diff --git a/CHANGELOG.md b/CHANGELOG.md index 5a4f02a7..45ebbeb2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,15 @@ ## Next +## Added +- Integrated json-repair package to handle and repair invalid JSON generated by LLMs. +- Introduced InvalidJSONError exception for handling cases where JSON repair fails. +- Ability to create a Pipeline or SimpleKGPipeline from a config file. See [the example](examples/build_graph/from_config_files/simple_kg_pipeline_from_config_file.py). +- Added `OllamaLLM` and `OllamaEmbeddings` classes to make Ollama support more explicit. Implementations using the `OpenAILLM` and `OpenAIEmbeddings` classes will still work. + +## Changed +- Updated LLM prompts to include stricter instructions for generating valid JSON. + ### Fixed - Added schema functions to the documentation. diff --git a/README.md b/README.md index 2c5cd53b..74a2d6dd 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,33 @@ pip install neo4j-graphrag ### Optional Dependencies +This package has some optional features that can be enabled using +the extra dependencies described below: + +- LLM providers (at least one is required for RAG and KG Builder Pipeline): + - **ollama**: LLMs from Ollama + - **openai**: LLMs from OpenAI (including AzureOpenAI) + - **google**: LLMs from Vertex AI + - **cohere**: LLMs from Cohere + - **anthropic**: LLMs from Anthropic + - **mistralai**: LLMs from MistralAI +- **sentence-transformers** : to use embeddings from the `sentence-transformers` Python package +- Vector database (to use :ref:`External Retrievers`): + - **weaviate**: store vectors in Weaviate + - **pinecone**: store vectors in Pinecone + - **qdrant**: store vectors in Qdrant +- **experimental**: experimental features such as the Knowledge Graph creation pipelines. + - Warning: this dependency group requires `pygraphviz`. See below for installation instructions. + + +Install package with optional dependencies with (for instance): + +```shell +pip install "neo4j-graphrag[openai]" +# or +pip install "neo4j-graphrag[openai, experimental]" +``` + #### pygraphviz `pygraphviz` is used for visualizing pipelines. diff --git a/docs/source/api.rst b/docs/source/api.rst index 6cedc097..ca0647ee 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -9,17 +9,18 @@ API Documentation Components ********** -KGWriter -======== +DataLoader +========== -.. autoclass:: neo4j_graphrag.experimental.components.kg_writer.KGWriter - :members: run +.. autoclass:: neo4j_graphrag.experimental.components.pdf_loader.DataLoader + :members: run, get_document_metadata -Neo4jWriter -=========== -.. autoclass:: neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter - :members: run +PdfLoader +========= + +.. autoclass:: neo4j_graphrag.experimental.components.pdf_loader.PdfLoader + :members: run, load_file TextSplitter ============ @@ -85,6 +86,17 @@ LLMEntityRelationExtractor .. autoclass:: neo4j_graphrag.experimental.components.entity_relation_extractor.LLMEntityRelationExtractor :members: run +KGWriter +======== + +.. autoclass:: neo4j_graphrag.experimental.components.kg_writer.KGWriter + :members: run + +Neo4jWriter +=========== + +.. autoclass:: neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter + :members: run SinglePropertyExactMatchResolver ================================ @@ -112,6 +124,23 @@ SimpleKGPipeline :members: run_async +************ +Config files +************ + + +SimpleKGPipelineConfig +====================== + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_kg_builder.SimpleKGPipelineConfig + + +PipelineRunner +============== + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.runner.PipelineRunner + + .. _retrievers-section: ********** @@ -210,6 +239,12 @@ AzureOpenAIEmbeddings .. autoclass:: neo4j_graphrag.embeddings.openai.AzureOpenAIEmbeddings :members: +OllamaEmbeddings +================ + +.. autoclass:: neo4j_graphrag.embeddings.ollama.OllamaEmbeddings + :members: + VertexAIEmbeddings ================== @@ -257,6 +292,12 @@ AzureOpenAILLM :members: :undoc-members: get_messages, client_class, async_client_class +OllamaLLM +--------- + +.. autoclass:: neo4j_graphrag.llm.ollama_llm.OllamaLLM + :members: + VertexAILLM ----------- @@ -297,12 +338,21 @@ RagTemplate .. autoclass:: neo4j_graphrag.generation.prompts.RagTemplate :members: + :exclude-members: format ERExtractionTemplate -------------------- .. autoclass:: neo4j_graphrag.generation.prompts.ERExtractionTemplate :members: + :exclude-members: format + +Text2CypherTemplate +-------------------- + +.. autoclass:: neo4j_graphrag.generation.prompts.Text2CypherTemplate + :members: + :exclude-members: format **** @@ -384,6 +434,8 @@ Errors * :class:`neo4j_graphrag.experimental.pipeline.exceptions.PipelineStatusUpdateError` + * :class:`neo4j_graphrag.experimental.pipeline.exceptions.InvalidJSONError` + Neo4jGraphRagError ================== @@ -414,7 +466,7 @@ FilterValidationError EmbeddingsGenerationError -======================== +========================= .. autoclass:: neo4j_graphrag.exceptions.EmbeddingsGenerationError :show-inheritance: @@ -509,3 +561,10 @@ PipelineStatusUpdateError .. autoclass:: neo4j_graphrag.experimental.pipeline.exceptions.PipelineStatusUpdateError :show-inheritance: + + +InvalidJSONError +================ + +.. autoclass:: neo4j_graphrag.experimental.pipeline.exceptions.InvalidJSONError + :show-inheritance: diff --git a/docs/source/images/kg_builder_pipeline.png b/docs/source/images/kg_builder_pipeline.png index b8faf2ba..935c2a12 100644 Binary files a/docs/source/images/kg_builder_pipeline.png and b/docs/source/images/kg_builder_pipeline.png differ diff --git a/docs/source/index.rst b/docs/source/index.rst index df94a066..f48bf281 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -72,6 +72,37 @@ To install the latest stable version, use: It is always recommended to install python packages for user space in a virtual environment. +********************* +Optional Dependencies +********************* + +Extra dependencies can be installed with: + +.. code:: bash + + pip install "neo4j-graphrag[openai]" + # or + pip install "neo4j-graphrag[openai, experimental]" + + +List of extra dependencies: + +- LLM providers (at least one is required for RAG and KG Builder Pipeline): + - **ollama**: LLMs from Ollama + - **openai**: LLMs from OpenAI (including AzureOpenAI) + - **google**: LLMs from Vertex AI + - **cohere**: LLMs from Cohere + - **anthropic**: LLMs from Anthropic + - **mistralai**: LLMs from MistralAI +- **sentence-transformers** : to use embeddings from the `sentence-transformers` Python package +- Vector database (to use :ref:`External Retrievers`): + - **weaviate**: store vectors in Weaviate + - **pinecone**: store vectors in Pinecone + - **qdrant**: store vectors in Qdrant +- **experimental**: experimental features such as the Knowledge Graph creation pipelines. + - Warning: this requires `pygraphviz`. Installation instructions can be found `here `_. + + ******** Examples ******** diff --git a/docs/source/types.rst b/docs/source/types.rst index 6322b29b..253994ad 100644 --- a/docs/source/types.rst +++ b/docs/source/types.rst @@ -82,3 +82,62 @@ SchemaConfig ============ .. autoclass:: neo4j_graphrag.experimental.components.schema.SchemaConfig + +LexicalGraphConfig +=================== + +.. autoclass:: neo4j_graphrag.experimental.components.types.LexicalGraphConfig + + +Neo4jDriverType +=============== + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverType + + +Neo4jDriverConfig +================= + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverConfig + + +LLMType +======= + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.LLMType + + +LLMConfig +========= + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.LLMConfig + + +EmbedderType +============ + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.EmbedderType + + +EmbedderConfig +============== + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.EmbedderConfig + + +ComponentType +============= + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.ComponentType + + +ComponentConfig +=============== + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.object_config.ComponentConfig + + +ParamFromEnvConfig +================== + +.. autoclass:: neo4j_graphrag.experimental.pipeline.config.param_resolver.ParamFromEnvConfig diff --git a/docs/source/user_guide_kg_builder.rst b/docs/source/user_guide_kg_builder.rst index b758767a..87ce2f42 100644 --- a/docs/source/user_guide_kg_builder.rst +++ b/docs/source/user_guide_kg_builder.rst @@ -18,11 +18,11 @@ Pipeline Structure A Knowledge Graph (KG) construction pipeline requires a few components (some of the below components are optional): -- **Document parser**: extract text from files (PDFs, ...). -- **Document chunker**: split the text into smaller pieces of text, manageable by the LLM context window (token limit). +- **Data loader**: extract text from files (PDFs, ...). +- **Text splitter**: split the text into smaller pieces of text (chunks), manageable by the LLM context window (token limit). - **Chunk embedder** (optional): compute the chunk embeddings. - **Schema builder**: provide a schema to ground the LLM extracted entities and relations and obtain an easily navigable KG. -- **LexicalGraphBuilder**: build the lexical graph (Document, Chunk and their relationships) (optional). +- **Lexical graph builder**: build the lexical graph (Document, Chunk and their relationships) (optional). - **Entity and relation extractor**: extract relevant entities and relations from the text. - **Knowledge Graph writer**: save the identified entities and relations. - **Entity resolver**: merge similar entities into a single node. @@ -34,7 +34,486 @@ A Knowledge Graph (KG) construction pipeline requires a few components (some of This package contains the interface and implementations for each of these components, which are detailed in the following sections. To see an end-to-end example of a Knowledge Graph construction pipeline, -refer to the `example folder `_ in the project GitHub repository. +visit the `example folder `_ +in the project's GitHub repository. + + +****************** +Simple KG Pipeline +****************** + +The simplest way to begin building a KG from unstructured data using this package +is utilizing the `SimpleKGPipeline` interface: + +.. code:: python + + from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline + + kg_builder = SimpleKGPipeline( + llm=llm, # an LLMInterface for Entity and Relation extraction + driver=neo4j_driver, # a neo4j driver to write results to graph + embedder=embedder, # an Embedder for chunks + from_pdf=True, # set to False if parsing an already extracted text + ) + await kg_builder.run_async(file_path=str(file_path)) + # await kg_builder.run_async(text="my text") # if using from_pdf=False + + +See: + +- :ref:`Using Another LLM Model` to learn how to instantiate the `llm` +- :ref:`Embedders` to learn how to instantiate the `embedder` + + +The following section outlines the configuration parameters for this class. + +Customizing the SimpleKGPipeline +================================ + +Graph Schema +------------ + +It is possible to guide the LLM by supplying a list of entities, relationships, +and instructions on how to connect them. However, note that the extracted graph +may not fully adhere to these guidelines. Entities and relationships can be +represented as either simple strings (for their labels) or dictionaries. If using +a dictionary, it must include a label key and can optionally include description +and properties keys, as shown below: + +.. code:: python + + ENTITIES = [ + # entities can be defined with a simple label... + "Person", + # ... or with a dict if more details are needed, + # such as a description: + {"label": "House", "description": "Family the person belongs to"}, + # or a list of properties the LLM will try to attach to the entity: + {"label": "Planet", "properties": [{"name": "weather", "type": "STRING"}]}, + ] + # same thing for relationships: + RELATIONS = [ + "PARENT_OF", + { + "label": "HEIR_OF", + "description": "Used for inheritor relationship between father and sons", + }, + {"label": "RULES", "properties": [{"name": "fromYear", "type": "INTEGER"}]}, + ] + +The `potential_schema` is defined by a list of triplet in the format: +`(source_node_label, relationship_label, target_node_label)`. For instance: + + +.. code:: python + + POTENTIAL_SCHEMA = [ + ("Person", "PARENT_OF", "Person"), + ("Person", "HEIR_OF", "House"), + ("House", "RULES", "Planet"), + ] + +This schema information can be provided to the `SimpleKGBuilder` as demonstrated below: + +.. code:: python + + kg_builder = SimpleKGPipeline( + # ... + entities=ENTITIES, + relations=RELATIONS, + potential_schema=POTENTIAL_SCHEMA, + # ... + ) + +Prompt Template, Lexical Graph Config and Error Behavior +-------------------------------------------------------- + +These parameters are part of the `EntityAndRelationExtractor` component. +For detailed information, refer to the section on :ref:`Entity and Relation Extractor`. +They are also accessible via the `SimpleKGPipeline` interface. + +.. code:: python + + kg_builder = SimpleKGPipeline( + # ... + prompt_template="", + lexical_graph_config=my_config, + on_error="RAISE", + # ... + ) + +Skip Entity Resolution +---------------------- + +By default, after each run, an Entity Resolution step is performed to merge nodes +that share the same label and name property. To disable this behavior, adjust +the following parameter: + +.. code:: python + + kg_builder = SimpleKGPipeline( + # ... + perform_entity_resolution=False, + # ... + ) + +Neo4j Database +-------------- + +To write to a non-default Neo4j database, specify the database name using this parameter: + +.. code:: python + + kg_builder = SimpleKGPipeline( + # ... + neo4j_database="myDb", + # ... + ) + +Using Custom Components +----------------------- + +For advanced customization or when using a custom implementation, you can pass +instances of specific components to the `SimpleKGPipeline`. The components that can +customized at the moment are: + +- `text_splitter`: must be an instance of :ref:`TextSplitter` +- `pdf_loader`: must be an instance of :ref:`PdfLoader` +- `kg_writer`: must be an instance of :ref:`KGWriter` + +For instance, the following code can be used to customize the chunk size and +chunk overlap in the text splitter component: + +.. code:: python + + from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import ( + FixedSizeSplitter, + ) + + text_splitter = FixedSizeSplitter(chunk_size=500, chunk_overlap=100) + + kg_builder = SimpleKGPipeline( + # ... + text_splitter=text_splitter, + # ... + ) + + +Using a Config file +=================== + +.. code:: python + + from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner + + file_path = "my_config.json" + + pipeline = PipelineRunner.from_config_file(file_path) + await pipeline.run({"text": "my text"}) + + +The config file can be written in either JSON or YAML format. + +Here is an example of a base configuration file in JSON format: + +.. code:: json + + { + "version_": 1, + "template_": "SimpleKGPipeline", + "neo4j_config": {}, + "llm_config": {}, + "embedder_config": {} + } + +And like this in YAML: + +.. code:: yaml + + version_: 1 + template_: SimpleKGPipeline + neo4j_config: + llm_config: + embedder_config: + + +Defining a Neo4j Driver +----------------------- + +Below is an example of configuring a Neo4j driver in a JSON configuration file: + +.. code:: json + + { + "neo4j_config": { + "params_": { + "uri": "bolt://...", + "user": "neo4j", + "password": "password" + } + } + } + +Same for YAML: + +.. code:: yaml + + neo4j_config: + params_: + uri: bolt:// + user: neo4j + password: password + +In some cases, it may be necessary