSFORMERS() + model_kwargs = {"model": "BAAI/bge-reranker-base"} + + reranker = RerankerRetriever( + top_k=2, + model_client=model_client, + model_kwargs=model_kwargs, + documents=documents, + document_map_func=document_map_func, + ) + print(reranker) + +The printout: + +.. code-block:: + + RerankerRetriever( + top_k=2, model_kwargs={'model': 'BAAI/bge-reranker-base', 'documents': ['Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure.', 'Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels.', 'While solar energy offers substantial environmental benefits, such as reducing carbon footprints and pollution, it also has downsides. The production of solar panels can lead to hazardous waste, and large solar farms require significant land, which can disrupt local ecosystems.', 'Renewable energy sources like wind, solar, and hydro power play a crucial role in combating climate change. They do not produce greenhouse gases during operation, making them essential for sustainable development. However, the initial setup and material sourcing for these technologies can still have environmental impacts.']}, model_client=TransformersClient(), total_documents=4 + (model_client): TransformersClient() + ) + +Here is the retrieval result: + +.. code-block:: + + [RetrieverOutput(doc_indices=[0, 3], doc_scores=[0.9996004700660706, 0.9950029253959656], query='What are the benefits of renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[2, 0], doc_scores=[0.9994490742683411, 0.9994476437568665], query='How do solar panels impact the environment?', documents=None)] + +It missed one at the second query, but it is at the top 3. +Semantically, these documents might be close. +If we use top_k = 3, the genearator might be able to filter out the irrelevant one and eventually give out the right final response. +Also, if we use both the `title` and `content`, it will also got the right response. + + + +LLM as Retriever +^^^^^^^^^^^^^^^^^^^^^^^^ + +There are differen ways to use LLM as a retriever: + +1. Directly show it of all documents and query and ask it to return the indices of the top_k as a list. +2. Put the query and document a pair and ask it to do a `yes` and `no`. Additionally, we can use its `logprobs` of the `yes` token to get a probability-like score. We will implement this in the near future, for now, you can refer [8]_ to implement it yourself. + +For the first case, with out prompt and zero-shot, `gpt-3.5-turbo` is not working as well as `gpt-4o` which got both answers right. +Here is our code: + +.. code-block:: python + + from lightrag.components.retriever import LLMRetriever + + model_client = ModelClientType.OPENAI() + model_kwargs = { + "model": "gpt-4o", + } + document_map_func = lambda x: x["content"] + llm_retriever = LLMRetriever( + top_k=2, + model_client=model_client, + model_kwargs=model_kwargs, + documents=documents, + document_map_func=document_map_func + ) + print(llm_retriever) + +The printout: + +.. code-block:: + + LLMRetriever( + top_k=2, total_documents=4, + (generator): Generator( + model_kwargs={'model': 'gpt-4o'}, + (prompt): Prompt( + template: + You are a retriever. Given a list of documents, you will retrieve the top_k {{top_k}} most relevant documents and output the indices (int) as a list: + [] + + {% for doc in documents %} + ```Index {{ loop.index - 1 }}. {{ doc }}``` + {% endfor %} + + + Query: {{ input_str }} + You: + , preset_prompt_kwargs: {'top_k': 2, 'documents': ['Renewable energy technologies not only help in reducing greenhouse gas emissions but also contribute significantly to the economy by creating jobs in the manufacturing and installation sectors. The growth in renewable energy usage boosts local economies through increased investment in technology and infrastructure.', 'Solar panels convert sunlight into electricity by allowing photons, or light particles, to knock electrons free from atoms, generating a flow of electricity. Solar panels are a type of renewable energy technology that has been found to have a significant positive effect on the environment by reducing the reliance on fossil fuels.', 'While solar energy offers substantial environmental benefits, such as reducing carbon footprints and pollution, it also has downsides. The production of solar panels can lead to hazardous waste, and large solar farms require significant land, which can disrupt local ecosystems.', 'Renewable energy sources like wind, solar, and hydro power play a crucial role in combating climate change. They do not produce greenhouse gases during operation, making them essential for sustainable development. However, the initial setup and material sourcing for these technologies can still have environmental impacts.']}, prompt_variables: ['documents', 'top_k', 'input_str'] + ) + (model_client): OpenAIClient() + (output_processors): ListParser() + ) + ) + +Here is the response: + +.. code-block:: + + [RetrieverOutput(doc_indices=[0, 3], doc_scores=None, query='What are the benefits of renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[1, 2], doc_scores=None, query='How do solar panels impact the environment?', documents=None)] + +We can call the retriever with different model without reinitializing the retriever. Here is how we do it with `gpt-3.5-turbo`: + +.. code-block:: python + + model_kwargs = { + "model": "gpt-3.5-turbo", + } + output_1 = llm_retriever(model_kwargs=model_kwargs, input=query_1) + output_2 = llm_retriever(model_kwargs=model_kwargs, input=query_2) + +The response is: + +.. code-block:: + + [RetrieverOutput(doc_indices=[0, 1], doc_scores=None, query='What are the benefits of renewable energy?', documents=None)] + [RetrieverOutput(doc_indices=[1, 2], doc_scores=None, query='How do solar panels impact the environment?', documents=None)] + + +PostgresRetriever +^^^^^^^^^^^^^^^^^^^^^^^^ + + +Use Score Threshold instead of top_k +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +In some cases, when the retriever has a computed score and you might prefer to use the score instead of ``top_k`` to filter out the relevant documents. +To do so, you can simplify set the ``top_k`` to the full size of the documents and use a post-processing step or a component(to chain with the retriever) to filter out the documents with the score below the threshold. + + +Use together with Database +----------------------------- +When the scale of data is large, we will use a database to store the computed embeddings and indexes from the documents. + +With LocalDB +^^^^^^^^^^^^^^^^^^^^^^^^ +We have previously computed embeddings, now let us :class:`core.db.LocalDB` to help with the persistence. +(Although you can totally persist them yourself such as using pickle). +Additionally, ``LocalDB`` help us keep track of our initial documents and its transformed documents. + + +.. admonition:: References + :class: highlight + + 1. Full-text search on PostgreSQL: https://www.postgresql.org/docs/current/textsearch.html + 2. BM25: https://en.wikipedia.org/wiki/Okapi_BM25 + 3. Representative learning models: https://arxiv.org/abs/2104.08663 [Find the right reference] + 4. Reranking models: https://arxiv.org/abs/2104.08663 [Find the right reference] + 5. FAISS: https://github.com/facebookresearch/faiss + 6. Lost-in-the-middle: https://arxiv.org/abs/2104.08663 [Find the right reference] + 7. RAG: https://arxiv.org/abs/2104.08663 [Find the first paper on RAG] + 8. Use LLM as Reranker along with logprobs: https://cookbook.openai.com/examples/search_reranking_with_cross-encoders/ + 9. Rank_bm25: https://github.com/dorianbrown/rank_bm25 + 10. https://huggingface.co/BAAI/bge-reranker-base + 11. Cohere reranker: https://docs.cohere.com/reference/rerank + + +.. admonition:: API References + :class: highlight + + - :class:`core.retriever.Retriever` + - :ref:`core.types` + - :class:`components.retriever.faiss_retriever.FAISSRetriever` + - :class:`components.retriever.bm25_retriever.BM25Retriever` + - :class:`components.retriever.reranker_retriever.RerankerRetriever` + - :class:`components.retriever.llm_retriever.LLMRetriever` diff --git a/_sources/developer_notes/retriever_xy.rst.txt b/_sources/developer_notes/retriever_xy.rst.txt new file mode 100644 index 000000000..ff67c41b5 --- /dev/null +++ b/_sources/developer_notes/retriever_xy.rst.txt @@ -0,0 +1,428 @@ +Retriever +=================== + +In this tutorial, we will explain each component in ``LightRAG's Retriever`` and show you how to implement it in your LLM applications. + +LLMs develop fast, but they have limitations. + +**Content Window Limit:** Although the trend is, LLM models' content window keeps growing, there is still a context limit. + +**Signal to Noise Ratio** Meanwhile, LLMs perform better when the provided contents are relevant to the task. + +To improve LLMs performances in production, Retrieval Augmented Generation (RAG), a system that augments LLMs by adding extra context from another source, becomes popular. +**Retrieval**, one of the most important components of RAG, is the process to fetch the extra relevant information to the model. +The common solution for Retrieval is to chunk the documents into smaller contexts, store these pieces in databases such as vectorstore, Graph DB and Relational DB depending on the use case, and create significant embedding representations for these chunks in order to retrieve. + +``LightRAG`` aims to find the optimal way to pass the task-requiring data into LLMs. + +1. Document Splitter +---------------------- + +The DocumentSplitter in LightRAG is designed to preprocess text by splitting long documents into smaller chunks. +This improves the performance of embedding models and ensures they operate within their maximum context length limits. + +``LightRAG's DocumentSplitter`` splits a list of documents (:obj:`core.base_data_class.Document`) into a list of shorter documents. +The document object to manage id, document content,optional meta data, document's embedding vectors, etc. +Instead of maintaining the complex relationship between parent, child, previous, and next documents, ``LightRAG`` mainly manages the related documents with ``parent_doc_id`` (id of the Document where the chunk is from) and ``order`` (order of the chunked document in the original document). + +**Key Arguments:** + +* ``split_by`` is the unit by which the document should be split. We implemented a string split function inside to break the text into a ``list``. The splitted ``list`` will get concatenated based on the specified ``split_length`` later. +Check the following table for ``split_by`` options: + +.. list-table:: Text Splitting Options + :widths: 10 15 75 + :header-rows: 1 + + * - Option + - Split by + - Example + * - **page** + - ``\f`` + - ``Hello, world!\fNew page starts here.`` to ``['Hello, world!\x0c', 'New page starts here.']`` + * - **passage** + - ``\n\n`` + - ``Hello, world!\n\nNew paragraph starts here`` to ``['Hello, world!\n\n', 'New paragraph starts here.']`` + * - **sentence** + - ``.`` + - ``Hello, world. This is LightRAG.`` to ``['Hello, world.', ' This is LightRAG.', '']`` + * - **word** + - ```` + - ``Hello, world. This is LightRAG.`` to ``['Hello, ', 'world. ', 'This ', 'is ', 'LightRAG.']`` + +We will use ``word`` in our example. + +* ``split_length`` is the the maximum number of units in each split. + +* ``split_overlap`` is the number of units that each split should overlap. Including context at the borders prevents sudden meaning shift in text between sentences/context, especially in sentiment analysis. In ``LightRAG`` we use ``windowed`` function in ``more-itertools`` package to build a sliding window for the texts to keep the overlaps. The window step size = ``split_length - split_overlap``. + +After splitting the long text into a list and using a sliding window to generate the text lists with specified overlap length, the text list will be concatenated into text pieces again. +Here is a quick example: + +``Review: The theater service is terrible. The movie is good.`` Set ``split_by: word``, ``split_length: 6``, ``split_overlap: 2``. + +With our ``DocumentSplitter`` logic, the output will be: ``Review: The theater service is terrible.``, ``is terrible. The movie is good.`` +It prevents the model of misunderstand the context. If we don't have overlap, the second sentence will be ``The movie is good.`` and the embedding model might only consider this document is merely ``Positive``. + +Now let's see the code example. First, import the components. + +.. code:: python + + from core.document_splitter import DocumentSplitter + from core.base_data_class import Document + +Then, configure the splitter settings. + +.. code:: python + + text_splitter_settings = { + "split_by": "word", + "split_length": 15, + "split_overlap": 2, + } + +Next, define the document splitter and set up the documents. + +.. code:: python + + text_splitter = DocumentSplitter( + split_by=text_splitter_settings["split_by"], + split_length=text_splitter_settings["split_length"], + split_overlap=text_splitter_settings["split_overlap"], + ) + + example1 = Document( + text="Review: I absolutely loved the friendly staff and the welcoming atmosphere! Sentiment: Positive", + ) + example2 = Document( + text="Review: It was an awful experience, the food was bland and overpriced. Sentiment: Negative", + ) + example3 = Document( + text="Review: What a fantastic movie! Had a great time and would watch it again! Sentiment: Positive", + ) + example4 = Document( + text="Review: The store is not clean and smells bad. Sentiment: Negative", + ) + + documents = [example1, example2, example3, example4] + +Now you can use the splitter to create document chunks. + +.. code:: python + + splitted_docs = (text_splitter.call(documents=documents)) + + # output: + # splitted_doc: [Document(id=15d838c4-abda-4c39-b81f-9cd745effb43, meta_data=None, text=Review: I absolutely loved the friendly staff and the welcoming atmosphere! Sentiment: Positive, estimated_num_tokens=17), Document(id=e4850140-8762-4972-9bae-1dfe96ccb65f, meta_data=None, text=Review: It was an awful experience, the food was bland and overpriced. Sentiment: Negative, estimated_num_tokens=21), Document(id=6bd772b9-88b4-4dfa-a595-922c0f8a4efb, meta_data=None, text=Review: What a fantastic movie! Had a great time and would watch it again! Sentiment: , estimated_num_tokens=21), Document(id=b0d98c1b-13ac-4c92-882e-2ed0196b0c81, meta_data=None, text=again! Sentiment: Positive, estimated_num_tokens=6), Document(id=fdc2429b-17e7-4c00-991f-f89e0955e3a3, meta_data=None, text=Review: The store is not clean and smells bad. Sentiment: Negative, estimated_num_tokens=15)] + +2. Embedder +---------------- + +Now we have splitted long documents to shorter ones, the next part is to retrieve the relevant documents. +But how can we find "relevant" texts? A commonly applied approach in the NLP field is Embedding. + +For ``Embedder`` tutorial, please check `Embedder <./embedder.html>`_. + +3. LightRAG Retrievers +------------------------ +Given a query, the retriever is responsible to fetch the relevant documents. +Now we have document splitter and embedder, we can check the retrievers now. +LightRAG provides ``FAISSRetriever``, ``BM25Retriever``, and ``LLMRetriever``. +These retrievers are built on the basic :class:`Retriever`, with default index building and retrieve phases. +All these retrievers return a list of ``RetrieverOutput``, including indexes, scores, query and documents. + +#. FAISSRetriever + +The ``FAISSRetriever`` uses in-memory Faiss index to retrieve the top k chunks(see `research `_). It is particularly useful in applications involving large-scale vector. +The developers need to configure ``top_k``, ``dimensions`` and ``vectorizer`` first. +``vectorizer`` is basically an instance of the ``Embedder``. The ``FAISSRetriever`` itself will initialize ``faiss.IndexFlatIP`` with the specified ``dimensions`` to do `Exact Search for Inner Product`. + +LightRAG's ``FAISSRetriever`` provides :func:`build_index_from_documents ` to create index from embeddings(``vector`` field of each document). +It will create ``xb`` indexes(the same number with embeddings). After the indexes are added, the index state will be ``True``. + +Then, developers can pass the queries to :func:`retrieve `. This function embeds the queries, and performs inner product search for ``xq``(the number of queries) queries and return k most close vectors. +We choose cosine similarity and convert it to range [0, 1] by adding 1 and dividing by 2 to simulate probability. This is how we calculate the score. +Then we attach the score to each retrieval output. + +Then, to speed up the retrieval, it is a common practice to build indexes from the documents or chunks. +When the indexes are ready, we should pass the query to the retriever and get the top k documents closest to the query vector. + +Here is an example: + +.. code-block:: python + + from lightrag.core.embedder import Embedder + from lightrag.components.model_client import OpenAIClient + from lightrag.core.data_components import ToEmbedderResponse, ToEmbeddings + from lightrag.core.types import Document + from lightrag.core.document_splitter import DocumentSplitter + from lightrag.components.retriever import FAISSRetriever + + import dotenv + dotenv.load_dotenv(dotenv_path=".env", override=True) + + import os + os.environ["KMP_DUPLICATE_LIB_OK"] = "True" + + # To use ``FAISSRetriever``, we need to prepare the embeddings + # for documents or chunks following the previous steps. + + # configure the splitter setting + text_splitter_settings = { + "split_by": "word", + "split_length": 200, + "split_overlap": 100, + } + + # set up the document splitter + text_splitter = DocumentSplitter( + split_by=text_splitter_settings["split_by"], + split_length=text_splitter_settings["split_length"], + split_overlap=text_splitter_settings["split_overlap"], + ) + + doc1 = Document( + meta_data={"title": "Luna's Profile"}, + text="lots of more nonsense text." * 50 + + "Luna is a domestic shorthair." + + "lots of nonsense text." * 100 + + "Luna loves to eat Tuna." + + "lots of nonsense text." * 50, + id="doc1", + ) + doc2 = Document( + meta_data={"title": "Luna's Hobbies"}, + text="lots of more nonsense text." * 50 + + "Luna loves to eat lickable treats." + + "lots of more nonsense text." * 50 + + "Luna loves to play cat wand." + + "lots of more nonsense text." * 50 + + "Luna likes to sleep all the afternoon", + id="doc2", + ) + documents = [doc1, doc2] + + # split the documents + splitted_docs = (text_splitter.call(documents=documents)) + + # configure the vectorizer(embedding) setting + vectorizer_settings = { + "model_kwargs": { + "model": "text-embedding-3-small", + "dimensions": 256, + "encoding_format": "float", + }, + "batch_size": 100 + } + + # set up the embedder using openai model + vectorizer = Embedder( + model_client=OpenAIClient, + model_kwargs=vectorizer_settings["model_kwargs"], # set up model arguments + output_processors=ToEmbedderResponse(), # convert the model output to EmbedderResponse + ) + # Prepare embeddings for the documents + embedder_response_processor = ToEmbeddings( + vectorizer=vectorizer, + batch_size=vectorizer_settings["batch_size"], + ) + + # Apply embedding transformation + embeddings = embedder_response_processor(splitted_docs) + + # Initialize the FAISS retriever with the embeddings + faiss_retriever = FAISSRetriever( + top_k=2, + dimensions=vectorizer_settings["model_kwargs"]["dimensions"], + vectorizer=vectorizer + ) + + # build indexes for the documents + faiss_retriever.build_index_from_documents(embeddings) + + # set up queries + queries = ["what does luna like to eat?"] + + # get the retrieved results + faiss_query_result = faiss_retriever.retrieve(query_or_queries=queries) + + # Continue with the rest of your original code + print(f"*" * 50) + print("Faiss Retrieval Results:") + for result in faiss_query_result: + print(f"Query: {result.query}") + print(f"Document Indexes: {result.doc_indexes}, Scores: {result.doc_scores}") + # Fetch and print the document texts corresponding to the retrieved indexes + for idx in result.doc_indexes: + print(f"Document ID: {splitted_docs[idx].id} - Title: {splitted_docs[idx].meta_data['title']}") + print(f"Text: {splitted_docs[idx].text}") # Print first 200 characters of the document text + + print(f"*" * 50) + + # ************************************************** + # Faiss Retrieval Results: + # Query: what does luna like to eat? + # Document Indexes: [8 2], Scores: [0.741 0.724] + # Document ID: e3f04c8b-68ae-4dde-844a-439037e58842 - Title: Luna's Hobbies + # Text: text. Luna loves to eat lickable treats.