to avoid hard-coding sensitive values, +such as passwords or API keys, to ensure security. To address this, the configuration +parser supports parameter resolution methods. + +Parameter resolution +-------------------- + +To instruct the configuration parser to read a parameter from an environment variable, +use the following syntax: + +.. code:: json + + { + "neo4j_config": { + "params_": { + "uri": "bolt://...", + "user": "neo4j", + "password": { + "resolver_": "ENV", + "var_": "NEO4J_PASSWORD" + } + } + } + } + +And for YAML: + +.. code:: yaml + + neo4j_config: + params_: + uri: bolt:// + user: neo4j + password: + resolver_: ENV + var_: NEO4J_PASSWORD + +- The `resolver_=ENV` key is mandatory and its value cannot be altered. +- The `var_` key specifies the name of the environment variable to be read. + +This syntax can be applied to all parameters. + + +Defining an LLM +---------------- + +Below is an example of configuring an LLM in a JSON configuration file: + +.. code:: json + + { + "llm_config": { + "class_": "OpenAILLM", + "params_": { + "mode_name": "gpt-4o", + "api_key": { + "resolver_": "ENV", + "var_": "OPENAI_API_KEY", + }, + "model_params": { + "temperature": 0, + "max_tokens": 2000, + "response_format": {"type": "json_object"} + } + } + } + } + +And the equivalent YAML: + +.. code:: yaml + + llm_config: + class_: OpenAILLM + params_: + model_name: gpt-4o + api_key: + resolver_: ENV + var_: OPENAI_API_KEY + model_params: + temperature: 0 + max_tokens: 2000 + response_format: + type: json_object + +- The `class_` key specifies the path to the class to be instantiated. +- The `params_` key contains the parameters to be passed to the class constructor. + +When using an LLM implementation provided by this package, the full path in the `class_` key +can be omitted (the parser will automatically import from `neo4j_graphrag.llm`). +For custom implementations, the full path must be explicitly specified, +for example: `my_package.my_llm.MyLLM`. + +Defining an Embedder +-------------------- + +The same principles apply to `embedder_config`: + +.. code:: json + + { + "embedder_config": { + "class_": "OpenAIEmbeddings", + "params_": { + "mode": "text-embedding-ada-002", + "api_key": { + "resolver_": "ENV", + "var_": "OPENAI_API_KEY", + } + } + } + } + +Or the YAML version: + +.. code:: yaml + + embedder_config: + class_: OpenAIEmbeddings + params_: + api_key: + resolver_: ENV + var_: OPENAI_API_KEY + +- For embedder implementations from this package, the full path can be omitted in the `class_` key (the parser will import from `neo4j_graphrag.embeddings`). +- For custom implementations, the full path must be provided, for example: `my_package.my_embedding.MyEmbedding`. + + +Other configuration +------------------- + +The other parameters exposed in the :ref:`SimpleKGPipeline` can also be configured +within the configuration file. + +.. code:: json + + { + "from_pdf": false, + "perform_entity_resolution": true, + "neo4j_database": "myDb", + "on_error": "IGNORE", + "prompt_template": "...", + "entities": [ + "Person", + { + "label": "House", + "description": "Family the person belongs to", + "properties": [ + {"name": "name", "type": "STRING"} + ] + }, + { + "label": "Planet", + "properties": [ + {"name": "name", "type": "STRING"}, + {"name": "weather", "type": "STRING"} + ] + } + ], + "relations": [ + "PARENT_OF", + { + "label": "HEIR_OF", + "description": "Used for inheritor relationship between father and sons" + }, + { + "label": "RULES", + "properties": [ + {"name": "fromYear", "type": "INTEGER"} + ] + } + ], + "potential_schema": [ + ["Person", "PARENT_OF", "Person"], + ["Person", "HEIR_OF", "House"], + ["House", "RULES", "Planet"] + ], + "lexical_graph_config": { + "chunk_node_label": "TextPart" + } + } + + +or in YAML: + +.. code:: yaml + + from_pdf: false + perform_entity_resolution: true + neo4j_database: myDb + on_error: IGNORE + prompt_template: ... + entities: + - label: Person + - label: House + description: Family the person belongs to + properties: + - name: name + type: STRING + - label: Planet + properties: + - name: name + type: STRING + - name: weather + type: STRING + relations: + - label: PARENT_OF + - label: HEIR_OF + description: Used for inheritor relationship between father and sons + - label: RULES + properties: + - name: fromYear + type: INTEGER + potential_schema: + - ["Person", "PARENT_OF", "Person"] + - ["Person", "HEIR_OF", "House"] + - ["House", "RULES", "Planet"] + lexical_graph_config: + chunk_node_label: TextPart + + +It is also possible to further customize components, with a syntax similar to the one +used for `llm_config` or `embedder_config`: + +.. code:: json + + { + "text_splitter": { + "class_": "text_splitters.FixedSizeSplitter", + "params_": { + "chunk_size": 500, + "chunk_overlap": 100 + } + } + + } + +The YAML equivalent: + +.. code:: yaml + + text_splitter: + class_: text_splitters.fixed_size_splitter.FixedSizeSplitter + params_: + chunk_size: 100 + chunk_overlap: 10 + +The `neo4j_graphrag.experimental.components` prefix will be appended automatically +if needed. + ********************************** Knowledge Graph Builder Components @@ -63,10 +542,10 @@ They can also be used within a pipeline: pipeline.add_component(my_component, "component_name") -Document Parser -=============== +Data Loader +============ -Document parsers start from a file path and return the text extracted from this file. +Data loaders start from a file path and return the text extracted from this file. This package currently supports text extraction from PDFs: @@ -92,8 +571,8 @@ To implement your own loader, use the `DataLoader` interface: -Document Splitter -================= +Text Splitter +============== Document splitters, as the name indicate, split documents into smaller chunks that can be processed within the LLM token limits: diff --git a/docs/source/user_guide_rag.rst b/docs/source/user_guide_rag.rst index d939ed85..8074eef7 100644 --- a/docs/source/user_guide_rag.rst +++ b/docs/source/user_guide_rag.rst @@ -218,14 +218,13 @@ See :ref:`coherellm`. Using a Local Model via Ollama ------------------------------- -Similarly to the official OpenAI Python client, the `OpenAILLM` can be -used with Ollama. Assuming Ollama is running on the default address `127.0.0.1:11434`, +Assuming Ollama is running on the default address `127.0.0.1:11434`, it can be queried using the following: .. code:: python - from neo4j_graphrag.llm import OpenAILLM - llm = OpenAILLM(api_key="ollama", base_url="http://127.0.0.1:11434/v1", model_name="orca-mini") + from neo4j_graphrag.llm import OllamaLLM + llm = OllamaLLM(model_name="orca-mini") llm.invoke("say something") @@ -397,7 +396,7 @@ However, in most cases, a text (from the user) will be provided instead of a vec In this scenario, an `Embedder` is required. Search Similar Text ------------------------------ +-------------------- When searching for a text, specifying how the retriever transforms (embeds) the text into a vector is required. Therefore, the retriever requires knowledge of an embedder: @@ -418,7 +417,7 @@ into a vector is required. Therefore, the retriever requires knowledge of an emb Embedders ------------------------------ +--------- Currently, this package supports the following embedders: @@ -428,6 +427,7 @@ Currently, this package supports the following embedders: - :ref:`mistralaiembeddings` - :ref:`cohereembeddings` - :ref:`azureopenaiembeddings` +- :ref:`ollamaembeddings` The `OpenAIEmbeddings` was illustrated previously. Here is how to use the `SentenceTransformerEmbeddings`: @@ -438,31 +438,7 @@ The `OpenAIEmbeddings` was illustrated previously. Here is how to use the `Sente embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2") # Note: this is the default model -If another embedder is desired, a custom embedder can be created. For example, consider -the following implementation of an embedder that wraps the `OllamaEmbedding` model from LlamaIndex: - -.. code:: python - - from llama_index.embeddings.ollama import OllamaEmbedding - from neo4j_graphrag.embeddings.base import Embedder - - class OllamaEmbedder(Embedder): - def __init__(self, ollama_embedding): - self.embedder = ollama_embedding - - def embed_query(self, text: str) -> list[float]: - embedding = self.embedder.get_text_embedding_batch( - [text], show_progress=True - ) - return embedding[0] - - ollama_embedding = OllamaEmbedding( - model_name="llama3", - base_url="http://localhost:11434", - ollama_additional_kwargs={"mirostat": 0}, - ) - embedder = OllamaEmbedder(ollama_embedding) - vector = embedder.embed_query("some text") +If another embedder is desired, a custom embedder can be created, using the `Embedder` interface. Other Vector Retriever Configuration @@ -485,7 +461,7 @@ using the `return_properties` parameter: Pre-Filters ------------------------------ +----------- When performing a similarity search, one may have constraints to apply. For instance, filtering out movies released before 2000. This can be achieved diff --git a/examples/README.md b/examples/README.md index 95f9443a..a7308660 100644 --- a/examples/README.md +++ b/examples/README.md @@ -14,6 +14,7 @@ are listed in [the last section of this file](#customize). - [End to end PDF to graph simple pipeline](build_graph/simple_kg_builder_from_pdf.py) - [End to end text to graph simple pipeline](build_graph/simple_kg_builder_from_text.py) +- [Build KG pipeline from config file](build_graph/from_config_files/simple_kg_pipeline_from_config_file.py) ## Retrieve @@ -57,7 +58,8 @@ are listed in [the last section of this file](#customize). - [Control result format for VectorRetriever](customize/retrievers/result_formatter_vector_retriever.py) - [Control result format for VectorCypherRetriever](customize/retrievers/result_formatter_vector_cypher_retriever.py) - +- [Use pre-filters](customize/retrievers/use_pre_filters.py) +- [Text2Cypher: use a custom prompt](customize/retrievers/text2cypher_custom_prompt.py) ### LLMs @@ -73,7 +75,7 @@ are listed in [the last section of this file](#customize). ### Prompts -- [Using a custom prompt](old/graphrag_custom_prompt.py) +- [Using a custom prompt for RAG](customize/answer/custom_prompt.py) ### Embedders @@ -93,6 +95,7 @@ are listed in [the last section of this file](#customize). - [End to end example with explicit components and PDF input](./customize/build_graph/pipeline/kg_builder_from_pdf.py) - [Process multiple documents](./customize/build_graph/pipeline/kg_builder_two_documents_entity_resolution.py) - [Export lexical graph creation into another pipeline](./customize/build_graph/pipeline/text_to_lexical_graph_to_entity_graph_two_pipelines.py) +- [Build pipeline from config file](customize/build_graph/pipeline/from_config_files/pipeline_from_config_file.py) #### Components diff --git a/examples/build_graph/from_config_files/simple_kg_pipeline_config.json b/examples/build_graph/from_config_files/simple_kg_pipeline_config.json new file mode 100644 index 00000000..ef251624 --- /dev/null +++ b/examples/build_graph/from_config_files/simple_kg_pipeline_config.json @@ -0,0 +1,112 @@ +{ + "version_": "1", + "template_": "SimpleKGPipeline", + "neo4j_config": { + "params_": { + "uri": { + "resolver_": "ENV", + "var_": "NEO4J_URI" + }, + "user": { + "resolver_": "ENV", + "var_": "NEO4J_USER" + }, + "password": { + "resolver_": "ENV", + "var_": "NEO4J_PASSWORD" + } + } + }, + "llm_config": { + "class_": "OpenAILLM", + "params_": { + "api_key": { + "resolver_": "ENV", + "var_": "OPENAI_API_KEY" + }, + "model_name": "gpt-4o", + "model_params": { + "temperature": 0, + "max_tokens": 2000, + "response_format": {"type": "json_object"} + } + } + }, + "embedder_config": { + "class_": "OpenAIEmbeddings", + "params_": { + "api_key": { + "resolver_": "ENV", + "var_": "OPENAI_API_KEY" + } + } + }, + "from_pdf": false, + "entities": [ + "Person", + { + "label": "House", + "description": "Family the person belongs to", + "properties": [ + { + "name": "name", + "type": "STRING" + } + ] + }, + { + "label": "Planet", + "properties": [ + { + "name": "name", + "type": "STRING" + }, + { + "name": "weather", + "type": "STRING" + } + ] + } + ], + "relations": [ + "PARENT_OF", + { + "label": "HEIR_OF", + "description": "Used for inheritor relationship between father and sons" + }, + { + "label": "RULES", + "properties": [ + { + "name": "fromYear", + "type": "INTEGER" + } + ] + } + ], + "potential_schema": [ + [ + "Person", + "PARENT_OF", + "Person" + ], + [ + "Person", + "HEIR_OF", + "House" + ], + [ + "House", + "RULES", + "Planet" + ] + ], + "text_splitter": { + "class_": "text_splitters.fixed_size_splitter.FixedSizeSplitter", + "params_": { + "chunk_size": 100, + "chunk_overlap": 10 + } + }, + "perform_entity_resolution": true +} diff --git a/examples/build_graph/from_config_files/simple_kg_pipeline_config.yaml b/examples/build_graph/from_config_files/simple_kg_pipeline_config.yaml new file mode 100644 index 00000000..8917e8ca --- /dev/null +++ b/examples/build_graph/from_config_files/simple_kg_pipeline_config.yaml @@ -0,0 +1,63 @@ +version_: "1" +template_: SimpleKGPipeline +neo4j_config: + params_: + uri: + resolver_: ENV + var_: NEO4J_URI + user: + resolver_: ENV + var_: NEO4J_USER + password: + resolver_: ENV + var_: NEO4J_PASSWORD +llm_config: + class_: OpenAILLM + params_: + api_key: + resolver_: ENV + var_: OPENAI_API_KEY + model_name: gpt-4o + model_params: + temperature: 0 + max_tokens: 2000 + response_format: + type: json_object +embedder_config: + class_: OpenAIEmbeddings + params_: + api_key: + resolver_: ENV + var_: OPENAI_API_KEY +from_pdf: false +entities: + - label: Person + - label: House + description: Family the person belongs to + properties: + - name: name + type: STRING + - label: Planet + properties: + - name: name + type: STRING + - name: weather + type: STRING +relations: + - label: PARENT_OF + - label: HEIR_OF + description: Used for inheritor relationship between father and sons + - label: RULES + properties: + - name: fromYear + type: INTEGER +potential_schema: + - ["Person", "PARENT_OF", "Person"] + - ["Person", "HEIR_OF", "House"] + - ["House", "RULES", "Planet"] +text_splitter: + class_: text_splitters.fixed_size_splitter.FixedSizeSplitter + params_: + chunk_size: 100 + chunk_overlap: 10 +perform_entity_resolution: true diff --git a/examples/build_graph/from_config_files/simple_kg_pipeline_from_config_file.py b/examples/build_graph/from_config_files/simple_kg_pipeline_from_config_file.py new file mode 100644 index 00000000..62ba6c85 --- /dev/null +++ b/examples/build_graph/from_config_files/simple_kg_pipeline_from_config_file.py @@ -0,0 +1,47 @@ +"""In this example, the pipeline is defined in a JSON ('simple_kg_pipeline_config.json') +or YAML ('simple_kg_pipeline_config.yaml') file. + +According to the configuration file, some parameters will be read from the env vars +(Neo4j credentials and the OpenAI API key). +""" + +import asyncio +import logging + +## If env vars are in a .env file, uncomment: +## (requires pip install python-dotenv) +# from dotenv import load_dotenv +# load_dotenv() +# env vars manually set for testing: +import os +from pathlib import Path + +from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner +from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult + +logging.basicConfig() +logging.getLogger("neo4j_graphrag").setLevel(logging.DEBUG) + +os.environ["NEO4J_URI"] = "bolt://localhost:7687" +os.environ["NEO4J_USER"] = "neo4j" +os.environ["NEO4J_PASSWORD"] = "password" +# os.environ["OPENAI_API_KEY"] = "sk-..." + + +root_dir = Path(__file__).parent +file_path = root_dir / "simple_kg_pipeline_config.yaml" +# file_path = root_dir / "simple_kg_pipeline_config.json" + + +# Text to process +TEXT = """The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House Atreides, +an aristocratic family that rules the planet Caladan, the rainy planet, since 10191.""" + + +async def main() -> PipelineResult: + pipeline = PipelineRunner.from_config_file(file_path) + return await pipeline.run({"text": TEXT}) + + +if __name__ == "__main__": + print(asyncio.run(main())) diff --git a/examples/customize/build_graph/pipeline/from_config_files/pipeline_config.json b/examples/customize/build_graph/pipeline/from_config_files/pipeline_config.json new file mode 100644 index 00000000..ce815412 --- /dev/null +++ b/examples/customize/build_graph/pipeline/from_config_files/pipeline_config.json @@ -0,0 +1,68 @@ +{ + "version_": "1", + "template_": "none", + "name": "", + "neo4j_config": { + "params_": { + "uri": { + "resolver_": "ENV", + "var_": "NEO4J_URI" + }, + "user": { + "resolver_": "ENV", + "var_": "NEO4J_USER" + }, + "password": { + "resolver_": "ENV", + "var_": "NEO4J_PASSWORD" + } + } + }, + "extras": { + "database": "neo4j" + }, + "component_config": { + "splitter": { + "class_": "text_splitters.fixed_size_splitter.FixedSizeSplitter" + }, + "builder": { + "class_": "lexical_graph.LexicalGraphBuilder", + "params_": { + "config": { + "chunk_node_label": "TextPart" + } + } + }, + "writer": { + "name_": "writer", + "class_": "kg_writer.Neo4jWriter", + "params_": { + "driver": { + "resolver_": "CONFIG_KEY", + "key_": "neo4j_config.default" + }, + "neo4j_database": { + "resolver_": "CONFIG_KEY", + "key_": "extras.database" + } + } + } + }, + "connection_config": [ + { + "start": "splitter", + "end": "builder", + "input_config": { + "text_chunks": "splitter" + } + }, + { + "start": "builder", + "end": "writer", + "input_config": { + "graph": "builder.graph", + "lexical_graph_config": "builder.config" + } + } + ] +} diff --git a/examples/customize/build_graph/pipeline/from_config_files/pipeline_config.yaml b/examples/customize/build_graph/pipeline/from_config_files/pipeline_config.yaml new file mode 100644 index 00000000..87ac905e --- /dev/null +++ b/examples/customize/build_graph/pipeline/from_config_files/pipeline_config.yaml @@ -0,0 +1,45 @@ +version_: "1" +template_: none +neo4j_config: + params_: + uri: + resolver_: ENV + var_: NEO4J_URI + user: + resolver_: ENV + var_: NEO4J_USER + password: + resolver_: ENV + var_: NEO4J_PASSWORD +extras: + database: neo4j +component_config: + splitter: + class_: text_splitters.fixed_size_splitter.FixedSizeSplitter + params_: + chunk_size: 100 + chunk_overlap: 10 + builder: + class_: lexical_graph.LexicalGraphBuilder + params_: + config: + chunk_node_label: TextPart + writer: + class_: kg_writer.Neo4jWriter + params_: + driver: + resolver_: CONFIG_KEY + key_: neo4j_config.default + neo4j_database: + resolver_: CONFIG_KEY + key_: extras.database +connection_config: + - start: splitter + end: builder + input_config: + text_chunks: splitter + - start: builder + end: writer + input_config: + graph: builder.graph + lexical_graph_config: builder.config diff --git a/examples/customize/build_graph/pipeline/from_config_files/pipeline_from_config_file.py b/examples/customize/build_graph/pipeline/from_config_files/pipeline_from_config_file.py new file mode 100644 index 00000000..9a2a1680 --- /dev/null +++ b/examples/customize/build_graph/pipeline/from_config_files/pipeline_from_config_file.py @@ -0,0 +1,40 @@ +"""In this example, the pipeline is defined in a JSON file 'pipeline_config.json'. +According to the configuration file, some parameters will be read from the env vars +(Neo4j credentials and the OpenAI API key). +""" + +import asyncio + +## If env vars are in a .env file, uncomment: +## (requires pip install python-dotenv) +# from dotenv import load_dotenv +# load_dotenv() +# env vars manually set for testing: +import os +from pathlib import Path + +from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner +from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult + +os.environ["NEO4J_URI"] = "bolt://localhost:7687" +os.environ["NEO4J_USER"] = "neo4j" +os.environ["NEO4J_PASSWORD"] = "password" +# os.environ["OPENAI_API_KEY"] = "sk-..." + + +root_dir = Path(__file__).parent +# file_path = root_dir / "pipeline_config.json" +file_path = root_dir / "pipeline_config.yaml" + +# Text to process +TEXT = """The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House Atreides, +an aristocratic family that rules the planet Caladan, the rainy planet, since 10191.""" + + +async def main() -> PipelineResult: + pipeline = PipelineRunner.from_config_file(file_path) + return await pipeline.run({"splitter": {"text": TEXT}}) + + +if __name__ == "__main__": + print(asyncio.run(main())) diff --git a/examples/customize/embeddings/ollama_embeddings.py b/examples/customize/embeddings/ollama_embeddings.py index 2a5a0046..243cf4b6 100644 --- a/examples/customize/embeddings/ollama_embeddings.py +++ b/examples/customize/embeddings/ollama_embeddings.py @@ -1,15 +1,10 @@ """This example demonstrate how to embed a text into a vector -using OpenAI models and API. +using a local model served by Ollama. """ -from neo4j_graphrag.embeddings import OpenAIEmbeddings +from neo4j_graphrag.embeddings import OllamaEmbeddings -# not used but needs to be provided -api_key = "ollama" - -embeder = OpenAIEmbeddings( - base_url="http://localhost:11434/v1", - api_key=api_key, +embeder = OllamaEmbeddings( model="", ) res = embeder.embed_query("my question") diff --git a/examples/customize/llms/ollama_llm.py b/examples/customize/llms/ollama_llm.py index 56148042..f815d4db 100644 --- a/examples/customize/llms/ollama_llm.py +++ b/examples/customize/llms/ollama_llm.py @@ -1,12 +1,11 @@ -from neo4j_graphrag.llm import LLMResponse, OpenAILLM +"""This example demonstrate how to invoke an LLM using a local model +served by Ollama. +""" -# not used but needs to be provided -api_key = "ollama" +from neo4j_graphrag.llm import LLMResponse, OllamaLLM -llm = OpenAILLM( - base_url="http://localhost:11434/v1", +llm = OllamaLLM( model_name="", - api_key=api_key, ) res: LLMResponse = llm.invoke("What is the additive color model?") print(res.content) diff --git a/examples/customize/retrievers/text2cypher_custom_prompt.py b/examples/customize/retrievers/text2cypher_custom_prompt.py new file mode 100644 index 00000000..64ab5290 --- /dev/null +++ b/examples/customize/retrievers/text2cypher_custom_prompt.py @@ -0,0 +1,76 @@ +"""The example shows how to provide a custom prompt to Text2CypherRetriever. + +Example using the OpenAILLM, hence the OPENAI_API_KEY needs to be set in the +environment for this example to run. +""" + +import neo4j +from neo4j_graphrag.llm import OpenAILLM +from neo4j_graphrag.retrievers import Text2CypherRetriever +from neo4j_graphrag.schema import get_schema + +# Define database credentials +URI = "neo4j+s://demo.neo4jlabs.com" +AUTH = ("recommendations", "recommendations") +DATABASE = "recommendations" + +# Create LLM object +llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0}) + +# (Optional) Specify your own Neo4j schema +# (also see get_structured_schema and get_schema functions) +neo4j_schema = """ +Node properties: +User {name: STRING} +Person {name: STRING, born: INTEGER} +Movie {tagline: STRING, title: STRING, released: INTEGER} +Relationship properties: +ACTED_IN {roles: LIST} +DIRECTED {} +REVIEWED {summary: STRING, rating: INTEGER} +The relationships: +(:Person)-[:ACTED_IN]->(:Movie) +(:Person)-[:DIRECTED]->(:Movie) +(:User)-[:REVIEWED]->(:Movie) +""" + +prompt = """Task: Generate a Cypher statement for querying a Neo4j graph database from a user input. + +Do not use any properties or relationships not included in the schema. +Do not include triple backticks ``` or any additional text except the generated Cypher statement in your response. + +Always filter movies that have not already been reviewed by the user with name: '{user_name}' using for instance: +(m:Movie)<-[:REVIEWED]-(:User {{name: }}) + +Schema: +{schema} + +Input: +{query_text} + +Cypher query: +""" + +with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver: + # Initialize the retriever + retriever = Text2CypherRetriever( + driver=driver, + llm=llm, + neo4j_schema=neo4j_schema, + # here we provide a custom prompt + custom_prompt=prompt, + neo4j_database=DATABASE, + ) + + # Generate a Cypher query using the LLM, send it to the Neo4j database, and return the results + query_text = "Which movies did Hugo Weaving star in?" + print( + retriever.search( + query_text=query_text, + prompt_params={ + # you have to specify all placeholder except the {query_text} one + "schema": get_schema(driver), + "user_name": "the user asking question", + }, + ) + ) diff --git a/examples/retrieve/text2cypher_search.py b/examples/retrieve/text2cypher_search.py index e17cb65d..deb2f592 100644 --- a/examples/retrieve/text2cypher_search.py +++ b/examples/retrieve/text2cypher_search.py @@ -22,13 +22,11 @@ Movie {tagline: STRING, title: STRING, released: INTEGER} Relationship properties: ACTED_IN {roles: LIST} +DIRECTED {} REVIEWED {summary: STRING, rating: INTEGER} The relationships: (:Person)-[:ACTED_IN]->(:Movie) (:Person)-[:DIRECTED]->(:Movie) -(:Person)-[:PRODUCED]->(:Movie) -(:Person)-[:WROTE]->(:Movie) -(:Person)-[:FOLLOWS]->(:Person) (:Person)-[:REVIEWED]->(:Movie) """ diff --git a/poetry.lock b/poetry.lock index 3b0f41ad..21f8bf66 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,124 +1,110 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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= true} sentence-transformers = {version = "^3.0.0", optional = true } +json-repair = "^0.30.2" +types-pyyaml = "^6.0.12.20240917" +ollama = {version = "^0.4.4", optional = true} [tool.poetry.group.dev.dependencies] urllib3 = "<2" @@ -67,6 +70,7 @@ pinecone = ["pinecone-client"] google = ["google-cloud-aiplatform"] cohere = ["cohere"] anthropic = ["anthropic"] +ollama = ["ollama"] openai = ["openai"] mistralai = ["mistralai"] qdrant = ["qdrant-client"] diff --git a/src/neo4j_graphrag/embeddings/__init__.py b/src/neo4j_graphrag/embeddings/__init__.py index 6c30951d..9398eefe 100644 --- a/src/neo4j_graphrag/embeddings/__init__.py +++ b/src/neo4j_graphrag/embeddings/__init__.py @@ -15,6 +15,7 @@ from .base import Embedder from .cohere import CohereEmbeddings from .mistral import MistralAIEmbeddings +from .ollama import OllamaEmbeddings from .openai import AzureOpenAIEmbeddings, OpenAIEmbeddings from .sentence_transformers import SentenceTransformerEmbeddings from .vertexai import VertexAIEmbeddings @@ -22,6 +23,7 @@ __all__ = [ "Embedder", "SentenceTransformerEmbeddings", + "OllamaEmbeddings", "OpenAIEmbeddings", "AzureOpenAIEmbeddings", "VertexAIEmbeddings", diff --git a/src/neo4j_graphrag/embeddings/cohere.py b/src/neo4j_graphrag/embeddings/cohere.py index 3289ee3f..63906a5e 100644 --- a/src/neo4j_graphrag/embeddings/cohere.py +++ b/src/neo4j_graphrag/embeddings/cohere.py @@ -28,8 +28,8 @@ class CohereEmbeddings(Embedder): def __init__(self, model: str = "", **kwargs: Any) -> None: if cohere is None: raise ImportError( - "Could not import cohere python client. " - "Please install it with `pip install cohere`." + """Could not import cohere python client. + Please install it with `pip install "neo4j-graphrag[cohere]"`.""" ) self.model = model self.client = cohere.Client(**kwargs) diff --git a/src/neo4j_graphrag/embeddings/mistral.py b/src/neo4j_graphrag/embeddings/mistral.py index 5a9c76a0..09943019 100644 --- a/src/neo4j_graphrag/embeddings/mistral.py +++ b/src/neo4j_graphrag/embeddings/mistral.py @@ -39,8 +39,8 @@ class MistralAIEmbeddings(Embedder): def __init__(self, model: str = "mistral-embed", **kwargs: Any) -> None: if Mistral is None: raise ImportError( - "Could not import mistralai. " - "Please install it with `pip install mistralai`." + """Could not import mistralai. + Please install it with `pip install "neo4j-graphrag[mistralai]"`.""" ) api_key = kwargs.pop("api_key", None) if api_key is None: @@ -57,8 +57,7 @@ def embed_query(self, text: str, **kwargs: Any) -> list[float]: **kwargs (Any): Additional keyword arguments to pass to the Mistral AI client. """ embeddings_batch_response = self.mistral_client.embeddings.create( - model=self.model, - inputs=[text], + model=self.model, inputs=[text], **kwargs ) if embeddings_batch_response is None or not embeddings_batch_response.data: raise EmbeddingsGenerationError("Failed to retrieve embeddings.") diff --git a/src/neo4j_graphrag/embeddings/ollama.py b/src/neo4j_graphrag/embeddings/ollama.py new file mode 100644 index 00000000..a3d804a0 --- /dev/null +++ b/src/neo4j_graphrag/embeddings/ollama.py @@ -0,0 +1,65 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from typing import Any + +from neo4j_graphrag.embeddings.base import Embedder +from neo4j_graphrag.exceptions import EmbeddingsGenerationError + + +class OllamaEmbeddings(Embedder): + """ + Ollama embeddings class. + This class uses the ollama Python client to generate vector embeddings for text data. + + Args: + model (str): The name of the Mistral AI text embedding model to use. Defaults to "mistral-embed". + """ + + def __init__(self, model: str, **kwargs: Any) -> None: + try: + import ollama + except ImportError: + raise ImportError( + "Could not import ollama python client. " + "Please install it with `pip install ollama`." + ) + self.model = model + self.client = ollama.Client(**kwargs) + + def embed_query(self, text: str, **kwargs: Any) -> list[float]: + """ + Generate embeddings for a given query using an Ollama text embedding model. + + Args: + text (str): The text to generate an embedding for. + **kwargs (Any): Additional keyword arguments to pass to the Ollama client. + """ + embeddings_response = self.client.embed( + model=self.model, + input=text, + **kwargs, + ) + + if embeddings_response is None or embeddings_response.embeddings is None: + raise EmbeddingsGenerationError("Failed to retrieve embeddings.") + + embedding = embeddings_response.embeddings + if not isinstance(embedding, list): + raise EmbeddingsGenerationError("Embedding is not a list of floats.") + + return embedding diff --git a/src/neo4j_graphrag/embeddings/openai.py b/src/neo4j_graphrag/embeddings/openai.py index 6204acb3..4a4d6038 100644 --- a/src/neo4j_graphrag/embeddings/openai.py +++ b/src/neo4j_graphrag/embeddings/openai.py @@ -36,8 +36,8 @@ def __init__(self, model: str = "text-embedding-ada-002", **kwargs: Any) -> None import openai except ImportError: raise ImportError( - "Could not import openai python client. " - "Please install it with `pip install openai`." + """Could not import openai python client. + Please install it with `pip install "neo4j-graphrag[openai]"`.""" ) self.openai = openai self.model = model diff --git a/src/neo4j_graphrag/embeddings/sentence_transformers.py b/src/neo4j_graphrag/embeddings/sentence_transformers.py index 3e0f9202..f30ed5b7 100644 --- a/src/neo4j_graphrag/embeddings/sentence_transformers.py +++ b/src/neo4j_graphrag/embeddings/sentence_transformers.py @@ -28,8 +28,8 @@ def __init__( import torch except ImportError: raise ImportError( - "Could not import sentence_transformers python package. " - "Please install it with `pip install sentence-transformers`." + """Could not import sentence_transformers python package. + Please install it with `pip install "neo4j-graphrag[sentence-transformers]"`.""" ) self.torch = torch self.np = np diff --git a/src/neo4j_graphrag/embeddings/vertexai.py b/src/neo4j_graphrag/embeddings/vertexai.py index c04c665e..5aecdcfa 100644 --- a/src/neo4j_graphrag/embeddings/vertexai.py +++ b/src/neo4j_graphrag/embeddings/vertexai.py @@ -36,8 +36,8 @@ class VertexAIEmbeddings(Embedder): def __init__(self, model: str = "text-embedding-004") -> None: if vertexai is None: raise ImportError( - "Could not import Vertex AI Python client. " - "Please install it with `pip install google-cloud-aiplatform`." + """Could not import Vertex AI Python client. + Please install it with `pip install "neo4j-graphrag[google]"`.""" ) self.vertexai_model = ( vertexai.language_models.TextEmbeddingModel.from_pretrained(model) diff --git a/src/neo4j_graphrag/experimental/components/entity_relation_extractor.py b/src/neo4j_graphrag/experimental/components/entity_relation_extractor.py index 1fe49465..d4070aea 100644 --- a/src/neo4j_graphrag/experimental/components/entity_relation_extractor.py +++ b/src/neo4j_graphrag/experimental/components/entity_relation_extractor.py @@ -19,10 +19,10 @@ import enum import json import logging -import re from datetime import datetime -from typing import Any, List, Optional, Union +from typing import Any, List, Optional, Union, cast +import json_repair from pydantic import ValidationError, validate_call from neo4j_graphrag.exceptions import LLMGenerationError @@ -36,6 +36,7 @@ TextChunks, ) from neo4j_graphrag.experimental.pipeline.component import Component +from neo4j_graphrag.experimental.pipeline.exceptions import InvalidJSONError from neo4j_graphrag.generation.prompts import ERExtractionTemplate, PromptTemplate from neo4j_graphrag.llm import LLMInterface @@ -100,28 +101,15 @@ def balance_curly_braces(json_string: str) -> str: return "".join(fixed_json) -def fix_invalid_json(invalid_json_string: str) -> str: - # Fix missing quotes around field names - invalid_json_string = re.sub( - r"([{,]\s*)(\w+)(\s*:)", r'\1"\2"\3', invalid_json_string - ) +def fix_invalid_json(raw_json: str) -> str: + repaired_json = json_repair.repair_json(raw_json) + repaired_json = cast(str, repaired_json).strip() - # Fix missing quotes around string values, correctly ignoring null, true, false, and numeric values - invalid_json_string = re.sub( - r"(?