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more + # Document ID: f2d0f52a-4e69-4cc5-8f78-4499fa22525d - Title: Luna's Profile + # Text: text.Luna is a domestic shorthair.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots + # ************************************************** + +#. BM25Retriever + +The ``BM25Retriever`` leverages the `Okapi BM25 algorithm(Best Matching 25 ranking) `_, a widely-used ranking function in information retrieval that is particularly effective in contexts where document relevance to a query is crucial. + +This retriever is initialized with parameters that fine-tune its behavior: + +``top_k``: Number of top documents to retrieve. +``k1``: Controls term frequency saturation. +``b```: Part of the BM25 algorithm that controls the influence of document length on term frequency normalization. Larger b means lengthier documents have more impact on its effect. 0.5 < b < 0.8 is suggested to yields reasonably good results. +``alpha``: Sets a cutoff for the IDF scores, filtering out terms that are too common to be informative. +IDF refers to `Inverse document frequency `_. It measures how much information the word provides. +Lower the IDF score means the word is used a lot and less important in the document. +Please check :class:`BM25Retriever` to see how we calculate the IDF score. +``split_function``: Tokenization is customizable via the ``split_function``, which defaults to splitting text by tokens. Here's an example using a custom tokenizer: +The following example shows how the token splitting works. This tokenizer converts text into a series of token IDs, which are numeric representations of the tokens. + +.. code-block:: python + + from lightrag.core.tokenizer import Tokenizer + from typing import List + def split_text_by_token_fn(tokenizer: Tokenizer, x: str) -> List[str]: + return tokenizer(x) + + tokenizer = Tokenizer(name="o200k_base") + sentence = "Hello world. This is LightRAG." + print(split_text_by_token_fn(tokenizer=tokenizer, x=sentence)) + + # [13225, 2375, 13, 1328, 382, 12936, 49, 2971, 13], these numbers represent token ids + +Tokenization can be customized through ``split_function``. + +Similar to ``FAISSRetriever``, developers can build index from documents. In ``BM25Retriever`` allows direct documents inputs without need for preparing embeddings beforehand. +The ``build_index_from_documents`` first tokenizes the documents, then analyzes each to compute token frequencies necessary for IDF calculation. +And we filter the IDF based on the specified ``alpha``. +The ``t2d`` represents the token and its frequency in documents. +For example, t2d={"apple":{0:1}} means, the word apple appears once in the 0th document. +With the frequency we can calculate idf. The ``idf`` dictionary is to record the idf score for each token, such as {"apple": 0.9}, it means in the corpus, the token apple has idf score=0.9. + +``load_index``, ``save_index`` and ``reset_index`` are supported. + + +When a query is received, each token of the query is first transformed into its corresponding token using the same ``split_function`` configured during initialization. + +If a token from the query also appears in the documents of the corpus, +the retriever iterates over the documents containing the token, +applying the BM25 formula to calculate and accumulate scores based on the token's frequency. +For instance, document 1 = "apple, apple, banana", document 2 = "apple, orange". +If the query is "apple, orange", the score of document 1 be the accumulated score from 2 "apple". The score of document 2 will be the accumulated score from "apple" and "orange". +The document's score increases for each occurrence of these tokens. +This cumulative scoring approach ensures that documents containing more query-related tokens are ranked higher. +Finally, the ``k`` documents with the highest cumulative scores are identified and returned in a ``RetrieverOutput``, +which means most relevant to the query. + +#. LLMRetriever + +Unlike ``FAISSRetriever`` and ``BM25Retriever``, the ``LLMRetriever`` utilizes LLM models to perform retrieval. + +This model-driven approach does not rely on traditional similarity/IDF scores but instead uses the model's understanding of the content. + +Besides ``top_k``, developers need to configure the generator arguments to call LLMs, including: +``model_client``: Model provider such as OpenAIClient, or GroqAPIClient. +``model_kwargs``: Model related arguments such the ``temperature``. +``template``: The prompt template used in the generator to guide the model's focus during retrieval. +``preset_prompt_kwargs``: Includes preset arguments for prompt customization, such as ``task_desc_str`` for task descriptions and ``input_str`` for user queries. +``output_processors``: A component by default ``ListParser`` that processes the model's output into a list of document indices. You should configure this parser based on how you instruct the model to output in the prompt. + +**Index Building:** When ``build_info_from_documents`` is called, the retriever configures a designed prompt that informs the model of the documents' context. This enables the model to understand and organize the information before any query is processed. +**Retrieve:** Developers can submit queries as a list. The queries will be processed by using the configured model and template. +The retrieve phase will return the k most relevant **document indices** based on the context provided during indexing. +Developers should be aware of the flexibility of prompt instruction and ``output_processors`` setting and process the output indices. + +Here is an example for ``LLMRetriever``: + +.. code-block:: python + + from lightrag.components.model_client import OpenAIClient + from lightrag.core.types import Document + from lightrag.core.document_splitter import DocumentSplitter + from lightrag.components.retriever import LLMRetriever + from lightrag.core.string_parser import ListParser + + import dotenv + dotenv.load_dotenv(dotenv_path=".env", override=True) + + # Document preparation and splitting + splitter_settings = {"split_by": "word", "split_length": 200, "split_overlap": 100} + text_splitter = DocumentSplitter(**splitter_settings) + documents = [ + Document(id="doc1", meta_data={"title": "Luna's Profile"}, text= + "lots of more nonsense text." * 50 + + "Luna is a domestic shorthair." + + "lots of nonsense text." * 50 + + "Luna loves to eat Tuna." + + "lots of nonsense text." * 50), + Document(id="doc2", meta_data={"title": "Luna's Hobbies"}, text= + "lots of more nonsense text." * 50 + + "Luna loves to eat lickable treats." + + "lots of more nonsense text." * 50 + + "Luna loves to play cat wand." + + "lots of more nonsense text." * 50 + + "Luna likes to sleep all the afternoon"), + ] + + # split the documents + splitted_docs = text_splitter.call(documents) + + # configure the model + gpt_model_kwargs = { + "model": "gpt-3.5-turbo", + "temperature": 0.0, + } + # set up the retriever + llm_retriever = LLMRetriever( + top_k=1, + model_client=OpenAIClient(), + model_kwargs=gpt_model_kwargs, + output_processors = ListParser() + ) + + # build indexes for the splitted documents + llm_retriever.build_index_from_documents(documents=splitted_docs) + + # set up queries + queries = ["what does luna like to eat?", "what does Luna look like?"] + + + # get the retrieved list of GeneratorOutput, each contains list of indices + llm_query_output = llm_retriever.retrieve(query_or_queries=queries) + # print(llm_query_indices) + print("*" * 50) + for query, result in zip(queries, llm_query_output): + result = result.data # get list of indices from generatoroutput + print(f"Query: {query}") + if result: + # Retrieve the indices from the result + document_indices = result + for idx in document_indices: + # Ensure the index is within the range of splitted_docs + if idx < len(splitted_docs): + doc = splitted_docs[idx] + print(f"Document ID: {doc.id} - Title: {doc.meta_data['title']}") + print(f"Text: {doc.text}") # Print the first 200 characters + else: + print(f"Index {idx} out of range.") + else: + print("No documents retrieved for this query.") + print("*" * 50) + + # ************************************************** + # Query: what does luna like to eat? + # Document ID: 557cc52b-a2b7-4780-bbc3-f1be8330c167 - Title: Luna's Profile + # Text: text.Luna is a domestic shorthair.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.Luna loves to eat Tuna.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense + # ************************************************** + # Query: what does Luna look like? + # Document ID: 7de4b00a-e539-4df0-adc9-b4c312bed365 - Title: Luna's Profile + # Text: text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.lots of more nonsense text.Luna is a domestic shorthair.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense text.lots of nonsense + # ************************************************** diff --git a/_sources/developer_notes/text_splitter.rst.txt b/_sources/developer_notes/text_splitter.rst.txt new file mode 100644 index 000000000..b6904110c --- /dev/null +++ b/_sources/developer_notes/text_splitter.rst.txt @@ -0,0 +1,147 @@ +Text Splitter +----------------- +.. admonition:: Author + :class: highlight + + `Xiaoyi Gu `_ + +In this tutorial, we will learn: + +#. TextSplitter Overview + +#. How does it work + +#. How to use it + +TextSplitter Overview +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +LLMs’s context window is limited and the performance often drops with very long and nonsense input. +Shorter content is more manageable and fits memory constraint. +The goal of the text splitter is to chunk large data into smaller ones, potentially improving embedding and retrieving. + +The ``TextSplitter`` is designed to efficiently process and chunk **plain text**. +It leverages configurable separators to facilitate the splitting of :obj:`document object ` into smaller manageable document chunks. + +How does it work +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +``TextSplitter`` first utilizes ``split_by`` to specify the text-splitting criterion and breaks the long text into smaller texts. +Then we create a sliding window with length= ``chunk_size``. It moves at step= ``chunk_size`` - ``chunk_overlap``. +The texts inside each window will get merged to a smaller chunk. The generated chunks from the splitted text will be returned. + +**Splitting Types** + +``TextSplitter`` supports 2 types of splitting. + +* **Type 1:** Specify the exact text splitting point such as space<" "> and periods<".">. It is intuitive, for example, split_by "word": + +:: + + "Hello, world!" -> ["Hello, " ,"world!"] + +* **Type 2:** Use :class:`tokenizer `. It works as: + +:: + + "Hello, world!" -> ['Hello', ',', ' world', '!'] + +This aligns with how models see text in the form of tokens (`Reference `_), +Tokenizer reflects the real token numbers the models take in and helps the developers control budgets. + +**Definitions** + +* **split_by** specifies the split rule, i.e. the smallest unit during splitting. We support ``"word"``, ``"sentence"``, ``"page"``, ``"passage"``, and ``"token"``. The splitter utilizes the corresponding separator from the ``SEPARATORS`` dictionary. +For Type 1 splitting, we apply ``Python str.split()`` to break the text. + +* **SEPARATORS**: Maps ``split_by`` criterions to their exact text separators, e.g., spaces <" "> for "word" or periods <"."> for "sentence". + +.. note:: + For option ``token``, its separator is "" because we directly split by a tokenizer, instead of text point. + +* **chunk_size** is the the maximum number of units in each chunk. + +* **chunk_overlap** is the number of units that each chunk should overlap. Including context at the borders prevents sudden meaning shift in text between sentences/context, especially in sentiment analysis. + +Here are examples of how ``split_by``, ``chunk_size`` works with ``chunk_overlap``. +Document Text: + +:: + + Hello, this is lightrag. Please implement your splitter here. + + +.. list-table:: Chunking Example Detailed + :widths: 15 15 15 55 + :header-rows: 1 + + * - Split By + - Chunk Size + - Chunk Overlap + - Resulting Chunks + * - word + - 5 + - 2 + - "Hello, this is lightrag. Please", "lightrag. Please implement your splitter", "your splitter here." + * - sentence + - 1 + - 0 + - "Hello, this is lightrag.", "Please implement your splitter here." + * - token + - 5 + - 2 + - "Hello, this is l", "is lightrag.", "trag. Please implement your", "implement your splitter here." + +When splitting by ``word`` with ``chunk_size`` = 5 and ``chunk_overlap`` = 2, +each chunk will repeat 2 words from the previous chunk. These 2 words are set by ``chunk_overlap``. +This means each chunk has ``5-2=3`` word(split unit) difference compared with its previous. + +When splitting using tokenizer, each chunk still keeps 5 tokens. +For example, the tokenizer transforms ``lightrag`` to ['l', 'igh', 'trag']. So the second chunk is actually ``is`` + ``l`` + ``igh`` + ``trag`` + ``.``. + +.. note:: + ``chunk_overlap`` should always be smaller than ``chunk_size``, otherwise the window won't move and the splitting stucks. + When ``split_by`` = ``token``, the punctuation is considered as a token. + +How to use it +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +What you need is to specify the arguments and input your documents this way: + +.. code-block:: python + + from lightrag.components.data_process.text_splitter import TextSplitter + from lightrag.core.types import Document + + # Configure the splitter settings + text_splitter = TextSplitter( + split_by="word", + chunk_size=5, + chunk_overlap=1 + ) + + # Example document + doc = Document( + text="Example text. More example text. Even more text to illustrate.", + id="doc1" + ) + + # Execute the splitting + splitted_docs = text_splitter.call(documents=[doc]) + + for doc in splitted_docs: + print(doc) + + # Output: + # Document(id=44a8aa37-0d16-40f0-9ca4-2e25ae5336c8, text='Example text. More example text. ', meta_data=None, vector=[], parent_doc_id=doc1, order=0, score=None) + # Document(id=ca0af45b-4f88-49b5-97db-163da9868ea4, text='text. Even more text to ', meta_data=None, vector=[], parent_doc_id=doc1, order=1, score=None) + # Document(id=e7b617b2-3927-4248-afce-ec0fc247ac8b, text='to illustrate.', meta_data=None, vector=[], parent_doc_id=doc1, order=2, score=None) + +Integration with Other Document Types +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +This functionality is ideal for segmenting texts into sentences, words, pages, or passages, which can then be processed further for NLP applications. +For **PDFs**, developers will need to extract the text before using the splitter. Libraries like ``PyPDF2`` or ``PDFMiner`` can be utilized for this purpose. +``LightRAG``'s future implementations will introduce splitters for ``JSON``, ``HTML``, ``markdown``, and ``code``. + +Customization Tips +~~~~~~~~~~~~~~~~~~~~~ +You can also customize the ``SEPARATORS``. For example, by defining ``SEPARATORS`` = {"question": "?"} and setting ``split_by`` = "question", the document will be split at each ``?``, ideal for processing text structured +as a series of questions. If you need to customize :class:`tokenizer `, please check `Reference `_. + \ No newline at end of file diff --git a/_sources/developer_notes/tool_helper.rst.txt b/_sources/developer_notes/tool_helper.rst.txt new file mode 100644 index 000000000..587a732e8 --- /dev/null +++ b/_sources/developer_notes/tool_helper.rst.txt @@ -0,0 +1,813 @@ +Function calls +=========================== +Tools are means LLM can use to interact with the world beyond of its internal knowledge. Technically speaking, retrievers are tools to help LLM to get more relevant context, and memory is a tool for LLM to carry out a conversation. +Deciding when, which, and how to use a tool, and even to creating a tool is an agentic behavior: +Function calls is a process of showing LLM a list of funciton definitions and prompt it to choose one or few of them. +Many places use tools and function calls interchangably. + +In this note we will covert function calls, including + +1. Function call walkthrough +2. Overall design +3. Function call in action + + +Quick Walkthrough +-------------------- +Users might already know of OpenAI's function call feature via its API (https://platform.openai.com/docs/guides/function-calling). + +.. code-block:: python + + def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + import json + + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit}) + elif "san francisco" in location.lower(): + return json.dumps( + {"location": "San Francisco", "temperature": "72", "unit": unit} + ) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": unit}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + +For the above function, it is shown to LLM in the following format: + +.. code-block:: python + + function_definition = { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + + +Then the API will respond with a list of function names and parameters: + +.. code-block:: python + + Function(arguments='{"location": "San Francisco, CA"}', name='get_current_weather') + +Then the output will need to be parsed into arguments which are then passed to the function: + +.. code-block:: python + + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + +LightRAG Scope and Design +---------------------------- +Even with API, users have to (1) create the function definition, (2) Parse the response, (3) Execute the function. +What is missing in using API is: (1) How the function definitions are shown to LLM, (2) How the output format is instructured. + +LightRAG will provide built-in capabilities to do function calls simplily via prompt without relying on the tools API. + +**Design Goals** + +Asking LLM to call a function with keyword arguments is the simplest way of achieving the function call. +But it is limiting: + +1. What if the argument value is a more complicated data structure? +2. What if you want to use a variable as an argument? + +LightRAG will also provide ``FunctionExpression`` where calling a function is asking LLM to write the code snippet of the function call directly: + +.. code-block:: python + + 'get_current_weather("San Francisco, CA", unit="celsius")' + +This is not only more flexible, but also it is also a more efficient/compact way to call a function. + +.. As a library, we prioritize the built-in function call capabilities via the normal prompt-response. +.. Function calls are often just a prerequisite for more complext agent behaviors. +.. This means we need to know how to form a ``prompt``, how to define ``functions`` or ``tools``, how to parse them out from the response, and how to execute them securely in your LLM applications. +.. We encourage our users to handle function calls on their own and we make the effort to make it easy to do so. + +.. 