<=:\s)(?!(null|true|false|\d+\.?\d*))([a-zA-Z_][a-zA-Z0-9_]*)\s*(?=[,}])", - r'"\2"', - invalid_json_string, - ) - - # Correct the specific issue: remove trailing commas within arrays or objects before closing braces or brackets - invalid_json_string = re.sub(r",\s*(?=[}\]])", "", invalid_json_string) - - # Normalize excessive curly braces - invalid_json_string = re.sub(r"{{+", "{", invalid_json_string) - invalid_json_string = re.sub(r"}}+", "}", invalid_json_string) - - # Balance curly braces - return balance_curly_braces(invalid_json_string) + if repaired_json == '""': + raise InvalidJSONError("JSON repair resulted in an empty or invalid JSON.") + if not repaired_json: + raise InvalidJSONError("JSON repair resulted in an empty string.") + return repaired_json class EntityRelationExtractor(Component, abc.ABC): @@ -223,24 +211,18 @@ async def extract_for_chunk( ) llm_result = await self.llm.ainvoke(prompt) try: - result = json.loads(llm_result.content) - except json.JSONDecodeError: - logger.info( - f"LLM response is not valid JSON {llm_result.content} for chunk_index={chunk.index}. Trying to fix it." - ) - fixed_content = fix_invalid_json(llm_result.content) - try: - result = json.loads(fixed_content) - except json.JSONDecodeError as e: - if self.on_error == OnError.RAISE: - raise LLMGenerationError( - f"LLM response is not valid JSON {fixed_content}: {e}" - ) - else: - logger.error( - f"LLM response is not valid JSON {llm_result.content} for chunk_index={chunk.index}" - ) - result = {"nodes": [], "relationships": []} + llm_generated_json = fix_invalid_json(llm_result.content) + result = json.loads(llm_generated_json) + except (json.JSONDecodeError, InvalidJSONError) as e: + if self.on_error == OnError.RAISE: + raise LLMGenerationError( + f"LLM response is not valid JSON {llm_result.content}: {e}" + ) + else: + logger.error( + f"LLM response is not valid JSON {llm_result.content} for chunk_index={chunk.index}" + ) + result = {"nodes": [], "relationships": []} try: chunk_graph = Neo4jGraph(**result) except ValidationError as e: diff --git a/src/neo4j_graphrag/experimental/components/lexical_graph.py b/src/neo4j_graphrag/experimental/components/lexical_graph.py index 92681a8b..ce96b9fd 100644 --- a/src/neo4j_graphrag/experimental/components/lexical_graph.py +++ b/src/neo4j_graphrag/experimental/components/lexical_graph.py @@ -42,6 +42,7 @@ class LexicalGraphBuilder(Component): - A relationship between a chunk and the next one in the document """ + @validate_call def __init__( self, config: LexicalGraphConfig = LexicalGraphConfig(), diff --git a/src/neo4j_graphrag/experimental/components/schema.py b/src/neo4j_graphrag/experimental/components/schema.py index 64e908ed..0f118545 100644 --- a/src/neo4j_graphrag/experimental/components/schema.py +++ b/src/neo4j_graphrag/experimental/components/schema.py @@ -17,9 +17,14 @@ from typing import Any, Dict, List, Literal, Optional, Tuple from pydantic import BaseModel, ValidationError, model_validator, validate_call +from typing_extensions import Self from neo4j_graphrag.exceptions import SchemaValidationError from neo4j_graphrag.experimental.pipeline.component import Component, DataModel +from neo4j_graphrag.experimental.pipeline.types import ( + EntityInputType, + RelationInputType, +) class SchemaProperty(BaseModel): @@ -55,6 +60,14 @@ class SchemaEntity(BaseModel): description: str = "" properties: List[SchemaProperty] = [] + @classmethod + def from_text_or_dict(cls, input: EntityInputType) -> Self: + if isinstance(input, SchemaEntity): + return input + if isinstance(input, str): + return cls(label=input) + return cls.model_validate(input) + class SchemaRelation(BaseModel): """ @@ -65,6 +78,14 @@ class SchemaRelation(BaseModel): description: str = "" properties: List[SchemaProperty] = [] + @classmethod + def from_text_or_dict(cls, input: RelationInputType) -> Self: + if isinstance(input, SchemaRelation): + return input + if isinstance(input, str): + return cls(label=input) + return cls.model_validate(input) + class SchemaConfig(DataModel): """ diff --git a/src/neo4j_graphrag/experimental/pipeline/config/__init__.py b/src/neo4j_graphrag/experimental/pipeline/config/__init__.py new file mode 100644 index 00000000..c0199c14 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/src/neo4j_graphrag/experimental/pipeline/config/base.py b/src/neo4j_graphrag/experimental/pipeline/config/base.py new file mode 100644 index 00000000..665a56d0 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/base.py @@ -0,0 +1,62 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Abstract class for all pipeline configs.""" + +from __future__ import annotations + +import logging +from typing import Any + +from pydantic import BaseModel, PrivateAttr + +from neo4j_graphrag.experimental.pipeline.config.param_resolver import ( + ParamConfig, + ParamToResolveConfig, +) + +logger = logging.getLogger(__name__) + + +class AbstractConfig(BaseModel): + """Base class for all configs. + Provides methods to get a class from a string and resolve a parameter defined by + a dict with a 'resolver_' key. + + Each subclass must implement a 'parse' method that returns the relevant object. + """ + + _global_data: dict[str, Any] = PrivateAttr({}) + """Additional parameter ignored by all Pydantic model_* methods.""" + + def resolve_param(self, param: ParamConfig) -> Any: + """Finds the parameter value from its definition.""" + if not isinstance(param, ParamToResolveConfig): + # some parameters do not have to be resolved, real + # values are already provided + return param + return param.resolve(self._global_data) + + def resolve_params(self, params: dict[str, ParamConfig]) -> dict[str, Any]: + """Resolve all parameters + + Returning dict[str, Any] because parameters can be anything (str, float, list, dict...) + """ + return { + param_name: self.resolve_param(param) + for param_name, param in params.items() + } + + def parse(self, resolved_data: dict[str, Any] | None = None) -> Any: + raise NotImplementedError() diff --git a/src/neo4j_graphrag/experimental/pipeline/config/config_reader.py b/src/neo4j_graphrag/experimental/pipeline/config/config_reader.py new file mode 100644 index 00000000..c3df28d9 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/config_reader.py @@ -0,0 +1,85 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Read JSON or YAML files and returns a dict. +No data validation performed at this stage. +""" + +import json +import logging +from pathlib import Path +from typing import Any, Optional + +import fsspec +import yaml +from fsspec.implementations.local import LocalFileSystem + +logger = logging.getLogger(__name__) + + +class ConfigReader: + """Reads config from a file (JSON or YAML format) + and returns a dict. + + File format is guessed from the extension. Supported extensions are + (lower or upper case): + + - .json + - .yaml, .yml + + Example: + + .. code-block:: python + + from pathlib import Path + from neo4j_graphrag.experimental.pipeline.config.reader import ConfigReader + reader = ConfigReader() + reader.read(Path("my_file.json")) + + If reading a file with a different extension but still in JSON or YAML format, + it is possible to call directly the `read_json` or `read_yaml` methods: + + .. code-block:: python + + reader.read_yaml(Path("my_file.txt")) + + """ + + def __init__(self, fs: Optional[fsspec.AbstractFileSystem] = None) -> None: + self.fs = fs or LocalFileSystem() + + def read_json(self, file_path: str) -> Any: + logger.debug(f"CONFIG_READER: read from json {file_path}") + with self.fs.open(file_path, "r") as f: + return json.load(f) + + def read_yaml(self, file_path: str) -> Any: + logger.debug(f"CONFIG_READER: read from yaml {file_path}") + with self.fs.open(file_path, "r") as f: + return yaml.safe_load(f) + + def _guess_format_and_read(self, file_path: str) -> dict[str, Any]: + p = Path(file_path) + extension = p.suffix.lower() + # Note: .suffix returns an empty string if Path has no extension + # if not returning a dict, parsing will fail later on + if extension in [".json"]: + return self.read_json(file_path) # type: ignore[no-any-return] + if extension in [".yaml", ".yml"]: + return self.read_yaml(file_path) # type: ignore[no-any-return] + raise ValueError(f"Unsupported extension: {extension}") + + def read(self, file_path: str) -> dict[str, Any]: + data = self._guess_format_and_read(file_path) + return data diff --git a/src/neo4j_graphrag/experimental/pipeline/config/object_config.py b/src/neo4j_graphrag/experimental/pipeline/config/object_config.py new file mode 100644 index 00000000..cb47b380 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/object_config.py @@ -0,0 +1,266 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Config for all parameters that can be both provided as object instance or +config dict with 'class_' and 'params_' keys. + +Nomenclature in this file: + +- `*Config` models are used to represent "things" as dict to be used in a config file. + e.g.: + - neo4j.Driver => {"uri": "", "user": "", "password": ""} + - LLMInterface => {"class_": "OpenAI", "params_": {"model_name": "gpt-4o"}} +- `*Type` models are wrappers around an object and a 'Config' the object can be created + from. They are used to allow the instantiation of "PipelineConfig" either from + instantiated objects (when used in code) and from a config dict (when used to + load config from file). +""" + +from __future__ import annotations + +import importlib +import logging +from typing import ( + Any, + ClassVar, + Generic, + Optional, + TypeVar, + Union, + cast, +) + +import neo4j +from pydantic import ( + ConfigDict, + Field, + RootModel, + field_validator, +) + +from neo4j_graphrag.embeddings import Embedder +from neo4j_graphrag.experimental.pipeline import Component +from neo4j_graphrag.experimental.pipeline.config.base import AbstractConfig +from neo4j_graphrag.experimental.pipeline.config.param_resolver import ( + ParamConfig, +) +from neo4j_graphrag.llm import LLMInterface + +logger = logging.getLogger(__name__) + + +T = TypeVar("T") +"""Generic type to help mypy with the parse method when we know the exact +expected return type (e.g. for the Neo4jDriverConfig below). +""" + + +class ObjectConfig(AbstractConfig, Generic[T]): + """A config class to represent an object from a class name + and its constructor parameters. + """ + + class_: str | None = Field(default=None, validate_default=True) + """Path to class to be instantiated.""" + params_: dict[str, ParamConfig] = {} + """Initialization parameters.""" + + DEFAULT_MODULE: ClassVar[str] = "." + """Default module to import the class from.""" + INTERFACE: ClassVar[type] = object + """Constraint on the class (must be a subclass of).""" + REQUIRED_PARAMS: ClassVar[list[str]] = [] + """List of required parameters for this object constructor.""" + + @field_validator("params_") + @classmethod + def validate_params(cls, params_: dict[str, Any]) -> dict[str, Any]: + """Make sure all required parameters are provided.""" + for p in cls.REQUIRED_PARAMS: + if p not in params_: + raise ValueError(f"Missing parameter {p}") + return params_ + + def get_module(self) -> str: + return self.DEFAULT_MODULE + + def get_interface(self) -> type: + return self.INTERFACE + + @classmethod + def _get_class(cls, class_path: str, optional_module: Optional[str] = None) -> type: + """Get class from string and an optional module + + Will first try to import the class from `class_path` alone. If it results in an ImportError, + will try to import from `f'{optional_module}.{class_path}'` + + Args: + class_path (str): Class path with format 'my_module.MyClass'. + optional_module (Optional[str]): Optional module path. Used to provide a default path for some known objects and simplify the notation. + + Raises: + ValueError: if the class can't be imported, even using the optional module. + """ + *modules, class_name = class_path.rsplit(".", 1) + module_name = modules[0] if modules else optional_module + if module_name is None: + raise ValueError("Must specify a module to import class from") + try: + module = importlib.import_module(module_name) + klass = getattr(module, class_name) + except (ImportError, AttributeError): + if optional_module and module_name != optional_module: + full_klass_path = optional_module + "." + class_path + return cls._get_class(full_klass_path) + raise ValueError(f"Could not find {class_name} in {module_name}") + return cast(type, klass) + + def parse(self, resolved_data: dict[str, Any] | None = None) -> T: + """Import `class_`, resolve `params_` and instantiate object.""" + self._global_data = resolved_data or {} + logger.debug(f"OBJECT_CONFIG: parsing {self} using {resolved_data}") + if self.class_ is None: + raise ValueError(f"`class_` is not required to parse object {self}") + klass = self._get_class(self.class_, self.get_module()) + if not issubclass(klass, self.get_interface()): + raise ValueError( + f"Invalid class '{klass}'. Expected a subclass of '{self.get_interface()}'" + ) + params = self.resolve_params(self.params_) + try: + obj = klass(**params) + except TypeError as e: + logger.error( + "OBJECT_CONFIG: failed to instantiate object due to improperly configured parameters" + ) + raise e + return cast(T, obj) + + +class Neo4jDriverConfig(ObjectConfig[neo4j.Driver]): + REQUIRED_PARAMS = ["uri", "user", "password"] + + @field_validator("class_", mode="before") + @classmethod + def validate_class(cls, class_: Any) -> str: + """`class_` parameter is not used because we're always using the sync driver.""" + if class_: + logger.info("Parameter class_ is not used for Neo4jDriverConfig") + # not used + return "not used" + + def parse(self, resolved_data: dict[str, Any] | None = None) -> neo4j.Driver: + params = self.resolve_params(self.params_) + # we know these params are there because of the required params validator + uri = params.pop("uri") + user = params.pop("user") + password = params.pop("password") + driver = neo4j.GraphDatabase.driver(uri, auth=(user, password), **params) + return driver + + +# note: using the notation with RootModel + root: field +# instead of RootModel[] for clarity +# but this requires the type: ignore comment below +class Neo4jDriverType(RootModel): # type: ignore[type-arg] + """A model to wrap neo4j.Driver and Neo4jDriverConfig objects. + + The `parse` method always returns a neo4j.Driver. + """ + + root: Union[neo4j.Driver, Neo4jDriverConfig] + + model_config = ConfigDict(arbitrary_types_allowed=True) + + def parse(self, resolved_data: dict[str, Any] | None = None) -> neo4j.Driver: + if isinstance(self.root, neo4j.Driver): + return self.root + # self.root is a Neo4jDriverConfig object + return self.root.parse(resolved_data) + + +class LLMConfig(ObjectConfig[LLMInterface]): + """Configuration for any LLMInterface object. + + By default, will try to import from `neo4j_graphrag.llm`. + """ + + DEFAULT_MODULE = "neo4j_graphrag.llm" + INTERFACE = LLMInterface + + +class LLMType(RootModel): # type: ignore[type-arg] + """A model to wrap LLMInterface and LLMConfig objects. + + The `parse` method always returns an object inheriting from LLMInterface. + """ + + root: Union[LLMInterface, LLMConfig] + + model_config = ConfigDict(arbitrary_types_allowed=True) + + def parse(self, resolved_data: dict[str, Any] | None = None) -> LLMInterface: + if isinstance(self.root, LLMInterface): + return self.root + return self.root.parse(resolved_data) + + +class EmbedderConfig(ObjectConfig[Embedder]): + """Configuration for any Embedder object. + + By default, will try to import from `neo4j_graphrag.embeddings`. + """ + + DEFAULT_MODULE = "neo4j_graphrag.embeddings" + INTERFACE = Embedder + + +class EmbedderType(RootModel): # type: ignore[type-arg] + """A model to wrap Embedder and EmbedderConfig objects. + + The `parse` method always returns an object inheriting from Embedder. + """ + + root: Union[Embedder, EmbedderConfig] + + model_config = ConfigDict(arbitrary_types_allowed=True) + + def parse(self, resolved_data: dict[str, Any] | None = None) -> Embedder: + if isinstance(self.root, Embedder): + return self.root + return self.root.parse(resolved_data) + + +class ComponentConfig(ObjectConfig[Component]): + """A config model for all components. + + In addition to the object config, components can have pre-defined parameters + that will be passed to the `run` method, ie `run_params_`. + """ + + run_params_: dict[str, ParamConfig] = {} + + DEFAULT_MODULE = "neo4j_graphrag.experimental.components" + INTERFACE = Component + + +class ComponentType(RootModel): # type: ignore[type-arg] + root: Union[Component, ComponentConfig] + + model_config = ConfigDict(arbitrary_types_allowed=True) + + def parse(self, resolved_data: dict[str, Any] | None = None) -> Component: + if isinstance(self.root, Component): + return self.root + return self.root.parse(resolved_data) diff --git a/src/neo4j_graphrag/experimental/pipeline/config/param_resolver.py b/src/neo4j_graphrag/experimental/pipeline/config/param_resolver.py new file mode 100644 index 00000000..24190add --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/param_resolver.py @@ -0,0 +1,60 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import enum +import os +from typing import Any, ClassVar, Literal, Union + +from pydantic import BaseModel + + +class ParamResolverEnum(str, enum.Enum): + ENV = "ENV" + CONFIG_KEY = "CONFIG_KEY" + + +class ParamToResolveConfig(BaseModel): + def resolve(self, data: dict[str, Any]) -> Any: + raise NotImplementedError + + +class ParamFromEnvConfig(ParamToResolveConfig): + resolver_: Literal[ParamResolverEnum.ENV] = ParamResolverEnum.ENV + var_: str + + def resolve(self, data: dict[str, Any]) -> Any: + return os.environ.get(self.var_) + + +class ParamFromKeyConfig(ParamToResolveConfig): + resolver_: Literal[ParamResolverEnum.CONFIG_KEY] = ParamResolverEnum.CONFIG_KEY + key_: str + + KEY_SEP: ClassVar[str] = "." + + def resolve(self, data: dict[str, Any]) -> Any: + d = data + for k in self.key_.split(self.KEY_SEP): + d = d[k] + return d + + +ParamConfig = Union[ + float, + str, + ParamFromEnvConfig, + ParamFromKeyConfig, + dict[str, Any], +] diff --git a/src/neo4j_graphrag/experimental/pipeline/config/pipeline_config.py b/src/neo4j_graphrag/experimental/pipeline/config/pipeline_config.py new file mode 100644 index 00000000..c3871179 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/pipeline_config.py @@ -0,0 +1,199 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from typing import Any, ClassVar, Literal, Optional, Union + +import neo4j +from pydantic import field_validator + +from neo4j_graphrag.embeddings import Embedder +from neo4j_graphrag.experimental.pipeline.config.base import AbstractConfig +from neo4j_graphrag.experimental.pipeline.config.object_config import ( + ComponentType, + EmbedderType, + LLMType, + Neo4jDriverType, +) +from neo4j_graphrag.experimental.pipeline.config.types import PipelineType +from neo4j_graphrag.experimental.pipeline.types import ( + ComponentDefinition, + ConnectionDefinition, + PipelineDefinition, +) +from neo4j_graphrag.llm import LLMInterface + +logger = logging.getLogger(__name__) + + +class AbstractPipelineConfig(AbstractConfig): + """This class defines the fields possibly used by all pipelines: neo4j drivers, LLMs... + neo4j_config, llm_config can be provided by user as a single item or a dict of items. + Validators deal with type conversion so that the field in all instances is a dict of items. + """ + + neo4j_config: dict[str, Neo4jDriverType] = {} + llm_config: dict[str, LLMType] = {} + embedder_config: dict[str, EmbedderType] = {} + # extra parameters values that can be used in different places of the config file + extras: dict[str, Any] = {} + + DEFAULT_NAME: ClassVar[str] = "default" + """Name of the default item in dict + """ + + @field_validator("neo4j_config", mode="before") + @classmethod + def validate_drivers( + cls, drivers: Union[Neo4jDriverType, dict[str, Any]] + ) -> dict[str, Any]: + if not isinstance(drivers, dict) or "params_" in drivers: + return {cls.DEFAULT_NAME: drivers} + return drivers + + @field_validator("llm_config", mode="before") + @classmethod + def validate_llms(cls, llms: Union[LLMType, dict[str, Any]]) -> dict[str, Any]: + if not isinstance(llms, dict) or "class_" in llms: + return {cls.DEFAULT_NAME: llms} + return llms + + @field_validator("embedder_config", mode="before") + @classmethod + def validate_embedders( + cls, embedders: Union[EmbedderType, dict[str, Any]] + ) -> dict[str, Any]: + if not isinstance(embedders, dict) or "class_" in embedders: + return {cls.DEFAULT_NAME: embedders} + return embedders + + def _resolve_component_definition( + self, name: str, config: ComponentType + ) -> ComponentDefinition: + component = config.parse(self._global_data) + if hasattr(config.root, "run_params_"): + component_run_params = self.resolve_params(config.root.run_params_) + else: + component_run_params = {} + component_def = ComponentDefinition( + name=name, + component=component, + run_params=component_run_params, + ) + logger.debug(f"PIPELINE_CONFIG: resolved component {component_def}") + return component_def + + def _parse_global_data(self) -> dict[str, Any]: + """Global data contains data that can be referenced in other parts of the + config. + + Typically, neo4j drivers, LLMs and embedders can be referenced in component + input parameters. + """ + # 'extras' parameters can be referenced in other configs, + # that's why they are parsed before the others + # e.g., an API key used for both LLM and Embedder can be stored only + # once in extras. + extra_data = { + "extras": self.resolve_params(self.extras), + } + logger.debug(f"PIPELINE_CONFIG: resolved 'extras': {extra_data}") + drivers: dict[str, neo4j.Driver] = { + driver_name: driver_config.parse(extra_data) + for driver_name, driver_config in self.neo4j_config.items() + } + llms: dict[str, LLMInterface] = { + llm_name: llm_config.parse(extra_data) + for llm_name, llm_config in self.llm_config.items() + } + embedders: dict[str, Embedder] = { + embedder_name: embedder_config.parse(extra_data) + for embedder_name, embedder_config in self.embedder_config.items() + } + global_data = { + **extra_data, + "neo4j_config": drivers, + "llm_config": llms, + "embedder_config": embedders, + } + logger.debug(f"PIPELINE_CONFIG: resolved globals: {global_data}") + return global_data + + def _get_components(self) -> list[ComponentDefinition]: + return [] + + def _get_connections(self) -> list[ConnectionDefinition]: + return [] + + def parse( + self, resolved_data: Optional[dict[str, Any]] = None + ) -> PipelineDefinition: + """Parse the full config and returns a PipelineDefinition object, containing instantiated + components and a list of connections. + """ + self._global_data = self._parse_global_data() + return PipelineDefinition( + components=self._get_components(), + connections=self._get_connections(), + ) + + def get_run_params(self, user_input: dict[str, Any]) -> dict[str, Any]: + return user_input + + async def close(self) -> None: + drivers = self._global_data.get("neo4j_config", {}) + for driver_name in drivers: + driver = drivers[driver_name] + logger.debug(f"PIPELINE_CONFIG: closing driver {driver_name}: {driver}") + driver.close() + + def get_neo4j_driver_by_name(self, name: str) -> neo4j.Driver: + drivers: dict[str, neo4j.Driver] = self._global_data.get("neo4j_config", {}) + return drivers[name] + + def get_default_neo4j_driver(self) -> neo4j.Driver: + return self.get_neo4j_driver_by_name(self.DEFAULT_NAME) + + def get_llm_by_name(self, name: str) -> LLMInterface: + llms: dict[str, LLMInterface] = self._global_data.get("llm_config", {}) + return llms[name] + + def get_default_llm(self) -> LLMInterface: + return self.get_llm_by_name(self.DEFAULT_NAME) + + def get_embedder_by_name(self, name: str) -> Embedder: + embedders: dict[str, Embedder] = self._global_data.get("embedder_config", {}) + return embedders[name] + + def get_default_embedder(self) -> Embedder: + return self.get_embedder_by_name(self.DEFAULT_NAME) + + +class PipelineConfig(AbstractPipelineConfig): + """Configuration class for raw pipelines. + Config must contain all components and connections.""" + + component_config: dict[str, ComponentType] + connection_config: list[ConnectionDefinition] + template_: Literal[PipelineType.NONE] = PipelineType.NONE + + def _get_connections(self) -> list[ConnectionDefinition]: + return self.connection_config + + def _get_components(self) -> list[ComponentDefinition]: + return [ + self._resolve_component_definition(name, component_config) + for name, component_config in self.component_config.items() + ] diff --git a/src/neo4j_graphrag/experimental/pipeline/config/runner.py b/src/neo4j_graphrag/experimental/pipeline/config/runner.py new file mode 100644 index 00000000..a1a22585 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/runner.py @@ -0,0 +1,132 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Pipeline config wrapper (router based on 'template_' key) +and pipeline runner. +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import ( + Annotated, + Any, + Union, +) + +from pydantic import ( + BaseModel, + Discriminator, + Field, + Tag, +) +from pydantic.v1.utils import deep_update +from typing_extensions import Self + +from neo4j_graphrag.experimental.pipeline import Pipeline +from neo4j_graphrag.experimental.pipeline.config.config_reader import ConfigReader +from neo4j_graphrag.experimental.pipeline.config.pipeline_config import ( + AbstractPipelineConfig, + PipelineConfig, +) +from neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_kg_builder import ( + SimpleKGPipelineConfig, +) +from neo4j_graphrag.experimental.pipeline.config.types import PipelineType +from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult +from neo4j_graphrag.experimental.pipeline.types import PipelineDefinition + +logger = logging.getLogger(__name__) + + +def _get_discriminator_value(model: Any) -> PipelineType: + template_ = None + if "template_" in model: + template_ = model["template_"] + if hasattr(model, "template_"): + template_ = model.template_ + return PipelineType(template_) or PipelineType.NONE + + +class PipelineConfigWrapper(BaseModel): + """The pipeline config wrapper will parse the right pipeline config based on the `template_` field.""" + + config: Union[ + Annotated[PipelineConfig, Tag(PipelineType.NONE)], + Annotated[SimpleKGPipelineConfig, Tag(PipelineType.SIMPLE_KG_PIPELINE)], + ] = Field(discriminator=Discriminator(_get_discriminator_value)) + + def parse(self, resolved_data: dict[str, Any] | None = None) -> PipelineDefinition: + return self.config.parse(resolved_data) + + def get_run_params(self, user_input: dict[str, Any]) -> dict[str, Any]: + return self.config.get_run_params(user_input) + + +class PipelineRunner: + """Pipeline runner builds a pipeline from different objects and exposes a run method to run pipeline + + Pipeline can be built from: + - A PipelineDefinition (`__init__` method) + - A PipelineConfig (`from_config` method) + - A config file (`from_config_file` method) + """ + + def __init__( + self, + pipeline_definition: PipelineDefinition, + config: AbstractPipelineConfig | None = None, + do_cleaning: bool = False, + ) -> None: + self.config = config + self.pipeline = Pipeline.from_definition(pipeline_definition) + self.run_params = pipeline_definition.get_run_params() + self.do_cleaning = do_cleaning + + @classmethod + def from_config( + cls, config: AbstractPipelineConfig | dict[str, Any], do_cleaning: bool = False + ) -> Self: + wrapper = PipelineConfigWrapper.model_validate({"config": config}) + return cls(wrapper.parse(), config=wrapper.config, do_cleaning=do_cleaning) + + @classmethod + def from_config_file(cls, file_path: Union[str, Path]) -> Self: + if not isinstance(file_path, str): + file_path = str(file_path) + data = ConfigReader().read(file_path) + return cls.from_config(data, do_cleaning=True) + + async def run(self, user_input: dict[str, Any]) -> PipelineResult: + # pipeline_conditional_run_params = self. + if self.config: + run_param = deep_update( + self.run_params, self.config.get_run_params(user_input) + ) + else: + run_param = deep_update(self.run_params, user_input) + logger.info( + f"PIPELINE_RUNNER: starting pipeline {self.pipeline} with run_params={run_param}" + ) + result = await self.pipeline.run(data=run_param) + if self.do_cleaning: + await self.close() + return result + + async def close(self) -> None: + logger.debug("PIPELINE_RUNNER: cleaning up (closing instantiated drivers...)") + if self.config: + await self.config.close() diff --git a/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/__init__.py b/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/__init__.py new file mode 100644 index 00000000..125a1c87 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .simple_kg_builder import SimpleKGPipelineConfig + +__all__ = [ + "SimpleKGPipelineConfig", +] diff --git a/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/base.py b/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/base.py new file mode 100644 index 00000000..69fbc751 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/base.py @@ -0,0 +1,63 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +from typing import Any, ClassVar, Optional + +from neo4j_graphrag.experimental.pipeline.config.pipeline_config import ( + AbstractPipelineConfig, +) +from neo4j_graphrag.experimental.pipeline.types import ComponentDefinition + +logger = logging.getLogger(__name__) + + +class TemplatePipelineConfig(AbstractPipelineConfig): + """This class represent a 'template' pipeline, ie pipeline with pre-defined default + components and fixed connections. + + Component names are defined in the COMPONENTS class var. For each of them, + a `_get_` method must be implemented that returns the proper + component. Optionally, `_get_run_params_for_` can be implemented + to deal with parameters required by the component's run method and predefined on + template initialization. + """ + + COMPONENTS: ClassVar[list[str]] = [] + + def _get_component(self, component_name: str) -> Optional[ComponentDefinition]: + method = getattr(self, f"_get_{component_name}") + component = method() + if component is None: + return None + method = getattr(self, f"_get_run_params_for_{component_name}", None) + run_params = method() if method else {} + component_definition = ComponentDefinition( + name=component_name, + component=component, + run_params=run_params, + ) + logger.debug(f"TEMPLATE_PIPELINE: resolved component {component_definition}") + return component_definition + + def _get_components(self) -> list[ComponentDefinition]: + components = [] + for component_name in self.COMPONENTS: + comp = self._get_component(component_name) + if comp is not None: + components.append(comp) + return components + + def get_run_params(self, user_input: dict[str, Any]) -> dict[str, Any]: + return {} diff --git a/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/simple_kg_builder.py b/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/simple_kg_builder.py new file mode 100644 index 00000000..73edfd9a --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/template_pipeline/simple_kg_builder.py @@ -0,0 +1,228 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, ClassVar, Literal, Optional, Sequence, Union + +from pydantic import ConfigDict + +from neo4j_graphrag.experimental.components.embedder import TextChunkEmbedder +from neo4j_graphrag.experimental.components.entity_relation_extractor import ( + EntityRelationExtractor, + LLMEntityRelationExtractor, + OnError, +) +from neo4j_graphrag.experimental.components.kg_writer import KGWriter, Neo4jWriter +from neo4j_graphrag.experimental.components.pdf_loader import PdfLoader +from neo4j_graphrag.experimental.components.resolver import ( + EntityResolver, + SinglePropertyExactMatchResolver, +) +from neo4j_graphrag.experimental.