1. Get **maximum control and transparency** over your prompt and for researchers to help improve these capabilities. +.. 2. Model-agnositc: Can switch to any model, either local or API based, without changing the code. +.. 3. More powerful. + + + +**Data Models** + +We have four ``DataClass`` models: :class:`core.types.FunctionDefinition`, :class:`core.types.Function`, :class:`core.types.FunctionExpression`, and :class:`core.types.FunctionOutput` to handle function calls. + +These classes not only help with data structuring but also by being a subclass of ``DataClass``, it can be easily used in the prompt. +``Function`` has three important attributes: ``name``, ``args``, and ``kwargs`` for the function name, positional arguments and keyword arguments. +``FunctionExpression`` only has one action for the function call expression. +Both can be used to format the output in the prompt. We will demonstrate how to use it later. + +**Components** + +We have two components: :class:`core.func_tool.FunctionTool` and :class:`core.tool_manager.ToolManager` to streamline the lifecyle of (1) +creating the function definition (2) formatting the prompt with the definitions and output format (3) parsing the response (4) executing the function. + +``FunctionTool`` is a container of a single function. It handles the function definition and executing of the function. It supports both sync and async functions. +``ToolManager`` manages all tools. And it handles the execution and context_map that is used to parse the functions sercurely. + +``ToolManager`` is simplified way to do function calls. + +.. list-table:: + :header-rows: 1 + + * - + - Attribute/Method + - Description + * - Attributes + - ``tools`` + - A list of tools managed by ToolManager. Each tool is an instance or a derivative of ``FunctionTool``. + * - + - ``context`` + - A dictionary combining tool definitions and additional context, used for executing function expressions. + * - Methods + - ``__init__`` + - Initializes a new ToolManager instance with tools and additional context. Tool can be ``FunctionTool`` or any function. + * - + - ``yaml_definitions`` + - Returns the YAML definitions of all tools managed by ToolManager. + * - + - ``json_definitions`` + - Returns the JSON definitions of all tools managed by ToolManager. + * - + - ``function_definitions`` + - Returns a list of function definitions for all tools. + * - + - ``parse_func_expr`` + - Parses a ``FunctionExpression`` and returns a ``Function`` object ready for execution. + * - + - ``execute_func`` + - Executes a given ``Function`` object and returns its output wrapped in ``FunctionOutput``. Support both sync and async functions. + * - + - ``execute_func_expr`` + - Parses and executes a ``FunctionExpression`` directly, returning the execution result as ``FunctionOutput``. Support both sync and async functions. + * - + - ``execute_func_expr_via_sandbox`` + - Execute the function expression via sandbox. Only support sync functions. + * - + - ``execute_func_expr_via_eval`` + - Execute the function expression via eval. Only support sync functions. + +Function Call in Action +-------------------------- + +We will use the following functions as examples across this note: + +.. code-block:: python + + from dataclasses import dataclass + import numpy as np + import time + import asyncio + + + def multiply(a: int, b: int) -> int: + """Multiply two numbers.""" + time.sleep(1) + return a * b + + + def add(a: int, b: int) -> int: + """Add two numbers.""" + time.sleep(1) + return a + b + + + async def divide(a: float, b: float) -> float: + """Divide two numbers.""" + await asyncio.sleep(1) + return float(a) / b + + + async def search(query: str) -> List[str]: + """Search for query and return a list of results.""" + await asyncio.sleep(1) + return ["result1" + query, "result2" + query] + + + def numpy_sum(arr: np.ndarray) -> float: + """Sum the elements of an array.""" + return np.sum(arr) + + + x = 2 + + @dataclass + class Point: + x: int + y: int + + + def add_points(p1: Point, p2: Point) -> Point: + return Point(p1.x + p2.x, p1.y + p2.y) + +We delibrately cover both async and sync, examples of using variables and more complicated data structures as arguments. +We will demonstrate the structure and how to use each data model and component to call the above functions in different ways. + +1. FunctionTool +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +First, let's see how we help describe the function to LLM. + +Use the above functions as examples, ``FunctionTool`` will generate the ``FunctionDefinition`` for each function automatically if the user did not pass it in. + +.. code-block:: python + + from lightrag.core.func_tool import FunctionTool + + functions =[multiply, add, divide, search, numpy_sum, add_points] + tools = [ + FunctionTool(fn=fn) for fn in functions + ] + for tool in tools: + print(tool) + +The printout shows three attributes for each function: ``fn``, ``_is_async``, and ``definition``. + +.. code-block:: + + FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='multiply', func_desc='multiply(a: int, b: int) -> int\nMultiply two numbers.', func_parameters={'type': 'object', 'properties': {'a': {'type': 'int'}, 'b': {'type': 'int'}}, 'required': ['a', 'b']})) + FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='add', func_desc='add(a: int, b: int) -> int\nAdd two numbers.', func_parameters={'type': 'object', 'properties': {'a': {'type': 'int'}, 'b': {'type': 'int'}}, 'required': ['a', 'b']})) + FunctionTool(fn: , async: True, definition: FunctionDefinition(func_name='divide', func_desc='divide(a: float, b: float) -> float\nDivide two numbers.', func_parameters={'type': 'object', 'properties': {'a': {'type': 'float'}, 'b': {'type': 'float'}}, 'required': ['a', 'b']})) + FunctionTool(fn: , async: True, definition: FunctionDefinition(func_name='search', func_desc='search(query: str) -> List[str]\nSearch for query and return a list of results.', func_parameters={'type': 'object', 'properties': {'query': {'type': 'str'}}, 'required': ['query']})) + FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='numpy_sum', func_desc='numpy_sum(arr: numpy.ndarray) -> float\nSum the elements of an array.', func_parameters={'type': 'object', 'properties': {'arr': {'type': 'ndarray'}}, 'required': ['arr']})) + FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='add_points', func_desc='add_points(p1: __main__.Point, p2: __main__.Point) -> __main__.Point\nNone', func_parameters={'type': 'object', 'properties': {'p1': {'type': 'Point', 'properties': {'x': {'type': 'int'}, 'y': {'type': 'int'}}, 'required': ['x', 'y']}, 'p2': {'type': 'Point', 'properties': {'x': {'type': 'int'}, 'y': {'type': 'int'}}, 'required': ['x', 'y']}}, 'required': ['p1', 'p2']})) + +View the definition for ``add_point`` and also the ``get_current_weather`` function in dict format: + +.. code-block:: python + + print(tools[-2].definition.to_dict()) + +The output will be: + +.. code-block:: + + { + "func_name": "numpy_sum", + "func_desc": "numpy_sum(arr: numpy.ndarray) -> float\nSum the elements of an array.", + "func_parameters": { + "type": "object", + "properties": {"arr": {"type": "ndarray"}}, + "required": ["arr"], + }, + } + +Using ``to_json`` and ``to_yaml`` will directly get us the string that can be fed into the prompt. +And we prefer to use ``yaml`` format here as it is more token efficient: + + +We choose to describe the function not only with the docstring which is `Sum the elements of an array.` but also with the function signature which is `numpy_sum(arr: numpy.ndarray) -> float`. +This quitely gives the LLM a view of the function at the code level and it helps with the function call. + +.. note:: + Users should better use type hints and a good docstring to help LLM understand the function better. + +In comparison, here is our definition for ``get_current_weather``: + +.. code-block:: + + { + "func_name": "get_current_weather", + "func_desc": "get_current_weather(location, unit='fahrenheit')\nGet the current weather in a given location", + "func_parameters": { + "type": "object", + "properties": { + "location": {"type": "Any"}, + "unit": {"type": "Any", "default": "fahrenheit"}, + }, + "required": ["location"], + }, + } + +To execute function using function names requres us to manage a function map. Instead of using the raw function, we use ``FunctionTool`` instead for this context map. + +.. code-block:: python + + context_map = {tool.definition.func_name: tool for tool in tools} + +To execute a function, we can do: + +.. code-block:: python + + function_name = "add" + function_to_call = context_map[function_name] + function_args = {"a": 1, "b": 2} + function_response = function_to_call.call(**function_args) + +If we use async function, we can use ``acall``. +``execute`` is a wrapper that you can call a function in both sync and async way regardless of the function type. +Check out the API documentation for more details. + +2. ToolManager +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Using ``ToolManager`` on all the above function: + +.. code-block:: python + + from lightrag.core.tool_manager import ToolManager + + tool_manager = ToolManager(tools=functions) + print(tool_manager) + +The tool manager can take both ``FunctionTool``, function and async function. +The printout: + +.. code-block:: + + ToolManager(Tools: [FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='multiply', func_desc='multiply(a: int, b: int) -> int\nMultiply two numbers.', func_parameters={'type': 'object', 'properties': {'a': {'type': 'int'}, 'b': {'type': 'int'}}, 'required': ['a', 'b']})), FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='add', func_desc='add(a: int, b: int) -> int\nAdd two numbers.', func_parameters={'type': 'object', 'properties': {'a': {'type': 'int'}, 'b': {'type': 'int'}}, 'required': ['a', 'b']})), FunctionTool(fn: , async: True, definition: FunctionDefinition(func_name='divide', func_desc='divide(a: float, b: float) -> float\nDivide two numbers.', func_parameters={'type': 'object', 'properties': {'a': {'type': 'float'}, 'b': {'type': 'float'}}, 'required': ['a', 'b']})), FunctionTool(fn: , async: True, definition: FunctionDefinition(func_name='search', func_desc='search(query: str) -> List[str]\nSearch for query and return a list of results.', func_parameters={'type': 'object', 'properties': {'query': {'type': 'str'}}, 'required': ['query']})), FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='numpy_sum', func_desc='numpy_sum(arr: numpy.ndarray) -> float\nSum the elements of an array.', func_parameters={'type': 'object', 'properties': {'arr': {'type': 'ndarray'}}, 'required': ['arr']})), FunctionTool(fn: , async: False, definition: FunctionDefinition(func_name='add_points', func_desc='add_points(p1: __main__.Point, p2: __main__.Point) -> __main__.Point\nNone', func_parameters={'type': 'object', 'properties': {'p1': {'type': 'Point', 'properties': {'x': {'type': 'int'}, 'y': {'type': 'int'}}, 'required': ['x', 'y']}, 'p2': {'type': 'Point', 'properties': {'x': {'type': 'int'}, 'y': {'type': 'int'}}, 'required': ['x', 'y']}}, 'required': ['p1', 'p2']}))], Additional Context: {}) + + + +We will show more how it can be used in the next section. + +3. Function Call end-to-end +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Now, let us add prompt and start to do function calls via LLMs. +We use the following prompt to do a single function call. + +.. code-block:: python + + template = r"""You have these tools available: + {% if tools %} + + {% for tool in tools %} + {{ loop.index }}. + {{tool}} + ------------------------ + {% endfor %} + + {% endif %} + + {{output_format_str}} + + + User: {{input_str}} + You: + """ + +**Pass tools in the prompt** + +We use `yaml` format here and show an example with less tools. + +.. code-block:: python + + from lightrag.core.prompt_builder import Prompt + + prompt = Prompt(template=template) + small_tool_manager = ToolManager(tools=tools[:2]) + + renered_prompt = prompt(tools=small_tool_manager.yaml_definitions) + print(renered_prompt) + +The output is: + +.. code-block:: + + You have these tools available: + + 1. + func_name: multiply + func_desc: 'multiply(a: int, b: int) -> int + + Multiply two numbers.' + func_parameters: + type: object + properties: + a: + type: int + b: + type: int + required: + - a + - b + + ------------------------ + 2. + func_name: add + func_desc: 'add(a: int, b: int) -> int + + Add two numbers.' + func_parameters: + type: object + properties: + a: + type: int + b: + type: int + required: + - a + - b + + ------------------------ + + + None + + + User: None + You: + +**Pass the output format** + +We have two ways to instruct LLM to call the function: + +(1) using the function name and arguments, we will leverage ``Function`` as LLM's output data type. + +.. code-block:: python + + from lightrag.core.types import Function + + output_data_class = Function + output_format_str = output_data_class.to_json_signature(exclude=["thought", "kwargs"]) + + renered_prompt= prompt(output_format_str=output_format_str) + print(renered_prompt) + +We execluded both the ``thought`` and ``args`` as it is easier to use ``kwargs`` to represent the arguments. +The output is: + +.. code-block:: + + You have these tools available: + + { + "name": "The name of the function (str) (optional)", + "args": "The positional arguments of the function (Optional) (optional)", + "kwargs": "The keyword arguments of the function (Optional) (optional)" + } + + + User: None + You: + + + +(2) using the function call expression for which we will use ``FunctionExpression``. + +.. code-block:: python + + from lightrag.core.types import FunctionExpression + + output_data_class = FunctionExpression + output_format_str = output_data_class.to_json_signature(exclude=["thought"]) + print(prompt(output_format_str=output_format_str)) + +The output is: + +.. code-block:: + + You have these tools available: + + { + "action": "FuncName() Valid function call expression. Example: \"FuncName(a=1, b=2)\" Follow the data type specified in the function parameters. e.g. for Type object with x,y properties, use \"ObjectType(x=1, y=2) (str) (required)" + } + + + User: None + You: + +We will use :class:`components.output_parsers.outputs.JsonOutputParser` to streamline the formatting of our output data type. + +.. code-block:: python + + from lightrag.components.output_parsers import JsonOutputParser + + func_parser = JsonOutputParser(data_class=Function) + instructions = func_parser.format_instructions(exclude=["thought"]) + print(instructions) + +The output is: + +.. code-block:: + + Your output should be formatted as a standard JSON instance with the following schema: + ``` + { + "name": "The name of the function (str) (optional)", + "args": "The positional arguments of the function (Optional) (optional)", + "kwargs": "The keyword arguments of the function (Optional) (optional)" + } + ``` + -Make sure to always enclose the JSON output in triple backticks (```). Please do not add anything other than valid JSON output! + -Use double quotes for the keys and string values. + -Follow the JSON formatting conventions. + + +With Function output format +************************************************** + +.. code-block:: python + + from lightrag.core.generator import Generator + from lightrag.core.types import ModelClientType + + model_kwargs = {"model": "gpt-3.5-turbo"} + prompt_kwargs = { + "tools": tool_manager.yaml_definitions, + "output_format_str": func_parser.format_instructions( + exclude=["thought", "args"] + ), + } + generator = Generator( + model_client=ModelClientType.OPENAI(), + model_kwargs=model_kwargs, + template=template, + prompt_kwargs=prompt_kwargs, + output_processors=func_parser, + ) + +**Run Queries** + +We will use ``Function.from_dict`` to get the final output type from the json object. Here we use :meth:`core.tool_manager.ToolManager.execute_func` to execute it directly. + +.. code-block:: python + + queries = [ + "add 2 and 3", + "search for something", + "add points (1, 2) and (3, 4)", + "sum numpy array with arr = np.array([[1, 2], [3, 4]])", + "multiply 2 with local variable x", + "divide 2 by 3", + "Add 5 to variable y", + ] + + for idx, query in enumerate(queries): + prompt_kwargs = {"input_str": query} + print(f"\n{idx} Query: {query}") + print(f"{'-'*50}") + try: + result = generator(prompt_kwargs=prompt_kwargs) + # print(f"LLM raw output: {result.raw_response}") + func = Function.from_dict(result.data) + print(f"Function: {func}") + func_output = tool_manager.execute_func(func) + print(f"Function output: {func_output}") + except Exception as e: + print( + f"Failed to execute the function for query: {query}, func: {result.data}, error: {e}" + ) + +From the output shown below, we get valide ``Function`` parsed as output for all queries. +However, we see it failed three function execution: +(1)function `add_points` due to its argument type is a data class, and `multiply` and the last `add` due to it is difficult to represent the local variable `x` and `y` in the function call. + +.. code-block:: + + 0 Query: add 2 and 3 + -------------------------------------------------- + Function: Function(thought=None, name='add', args=[], kwargs={'a': 2, 'b': 3}) + Function output: FunctionOutput(name='add', input=Function(thought=None, name='add', args=(), kwargs={'a': 2, 'b': 3}), parsed_input=None, output=5, error=None) + + 1 Query: search for something + -------------------------------------------------- + Function: Function(thought=None, name='search', args=[], kwargs={'query': 'something'}) + Function output: FunctionOutput(name='search', input=Function(thought=None, name='search', args=(), kwargs={'query': 'something'}), parsed_input=None, output=['result1something', 'result2something'], error=None) + + 2 Query: add points (1, 2) and (3, 4) + -------------------------------------------------- + Function: Function(thought=None, name='add_points', args=[], kwargs={'p1': {'x': 1, 'y': 2}, 'p2': {'x': 3, 'y': 4}}) + Error at calling : 'dict' object has no attribute 'x' + Function output: FunctionOutput(name='add_points', input=Function(thought=None, name='add_points', args=(), kwargs={'p1': {'x': 1, 'y': 2}, 'p2': {'x': 3, 'y': 4}}), parsed_input=None, output=None, error="'dict' object has no attribute 'x'") + + 3 Query: sum numpy array with arr = np.array([[1, 2], [3, 4]]) + -------------------------------------------------- + Function: Function(thought=None, name='numpy_sum', args=[], kwargs={'arr': [[1, 2], [3, 4]]}) + Function output: FunctionOutput(name='numpy_sum', input=Function(thought=None, name='numpy_sum', args=(), kwargs={'arr': [[1, 2], [3, 4]]}), parsed_input=None, output=10, error=None) + + 4 Query: multiply 2 with local variable x + -------------------------------------------------- + Function: Function(thought=None, name='multiply', args=[], kwargs={'a': 2, 'b': 'x'}) + Function output: FunctionOutput(name='multiply', input=Function(thought=None, name='multiply', args=(), kwargs={'a': 2, 'b': 'x'}), parsed_input=None, output='xx', error=None) + + 5 Query: divide 2 by 3 + -------------------------------------------------- + Function: Function(thought=None, name='divide', args=[], kwargs={'a': 2.0, 'b': 3.0}) + Function output: FunctionOutput(name='divide', input=Function(thought=None, name='divide', args=(), kwargs={'a': 2.0, 'b': 3.0}), parsed_input=None, output=0.6666666666666666, error=None) + + 6 Query: Add 5 to variable y + -------------------------------------------------- + Function: Function(thought=None, name='add', args=[], kwargs={'a': 5, 'b': 'y'}) + Error at calling : unsupported operand type(s) for +: 'int' and 'str' + Function output: FunctionOutput(name='add', input=Function(thought=None, name='add', args=(), kwargs={'a': 5, 'b': 'y'}), parsed_input=None, output=None, error="unsupported operand type(s) for +: 'int' and 'str'") + + +.. note:: + If users prefer to use Function, to incress the success rate, make sure your function arguments are dict based for class object. You can always convert it to a class from a dict. + + +Function output format +************************************************** +We will adapt the above code easily using tool manager to use ``FunctionExpression`` as the output format. +We will use FunctionExpression this time in the parser. And we added the necessary context to handle the local variable `x`, `y`, and `np.array`. + +.. code-block:: python + + tool_manager = ToolManager( + tools=functions, + additional_context={"x": x, "y": 0, "np.array": np.array, "np": np}, + ) + func_parser = JsonOutputParser(data_class=FunctionExpression) + +Additionally, we can also pass the ``additional_context`` to LLM using the follow prompt after the + +.. code-block:: python + + context = r""" + Your function expression also have access to these context: + {{context_str}} + + """ + +This time, let us try to execute all function concurrently and treating them all as async functions. + +.. code-block:: python + + async def run_async_function_call(self, generator, tool_manager): + answers = [] + start_time = time.time() + tasks = [] + for idx, query in enumerate(queries): + tasks.append(self.process_query(idx, query, generator, tool_manager)) + + results = await asyncio.gather(*tasks) + answers.extend(results) + end_time = time.time() + print(f"Total time taken: {end_time - start_time :.2f} seconds") + return answers + + async def process_query(self, idx, query, generator, tool_manager: ToolManager): + print(f"\n{idx} Query: {query}") + print(f"{'-'*50}") + try: + result = generator(prompt_kwargs={"input_str": query}) + func_expr = FunctionExpression.from_dict(result.data) + print(f"Function_expr: {func_expr}") + func = tool_manager.parse_func_expr(func_expr) + func_output = await tool_manager.execute_func(func) + print(f"Function output: {func_output}") + return func_output + except Exception as e: + print( + f"Failed to execute the function for query: {query}, func: {result.data}, error: {e}" + ) + return None + +In this case, we used :meth:`core.tool_manager.ToolManager.parse_func_expr` and :meth:`core.tool_manager.ToolManager.execute_func` to execute the function. +Or we can directly use :meth:`core.tool_manager.ToolManager.execute_func_expr` to execute the function expression. Both are equivalent. + +From the output shown below, this time we get all function calls executed successfully. + +.. code-block:: + + 0 Query: add 2 and 3 + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='add(a=2, b=3)') + + 1 Query: search for something + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='search(query="something")') + + 2 Query: add points (1, 2) and (3, 4) + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='add_points(p1=Point(x=1, y=2), p2=Point(x=3, y=4))') + + 3 Query: sum numpy array with arr = np.array([[1, 2], [3, 4]]) + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='numpy_sum(arr=np.array([[1, 2], [3, 4]]))') + + 4 Query: multiply 2 with local variable x + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='multiply(a=2, b=2)') + + 5 Query: divide 2 by 3 + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='divide(a=2.0, b=3.0)') + + 6 Query: Add 5 to variable y + -------------------------------------------------- + Function_expr: FunctionExpression(thought=None, action='add(a=0, b=5)') + Function output: FunctionOutput(name='add_points', input=Function(thought=None, name='add_points', args=(), kwargs={'p1': Point(x=1, y=2), 'p2': Point(x=3, y=4)}), parsed_input=None, output=Point(x=4, y=6), error=None) + Function output: FunctionOutput(name='numpy_sum', input=Function(thought=None, name='numpy_sum', args=(), kwargs={'arr': array([[1, 2], + [3, 4]])}), parsed_input=None, output=10, error=None) + Function output: FunctionOutput(name='add', input=Function(thought=None, name='add', args=(), kwargs={'a': 2, 'b': 3}), parsed_input=None, output=5, error=None) + Function output: FunctionOutput(name='multiply', input=Function(thought=None, name='multiply', args=(), kwargs={'a': 2, 'b': 2}), parsed_input=None, output=4, error=None) + Function output: FunctionOutput(name='search', input=Function(thought=None, name='search', args=(), kwargs={'query': 'something'}), parsed_input=None, output=['result1something', 'result2something'], error=None) + Function output: FunctionOutput(name='divide', input=Function(thought=None, name='divide', args=(), kwargs={'a': 2.0, 'b': 3.0}), parsed_input=None, output=0.6666666666666666, error=None) + Function output: FunctionOutput(name='add', input=Function(thought=None, name='add', args=(), kwargs={'a': 0, 'b': 5}), parsed_input=None, output=5, error=None) + + +Parallel Function Calls +------------------------- + +We will slightly adapt the output format instruction to get it output json array, which can still be parsed with a json parser. + +.. code-block:: python + + multple_function_call_template = r"""You have these tools available: + {% if tools %} + + {% for tool in tools %} + {{ loop.index }}. + {{tool}} + ------------------------ + {% endfor %} + + {% endif %} + + Here is how you call one function. + {{output_format_str}} + Awlays return a List using `[]` of the above JSON objects. You can have length of 1 or more. + Do not call multiple functions in one action field. + + + {{input_str}} + You: + """ + +This case, we will show the response from using `execute_func_expr_via_sandbox` to execute the function expression. + +.. code-block:: python + + for idx in range(0, len(queries), 2): + query = " and ".join(queries[idx : idx + 2]) + prompt_kwargs = {"input_str": query} + print(f"\n{idx} Query: {query}") + print(f"{'-'*50}") + try: + result = generator(prompt_kwargs=prompt_kwargs) + # print(f"LLM raw output: {result.raw_response}") + func_expr: List[FunctionExpression] = [ + FunctionExpression.from_dict(item) for item in result.data + ] + print(f"Function_expr: {func_expr}") + for expr in func_expr: + func_output = tool_manager.execute_func_expr_via_sandbox(expr) + print(f"Function output: {func_output}") + except Exception as e: + print( + f"Failed to execute the function for query: {query}, func: {result.data}, error: {e}" + ) + +The output has one failed run, that is the ``add_point`` as it always like to use ``Dict`` to represent the data, even if it is a data class. + +.. code-block:: python + + 0 Query: add 2 and 3 and search for something + -------------------------------------------------- + Function_expr: [FunctionExpression(thought=None, action='add(a=2, b=3)')] + Function output: FunctionOutput(name='add(a=2, b=3)', input=FunctionExpression(thought=None, action='add(a=2, b=3)'), parsed_input=None, output=FunctionOutput(name='add', input=Function(thought=None, name='add', args=(), kwargs={'a': 2, 'b': 3}), parsed_input=None, output=5, error=None), error=None) + + 2 Query: add points (1, 2) and (3, 4) and sum numpy array with arr = np.array([[1, 2], [3, 4]]) + -------------------------------------------------- + Function_expr: [FunctionExpression(thought=None, action='add_points(p1={"x": 1, "y": 2}, p2={"x": 3, "y": 4})'), FunctionExpression(thought=None, action='numpy_sum(arr=[[1, 2], [3, 4]])')] + Error at calling : 'dict' object has no attribute 'x' + Function output: FunctionOutput(name='add_points(p1={"x": 1, "y": 2}, p2={"x": 3, "y": 4})', input=FunctionExpression(thought=None, action='add_points(p1={"x": 1, "y": 2}, p2={"x": 3, "y": 4})'), parsed_input=None, output=FunctionOutput(name='add_points', input=Function(thought=None, name='add_points', args=(), kwargs={'p1': {'x': 1, 'y': 2}, 'p2': {'x': 3, 'y': 4}}), parsed_input=None, output=None, error="'dict' object has no attribute 'x'"), error=None) + Function output: FunctionOutput(name='numpy_sum(arr=[[1, 2], [3, 4]])', input=FunctionExpression(thought=None, action='numpy_sum(arr=[[1, 2], [3, 4]])'), parsed_input=None, output=FunctionOutput(name='numpy_sum', input=Function(thought=None, name='numpy_sum', args=(), kwargs={'arr': [[1, 2], [3, 4]]}), parsed_input=None, output=10, error=None), error=None) + + 4 Query: multiply 2 with local variable x and divide 2 by 3 + -------------------------------------------------- + Function_expr: [FunctionExpression(thought=None, action='multiply(a=2, b=x)'), FunctionExpression(thought=None, action='divide(a=2.0, b=3.0)')] + Function output: FunctionOutput(name='multiply(a=2, b=x)', input=FunctionExpression(thought=None, action='multiply(a=2, b=x)'), parsed_input=None, output=FunctionOutput(name='multiply', input=Function(thought=None, name='multiply', args=(), kwargs={'a': 2, 'b': 2}), parsed_input=None, output=4, error=None), error=None) + Function output: FunctionOutput(name='divide(a=2.0, b=3.0)', input=FunctionExpression(thought=None, action='divide(a=2.0, b=3.0)'), parsed_input=None, output=FunctionOutput(name='divide', input=Function(thought=None, name='divide', args=(), kwargs={'a': 2.0, 'b': 3.0}), parsed_input=None, output=0.6666666666666666, error=None), error=None) + + 6 Query: Add 5 to variable y + -------------------------------------------------- + Function_expr: [FunctionExpression(thought=None, action='add(a=5, b=y)')] + Function output: FunctionOutput(name='add(a=5, b=y)', input=FunctionExpression(thought=None, action='add(a=5, b=y)'), parsed_input=None, output=FunctionOutput(name='add', input=Function(thought=None, name='add', args=(), kwargs={'a': 5, 'b': 0}), parsed_input=None, output=5, error=None), error=None) + + +.. admonition:: References + :class: highlight + + 1. OpenAI tools API: https://beta.openai.com/docs/api-reference/tools + +.. admonition:: API References + :class: highlight + + - :class:`core.types.FunctionDefinition` + - :class:`core.types.Function` + - :class:`core.types.FunctionExpression` + - :class:`core.types.FunctionOutput` + - :class:`core.tool_manager.FunctionTool` + - :class:`core.tool_manager.ToolManager` diff --git a/_sources/get_started/community.rst.txt b/_sources/get_started/community.rst.txt new file mode 100644 index 000000000..2f529eb6c --- /dev/null +++ b/_sources/get_started/community.rst.txt @@ -0,0 +1,19 @@ +Community +============ + +**Learn, share and collaborate with the LightRAG AI community** + +You can join our community on various platforms: + +* `Discord `_ +* `GitHub Discussion `_ + +.. _discord-link: + +Join our **Discord** to engage in real-time conversations with other members. + +.. _github-link: + +Participate in **GitHub Discussions** to share ideas, ask questions, and collaborate on projects. + +.. blogs diff --git a/_sources/get_started/index.rst.txt b/_sources/get_started/index.rst.txt new file mode 100644 index 000000000..4e0ffa41f --- /dev/null +++ b/_sources/get_started/index.rst.txt @@ -0,0 +1,13 @@ +Get Started +============================= + +Here is the content of our documentation project. + + + +.. toctree:: + :maxdepth: 2 + + lightrag_in_10_mins + installation + community diff --git a/_sources/get_started/installation.rst.txt b/_sources/get_started/installation.rst.txt new file mode 100644 index 000000000..dd0ea6264 --- /dev/null +++ b/_sources/get_started/installation.rst.txt @@ -0,0 +1,82 @@ +Installation +============ + +LightRAG can be installed either as a package using pip or set up for development by cloning from GitHub. Follow the appropriate instructions below based on your needs. + +Pip Installation +-------------------------------- + +For general users who simply want to use LightRAG, the easiest method is to install it directly via pip: + +.. code-block:: bash + + pip install lightrag + +After installing the package, you need to set up your environment variables for the project to function properly: + +1. **Create an Environment File:** + + Create a `.env` file in your project directory (where your scripts using LightRAG will run): + + .. code-block:: bash + + touch .env + # Open .env and add necessary configurations such as API keys + +2. **Configure Your `.env` File:** + + Add the necessary API keys and other configurations required by LightRAG. This usually includes setting up credentials for accessing various APIs that LightRAG interacts with. + +3. **Load Environment Variables:** + + Make sure your application or scripts load the environment variables from the `.env` file at runtime. If you are using Python, libraries like `python-dotenv` can be used: + + .. code-block:: bash + + pip install python-dotenv + +Then, in your Python script, ensure you load the variables: + +.. code-block:: python + + from dotenv import load_dotenv + load_dotenv() # This loads the environment variables from `.env`. + +This setup ensures that LightRAG can access all necessary configurations during runtime. + + +Poetry Installation +-------------------------- + +Developers and contributors who need access to the source code or wish to contribute to the project should set up their environment as follows: + +1. **Clone the Repository:** + + Start by cloning the LightRAG repository to your local machine: + + .. code-block:: bash + + git clone https://github.com/SylphAI-Inc/LightRAG + cd LightRAG + +2. **Configure API Keys:** + + Copy the example environment file and add your API keys: + + .. code-block:: bash + + cp .env.example .env + # Open .env and fill in your API keys + +3. **Install Dependencies:** + + Use Poetry to install the dependencies and set up the virtual environment: + + .. code-block:: bash + + poetry install + poetry shell + +4. **Verification:** + + Now, you should be able to run any file within the repository or execute tests to confirm everything is set up correctly. \ No newline at end of file diff --git a/_sources/get_started/introduction.rst.txt b/_sources/get_started/introduction.rst.txt new file mode 100644 index 000000000..4da292241 --- /dev/null +++ b/_sources/get_started/introduction.rst.txt @@ -0,0 +1,144 @@ +================ +Introduction +================ + +LightRAG +======== + +`1000 lines of code are all you +need `__. +No lock-in to vendors and frameworks, only the best practices of +**productionable RAG and Agent**. + +What is LightRAG? +----------------- + +LightRAG comes from the best of the AI research and engineering. +Fundamentally, we ask ourselves: what kind of system that combines the +best of research (such as LLM), engineering (such as ‘jinja’) to build +the best applications? We are not a framework. We do not want you to +directly install the package. We want you to carefully decide to take +modules and structures from here to build your own library and +applications. This is a cookbook organized uniquely for easy +understanding: you can read the 1000 lines of code to see a typical RAG +end-to-end without jumping between files and going through multi-level +class inheritance. If we build our system expanding from +``light_rag.py``, we as a community will share the same RAG languages, +and share other building blocks and use cases easily without depending +on a complex framework. + +**Our principles:** + +- We recognize that building a RAG is a highly complex and customized + process, and no framework can offer that flexibility. +- We seek to understand, not to establish. +- We seek to be flexible, not rigid. +- We seek to be organized and clear, not complex and confusing. +- We seek to be open, with open contributions from the community and + research on use cases, maybe we will come up with a more established + framework in the future. +- We seek to learn from code; we don’t read documentation that calls + very high-level functions and classes. + +Note: If we can’t understand your code quickly and easily, you should +not add it to the library. + +This is a new beginning, where all developers can come together, enjoy +learning and building, have fun, and build the best RAGs in the world. + +We stay neutral to all frameworks and all vendors, and we do not allow +vendors to merge code without a clear, articulate community vouch for +their performance, and comparison with other vendor or open-source +solutions. + +**Our opinions:** + +We are very opinionated but we ground our opinions on best practices. +Here are some of our unique opinions: - We code in a way that we can +switch between model providers or between oss and proprietary models +easily. + +How? LLM is a “text-in-text-out” model. ``Prompts`` are the new model +parameters–The in-context learning. We want full-control over it. Any +model provider API that manipulates our input prompts and output text, +we dont use it. Three examples are OpenAI’s ``role``, ``function call`` +(``tool``), ``json output mode``. The problem with this: our prompt +cannot be directly adapted to other LLM providers and we lose +transparency, adding more uncontrollable variables to our system. - We +write the ``prompts`` all together like writing a document instead of +separating them into multiple strings or variables. We think ``jinja2`` +speaks the best of the prompt language. [Here show how Llamaindex addes +different prompts together but we put all of them together.] Yes, we +think manual prompt enginerring is just a stage, like manually label +your data. The future is another LLM can take your description and will +be optimized to do your prompt, or another ``hypernetwork`` will convert +the lengthy prompt into parameters that can be plugged into the model. + +Structure +========= + +.. raw:: html + +

+ +LightRAG structure + +.. raw:: html + +