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaRelation, +) +from neo4j_graphrag.experimental.components.text_splitters.base import TextSplitter +from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import ( + FixedSizeSplitter, +) +from neo4j_graphrag.experimental.components.types import LexicalGraphConfig +from neo4j_graphrag.experimental.pipeline.config.object_config import ComponentType +from neo4j_graphrag.experimental.pipeline.config.template_pipeline.base import ( + TemplatePipelineConfig, +) +from neo4j_graphrag.experimental.pipeline.config.types import PipelineType +from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError +from neo4j_graphrag.experimental.pipeline.types import ( + ConnectionDefinition, + EntityInputType, + RelationInputType, +) +from neo4j_graphrag.generation.prompts import ERExtractionTemplate + + +class SimpleKGPipelineConfig(TemplatePipelineConfig): + COMPONENTS: ClassVar[list[str]] = [ + "pdf_loader", + "splitter", + "chunk_embedder", + "schema", + "extractor", + "writer", + "resolver", + ] + + template_: Literal[PipelineType.SIMPLE_KG_PIPELINE] = ( + PipelineType.SIMPLE_KG_PIPELINE + ) + + from_pdf: bool = False + entities: Sequence[EntityInputType] = [] + relations: Sequence[RelationInputType] = [] + potential_schema: Optional[list[tuple[str, str, str]]] = None + on_error: OnError = OnError.IGNORE + prompt_template: Union[ERExtractionTemplate, str] = ERExtractionTemplate() + perform_entity_resolution: bool = True + lexical_graph_config: Optional[LexicalGraphConfig] = None + neo4j_database: Optional[str] = None + + pdf_loader: Optional[ComponentType] = None + kg_writer: Optional[ComponentType] = None + text_splitter: Optional[ComponentType] = None + + model_config = ConfigDict(arbitrary_types_allowed=True) + + def _get_pdf_loader(self) -> Optional[PdfLoader]: + if not self.from_pdf: + return None + if self.pdf_loader: + return self.pdf_loader.parse(self._global_data) # type: ignore + return PdfLoader() + + def _get_splitter(self) -> TextSplitter: + if self.text_splitter: + return self.text_splitter.parse(self._global_data) # type: ignore + return FixedSizeSplitter() + + def _get_chunk_embedder(self) -> TextChunkEmbedder: + return TextChunkEmbedder(embedder=self.get_default_embedder()) + + def _get_schema(self) -> SchemaBuilder: + return SchemaBuilder() + + def _get_run_params_for_schema(self) -> dict[str, Any]: + return { + "entities": [SchemaEntity.from_text_or_dict(e) for e in self.entities], + "relations": [SchemaRelation.from_text_or_dict(r) for r in self.relations], + "potential_schema": self.potential_schema, + } + + def _get_extractor(self) -> EntityRelationExtractor: + return LLMEntityRelationExtractor( + llm=self.get_default_llm(), + prompt_template=self.prompt_template, + on_error=self.on_error, + ) + + def _get_writer(self) -> KGWriter: + if self.kg_writer: + return self.kg_writer.parse(self._global_data) # type: ignore + return Neo4jWriter( + driver=self.get_default_neo4j_driver(), + neo4j_database=self.neo4j_database, + ) + + def _get_resolver(self) -> Optional[EntityResolver]: + if not self.perform_entity_resolution: + return None + return SinglePropertyExactMatchResolver( + driver=self.get_default_neo4j_driver(), + neo4j_database=self.neo4j_database, + ) + + def _get_connections(self) -> list[ConnectionDefinition]: + connections = [] + if self.from_pdf: + connections.append( + ConnectionDefinition( + start="pdf_loader", + end="splitter", + input_config={"text": "pdf_loader.text"}, + ) + ) + connections.append( + ConnectionDefinition( + start="schema", + end="extractor", + input_config={ + "schema": "schema", + "document_info": "pdf_loader.document_info", + }, + ) + ) + else: + connections.append( + ConnectionDefinition( + start="schema", + end="extractor", + input_config={ + "schema": "schema", + }, + ) + ) + connections.append( + ConnectionDefinition( + start="splitter", + end="chunk_embedder", + input_config={ + "text_chunks": "splitter", + }, + ) + ) + connections.append( + ConnectionDefinition( + start="chunk_embedder", + end="extractor", + input_config={ + "chunks": "chunk_embedder", + }, + ) + ) + connections.append( + ConnectionDefinition( + start="extractor", + end="writer", + input_config={ + "graph": "extractor", + }, + ) + ) + + if self.perform_entity_resolution: + connections.append( + ConnectionDefinition( + start="writer", + end="resolver", + input_config={}, + ) + ) + + return connections + + def get_run_params(self, user_input: dict[str, Any]) -> dict[str, Any]: + run_params = {} + if self.lexical_graph_config: + run_params["extractor"] = { + "lexical_graph_config": self.lexical_graph_config + } + text = user_input.get("text") + file_path = user_input.get("file_path") + if not ((text is None) ^ (file_path is None)): + # exactly one of text or user_input must be set + raise PipelineDefinitionError( + "Use either 'text' (when from_pdf=False) or 'file_path' (when from_pdf=True) argument." + ) + if self.from_pdf: + if not file_path: + raise PipelineDefinitionError( + "Expected 'file_path' argument when 'from_pdf' is True." + ) + run_params["pdf_loader"] = {"filepath": file_path} + else: + if not text: + raise PipelineDefinitionError( + "Expected 'text' argument when 'from_pdf' is False." + ) + run_params["splitter"] = {"text": text} + return run_params diff --git a/src/neo4j_graphrag/experimental/pipeline/config/types.py b/src/neo4j_graphrag/experimental/pipeline/config/types.py new file mode 100644 index 00000000..48f91f48 --- /dev/null +++ b/src/neo4j_graphrag/experimental/pipeline/config/types.py @@ -0,0 +1,26 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import enum + + +class PipelineType(str, enum.Enum): + """Pipeline type: + + NONE => Pipeline + SIMPLE_KG_PIPELINE ~> SimpleKGPipeline + """ + + NONE = "none" + SIMPLE_KG_PIPELINE = "SimpleKGPipeline" diff --git a/src/neo4j_graphrag/experimental/pipeline/exceptions.py b/src/neo4j_graphrag/experimental/pipeline/exceptions.py index 3f1419dc..713f6cb6 100644 --- a/src/neo4j_graphrag/experimental/pipeline/exceptions.py +++ b/src/neo4j_graphrag/experimental/pipeline/exceptions.py @@ -31,3 +31,9 @@ class PipelineStatusUpdateError(Neo4jGraphRagError): """Raises when trying an invalid change of state (e.g. DONE => DOING)""" pass + + +class InvalidJSONError(Neo4jGraphRagError): + """Raised when JSON repair fails to produce valid JSON.""" + + pass diff --git a/src/neo4j_graphrag/experimental/pipeline/kg_builder.py b/src/neo4j_graphrag/experimental/pipeline/kg_builder.py index 58868cb3..3fca0215 100644 --- a/src/neo4j_graphrag/experimental/pipeline/kg_builder.py +++ b/src/neo4j_graphrag/experimental/pipeline/kg_builder.py @@ -18,30 +18,16 @@ from typing import Any, List, Optional, Sequence, Union import neo4j -from pydantic import BaseModel, ConfigDict, Field +from pydantic import ValidationError from neo4j_graphrag.embeddings import Embedder -from neo4j_graphrag.experimental.components.embedder import TextChunkEmbedder -from neo4j_graphrag.experimental.components.entity_relation_extractor import ( - LLMEntityRelationExtractor, - OnError, -) -from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter -from neo4j_graphrag.experimental.components.pdf_loader import PdfLoader -from neo4j_graphrag.experimental.components.resolver import ( - SinglePropertyExactMatchResolver, -) -from neo4j_graphrag.experimental.components.schema import ( - SchemaBuilder, - SchemaEntity, - SchemaRelation, -) -from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import ( - FixedSizeSplitter, -) from neo4j_graphrag.experimental.components.types import LexicalGraphConfig +from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner +from neo4j_graphrag.experimental.pipeline.config.template_pipeline import ( + SimpleKGPipelineConfig, +) from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError -from neo4j_graphrag.experimental.pipeline.pipeline import Pipeline, PipelineResult +from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult from neo4j_graphrag.experimental.pipeline.types import ( EntityInputType, RelationInputType, @@ -50,26 +36,6 @@ from neo4j_graphrag.llm.base import LLMInterface -class SimpleKGPipelineConfig(BaseModel): - llm: LLMInterface - driver: neo4j.Driver - from_pdf: bool - embedder: Embedder - entities: list[SchemaEntity] = Field(default_factory=list) - relations: list[SchemaRelation] = Field(default_factory=list) - potential_schema: list[tuple[str, str, str]] = Field(default_factory=list) - pdf_loader: Any = None - kg_writer: Any = None - text_splitter: Any = None - on_error: OnError = OnError.RAISE - prompt_template: Union[ERExtractionTemplate, str] = ERExtractionTemplate() - perform_entity_resolution: bool = True - lexical_graph_config: Optional[LexicalGraphConfig] = None - neo4j_database: Optional[str] = None - - model_config = ConfigDict(arbitrary_types_allowed=True) - - class SimpleKGPipeline: """ A class to simplify the process of building a knowledge graph from text documents. @@ -120,133 +86,29 @@ def __init__( lexical_graph_config: Optional[LexicalGraphConfig] = None, neo4j_database: Optional[str] = None, ): - self.potential_schema = potential_schema or [] - self.entities = [self.to_schema_entity(e) for e in entities or []] - self.relations = [self.to_schema_relation(r) for r in relations or []] - try: - on_error_enum = OnError(on_error) - except ValueError: - raise PipelineDefinitionError( - f"Invalid value for on_error: {on_error}. Expected one of {OnError.possible_values()}." - ) - - config = SimpleKGPipelineConfig( - llm=llm, - driver=driver, - entities=self.entities, - relations=self.relations, - potential_schema=self.potential_schema, - from_pdf=from_pdf, - pdf_loader=pdf_loader, - kg_writer=kg_writer, - text_splitter=text_splitter, - on_error=on_error_enum, - prompt_template=prompt_template, - embedder=embedder, - perform_entity_resolution=perform_entity_resolution, - lexical_graph_config=lexical_graph_config, - neo4j_database=neo4j_database, - ) - - self.from_pdf = config.from_pdf - self.llm = config.llm - self.driver = config.driver - self.embedder = config.embedder - self.text_splitter = config.text_splitter or FixedSizeSplitter() - self.on_error = config.on_error - self.pdf_loader = config.pdf_loader if pdf_loader is not None else PdfLoader() - self.kg_writer = ( - config.kg_writer - if kg_writer is not None - else Neo4jWriter(driver, neo4j_database=config.neo4j_database) - ) - self.prompt_template = config.prompt_template - self.perform_entity_resolution = config.perform_entity_resolution - self.lexical_graph_config = config.lexical_graph_config - self.neo4j_database = config.neo4j_database - - self.pipeline = self._build_pipeline() - - @staticmethod - def to_schema_entity(entity: EntityInputType) -> SchemaEntity: - if isinstance(entity, dict): - return SchemaEntity.model_validate(entity) - return SchemaEntity(label=entity) - - @staticmethod - def to_schema_relation(relation: RelationInputType) -> SchemaRelation: - if isinstance(relation, dict): - return SchemaRelation.model_validate(relation) - return SchemaRelation(label=relation) - - def _build_pipeline(self) -> Pipeline: - pipe = Pipeline() - - pipe.add_component(self.text_splitter, "splitter") - pipe.add_component(SchemaBuilder(), "schema") - pipe.add_component( - LLMEntityRelationExtractor( - llm=self.llm, - on_error=self.on_error, - prompt_template=self.prompt_template, - ), - "extractor", - ) - pipe.add_component(TextChunkEmbedder(embedder=self.embedder), "chunk_embedder") - pipe.add_component(self.kg_writer, "writer") - - if self.from_pdf: - pipe.add_component(self.pdf_loader, "pdf_loader") - - pipe.connect( - "pdf_loader", - "splitter", - input_config={"text": "pdf_loader.text"}, - ) - - pipe.connect( - "schema", - "extractor", - input_config={ - "schema": "schema", - "document_info": "pdf_loader.document_info", - }, - ) - else: - pipe.connect( - "schema", - "extractor", - input_config={ - "schema": "schema", - }, - ) - - pipe.connect( - "splitter", "chunk_embedder", input_config={"text_chunks": "splitter"} - ) - - pipe.connect( - "chunk_embedder", "extractor", input_config={"chunks": "chunk_embedder"} - ) - - # Connect extractor to writer - pipe.connect( - "extractor", - "writer", - input_config={"graph": "extractor"}, - ) - - if self.perform_entity_resolution: - pipe.add_component( - SinglePropertyExactMatchResolver( - self.driver, neo4j_database=self.neo4j_database - ), - "resolver", + config = SimpleKGPipelineConfig( + # argument type are fixed in the Config object + llm_config=llm, # type: ignore[arg-type] + neo4j_config=driver, # type: ignore[arg-type] + embedder_config=embedder, # type: ignore[arg-type] + entities=entities or [], + relations=relations or [], + potential_schema=potential_schema, + from_pdf=from_pdf, + pdf_loader=pdf_loader, + kg_writer=kg_writer, + text_splitter=text_splitter, + on_error=on_error, # type: ignore[arg-type] + prompt_template=prompt_template, + perform_entity_resolution=perform_entity_resolution, + lexical_graph_config=lexical_graph_config, + neo4j_database=neo4j_database, ) - pipe.connect("writer", "resolver", {}) + except ValidationError as e: + raise PipelineDefinitionError() from e - return pipe + self.runner = PipelineRunner.from_config(config) async def run_async( self, file_path: Optional[str] = None, text: Optional[str] = None @@ -261,39 +123,4 @@ async def run_async( Returns: PipelineResult: The result of the pipeline execution. """ - pipe_inputs = self._prepare_inputs(file_path=file_path, text=text) - return await self.pipeline.run(pipe_inputs) - - def _prepare_inputs( - self, file_path: Optional[str], text: Optional[str] - ) -> dict[str, Any]: - if self.from_pdf: - if file_path is None or text is not None: - raise PipelineDefinitionError( - "Expected 