+ +Foundation +---------- + +- ``lightrag/``: All core data structures, the core 1000 lines of code + to cover the essence of a performant RAG. ## Building blocks +- ``extend/``: All modules that can be used to extend the core RAG. + + 1. Mainly including functional modules: we can extend different + ``Embedder``, ``Retriever``, ``Generator``, ``Agent``, + ``Reranker``, etc. + 2. Tracking or monitoring modules: such as ``CallbackManager``, + ``Phoenix`` integration. When necessary, please add a + ``README.md`` to explain why this module is necessary and how to + use it. + +- ``tests/``: All tests for the core RAG and its extensions. Includng + ``dummy modules`` for testing new modules. + +End-to-end applications +----------------------- + +- ``use_cases/``: All use cases that can be solved using LightRAG. For + instance, we can solve ``Question Answering``, ``Summarization``, + ``Information Extraction``, etc. + +To add a new use case, you can add a new folder in ``use_cases/`` with +the name ``application_name`` and add the following files: - +``/prompt``: a directory containing all prompts used in the application. +- ``/data``: a directory containing all data used in the application, or +instructions on how to download the data. - ``/main.py``: a file +containing the main code to run the application. + +What is not part of LightRAG? +============================= + +- Data processing: For instance, llamaindex has + ``from llama_index.core.ingestion import IngestionPipeline`` which + transforms the data that are either in the ``Document`` or ``Chunk``. + We do not cover this in LightRAG. Similarly, + ``from llama_index.core.postprocessor import SimilarityPostprocessor`` + which processes the retrieved ``chunk``, sometimes with further + filtering. + +How to start? +============= + +1. Clone the repository. +2. Setup API keys by make a copy of ``.env.example`` to ``.env`` and + fill in the necessary API keys. +3. Setup the Python environment using ``poetry install``. And activate + the environment using ``poetry shell``. +4. (For contributors only) Install pre-commit into your git hooks using + ``pre-commit install``, which will automatically check the code + standard on every commit. +5. Now you should run any file in the repo. diff --git a/_sources/get_started/lightrag_in_10_mins.rst.txt b/_sources/get_started/lightrag_in_10_mins.rst.txt new file mode 100644 index 000000000..1bdec107c --- /dev/null +++ b/_sources/get_started/lightrag_in_10_mins.rst.txt @@ -0,0 +1,13 @@ +LightRAG in 10 minutes +============================= +[Li] + + +Will use end to end trec classifier as an example to demonstrate: + +1. Look at the data and task, create `data class`, `prompt`, and `task`, and set up `log` and tracing. +2. Create datasets with `train`, `eval`, and `test` splits. +3. Eval zero-shot with manual prompts. + + +The content will be from `/use_cases/classification/readme.md`. \ No newline at end of file diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 000000000..b23ce133f --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,176 @@ +.. LightRAG documentation master file, created by + sphinx-quickstart on Thu May 9 15:45:29 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. +======================= +LightRAG Home +======================= + + +LightRAG is the "PyTorch" library for building large langage model(LLM) applications. It is super light, modular and robust like "PyTorch", and offers essential components for `Retriever`-`Agent`-`Generator` (RAG). + +You have a similar coding experience as PyTorch. Here is a side to side comparison of writing a PyTorch module and a LightRAG component: + +.. grid:: 2 + :gutter: 1 + + .. grid-item-card:: PyTorch + + .. code-block:: python + + import torch + import torch.nn as nn + + class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 32, 3, 1) + self.conv2 = nn.Conv2d(32, 64, 3, 1) + self.dropout1 = nn.Dropout2d(0.25) + self.dropout2 = nn.Dropout2d(0.5) + self.fc1 = nn.Linear(9216, 128) + self.fc2 = nn.Linear(128, 10) + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = self.dropout1(x) + x = self.dropout2(x) + x = self.fc1(x) + return self.fc2(x) + + .. grid-item-card:: LightRAG + + .. code-block:: python + + from core.component import Component, Generator + from components.model_client import OpenAIClient + + class SimpleQA(Component): + def __init__(self): + super().__init__() + self.generator = Generator( + model_client=OpenAIClient(), + model_kwargs={'model': 'gpt-3.5-turbo'} + ) + + def call(self, query): + return self.generator.call({'input_str': query}) + + async def acall(self, query): + return await self.generator.acall({'input_str': query}) + + qa = SimpleQA() + print(qa) + + + + +**Why LightRAG?** + + +1. **Clarity and Simplicity** + + We understand that developers building real-world Large Language Model (LLM) applications are the real heroes. Just like AI researchers and engineers who build models on top of PyTorch, developers require **Maximum Flexibility and Customizability**: Each developer has unique data needs to build their own models/components, experiment with In-context Learning (ICL) or model finetuning, and deploy the LLM applications to production. This means the library must provide fundamental lower-level building blocks and strive for clarity and simplicity: + + - We maintain no more than two levels of subclasses. + - Each core abstract class is designed to be robust and flexible. + - We use 10X less code than other libraries to achieve 10X more robustness and flexibility. + + +2. **Control and Transparency** + + Coming from a deep AI research background, we understand that the more control and transparency developers have over their prompts, the better. In default: + + - LightRAG simplifies what developers need to send to LLM proprietary APIs to just two messages each time: a `system message` and a `user message`. This minimizes reliance on and manipulation by API providers. + - LightRAG provides advanced tooling for developers to build `agents`, `tools/function calls`, etc., without relying on any proprietary API provider's 'advanced' features such as `OpenAI` assistant, tools, and JSON format. + +3. **Suitted for Both Researchers and Production Engineers** + + On top of the easiness to use, we in particular optimize the configurability of components for researchers to build their solutions and to benchmark existing solutions. + Like how PyTorch has united both researchers and production teams, it enables smooth transition from research to production. + With researchers building on LightRAG, production engineers can easily take over the method and test and iterate on their production data. + Researchers will want their code to be adapted into more products too. + + + +**LightRAG vs other LLM libraries:** + + +**LightRAG library structures as follows:** + +#TODO: One diagram to make people understand lightrag faster + +* `core` - Base abstractions, core functions, and core components like `Generator` and `Embedder` to support more advanced components. +* `components` - Components that are built on top of the core directive. Users will install relevant depencides on their own for some components. + + +**LightRAG documentation is divided into two parts:** + +* **Developer Documentation**: This documentation explains how LightRAG is designed in more depth and is especially useful for developers who want to contribute to LightRAG. + +* **User Documentation**: This documentation is for users who want to use LightRAG to build their applications. + +We encourage all users to at least skim through the developer documentation. Different from "PyTorch" where a normal user does not have to customize a building module for neural network, +LLM applications have much bigger scope and varies even more to different product environments, so developers customizing components on their own is much more common. + + + +.. toctree:: + :glob: + :maxdepth: 1 + :caption: New Users + + + get_started/index + + + + +.. toctree:: + :glob: + :maxdepth: 1 + :caption: Tutorials - How each part works + + developer_notes/index + + + + + + +.. toctree:: + :maxdepth: 1 + :caption: Use Cases - How different parts are used to build various LLM applications + + tutorials/index + + +.. toctree:: + :maxdepth: 1 + :caption: API Reference + + apis/index + +.. todo:: + + .. toctree:: + :maxdepth: 1 + :caption: Benchmarks + + Manually add documents for the code in benchmarks + + +.. :glob: +.. :maxdepth: 1 +.. :caption: Resources + +.. resources/index + + +.. toctree:: + :glob: + :maxdepth: 1 + :caption: For Contributors + + contributor/index diff --git a/_sources/resources/index.rst.txt b/_sources/resources/index.rst.txt new file mode 100644 index 000000000..92ee6d580 --- /dev/null +++ b/_sources/resources/index.rst.txt @@ -0,0 +1,11 @@ +Resources +============================= + +Here is the content of our documentation project. + + + +.. toctree:: + :maxdepth: 2 + + resources diff --git a/_sources/resources/resources.rst.txt b/_sources/resources/resources.rst.txt new file mode 100644 index 000000000..08f77d359 --- /dev/null +++ b/_sources/resources/resources.rst.txt @@ -0,0 +1,9 @@ +Resources +========= + +Please check the GitHub for more information: +`GitHub repository `_ + + + + diff --git a/_sources/tutorials/eval_a_rag.rst.txt b/_sources/tutorials/eval_a_rag.rst.txt new file mode 100644 index 000000000..0a91ce5b8 --- /dev/null +++ b/_sources/tutorials/eval_a_rag.rst.txt @@ -0,0 +1,185 @@ +Evaluating a RAG Pipeline +========================== + +In LightRAG, we provide a set of metrics in :ref:`our evaluators `. In this tutorial, we will show how to use them to evaluate the performance of the retriever and generator components of a RAG pipeline + +The full code for this tutorial can be found in `use_cases/rag_hotpotqa.py `_. + +RAG (Retrieval-Augmented Generation) pipelines leverage a retriever to fetch relevant context from a knowledge base (e.g., a document database) which is then fed to an LLM generator with the query to produce the answer. This allows the model to generate more contextually relevant answers. + +Thus, to evaluate a RAG pipeline, we can assess both the quality of the retrieved context and the quality of the final generated answer. Speciafically, in this tutorial, we will use the following evaluators and their corresponding metrics. + +- :class:`RetrieverEvaluator `: This evaluator is used to evaluate the performance of the retriever component of the RAG pipeline. It has the following metric functions: + - :obj:`compute_recall`: This function computes the recall of the retriever. It is defined as the number of relevant documents retrieved by the retriever divided by the total number of relevant documents in the knowledge base. + - :obj:`compute_context_relevance`: This function computes the relevance of the retrieved context. It is defined as the ratio of the number of relevant context tokens in the retrieved context to the total number of tokens in the retrieved context. +- :class:`AnswerMacthEvaluator `: This evaluator is used to evaluate the performance of the generator component of the RAG pipeline. It has the following metric functions: + - :obj:`compute_match_acc (if type is 'exact_match')`: This function computes the exact match accuracy of the generated answer. It is defined as the number of generated answers that exactly match the ground truth answer divided by the total number of generated answers. + - :obj:`compute_match_acc (if type is 'fuzzy_match')`: This function computes the fuzzy match accuracy of the generated answer. It is defined as the number of generated answers that contain the ground truth answer divided by the total number of generated answers. +- :class:`LLMasJudge `: This evaluator uses an LLM to get the judgement of the predicted answer for a list of questions. The task description and the judgement query of the LLM judge can be customized. + - :obj:`compute_judgement`: This function computes the judgement of the predicted answer. It is defined as the number of generated answers that are judged as correct by the LLM divided by the total number of generated answers. + + +Let's walk through the code to evaluate a RAG pipeline step by step. + +**Step 1: import dependencies.** +We import the necessary dependencies for our evaluation script. These include modules for loading datasets, constructing a RAG pipeline, and evaluating the performance of the RAG pipeline. + +.. code-block:: + :linenos: + + import yaml + + from datasets import load_dataset + + from core.openai_client import OpenAIClient + from core.generator import Generator + from core.base_data_class import Document + from core.string_parser import JsonParser + from core.component import Sequential + from eval.evaluators import ( + RetrieverEvaluator, + AnswerMacthEvaluator, + LLMasJudge, + DEFAULT_LLM_EVALUATOR_PROMPT, + ) + from core.prompt_builder import Prompt + from use_cases.rag import RAG + +**Step 2: define the configuration.** +We load the configuration settings from `a YAML file `_. This file contains various parameters for the RAG pipeline. You can customize these settings based on your requirements. + +.. code-block:: + :linenos: + + with open("./configs/rag_hotpotqa.yaml", "r") as file: + settings = yaml.safe_load(file) + +**Step 3: load the dataset.** +In this tutorial, we use the `HotpotQA dataset `_ as an example. Each data sample in HotpotQA has *question*, *answer*, *context* and *supporting_facts* selected from the whole context. We load the HotpotQA dataset using the :obj:`load_dataset` function from the `datasets `_ module. We select a subset of the dataset as an example for evaluation purposes. + +.. code-block:: + :linenos: + + dataset = load_dataset(path="hotpot_qa", name="fullwiki") + dataset = dataset["train"].select(range(5)) + +**Step 4: build the document list for each sample in the dataset.** +For each sample in the dataset, we create a list of documents to retrieve from according to its corresponding *context* in the dataset. Each document has a title and a list of sentences. We use the :obj:`Document` class from the :obj:`core.base_data_class` module to represent each document. + +.. code-block:: + :linenos: + + for data in dataset: + num_docs = len(data["context"]["title"]) + doc_list = [ + Document( + meta_data={"title": data["context"]["title"][i]}, + text=" ".join(data["context"]["sentences"][i]), + ) + for i in range(num_docs) + ] + +**Step 5: build the RAG pipeline.** +We initialize the RAG pipeline by creating an instance of the :obj:`RAG` class with the loaded configuration settings. We then build the index using the document list created in the previous step. + +.. code-block:: + :linenos: + + for data in dataset: + # following the previous code snippet + rag = RAG(settings) + rag.build_index(doc_list) + +**Step 6: retrieve the context and generate the answer.** +For each sample in the dataset, we retrieve the context and generate the answer using the RAG pipeline. We can print the query, response, ground truth response, context string, and ground truth context string for each sample. + +To get the ground truth context string from the *supporting_facts* filed in HotpotQA. We have implemented a :obj:`get_supporting_sentences` function, which extract the supporting sentences from the context based on the *supporting_facts*. This function is specific to the HotpotQA dataset, which is available in `use_cases/rag_hotpotqa.py `_. + +.. code-block:: + :linenos: + + all_questions = [] + all_retrieved_context = [] + all_gt_context = [] + all_pred_answer = [] + all_gt_answer = [] + for data in dataset: + # following the previous code snippet + query = data["question"] + response, context_str = rag.call(query) + gt_context_sentence_list = get_supporting_sentences( + data["supporting_facts"], data["context"] + ) + all_questions.append(query) + all_retrieved_context.append(context_str) + all_gt_context.append(gt_context_sentence_list) + all_pred_answer.append(response["answer"]) + all_gt_answer.append(data["answer"]) + print(f"query: {query}") + print(f"response: {response['answer']}") + print(f"ground truth response: {data['answer']}") + print(f"context_str: {context_str}") + print(f"ground truth context_str: {gt_context_sentence_list}") + + +**Step 7: evaluate the performance of the RAG pipeline.** +We first evaluate the performance of the retriever component of the RAG pipeline. We compute the average recall and context relevance for each query using the :class:`RetrieverEvaluator ` class. + +.. code-block:: + :linenos: + + retriever_evaluator = RetrieverEvaluator() + avg_recall, recall_list = retriever_evaluator.compute_recall( + all_retrieved_context, all_gt_context + ) + avg_relevance, relevance_list = retriever_evaluator.compute_context_relevance( + all_retrieved_context, all_gt_context + ) + print(f"Average recall: {avg_recall}") + print(f"Average relevance: {avg_relevance}") + +Next, we evaluate the performance of the generator component of the RAG pipeline. We compute the average exact match accuracy for each query using the :class:`AnswerMacthEvaluator ` class. + +.. code-block:: + :linenos: + + generator_evaluator = AnswerMacthEvaluator(type="fuzzy_match") + answer_match_acc, match_acc_list = generator_evaluator.compute_match_acc( + all_pred_answer, all_gt_answer + ) + print(f"Answer match accuracy: {answer_match_acc}") + +Finally, we evaluate the performance of the generator component of the RAG pipeline using an LLM judge. We compute the average judgement for each query using the :class:`LLMasJudge ` class. + +Note that :obj:`task_desc_str` and :obj:`judgement_query` can be customized. + +.. code-block:: + :linenos: + + llm_evaluator = Generator( + model_client=OpenAIClient(), + prompt=Prompt(DEFAULT_LLM_EVALUATOR_PROMPT), + output_processors=Sequential(JsonParser()), + preset_prompt_kwargs={ + "task_desc_str": r""" + You are a helpful assistant. + Given the question, ground truth answer, and predicted answer, you need to answer the judgement query. + Output True or False according to the judgement query following this JSON format: + { + "judgement": True + } + """ + }, + model_kwargs=settings["llm_evaluator"], + ) + llm_judge = LLMasJudge(llm_evaluator) + judgement_query = ( + "For the question, does the predicted answer contain the ground truth answer?" + ) + avg_judgement, judgement_list = llm_judge.compute_judgement( + all_questions, all_pred_answer, all_gt_answer, judgement_query + ) + print(f"Average judgement: {avg_judgement}") + +**Conclusion.** +In this tutorial, we learned how to evaluate a RAG pipeline using the HotpotQA dataset. We walked through the code and explained each step of the evaluation process. You can use this tutorial as a starting point to evaluate your own RAG pipelines and customize the evaluation metrics based on your requirements. diff --git a/_sources/tutorials/index.rst.txt b/_sources/tutorials/index.rst.txt new file mode 100644 index 000000000..dc0edc48f --- /dev/null +++ b/_sources/tutorials/index.rst.txt @@ -0,0 +1,14 @@ +.. _use_cases: + +Use Cases +============================= + +*How different parts are used to build various LLM applications* + +* + +.. toctree:: + :maxdepth: 2 + + eval_a_rag + introduction_to_basedataclass \ No newline at end of file diff --git a/_sources/tutorials/introduction_to_basedataclass.rst.txt b/_sources/tutorials/introduction_to_basedataclass.rst.txt new file mode 100644 index 000000000..27d782d33 --- /dev/null +++ b/_sources/tutorials/introduction_to_basedataclass.rst.txt @@ -0,0 +1,225 @@ +Introduction to BaseDataClass +======================================= + +In this tutorial, we will discuss how to use ``BaseDataClass`` to streamline the data handling, serialization and description in LightRAG. + +`OpenAI's cookbook `_ emphasizes that LLM works better when operating **structured** and **consistent** data. +To solve this, ``LightRAG`` provides ``BaseDataClass`` for developers to manage data with control and flexibility, including: + +* getting structured dataclass/instance metadata(`signature` or `schema`) +* formatting class instance to ``yaml``, ``dict`` or ``json`` +* loading data from dictionary + +Like the role of ``Tensor`` in ``PyTorch``, ``BaseDataClass`` in ``LightRAG`` is the base class across all **dataclasses**. +``BaseDataClass`` offers to create `signature` or `schema` from both classeses and instances. It will also generate structured instances data. Developers can use ``BaseDataClass`` to easily define and describe dataclasses that handle the data input or output in LLM applications, keeping data consistent and structured. +In the following tutorial, we will investigate the functionality of ``BaseDataClass`` with examples. + +**1. Create Signature and Schema** + +* **Signature:** Signature has simpler content and structure and hence more token efficient than schema. ``LightRAG`` supports ``json`` and ``yaml`` formating. + +* **Schema:** Schema is enssentially a dictionary containing more keys to show detailed information. Because of the detailed content, schema can mislead the model if not used properly. + +Example to get signature and schema from a dataclass: + +.. code-block:: python + + from core.base_data_class import BaseDataClass + from dataclasses import dataclass, field + # Define a dataclass + @dataclass + class MyOutputs(BaseDataClass): + age: int = field(metadata={"desc": "The age of the person", "prefix": "Age:"}) + name: str = field(metadata={"desc": "The name of the person", "prefix": "Name:"}) + # Create signatures + print(f"json signature:") + print(MyOutputs.to_json_signature()) + print(f"yaml signature:") + print(MyOutputs.to_yaml_signature()) + # Create class schema + print(f"class schema:") + print(MyOutputs.get_data_class_schema()) + +.. grid:: 2 + :gutter: 1 + + .. grid-item-card:: Signature Output + + .. code-block:: python + + # json signature: + { + "age": "The age of the person (int) (required)", + "name": "The name of the person (str) (required)" + } + # yaml signature: + age: The age of the person (int) (required) + name: The name of the person (str) (required) + + + .. grid-item-card:: Schema Output + + .. code-block:: python + + { + 'age': {'type': 'int', 'description': '', 'required': True}, + 'name': {'type': 'str', 'description': '', 'required': True} + } + +As `signature` and `schema` are well-structured, developers can use them to instruct the model the output data format. + +Besides creating `signature` and `schema` for classes, ``BaseDataClass`` works for single instances as well. +Example to get signiture and schema from an instance: + +.. code-block:: python + + from core.base_data_class import BaseDataClass + from dataclasses import dataclass, field + # Define a dataclass + @dataclass + class MyOutputs(BaseDataClass): + age: int = field(metadata={"desc": "The age of the person", "prefix": "Age:"}) + name: str = field(metadata={"desc": "The name of the person", "prefix": "Name:"}) + + my_instance = MyOutputs(age=25, name="John Doe") + # my_instance json signiture + print(my_instance.to_json_signature()) + # my_instance yaml signiture + print(my_instance.to_yaml_signature()) + # my_instance schema + print(my_instance.get_data_class_schema()) + +.. grid:: 2 + :gutter: 1 + + .. grid-item-card:: Signature Output + + .. code-block:: python + + # json signature: + { + "age": "The age of the person (int) (required)", + "name": "The name of the person (str) (required)" + } + # yaml signature: + age: The age of the person (int) (required) + name: The name of the person (str) (required) + + .. grid-item-card:: Schema Output + + .. code-block:: python + + { + 'age': {'type': 'int', 'description': '', 'required': True}, + 'name': {'type': 'str', 'description': '', 'required': True} + } + +**2. Format Instances** + +Developers can use ``BaseDataClass`` not only to format the input or output, but also to format examples during tasks such as few-shot prompting. +Example to get structured instance examples(``yaml`` or ``json``): + +.. code-block:: python + + from core.base_data_class import BaseDataClass + from dataclasses import dataclass, field + + @dataclass + class MyOutputs(BaseDataClass): + age: int = field(metadata={"desc": "The age of the person", "prefix": "Age:"}) + name: str = field(metadata={"desc": "The name of the person", "prefix": "Name:"}) + + my_instance = MyOutputs(age=25, name="John Doe") + # my_instance json signiture + print(my_instance.to_json()) + # my_instance yaml signiture + print(my_instance.to_yaml()) + +.. grid:: 2 + :gutter: 1 + + .. grid-item-card:: json Output + + .. code-block:: python + + { + "age": 25, + "name": "John Doe" + } + + + .. grid-item-card:: yaml Output + + .. code-block:: python + + age: 25 + name: John Doe + +The examples demonstrate how ``BaseDataClass`` works for describing dataclasses and formatting instance to ``yaml`` and ``json``. +Developers should select schema or signature depends on the use case. + +**3. Load Data from Dictionary** + +If developers want to load data from a dictionary to a certain data class, they can run: +``loaded_example = MyOutputs.from_dict({"age":10, "name":"Harry"})``. + +(For details, please refer to :class:`core.base_data_class.BaseDataClass`.) + + +**4. Implement with Other Components** + +What's more, developers can use the dataclasses to interact with the ``Prompt`` and ``Generator`` classes, enhancing the consistency and structure of the application data flow. +(``LightRAG`` uses ``jinja2`` for prompt template, make sure you've checked ``jinja2`` template tutorial before reading the example.) + +Example: + +.. code-block:: python + + from core.base_data_class import BaseDataClass + from dataclasses import dataclass, field + from core.prompt_builder import Prompt + + # define a dataclass formatting the data + @dataclass + class JokeOutput(BaseDataClass): + setup: str = field(metadata={"desc": "question to set up a joke"}, default="") + punchline: str = field(metadata={"desc": "answer to resolve the joke"}, default="") + + # initialize an example + joke_example = JokeOutput( + setup="Why did the scarecrow win an award?", + punchline="Because he was outstanding in his field.", + ) + + OUTPUT_FORMAT = r""" + Your output should be formatted as a standard YAML instance with the following schema: + ``` + {{schema}} + ``` + {% if example %} + Here is an example: + ``` + {{example}} + ``` + {% endif %} + """ + + prompt_template = Prompt(template=OUTPUT_FORMAT) + prompt = prompt_template(schema=JokeOutput.to_yaml_signature(), example=joke_example.to_yaml()) + + print(prompt) + + # Your output should be formatted as a standard YAML instance with the following schema: + # ``` + # setup: question to set up a joke (str) (optional) + # punchline: answer to resolve the joke (str) (optional) + # ``` + # Here is an example: + # ``` + # punchline: Because he was outstanding in his field. + # setup: Why did the scarecrow win an award? + # ``` + +**5. Summary** + +In this tutorial, we've covered how to use ``BaseDataClass`` to create structured dataclass/instance `signature` and `schema`, format instance, load data from dictionary to the dataclass, and implement the ``BaseDataClass`` with other components. \ No newline at end of file diff --git a/_sources/tutorials/logging.rst.txt b/_sources/tutorials/logging.rst.txt new file mode 100644 index 000000000..e69de29bb diff --git a/_sphinx_design_static/design-tabs.js b/_sphinx_design_static/design-tabs.js new file mode 100644 index 000000000..b25bd6a4f --- /dev/null +++ b/_sphinx_design_static/design-tabs.js @@ -0,0 +1,101 @@ +// @ts-check + +// Extra JS capability for selected tabs to be synced +// The selection is stored in local storage so that it persists across page loads. + +/** + * @type {Record} + */ +let sd_id_to_elements = {}; +const storageKeyPrefix = "sphinx-design-tab-id-"; + +/** + * Create a key for a tab element. + * @param {HTMLElement} el - The tab element. + * 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-- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + 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white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js new file mode 100644 index 000000000..54b3c4638 --- /dev/null +++ b/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.8 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js new file mode 100644 index 000000000..2ea7ff3e2 --- /dev/null +++ b/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js new file mode 100644 index 000000000..dbe1aaad7 --- /dev/null +++ b/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_static/css/rtd_sphinx_search.min.css b/_static/css/rtd_sphinx_search.min.css new file mode 100644 index 000000000..b52e19095 --- /dev/null +++ b/_static/css/rtd_sphinx_search.min.css @@ 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return [syncGroup, syncId, syncGroup + "--" + syncId]; +} + +/** + * Initialize the tab selection. + * + */ +function ready() { + // Find all tabs with sync data + + /** @type {string[]} */ + let groups = []; + + document.querySelectorAll(".sd-tab-label").forEach((label) => { + if (label instanceof HTMLElement) { + let data = create_key(label); + if (data) { + let [group, id, key] = data; + + // add click event listener + // @ts-ignore + label.onclick = onSDLabelClick; + + // store map of key to elements + if (!sd_id_to_elements[key]) { + sd_id_to_elements[key] = []; + } + sd_id_to_elements[key].push(label); + + if (groups.indexOf(group) === -1) { + groups.push(group); + // Check if a specific tab has been selected via URL parameter + const tabParam = new URLSearchParams(window.location.search).get( + group + ); + if (tabParam) { + console.log( + "sphinx-design: Selecting tab id for group '" + + group + + "' from URL parameter: " + + tabParam + ); + window.sessionStorage.setItem(storageKeyPrefix + group, tabParam); + } + } + + // Check is a specific tab has been selected previously + let previousId = window.sessionStorage.getItem( + storageKeyPrefix + group + ); + if (previousId === id) { + // console.log( + // "sphinx-design: Selecting tab from session storage: " + id + // ); + // @ts-ignore + label.previousElementSibling.checked = true; + } + } + } + }); +} + +/** + * Activate other tabs with the same sync id. + * + * @this {HTMLElement} - The element that was clicked. + */ +function onSDLabelClick() { + let data = create_key(this); + if (!data) return; + let [group, id, key] = data; + for (const label of sd_id_to_elements[key]) { + if (label === this) continue; + // @ts-ignore + label.previousElementSibling.checked = true; + } + window.sessionStorage.setItem(storageKeyPrefix + group, id); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 000000000..4d67807d1 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 000000000..7e4c114f2 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/js/rtd_search_config.js b/_static/js/rtd_search_config.js new file mode 100644 index 000000000..c512b4db3 --- /dev/null +++ b/_static/js/rtd_search_config.js @@ -0,0 +1,2 @@ + +var RTD_SEARCH_CONFIG = {"default_filter": "project:/", "filters": []}; \ No newline at end of file diff --git a/_static/js/rtd_sphinx_search.min.js b/_static/js/rtd_sphinx_search.min.js new file mode 100644 index 000000000..9bb6a075d --- /dev/null +++ b/_static/js/rtd_sphinx_search.min.js @@ -0,0 +1 @@ +"use strict";function _createForOfIteratorHelper(e,t){var r;if("undefined"==typeof Symbol||null==e[Symbol.iterator]){if(Array.isArray(e)||(r=_unsupportedIterableToArray(e))||t&&e&&"number"==typeof e.length){r&&(e=r);var n=0,t=function(){};return{s:t,n:function(){return n>=e.length?{done:!0}:{done:!1,value:e[n++]}},e:function(e){throw e},f:t}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var a,o=!0,l=!1;return{s:function(){r=e[Symbol.iterator]()},n:function(){var e=r.next();return o=e.done,e},e:function(e){l=!0,a=e},f:function(){try{o||null==r.return||r.return()}finally{if(l)throw a}}}}function _slicedToArray(e,t){return _arrayWithHoles(e)||_iterableToArrayLimit(e,t)||_unsupportedIterableToArray(e,t)||_nonIterableRest()}function _nonIterableRest(){throw new TypeError("Invalid attempt to destructure non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}function _unsupportedIterableToArray(e,t){if(e){if("string"==typeof e)return _arrayLikeToArray(e,t);var r=Object.prototype.toString.call(e).slice(8,-1);return"Map"===(r="Object"===r&&e.constructor?e.constructor.name:r)||"Set"===r?Array.from(e):"Arguments"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?_arrayLikeToArray(e,t):void 0}}function _arrayLikeToArray(e,t){(null==t||t>e.length)&&(t=e.length);for(var r=0,n=new Array(t);rSearching ....",a.appendChild(e);return debounce(function(){updateUrl(),updateSearchBar();var e=t+"?"+new URLSearchParams(r).toString();fetch(e,{method:"GET"}).then(function(e){if(!e.ok)throw new Error;return e.json()}).then(function(e){var t;00 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/nbsphinx-code-cells.css b/_static/nbsphinx-code-cells.css new file mode 100644 index 000000000..a3fb27c30 --- /dev/null +++ b/_static/nbsphinx-code-cells.css @@ -0,0 +1,259 @@ +/* remove conflicting styling from Sphinx themes */ +div.nbinput.container div.prompt *, +div.nboutput.container div.prompt *, +div.nbinput.container div.input_area pre, +div.nboutput.container div.output_area pre, +div.nbinput.container div.input_area .highlight, +div.nboutput.container div.output_area .highlight { + border: none; + padding: 0; + margin: 0; + box-shadow: none; +} + +div.nbinput.container > div[class*=highlight], +div.nboutput.container > div[class*=highlight] { + margin: 0; +} + +div.nbinput.container div.prompt *, +div.nboutput.container div.prompt * { + background: none; +} + +div.nboutput.container div.output_area .highlight, +div.nboutput.container div.output_area pre { + background: unset; +} + +div.nboutput.container div.output_area div.highlight { + color: unset; /* override Pygments text color */ +} + +/* avoid gaps between output lines */ +div.nboutput.container div[class*=highlight] pre { + line-height: normal; +} + +/* input/output containers */ +div.nbinput.container, +div.nboutput.container { + display: -webkit-flex; + display: flex; + align-items: flex-start; + margin: 0; + width: 100%; +} +@media (max-width: 540px) { + div.nbinput.container, + div.nboutput.container { + flex-direction: column; + } +} + +/* input container */ +div.nbinput.container { + padding-top: 5px; +} + +/* last container */ +div.nblast.container { + padding-bottom: 5px; +} + +/* input prompt */ +div.nbinput.container div.prompt pre, +/* for sphinx_immaterial theme: */ +div.nbinput.container div.prompt pre > code { + color: #307FC1; +} + +/* output prompt */ +div.nboutput.container div.prompt pre, +/* for sphinx_immaterial theme: */ +div.nboutput.container div.prompt pre > code { + color: #BF5B3D; +} + +/* all prompts */ +div.nbinput.container div.prompt, +div.nboutput.container div.prompt { + width: 4.5ex; + padding-top: 5px; + position: relative; + user-select: 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display: none; +} + +/* Some additional styling taken form the Jupyter notebook CSS */ +.jp-RenderedHTMLCommon table, +div.rendered_html table { + border: none; + border-collapse: collapse; + border-spacing: 0; + color: black; + font-size: 12px; + table-layout: fixed; +} +.jp-RenderedHTMLCommon thead, +div.rendered_html thead { + border-bottom: 1px solid black; + vertical-align: bottom; +} +.jp-RenderedHTMLCommon tr, +.jp-RenderedHTMLCommon th, +.jp-RenderedHTMLCommon td, +div.rendered_html tr, +div.rendered_html th, +div.rendered_html td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} +.jp-RenderedHTMLCommon th, +div.rendered_html th { + font-weight: bold; +} +.jp-RenderedHTMLCommon tbody tr:nth-child(odd), +div.rendered_html tbody tr:nth-child(odd) { + background: #f5f5f5; +} +.jp-RenderedHTMLCommon tbody tr:hover, +div.rendered_html tbody tr:hover { + background: rgba(66, 165, 245, 0.2); +} + diff --git a/_static/nbsphinx-gallery.css b/_static/nbsphinx-gallery.css new file mode 100644 index 000000000..365c27a96 --- /dev/null +++ b/_static/nbsphinx-gallery.css @@ -0,0 +1,31 @@ +.nbsphinx-gallery { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(160px, 1fr)); + gap: 5px; + margin-top: 1em; + margin-bottom: 1em; +} + +.nbsphinx-gallery > a { + padding: 5px; + border: 1px dotted currentColor; + border-radius: 2px; + text-align: center; +} + +.nbsphinx-gallery > a:hover { + border-style: solid; +} + +.nbsphinx-gallery img { + max-width: 100%; + max-height: 100%; +} + +.nbsphinx-gallery > a > div:first-child { + display: flex; + align-items: start; + justify-content: center; + height: 120px; + margin-bottom: 5px; +} diff --git a/_static/pygments.css b/_static/pygments.css new file mode 100644 index 000000000..012e6a00a --- /dev/null +++ b/_static/pygments.css @@ -0,0 +1,152 @@ +html[data-theme="light"] .highlight pre { line-height: 125%; } 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ve{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=we.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===ti&&this.cycle()}static get Default(){return ri}static get DefaultType(){return ai}static get NAME(){return"carousel"}next(){this._slide(ze)}nextWhenVisible(){!document.hidden&&Bt(this._element)&&this.next()}prev(){this._slide(Re)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?fe.one(this._element,Ke,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void fe.one(this._element,Ke,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?ze:Re;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,Qe,(t=>this._keydown(t))),"hover"===this._config.pause&&(fe.on(this._element,Xe,(()=>this.pause())),fe.on(this._element,Ue,(()=>this._maybeEnableCycle()))),this._config.touch&&je.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of we.find(".carousel-item img",this._element))fe.on(t,Ge,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(qe)),rightCallback:()=>this._slide(this._directionToOrder(Ve)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new je(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=oi[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=we.findOne(ii,this._indicatorsElement);e.classList.remove(ei),e.removeAttribute("aria-current");const i=we.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(ei),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===ze,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const 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.collapse.collapsing").filter((t=>t!==this._element)).map((t=>Ei.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,hi).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(gi),this._element.classList.add(_i),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi,mi),this._element.style[e]="",fe.trigger(this._element,di)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,ui).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(_i),this._element.classList.remove(gi,mi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi),fe.trigger(this._element,fi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(mi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(vi);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(bi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Ei.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,pi,vi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))Ei.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(Ei);const Ai="dropdown",Ti=".bs.dropdown",Ci=".data-api",Oi="ArrowUp",xi="ArrowDown",ki=`hide${Ti}`,Li=`hidden${Ti}`,Si=`show${Ti}`,Di=`shown${Ti}`,$i=`click${Ti}${Ci}`,Ii=`keydown${Ti}${Ci}`,Ni=`keyup${Ti}${Ci}`,Pi="show",Mi='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',ji=`${Mi}.${Pi}`,Fi=".dropdown-menu",Hi=Kt()?"top-end":"top-start",Bi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",zi=Kt()?"bottom-start":"bottom-end",Ri=Kt()?"left-start":"right-start",qi=Kt()?"right-start":"left-start",Vi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Yi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Ki extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Fi)[0]||we.prev(this._element,Fi)[0]||we.findOne(Fi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Vi}static get DefaultType(){return Yi}static get NAME(){return Ai}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,Si,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Pi),this._element.classList.add(Pi),fe.trigger(this._element,Di,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,ki,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Pi),this._element.classList.remove(Pi),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,Li,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ai.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Ft(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Pi)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Ri;if(t.classList.contains("dropstart"))return qi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Bi:Hi:e?zi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===xi,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Ki.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(ji);for(const i of e){const e=Ki.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Oi,xi].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Mi)?this:we.prev(this,Mi)[0]||we.next(this,Mi)[0]||we.findOne(Mi,t.delegateTarget.parentNode),o=Ki.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Ii,Mi,Ki.dataApiKeydownHandler),fe.on(document,Ii,Fi,Ki.dataApiKeydownHandler),fe.on(document,$i,Ki.clearMenus),fe.on(document,Ni,Ki.clearMenus),fe.on(document,$i,Mi,(function(t){t.preventDefault(),Ki.getOrCreateInstance(this).toggle()})),Qt(Ki);const Qi="backdrop",Xi="show",Ui=`mousedown.bs.${Qi}`,Gi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ji={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Zi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Gi}static get DefaultType(){return Ji}static get NAME(){return Qi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Xi),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Xi),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Ui),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Ui,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const tn=".bs.focustrap",en=`focusin${tn}`,nn=`keydown.tab${tn}`,sn="backward",on={autofocus:!0,trapElement:null},rn={autofocus:"boolean",trapElement:"element"};class an extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return on}static get DefaultType(){return rn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,tn),fe.on(document,en,(t=>this._handleFocusin(t))),fe.on(document,nn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,tn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===sn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?sn:"forward")}}const ln=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",cn=".sticky-top",hn="padding-right",dn="margin-right";class un{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,hn,(e=>e+t)),this._setElementAttributes(ln,hn,(e=>e+t)),this._setElementAttributes(cn,dn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,hn),this._resetElementAttributes(ln,hn),this._resetElementAttributes(cn,dn)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const fn=".bs.modal",pn=`hide${fn}`,mn=`hidePrevented${fn}`,gn=`hidden${fn}`,_n=`show${fn}`,bn=`shown${fn}`,vn=`resize${fn}`,yn=`click.dismiss${fn}`,wn=`mousedown.dismiss${fn}`,En=`keydown.dismiss${fn}`,An=`click${fn}.data-api`,Tn="modal-open",Cn="show",On="modal-static",xn={backdrop:!0,focus:!0,keyboard:!0},kn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class Ln extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new un,this._addEventListeners()}static get Default(){return xn}static get DefaultType(){return kn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,_n,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(Tn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,pn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(Cn),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,fn),fe.off(this._dialog,fn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Zi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new an({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(Cn),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,bn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,En,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,vn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,wn,(t=>{fe.one(this._element,yn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Tn),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,gn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,mn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(On)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(On),this._queueCallback((()=>{this._element.classList.remove(On),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Ln.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,An,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,_n,(t=>{t.defaultPrevented||fe.one(e,gn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&Ln.getInstance(i).hide(),Ln.getOrCreateInstance(e).toggle(this)})),Ee(Ln),Qt(Ln);const Sn=".bs.offcanvas",Dn=".data-api",$n=`load${Sn}${Dn}`,In="show",Nn="showing",Pn="hiding",Mn=".offcanvas.show",jn=`show${Sn}`,Fn=`shown${Sn}`,Hn=`hide${Sn}`,Bn=`hidePrevented${Sn}`,Wn=`hidden${Sn}`,zn=`resize${Sn}`,Rn=`click${Sn}${Dn}`,qn=`keydown.dismiss${Sn}`,Vn={backdrop:!0,keyboard:!0,scroll:!1},Yn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Kn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Vn}static get DefaultType(){return Yn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,jn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new un).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Nn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(In),this._element.classList.remove(Nn),fe.trigger(this._element,Fn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,Hn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(Pn),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(In,Pn),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new un).reset(),fe.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Zi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Bn)}:null})}_initializeFocusTrap(){return new an({trapElement:this._element})}_addEventListeners(){fe.on(this._element,qn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Bn))}))}static jQueryInterface(t){return this.each((function(){const e=Kn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Rn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Wn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Mn);i&&i!==e&&Kn.getInstance(i).hide(),Kn.getOrCreateInstance(e).toggle(this)})),fe.on(window,$n,(()=>{for(const t of we.find(Mn))Kn.getOrCreateInstance(t).show()})),fe.on(window,zn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Kn.getOrCreateInstance(t).hide()})),Ee(Kn),Qt(Kn);const Qn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Xn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Un=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Gn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Xn.has(i)||Boolean(Un.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Jn={allowList:Qn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Zn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},ts={entry:"(string|element|function|null)",selector:"(string|element)"};class es extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Jn}static get DefaultType(){return Zn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},ts)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Gn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const is=new Set(["sanitize","allowList","sanitizeFn"]),ns="fade",ss="show",os=".tooltip-inner",rs=".modal",as="hide.bs.modal",ls="hover",cs="focus",hs={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ds={allowList:Qn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},us={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class fs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return ds}static get DefaultType(){return us}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(rs),as,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[cs]=!1,this._activeTrigger[ls]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ns,ss),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ns),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new es({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{[os]:this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ns)}_isShown(){return this.tip&&this.tip.classList.contains(ss)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=hs[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ls?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ls?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?cs:ls]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?cs:ls]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(rs),as,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=_e.getDataAttributes(this._element);for(const t of Object.keys(e))is.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".popover-header",ms=".popover-body",gs={...fs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},_s={...fs.DefaultType,content:"(null|string|element|function)"};class bs extends fs{static get Default(){return gs}static get DefaultType(){return _s}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{[ps]:this._getTitle(),[ms]:this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=bs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(bs);const vs=".bs.scrollspy",ys=`activate${vs}`,ws=`click${vs}`,Es=`load${vs}.data-api`,As="active",Ts="[href]",Cs=".nav-link",Os=`${Cs}, .nav-item > ${Cs}, .list-group-item`,xs={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ks={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ls extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xs}static get DefaultType(){return ks}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,ws),fe.on(this._config.target,ws,Ts,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(Ts,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(As),this._activateParents(t),fe.trigger(this._element,ys,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(As);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,Os))t.classList.add(As)}_clearActiveClass(t){t.classList.remove(As);const e=we.find(`${Ts}.${As}`,t);for(const t of e)t.classList.remove(As)}static jQueryInterface(t){return this.each((function(){const e=Ls.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,Es,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ls.getOrCreateInstance(t)})),Qt(Ls);const Ss=".bs.tab",Ds=`hide${Ss}`,$s=`hidden${Ss}`,Is=`show${Ss}`,Ns=`shown${Ss}`,Ps=`click${Ss}`,Ms=`keydown${Ss}`,js=`load${Ss}`,Fs="ArrowLeft",Hs="ArrowRight",Bs="ArrowUp",Ws="ArrowDown",zs="Home",Rs="End",qs="active",Vs="fade",Ys="show",Ks=".dropdown-toggle",Qs=`:not(${Ks})`,Xs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Us=`.nav-link${Qs}, .list-group-item${Qs}, [role="tab"]${Qs}, ${Xs}`,Gs=`.${qs}[data-bs-toggle="tab"], .${qs}[data-bs-toggle="pill"], .${qs}[data-bs-toggle="list"]`;class Js extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ms,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,Ds,{relatedTarget:t}):null;fe.trigger(t,Is,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(qs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ns,{relatedTarget:e})):t.classList.add(Ys)}),t,t.classList.contains(Vs)))}_deactivate(t,e){t&&(t.classList.remove(qs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,$s,{relatedTarget:e})):t.classList.remove(Ys)}),t,t.classList.contains(Vs)))}_keydown(t){if(![Fs,Hs,Bs,Ws,zs,Rs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([zs,Rs].includes(t.key))i=e[t.key===zs?0:e.length-1];else{const n=[Hs,Ws].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Js.getOrCreateInstance(i).show())}_getChildren(){return we.find(Us,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=we.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=we.findOne(t,i);s&&s.classList.toggle(n,e)};n(Ks,qs),n(".dropdown-menu",Ys),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(qs)}_getInnerElement(t){return t.matches(Us)?t:we.findOne(Us,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Js.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,Ps,Xs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Js.getOrCreateInstance(this).show()})),fe.on(window,js,(()=>{for(const t of we.find(Gs))Js.getOrCreateInstance(t)})),Qt(Js);const Zs=".bs.toast",to=`mouseover${Zs}`,eo=`mouseout${Zs}`,io=`focusin${Zs}`,no=`focusout${Zs}`,so=`hide${Zs}`,oo=`hidden${Zs}`,ro=`show${Zs}`,ao=`shown${Zs}`,lo="hide",co="show",ho="showing",uo={animation:"boolean",autohide:"boolean",delay:"number"},fo={animation:!0,autohide:!0,delay:5e3};class po extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return fo}static get DefaultType(){return uo}static get NAME(){return"toast"}show(){fe.trigger(this._element,ro).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(lo),qt(this._element),this._element.classList.add(co,ho),this._queueCallback((()=>{this._element.classList.remove(ho),fe.trigger(this._element,ao),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(fe.trigger(this._element,so).defaultPrevented||(this._element.classList.add(ho),this._queueCallback((()=>{this._element.classList.add(lo),this._element.classList.remove(ho,co),fe.trigger(this._element,oo)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(co),super.dispose()}isShown(){return this._element.classList.contains(co)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,to,(t=>this._onInteraction(t,!0))),fe.on(this._element,eo,(t=>this._onInteraction(t,!1))),fe.on(this._element,io,(t=>this._onInteraction(t,!0))),fe.on(this._element,no,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=po.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named 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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.3.3 (https://getbootstrap.com/)\n * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n const instanceMap = elementMap.get(element);\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n instanceMap.set(key, instance);\n },\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n return null;\n },\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key);\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n }\n return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n }\n\n // Get transition-duration of the element\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object));\n }\n return null;\n};\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])');\n if (!closedDetails) {\n return elementIsVisible;\n }\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n if (summary === null) {\n return false;\n }\n }\n return elementIsVisible;\n};\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n if (element.classList.contains('disabled')) {\n return true;\n }\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n if (element instanceof ShadowRoot) {\n return element;\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null;\n }\n return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement);\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n index += shouldGetNext ? 1 : -1;\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n return fn.apply(element, [event]);\n };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n hydrateObj(event, {\n delegateTarget: target\n });\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n return fn.apply(target, [event]);\n }\n }\n };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string';\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n callable = wrapFunction(callable);\n }\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n if (!fn) {\n return;\n }\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n const evt = hydrateObj(new Event(event, {\n bubbles,\n cancelable: true\n }), args);\n if (defaultPrevented) {\n evt.preventDefault();\n }\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n return evt;\n }\n};\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value;\n }\n });\n }\n }\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n if (value === 'false') {\n return false;\n }\n if (value === Number(value).toString()) {\n return Number(value);\n }\n if (value === '' || value === 'null') {\n return null;\n }\n if (typeof value !== 'string') {\n return value;\n }\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n return attributes;\n },\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n static get DefaultType() {\n return {};\n }\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n return config;\n }\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.3';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n if (!element) {\n return;\n }\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n static get VERSION() {\n return VERSION;\n }\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href');\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n }\n return selector ? selector.split(',').map(sel => parseSelector(sel)).join(',') : null;\n};\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n return parents;\n },\n prev(element, selector) {\n let previous = element.previousElementSibling;\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n previous = previous.previousElementSibling;\n }\n return [];\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n next = next.nextElementSibling;\n }\n return [];\n },\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n },\n getSelectorFromElement(element) {\n const selector = getSelector(element);\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null;\n }\n return null;\n },\n getElementFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.findOne(selector) : null;\n },\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element);\n return selector ? SelectorEngine.find(selector) : [];\n }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target);\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n if (closeEvent.defaultPrevented) {\n return;\n }\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n }\n\n // Private\n _destroyElement() {\n this._element.remove();\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n if (!element || !Swipe.isSupported()) {\n return;\n }\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n this._initEvents();\n }\n\n // Getters\n static get Default() {\n return Default$c;\n }\n static get DefaultType() {\n return DefaultType$c;\n }\n static get NAME() {\n return NAME$d;\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n this._handleSwipe();\n execute(this._config.endCallback);\n }\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n if (!direction) {\n return;\n }\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n this._addEventListeners();\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$b;\n }\n static get DefaultType() {\n return DefaultType$b;\n }\n static get NAME() {\n return NAME$c;\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT);\n }\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n prev() {\n this._slide(ORDER_PREV);\n }\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n this._clearInterval();\n }\n cycle() {\n this._clearInterval();\n this._updateInterval();\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n this.cycle();\n }\n to(index) {\n const items = this._getItems();\n if (index > items.length - 1 || index < 0) {\n return;\n }\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n const activeIndex = this._getItemIndex(this._getActive());\n if (activeIndex === index) {\n return;\n }\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n this._slide(order, items[index]);\n }\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause();\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n const direction = KEY_TO_DIRECTION[event.key];\n if (direction) {\n event.preventDefault();\n this._slide(this._directionToOrder(direction));\n }\n }\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n if (!element) {\n return;\n }\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n const activeElement = this._getActive();\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n if (nextElement === activeElement) {\n return;\n }\n const nextElementIndex = this._getItemIndex(nextElement);\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n const slideEvent = triggerEvent(EVENT_SLIDE);\n if (slideEvent.defaultPrevented) {\n return;\n }\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return;\n }\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n this._setActiveIndicatorElement(nextElementIndex);\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n if (isCycling) {\n this.cycle();\n }\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n if (slideIndex) {\n carousel.to(slideIndex);\n carousel._maybeEnableCycle();\n return;\n }\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n carousel._maybeEnableCycle();\n return;\n }\n carousel.prev();\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n this._initializeChildren();\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n if (this._config.toggle) {\n this.toggle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$a;\n }\n static get DefaultType() {\n return DefaultType$a;\n }\n static get NAME() {\n return NAME$b;\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n let activeChildren = [];\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n const dimension = this._getDimension();\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.style[dimension] = 0;\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n this._queueCallback(complete, this._element, true);\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n if (startEvent.defaultPrevented) {\n return;\n }\n const dimension = this._getDimension();\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger);\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n this._isTransitioning = true;\n const complete = () => {\n this._isTransitioning = false;\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n this._element.style[dimension] = '';\n this._queueCallback(complete, this._element, true);\n }\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n config.parent = getElement(config.parent);\n return config;\n }\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element);\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {};\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n }\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n }\n\n // Getters\n static get Default() {\n return Default$9;\n }\n static get DefaultType() {\n return DefaultType$9;\n }\n static get NAME() {\n return NAME$a;\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n if (showEvent.defaultPrevented) {\n return;\n }\n this._createPopper();\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n this._element.focus();\n this._element.setAttribute('aria-expanded', true);\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n this._element.classList.add(CLASS_NAME_SHOW$6);\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n const relatedTarget = {\n relatedTarget: this._element\n };\n this._completeHide(relatedTarget);\n }\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n super.dispose();\n }\n update() {\n this._inNavbar = this._detectNavbar();\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n if (this._popper) {\n this._popper.destroy();\n }\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n this._element.setAttribute('aria-expanded', 'false');\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n _getConfig(config) {\n config = super._getConfig(config);\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n return config;\n }\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n let referenceElement = this._element;\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n const popperConfig = this._getPopperConfig();\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n _getPlacement() {\n const parentDropdown = this._parent;\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n };\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n if (!items.length) {\n return;\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n if (!context || context._config.autoClose === false) {\n continue;\n }\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n const relatedTarget = {\n relatedTarget: context._element\n };\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n context._completeHide(relatedTarget);\n }\n }\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n if (isInput && !isEscapeEvent) {\n return;\n }\n event.preventDefault();\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n instance._selectMenuItem(event);\n return;\n }\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n};\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n }\n\n // Getters\n static get Default() {\n return Default$8;\n }\n static get DefaultType() {\n return DefaultType$8;\n }\n static get NAME() {\n return NAME$9;\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._append();\n const element = this._getElement();\n if (this._config.isAnimated) {\n reflow(element);\n }\n element.classList.add(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n dispose() {\n if (!this._isAppended) {\n return;\n }\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n this._element.remove();\n this._isAppended = false;\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n this._element = backdrop;\n }\n return this._element;\n }\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n _append() {\n if (this._isAppended) {\n return;\n }\n const element = this._getElement();\n this._config.rootElement.append(element);\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n};\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n }\n\n // Getters\n static get Default() {\n return Default$7;\n }\n static get DefaultType() {\n return DefaultType$7;\n }\n static get NAME() {\n return NAME$8;\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return;\n }\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n deactivate() {\n if (!this._isActive) {\n return;\n }\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n }\n\n // Private\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n const elements = SelectorEngine.focusableChildren(trapElement);\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n hide() {\n const width = this.getWidth();\n this._disableOverFlow();\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n isOverflowing() {\n return this.getWidth() > 0;\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n this._element.style.overflow = 'hidden';\n }\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n this._saveInitialAttribute(element, styleProperty);\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty);\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$6;\n }\n static get DefaultType() {\n return DefaultType$6;\n }\n static get NAME() {\n return NAME$7;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._isTransitioning = true;\n this._scrollBar.hide();\n document.body.classList.add(CLASS_NAME_OPEN);\n this._adjustDialog();\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._isShown = false;\n this._isTransitioning = true;\n this._focustrap.deactivate();\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n dispose() {\n EventHandler.off(window, EVENT_KEY$4);\n EventHandler.off(this._dialog, EVENT_KEY$4);\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n handleUpdate() {\n this._adjustDialog();\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n this._element.style.display = 'block';\n this._element.removeAttribute('aria-hidden');\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n reflow(this._element);\n this._element.classList.add(CLASS_NAME_SHOW$4);\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n return;\n }\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n _hideModal() {\n this._element.style.display = 'none';\n this._element.setAttribute('aria-hidden', true);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n this._isTransitioning = false;\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n this._resetAdjustments();\n this._scrollBar.reset();\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n if (hideEvent.defaultPrevented) {\n return;\n }\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY;\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n this._element.classList.add(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n this._element.focus();\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const scrollbarWidth = this._scrollBar.getWidth();\n const isBodyOverflowing = scrollbarWidth > 0;\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](relatedTarget);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n });\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._addEventListeners();\n }\n\n // Getters\n static get Default() {\n return Default$5;\n }\n static get DefaultType() {\n return DefaultType$5;\n }\n static get NAME() {\n return NAME$6;\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n if (showEvent.defaultPrevented) {\n return;\n }\n this._isShown = true;\n this._backdrop.show();\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n this._element.setAttribute('aria-modal', true);\n this._element.setAttribute('role', 'dialog');\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n this._element.classList.add(CLASS_NAME_SHOW$3);\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n this._queueCallback(completeCallBack, this._element, true);\n }\n hide() {\n if (!this._isShown) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n if (hideEvent.defaultPrevented) {\n return;\n }\n this._focustrap.deactivate();\n this._element.blur();\n this._isShown = false;\n this._element.classList.add(CLASS_NAME_HIDING);\n this._backdrop.hide();\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n this._element.removeAttribute('aria-modal');\n this._element.removeAttribute('role');\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n this._queueCallback(completeCallback, this._element, true);\n }\n dispose() {\n this._backdrop.dispose();\n this._focustrap.deactivate();\n super.dispose();\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n this.hide();\n };\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n if (this._config.keyboard) {\n this.hide();\n return;\n }\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n });\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config](this);\n });\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = SelectorEngine.getElementFromSelector(this);\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n if (isDisabled(this)) {\n return;\n }\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n });\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n dd: [],\n div: [],\n dl: [],\n dt: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n }\n return true;\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n }\n\n // Getters\n static get Default() {\n return Default$4;\n }\n static get DefaultType() {\n return DefaultType$4;\n }\n static get NAME() {\n return NAME$5;\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n hasContent() {\n return this.getContent().length > 0;\n }\n changeContent(content) {\n this._checkContent(content);\n this._config.content = {\n ...this._config.content,\n ...content\n };\n return this;\n }\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n const template = templateWrapper.children[0];\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n return template;\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n this._checkContent(config.content);\n }\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n if (!templateElement) {\n return;\n }\n content = this._resolvePossibleFunction(content);\n if (!content) {\n templateElement.remove();\n return;\n }\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n return;\n }\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n templateElement.textContent = content;\n }\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this]);\n }\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n templateElement.textContent = element.textContent;\n }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n super(element, config);\n\n // Private\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null;\n\n // Protected\n this.tip = null;\n this._setListeners();\n if (!this._config.selector) {\n this._fixTitle();\n }\n }\n\n // Getters\n static get Default() {\n return Default$3;\n }\n static get DefaultType() {\n return DefaultType$3;\n }\n static get NAME() {\n return NAME$4;\n }\n\n // Public\n enable() {\n this._isEnabled = true;\n }\n disable() {\n this._isEnabled = false;\n }\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n this._activeTrigger.click = !this._activeTrigger.click;\n if (this._isShown()) {\n this._leave();\n return;\n }\n this._enter();\n }\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n this._disposePopper();\n super.dispose();\n }\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper();\n const tip = this._getTipElement();\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n const {\n container\n } = this._config;\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n if (this._isHovered === false) {\n this._leave();\n }\n this._isHovered = false;\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n hide() {\n if (!this._isShown()) {\n return;\n }\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n if (hideEvent.defaultPrevented) {\n return;\n }\n const tip = this._getTipElement();\n tip.classList.remove(CLASS_NAME_SHOW$2);\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n if (!this._isHovered) {\n this._disposePopper();\n }\n this._element.removeAttribute('aria-describedby');\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n update() {\n if (this._popper) {\n this._popper.update();\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n return this.tip;\n }\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml();\n\n // TODO: remove this check in v6\n if (!tip) {\n return null;\n }\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n return tip;\n }\n setContent(content) {\n this._newContent = content;\n if (this._isShown()) {\n this._disposePopper();\n this.show();\n }\n }\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n return this._templateFactory;\n }\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element]);\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n _getOffset() {\n const {\n offset\n } = this._config;\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n return offset;\n }\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element]);\n }\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n };\n }\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n context._leave();\n });\n }\n }\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n _fixTitle() {\n const title = this._element.getAttribute('title');\n if (!title) {\n return;\n }\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title');\n }\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n this._isHovered = true;\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n this._isHovered = false;\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n this._typeCheckConfig(config);\n return config;\n }\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n return config;\n }\n _getDelegateConfig() {\n const config = {};\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value;\n }\n }\n config.selector = false;\n config.trigger = 'manual';\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config;\n }\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n this._popper = null;\n }\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n static get DefaultType() {\n return DefaultType$2;\n }\n static get NAME() {\n return NAME$3;\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent();\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n if (typeof config !== 'string') {\n return;\n }\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n data[config]();\n });\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n }\n\n // Getters\n static get Default() {\n return Default$1;\n }\n static get DefaultType() {\n return DefaultType$1;\n }\n static get NAME() {\n return NAME$2;\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables();\n this._maybeEnableSmoothScroll();\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n dispose() {\n this._observer.disconnect();\n super.dispose();\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body;\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n return config;\n }\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height;\n }\n });\n }\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n this._process(targetElement(entry));\n };\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n this._clearActiveClass(targetElement(entry));\n continue;\n }\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry);\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return;\n }\n continue;\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor);\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n this._clearActiveClass(this._config.target);\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n this._activateParents(target);\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both