'file_path' argument when 'from_pdf' is True." - ) - else: - if text is None or file_path is not None: - raise PipelineDefinitionError( - "Expected 'text' argument when 'from_pdf' is False." - ) - - pipe_inputs: dict[str, Any] = { - "schema": { - "entities": self.entities, - "relations": self.relations, - "potential_schema": self.potential_schema, - }, - } - - if self.from_pdf: - pipe_inputs["pdf_loader"] = {"filepath": file_path} - else: - pipe_inputs["splitter"] = {"text": text} - - if self.lexical_graph_config: - pipe_inputs["extractor"] = { - "lexical_graph_config": self.lexical_graph_config - } - - return pipe_inputs + return await self.runner.run({"file_path": file_path, "text": text}) diff --git a/src/neo4j_graphrag/experimental/pipeline/pipeline.py b/src/neo4j_graphrag/experimental/pipeline/pipeline.py index 5edc2783..e3ded494 100644 --- a/src/neo4j_graphrag/experimental/pipeline/pipeline.py +++ b/src/neo4j_graphrag/experimental/pipeline/pipeline.py @@ -44,9 +44,9 @@ ) from neo4j_graphrag.experimental.pipeline.stores import InMemoryStore, ResultStore from neo4j_graphrag.experimental.pipeline.types import ( - ComponentConfig, - ConnectionConfig, - PipelineConfig, + ComponentDefinition, + ConnectionDefinition, + PipelineDefinition, ) logger = logging.getLogger(__name__) @@ -349,16 +349,34 @@ def __init__(self, store: Optional[ResultStore] = None) -> None: @classmethod def from_template( - cls, pipeline_template: PipelineConfig, store: Optional[ResultStore] = None + cls, pipeline_template: PipelineDefinition, store: Optional[ResultStore] = None ) -> Pipeline: - """Create a Pipeline from a pydantic model defining the components and their connections""" + warnings.warn( + "from_template is deprecated, use from_definition instead", + DeprecationWarning, + stacklevel=2, + ) + return cls.from_definition(pipeline_template, store) + + @classmethod + def from_definition( + cls, + pipeline_definition: PipelineDefinition, + store: Optional[ResultStore] = None, + ) -> Pipeline: + """Create a Pipeline from a pydantic model defining the components and their connections + + Args: + pipeline_definition (PipelineDefinition): An object defining components and how they are connected to each other. + store (Optional[ResultStore]): Where the results are stored. By default, uses the InMemoryStore. + """ pipeline = Pipeline(store=store) - for component in pipeline_template.components: + for component in pipeline_definition.components: pipeline.add_component( component.component, component.name, ) - for edge in pipeline_template.connections: + for edge in pipeline_definition.connections: pipeline_edge = PipelineEdge( edge.start, edge.end, data={"input_config": edge.input_config} ) @@ -369,18 +387,18 @@ def show_as_dict(self) -> dict[str, Any]: component_config = [] for name, task in self._nodes.items(): component_config.append( - ComponentConfig(name=name, component=task.component) + ComponentDefinition(name=name, component=task.component) ) connection_config = [] for edge in self._edges: connection_config.append( - ConnectionConfig( + ConnectionDefinition( start=edge.start, end=edge.end, input_config=edge.data["input_config"] if edge.data else {}, ) ) - pipeline_config = PipelineConfig( + pipeline_config = PipelineDefinition( components=component_config, connections=connection_config ) return pipeline_config.model_dump() diff --git a/src/neo4j_graphrag/experimental/pipeline/types.py b/src/neo4j_graphrag/experimental/pipeline/types.py index ebdf141d..47aafd8b 100644 --- a/src/neo4j_graphrag/experimental/pipeline/types.py +++ b/src/neo4j_graphrag/experimental/pipeline/types.py @@ -14,29 +14,36 @@ # limitations under the License. from __future__ import annotations -from typing import Union +from collections import defaultdict +from typing import Any, Union from pydantic import BaseModel, ConfigDict from neo4j_graphrag.experimental.pipeline.component import Component -class ComponentConfig(BaseModel): +class ComponentDefinition(BaseModel): name: str component: Component + run_params: dict[str, Any] = {} model_config = ConfigDict(arbitrary_types_allowed=True) -class ConnectionConfig(BaseModel): +class ConnectionDefinition(BaseModel): start: str end: str input_config: dict[str, str] -class PipelineConfig(BaseModel): - components: list[ComponentConfig] - connections: list[ConnectionConfig] +class PipelineDefinition(BaseModel): + components: list[ComponentDefinition] + connections: list[ConnectionDefinition] + + def get_run_params(self) -> defaultdict[str, dict[str, Any]]: + return defaultdict( + dict, {c.name: c.run_params for c in self.components if c.run_params} + ) EntityInputType = Union[str, dict[str, Union[str, list[dict[str, str]]]]] diff --git a/src/neo4j_graphrag/generation/prompts.py b/src/neo4j_graphrag/generation/prompts.py index 5bd1d3c3..a73f6420 100644 --- a/src/neo4j_graphrag/generation/prompts.py +++ b/src/neo4j_graphrag/generation/prompts.py @@ -174,7 +174,11 @@ class ERExtractionTemplate(PromptTemplate): Do respect the source and target node types for relationship and the relationship direction. -Do not return any additional information other than the JSON in it. +Make sure you adhere to the following rules to produce valid JSON objects: +- Do not return any additional information other than the JSON in it. +- Omit any backticks around the JSON - simply output the JSON on its own. +- The JSON object must not wrapped into a list - it is its own JSON object. +- Property names must be enclosed in double quotes Examples: {examples} diff --git a/src/neo4j_graphrag/llm/__init__.py b/src/neo4j_graphrag/llm/__init__.py index a8fbdd61..a9ece5cc 100644 --- a/src/neo4j_graphrag/llm/__init__.py +++ b/src/neo4j_graphrag/llm/__init__.py @@ -16,6 +16,7 @@ from .base import LLMInterface from .cohere_llm import CohereLLM from .mistralai_llm import MistralAILLM +from .ollama_llm import OllamaLLM from .openai_llm import AzureOpenAILLM, OpenAILLM from .types import LLMResponse from .vertexai_llm import VertexAILLM @@ -25,6 +26,7 @@ "CohereLLM", "LLMResponse", "LLMInterface", + "OllamaLLM", "OpenAILLM", "VertexAILLM", "AzureOpenAILLM", diff --git a/src/neo4j_graphrag/llm/anthropic_llm.py b/src/neo4j_graphrag/llm/anthropic_llm.py index 1bfc20e5..446e447b 100644 --- a/src/neo4j_graphrag/llm/anthropic_llm.py +++ b/src/neo4j_graphrag/llm/anthropic_llm.py @@ -63,8 +63,8 @@ def __init__( ): if anthropic is None: raise ImportError( - "Could not import Anthropic Python client. " - "Please install it with `pip install anthropic`." + """Could not import Anthropic Python client. + Please install it with `pip install "neo4j-graphrag[anthropic]"`.""" ) super().__init__(model_name, model_params, system_instruction) self.anthropic = anthropic diff --git a/src/neo4j_graphrag/llm/cohere_llm.py b/src/neo4j_graphrag/llm/cohere_llm.py index 32cb1d5c..2ceaa402 100644 --- a/src/neo4j_graphrag/llm/cohere_llm.py +++ b/src/neo4j_graphrag/llm/cohere_llm.py @@ -59,8 +59,8 @@ def __init__( ) -> None: if cohere is None: raise ImportError( - "Could not import Cohere Python client. " - "Please install it with `pip install cohere`." + """Could not import cohere python client. + Please install it with `pip install "neo4j-graphrag[cohere]"`.""" ) super().__init__(model_name, model_params, system_instruction) self.cohere = cohere diff --git a/src/neo4j_graphrag/llm/mistralai_llm.py b/src/neo4j_graphrag/llm/mistralai_llm.py index aff20175..1835a2df 100644 --- a/src/neo4j_graphrag/llm/mistralai_llm.py +++ b/src/neo4j_graphrag/llm/mistralai_llm.py @@ -50,8 +50,8 @@ def __init__( """ if Mistral is None: raise ImportError( - "Could not import Mistral Python client. " - "Please install it with `pip install mistralai`." + """Could not import Mistral Python client. + Please install it with `pip install "neo4j-graphrag[mistralai]"`.""" ) super().__init__(model_name, model_params, system_instruction) api_key = kwargs.pop("api_key", None) @@ -93,10 +93,11 @@ def invoke(self, input: str, chat_history: Optional[list[dict[str, str]]] = None messages=messages, **self.model_params, ) - if response is None or response.choices is None or not response.choices: - content = "" - else: - content = response.choices[0].message.content or "" + content: str = "" + if response and response.choices: + possible_content = response.choices[0].message.content + if isinstance(possible_content, str): + content = possible_content return LLMResponse(content=content) except SDKError as e: raise LLMGenerationError(e) @@ -124,10 +125,11 @@ async def ainvoke( messages=messages, **self.model_params, ) - if response is None or response.choices is None or not response.choices: - content = "" - else: - content = response.choices[0].message.content or "" + content: str = "" + if response and response.choices: + possible_content = response.choices[0].message.content + if isinstance(possible_content, str): + content = possible_content return LLMResponse(content=content) except SDKError as e: raise LLMGenerationError(e) diff --git a/src/neo4j_graphrag/llm/ollama_llm.py b/src/neo4j_graphrag/llm/ollama_llm.py new file mode 100644 index 00000000..d82ccd25 --- /dev/null +++ b/src/neo4j_graphrag/llm/ollama_llm.py @@ -0,0 +1,76 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Optional + +from neo4j_graphrag.exceptions import LLMGenerationError + +from .base import LLMInterface +from .types import LLMResponse + + +class OllamaLLM(LLMInterface): + def __init__( + self, + model_name: str, + model_params: Optional[dict[str, Any]] = None, + **kwargs: Any, + ): + try: + import ollama + except ImportError: + raise ImportError( + "Could not import ollama Python client. " + "Please install it with `pip install ollama`." + ) + super().__init__(model_name, model_params, **kwargs) + self.ollama = ollama + self.client = ollama.Client( + **kwargs, + ) + self.async_client = ollama.AsyncClient( + **kwargs, + ) + + def invoke(self, input: str) -> LLMResponse: + try: + response = self.client.chat( + model=self.model_name, + messages=[ + { + "role": "user", + "content": input, + }, + ], + ) + content = response.message.content or "" + return LLMResponse(content=content) + except self.ollama.ResponseError as e: + raise LLMGenerationError(e) + + async def ainvoke(self, input: str) -> LLMResponse: + try: + response = await self.async_client.chat( + model=self.model_name, + messages=[ + { + "role": "user", + "content": input, + }, + ], + ) + content = response.message.content or "" + return LLMResponse(content=content) + except self.ollama.ResponseError as e: + raise LLMGenerationError(e) diff --git a/src/neo4j_graphrag/llm/openai_llm.py b/src/neo4j_graphrag/llm/openai_llm.py index 6496a726..8ac71ab8 100644 --- a/src/neo4j_graphrag/llm/openai_llm.py +++ b/src/neo4j_graphrag/llm/openai_llm.py @@ -54,8 +54,8 @@ def __init__( import openai except ImportError: raise ImportError( - "Could not import openai Python client. " - "Please install it with `pip install openai`." + """Could not import openai Python client. + Please install it with `pip install "neo4j-graphrag[openai]"`.""" ) self.openai = openai super().__init__(model_name, model_params, system_instruction) diff --git a/src/neo4j_graphrag/llm/vertexai_llm.py b/src/neo4j_graphrag/llm/vertexai_llm.py index 2900deec..77fd2720 100644 --- a/src/neo4j_graphrag/llm/vertexai_llm.py +++ b/src/neo4j_graphrag/llm/vertexai_llm.py @@ -68,8 +68,8 @@ def __init__( ): if GenerativeModel is None or ResponseValidationError is None: raise ImportError( - "Could not import Vertex AI Python client. " - "Please install it with `pip install google-cloud-aiplatform`." + """Could not import Vertex AI Python client. + Please install it with `pip install "neo4j-graphrag[google]"`.""" ) super().__init__(model_name, model_params) self.model = GenerativeModel( diff --git a/src/neo4j_graphrag/retrievers/text2cypher.py b/src/neo4j_graphrag/retrievers/text2cypher.py index 039f42f0..fb0a3521 100644 --- a/src/neo4j_graphrag/retrievers/text2cypher.py +++ b/src/neo4j_graphrag/retrievers/text2cypher.py @@ -48,7 +48,7 @@ class Text2CypherRetriever(Retriever): """ Allows for the retrieval of records from a Neo4j database using natural language. Converts a user's natural language query to a Cypher query using an LLM, - then retrieves records from a Neo4j database using the generated Cypher query + then retrieves records from a Neo4j database using the generated Cypher query. Args: driver (neo4j.Driver): The Neo4j Python driver. @@ -98,23 +98,23 @@ def __init__( self.examples = validated_data.examples self.result_formatter = validated_data.result_formatter self.custom_prompt = validated_data.custom_prompt - try: + if validated_data.custom_prompt: + neo4j_schema = "" + else: if ( - not validated_data.custom_prompt - ): # don't need schema for a custom prompt - self.neo4j_schema = ( - validated_data.neo4j_schema_model.neo4j_schema - if validated_data.neo4j_schema_model - else get_schema(validated_data.driver_model.driver) - ) + validated_data.neo4j_schema_model + and validated_data.neo4j_schema_model.neo4j_schema + ): + neo4j_schema = validated_data.neo4j_schema_model.neo4j_schema else: - self.neo4j_schema = "" - - except (Neo4jError, DriverError) as e: - error_message = getattr(e, "message", str(e)) - raise SchemaFetchError( - f"Failed to fetch schema for Text2CypherRetriever: {error_message}" - ) from e + try: + neo4j_schema = get_schema(validated_data.driver_model.driver) + except (Neo4jError, DriverError) as e: + error_message = getattr(e, "message", str(e)) + raise SchemaFetchError( + f"Failed to fetch schema for Text2CypherRetriever: {error_message}" + ) from e + self.neo4j_schema = neo4j_schema def get_search_results( self, query_text: str, prompt_params: Optional[Dict[str, Any]] = None @@ -142,12 +142,10 @@ def get_search_results( if prompt_params is not None: # parse the schema and examples inputs - examples_to_use = prompt_params.get("examples") or ( + examples_to_use = prompt_params.pop("examples", None) or ( "\n".join(self.examples) if self.examples else "" ) - schema_to_use = prompt_params.get("schema") or self.neo4j_schema - prompt_params.pop("examples", None) - prompt_params.pop("schema", None) + schema_to_use = prompt_params.pop("schema", None) or self.neo4j_schema else: examples_to_use = "\n".join(self.examples) if self.examples else "" schema_to_use = self.neo4j_schema diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index 70ca0193..42c21cf8 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -14,6 +14,7 @@ # limitations under the License. from __future__ import annotations +import os import random import string import uuid @@ -33,6 +34,8 @@ from ..e2e.utils import EMBEDDING_BIOLOGY +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) + @pytest.fixture(scope="module") def driver() -> Generator[Any, Any, Any]: @@ -48,6 +51,12 @@ def llm() -> MagicMock: return MagicMock(spec=LLMInterface) +@pytest.fixture +def embedder() -> Embedder: + embedder = MagicMock(spec=Embedder) + return embedder + + class RandomEmbedder(Embedder): def embed_query(self, text: str) -> list[float]: return [random.random() for _ in range(1536)] @@ -75,6 +84,31 @@ def retriever_mock() -> MagicMock: return MagicMock(spec=VectorRetriever) +@pytest.fixture +def harry_potter_text() -> str: + with open(os.path.join(BASE_DIR, "data/documents/harry_potter.txt"), "r") as f: + text = f.read() + return text + + +@pytest.fixture +def harry_potter_text_part1() -> str: + with open( + os.path.join(BASE_DIR, "data/documents/harry_potter_part1.txt"), "r" + ) as f: + text = f.read() + return text + + +@pytest.fixture +def harry_potter_text_part2() -> str: + with open( + os.path.join(BASE_DIR, "data/documents/harry_potter_part2.txt"), "r" + ) as f: + text = f.read() + return text + + @pytest.fixture(scope="module") def setup_neo4j_for_retrieval(driver: Driver) -> None: vector_index_name = "vector-index-name" diff --git a/tests/e2e/data/config_files/pipeline_config.json b/tests/e2e/data/config_files/pipeline_config.json new file mode 100644 index 00000000..fe36624d --- /dev/null +++ b/tests/e2e/data/config_files/pipeline_config.json @@ -0,0 +1,72 @@ +{ + "version_": "1", + "template_": "none", + "name": "", + "neo4j_config": { + "params_": { + "uri": { + "resolver_": "ENV", + "var_": "NEO4J_URI" + }, + "user": { + "resolver_": "ENV", + "var_": "NEO4J_USER" + }, + "password": { + "resolver_": "ENV", + "var_": "NEO4J_PASSWORD" + } + } + }, + "extras": { + "database": "neo4j" + }, + "component_config": { + "splitter": { + "class_": "text_splitters.fixed_size_splitter.FixedSizeSplitter", + "params_": { + "chunk_size": 100, + "chunk_overlap": 10 + } + }, + "builder": { + "class_": "lexical_graph.LexicalGraphBuilder", + "params_": { + "config": { + "chunk_node_label": "TextPart" + } + } + }, + "writer": { + "name_": "writer", + "class_": "kg_writer.Neo4jWriter", + "params_": { + "driver": { + "resolver_": "CONFIG_KEY", + "key_": "neo4j_config.default" + }, + "neo4j_database": { + "resolver_": "CONFIG_KEY", + "key_": "extras.database" + } + } + } + }, + "connection_config": [ + { + "start": "splitter", + "end": "builder", + "input_config": { + "text_chunks": "splitter" + } + }, + { + "start": "builder", + "end": "writer", + "input_config": { + "graph": "builder.graph", + "lexical_graph_config": "builder.config" + } + } + ] +} diff --git a/tests/e2e/data/config_files/pipeline_config.yaml b/tests/e2e/data/config_files/pipeline_config.yaml new file mode 100644 index 00000000..87ac905e --- /dev/null +++ b/tests/e2e/data/config_files/pipeline_config.yaml @@ -0,0 +1,45 @@ +version_: "1" +template_: none +neo4j_config: + params_: + uri: + resolver_: ENV + var_: NEO4J_URI + user: + resolver_: ENV + var_: NEO4J_USER + password: + resolver_: ENV + var_: NEO4J_PASSWORD +extras: + database: neo4j +component_config: + splitter: + class_: text_splitters.fixed_size_splitter.FixedSizeSplitter + params_: + chunk_size: 100 + chunk_overlap: 10 + builder: + class_: lexical_graph.LexicalGraphBuilder + params_: + config: + chunk_node_label: TextPart + writer: + class_: kg_writer.Neo4jWriter + params_: + driver: + resolver_: CONFIG_KEY + key_: neo4j_config.default + neo4j_database: + resolver_: CONFIG_KEY + key_: extras.database +connection_config: + - start: splitter + end: builder + input_config: + text_chunks: splitter + - start: builder + end: writer + input_config: + graph: builder.graph + lexical_graph_config: builder.config diff --git a/tests/e2e/data/config_files/simple_kg_pipeline_config.json b/tests/e2e/data/config_files/simple_kg_pipeline_config.json new file mode 100644 index 00000000..c2d629fb --- /dev/null +++ b/tests/e2e/data/config_files/simple_kg_pipeline_config.json @@ -0,0 +1,64 @@ +{ + "version_": "1", + "template_": "SimpleKGPipeline", + "neo4j_config": { + "params_": { + "uri": { + "resolver_": "ENV", + "var_": "NEO4J_URI" + }, + "user": { + "resolver_": "ENV", + "var_": "NEO4J_USER" + }, + "password": { + "resolver_": "ENV", + "var_": "NEO4J_PASSWORD" + } + } + }, + "llm_config": { + "class_": "OpenAILLM", + "params_": { + "api_key": { + "resolver_": "ENV", + "var_": "OPENAI_API_KEY" + }, + "model_name": "gpt-4o", + "model_params": { + "temperature": 0, + "max_tokens": 2000, + "response_format": {"type": "json_object"} + } + } + }, + "embedder_config": { + "class_": "OpenAIEmbeddings", + "params_": { + "api_key": { + "resolver_": "ENV", + "var_": "OPENAI_API_KEY" + } + } + }, + "from_pdf": true, + "entities": [ + "Person", + "Organization", + "Horcrux", + "Location" + ], + "relations": [ + "SITUATED_AT", + "INTERACTS", + "OWNS", + "LED_BY" + ], + "potential_schema": [ + ["Person", "SITUATED_AT", "Location"], + ["Person", "INTERACTS", "Person"], + ["Person", "OWNS", "Horcrux"], + ["Organization", "LED_BY", "Person"] + ], + "perform_entity_resolution": true +} diff --git a/tests/e2e/data/config_files/simple_kg_pipeline_config.yaml b/tests/e2e/data/config_files/simple_kg_pipeline_config.yaml new file mode 100644 index 00000000..abcf9632 --- /dev/null +++ b/tests/e2e/data/config_files/simple_kg_pipeline_config.yaml @@ -0,0 +1,50 @@ +version_: "1" +template_: SimpleKGPipeline +neo4j_config: + params_: + uri: + resolver_: ENV + var_: NEO4J_URI + user: + resolver_: ENV + var_: NEO4J_USER + password: + resolver_: ENV + var_: NEO4J_PASSWORD +llm_config: + class_: OpenAILLM + params_: + api_key: + resolver_: ENV + var_: OPENAI_API_KEY + model_name: gpt-4o + model_params: + temperature: 0 + max_tokens: 2000 + response_format: + type: json_object +embedder_config: + class_: OpenAIEmbeddings + params_: + api_key: + resolver_: ENV + var_: OPENAI_API_KEY +from_pdf: true +entities: + - Person + - Organization + - Location + - Horcrux +relations: + - SITUATED_AT + - INTERACTS + - OWNS + - LED_BY +potential_schema: + - ["Person", "SITUATED_AT", "Location"] + - ["Person", "INTERACTS", "Person"] + - ["Person", "OWNS", "Horcrux"] + - ["Organization", "LED_BY", "Person"] +text_splitter: + class_: text_splitters.fixed_size_splitter.FixedSizeSplitter +perform_entity_resolution: true diff --git a/tests/e2e/data/documents/harry_potter.pdf b/tests/e2e/data/documents/harry_potter.pdf new file mode 100644 index 00000000..49f0f6bd Binary files /dev/null and b/tests/e2e/data/documents/harry_potter.pdf differ diff --git a/tests/e2e/data/harry_potter.txt b/tests/e2e/data/documents/harry_potter.txt similarity index 100% rename from tests/e2e/data/harry_potter.txt rename to tests/e2e/data/documents/harry_potter.txt diff --git a/tests/e2e/experimental/__init__.py b/tests/e2e/experimental/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/e2e/experimental/pipeline/__init__.py b/tests/e2e/experimental/pipeline/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/e2e/experimental/pipeline/config/__init__.py b/tests/e2e/experimental/pipeline/config/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/e2e/experimental/pipeline/config/test_pipeline_runner_e2e.py b/tests/e2e/experimental/pipeline/config/test_pipeline_runner_e2e.py new file mode 100644 index 00000000..9122a935 --- /dev/null +++ b/tests/e2e/experimental/pipeline/config/test_pipeline_runner_e2e.py @@ -0,0 +1,208 @@ +import os +from typing import Any +from unittest.mock import AsyncMock, Mock, patch + +import neo4j +import pytest +from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner +from neo4j_graphrag.experimental.pipeline.pipeline import PipelineResult +from neo4j_graphrag.llm import LLMResponse + + +@pytest.fixture(scope="function", autouse=True) +def clear_db(driver: neo4j.Driver) -> Any: + driver.execute_query("MATCH (n) DETACH DELETE n") + yield + + +@pytest.mark.asyncio +async def test_pipeline_from_json_config(harry_potter_text: str, driver: Mock) -> None: + os.environ["NEO4J_URI"] = "neo4j://localhost:7687" + os.environ["NEO4J_USER"] = "neo4j" + os.environ["NEO4J_PASSWORD"] = "password" + + runner = PipelineRunner.from_config_file( + "tests/e2e/data/config_files/pipeline_config.json" + ) + res = await runner.run({"splitter": {"text": harry_potter_text}}) + assert isinstance(res, PipelineResult) + assert res.result["writer"]["metadata"] == { + "node_count": 11, + "relationship_count": 10, + } + nodes = driver.execute_query("MATCH (n) RETURN n") + assert len(nodes.records) == 11 + + +@pytest.mark.asyncio +async def test_pipeline_from_yaml_config(harry_potter_text: str, driver: Mock) -> None: + os.environ["NEO4J_URI"] = "neo4j://localhost:7687" + os.environ["NEO4J_USER"] = "neo4j" + os.environ["NEO4J_PASSWORD"] = "password" + + runner = PipelineRunner.from_config_file( + "tests/e2e/data/config_files/pipeline_config.yaml" + ) + res = await runner.run({"splitter": {"text": harry_potter_text}}) + assert isinstance(res, PipelineResult) + assert res.result["writer"]["metadata"] == { + "node_count": 11, + "relationship_count": 10, + } + + nodes = driver.execute_query("MATCH (n) RETURN n") + assert len(nodes.records) == 11 + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.runner.SimpleKGPipelineConfig.get_default_embedder" +) +@patch( + "neo4j_graphrag.experimental.pipeline.config.runner.SimpleKGPipelineConfig.get_default_llm" +) +@pytest.mark.asyncio +async def test_simple_kg_pipeline_from_json_config( + mock_llm: Mock, mock_embedder: Mock, harry_potter_text: str, driver: Mock +) -> None: + mock_llm.return_value.ainvoke = AsyncMock( + side_effect=[ + LLMResponse( + content="""{ + "nodes": [ + { + "id": "0", + "label": "Person", + "properties": { + "name": "Harry Potter" + } + }, + { + "id": "1", + "label": "Person", + "properties": { + "name": "Alastor Mad-Eye Moody" + } + }, + { + "id": "2", + "label": "Organization", + "properties": { + "name": "The Order of the Phoenix" + } + } + ], + "relationships": [ + { + "type": "KNOWS", + "start_node_id": "0", + "end_node_id": "1" + }, + { + "type": "LED_BY", + "start_node_id": "2", + "end_node_id": "1" + } + ] + }""" + ), + ] + ) + mock_embedder.return_value.embed_query.side_effect = [ + [1.0, 2.0], + ] + + os.environ["NEO4J_URI"] = "neo4j://localhost:7687" + os.environ["NEO4J_USER"] = "neo4j" + os.environ["NEO4J_PASSWORD"] = "password" + os.environ["OPENAI_API_KEY"] = "sk-my-secret-key" + + runner = PipelineRunner.from_config_file( + "tests/e2e/data/config_files/simple_kg_pipeline_config.json" + ) + res = await runner.run({"file_path": "tests/e2e/data/documents/harry_potter.pdf"}) + assert isinstance(res, PipelineResult) + # print(await runner.pipeline.store.get_result_for_component(res.run_id, "splitter")) + assert res.result["resolver"] == { + "number_of_nodes_to_resolve": 3, + "number_of_created_nodes": 3, + } + nodes = driver.execute_query("MATCH (n) RETURN n") + # 1 chunk + 1 document + 3 nodes + assert len(nodes.records) == 5 + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.runner.SimpleKGPipelineConfig.get_default_embedder" +) +@patch( + "neo4j_graphrag.experimental.pipeline.config.runner.SimpleKGPipelineConfig.get_default_llm" +) +@pytest.mark.asyncio +async def test_simple_kg_pipeline_from_yaml_config( + mock_llm: Mock, mock_embedder: Mock, harry_potter_text: str, driver: Mock +) -> None: + mock_llm.return_value.ainvoke = AsyncMock( + side_effect=[ + LLMResponse( + content="""{ + "nodes": [ + { + "id": "0", + "label": "Person", + "properties": { + "name": "Harry Potter" + } + }, + { + "id": "1", + "label": "Person", + "properties": { + "name": "Alastor Mad-Eye Moody" + } + }, + { + "id": "2", + "label": "Organization", + "properties": { + "name": "The Order of the Phoenix" + } + } + ], + "relationships": [ + { + "type": "KNOWS", + "start_node_id": "0", + "end_node_id": "1" + }, + { + "type": "LED_BY", + "start_node_id": "2", + "end_node_id": "1" + } + ] + }""" + ), + ] + ) + mock_embedder.return_value.embed_query.side_effect = [ + [1.0, 2.0], + ] + + os.environ["NEO4J_URI"] = "neo4j://localhost:7687" + os.environ["NEO4J_USER"] = "neo4j" + os.environ["NEO4J_PASSWORD"] = "password" + os.environ["OPENAI_API_KEY"] = "sk-my-secret-key" + + runner = PipelineRunner.from_config_file( + "tests/e2e/data/config_files/simple_kg_pipeline_config.yaml" + ) + res = await runner.run({"file_path": "tests/e2e/data/documents/harry_potter.pdf"}) + assert isinstance(res, PipelineResult) + # print(await runner.pipeline.store.get_result_for_component(res.run_id, "splitter")) + assert res.result["resolver"] == { + "number_of_nodes_to_resolve": 3, + "number_of_created_nodes": 3, + } + nodes = driver.execute_query("MATCH (n) RETURN n") + # 1 chunk + 1 document + 3 nodes + assert len(nodes.records) == 5 diff --git a/tests/e2e/test_kg_builder_pipeline_e2e.py b/tests/e2e/test_kg_builder_pipeline_e2e.py index 1713098b..bf74470c 100644 --- a/tests/e2e/test_kg_builder_pipeline_e2e.py +++ b/tests/e2e/test_kg_builder_pipeline_e2e.py @@ -124,31 +124,6 @@ def kg_builder_pipeline( return pipe -@pytest.fixture -def harry_potter_text() -> str: - with open(os.path.join(BASE_DIR, "data/harry_potter.txt"), "r") as f: - text = f.read() - return text - - -@pytest.fixture -def harry_potter_text_part1() -> str: - with open( - os.path.join(BASE_DIR, "data/documents/harry_potter_part1.txt"), "r" - ) as f: - text = f.read() - return text - - -@pytest.fixture -def harry_potter_text_part2() -> str: - with open( - os.path.join(BASE_DIR, "data/documents/harry_potter_part2.txt"), "r" - ) as f: - text = f.read() - return text - - @pytest.mark.asyncio @pytest.mark.usefixtures("setup_neo4j_for_kg_construction") async def test_pipeline_builder_happy_path( diff --git a/tests/e2e/test_simplekgpipeline_e2e.py b/tests/e2e/test_simplekgpipeline_e2e.py index def867f5..4d3059c7 100644 --- a/tests/e2e/test_simplekgpipeline_e2e.py +++ b/tests/e2e/test_simplekgpipeline_e2e.py @@ -14,36 +14,13 @@ # limitations under the License. from __future__ import annotations -import os from unittest.mock import MagicMock import neo4j import pytest -from neo4j_graphrag.embeddings.base import Embedder from neo4j_graphrag.experimental.components.types import LexicalGraphConfig from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline -from neo4j_graphrag.llm import LLMInterface, LLMResponse - -BASE_DIR = os.path.dirname(os.path.abspath(__file__)) - - -@pytest.fixture -def llm() -> LLMInterface: - llm = MagicMock(spec=LLMInterface) - return llm - - -@pytest.fixture -def embedder() -> Embedder: - embedder = MagicMock(spec=Embedder) - return embedder - - -@pytest.fixture -def harry_potter_text() -> str: - with open(os.path.join(BASE_DIR, "data/harry_potter.txt"), "r") as f: - text = f.read() - return text +from neo4j_graphrag.llm import LLMResponse @pytest.mark.asyncio diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index 829cad23..5069ab6e 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -19,6 +19,7 @@ import neo4j import pytest from neo4j_graphrag.embeddings.base import Embedder +from neo4j_graphrag.experimental.pipeline import Component from neo4j_graphrag.llm import LLMInterface from neo4j_graphrag.retrievers import ( HybridRetriever, @@ -98,3 +99,8 @@ def format_function(record: neo4j.Record) -> RetrieverResultItem: ) return format_function + + +@pytest.fixture(scope="function") +def component() -> MagicMock: + return MagicMock(spec=Component) diff --git a/tests/unit/embeddings/test_ollama_embedder.py b/tests/unit/embeddings/test_ollama_embedder.py new file mode 100644 index 00000000..dded2b87 --- /dev/null +++ b/tests/unit/embeddings/test_ollama_embedder.py @@ -0,0 +1,35 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import MagicMock, Mock, patch + +import pytest +from neo4j_graphrag.embeddings.ollama import OllamaEmbeddings + + +@patch("builtins.__import__", side_effect=ImportError) +def test_ollama_embedder_missing_dependency(mock_import: Mock) -> None: + with pytest.raises(ImportError): + OllamaEmbeddings(model="test") + + +@patch("builtins.__import__") +def test_ollama_embedder_happy_path(mock_import: Mock) -> None: + mock_import.return_value.Client.return_value.embed.return_value = MagicMock( + embeddings=[1.0, 2.0], + ) + embedder = OllamaEmbeddings(model="test") + res = embedder.embed_query("my text") + assert isinstance(res, list) + assert res == [1.0, 2.0] diff --git a/tests/unit/experimental/components/test_entity_relation_extractor.py b/tests/unit/experimental/components/test_entity_relation_extractor.py index 1ec56153..29ba6568 100644 --- a/tests/unit/experimental/components/test_entity_relation_extractor.py +++ b/tests/unit/experimental/components/test_entity_relation_extractor.py @@ -15,7 +15,7 @@ from __future__ import annotations import json -from unittest.mock import MagicMock +from unittest.mock import MagicMock, patch import pytest from neo4j_graphrag.exceptions import LLMGenerationError @@ -31,6 +31,7 @@ TextChunk, TextChunks, ) +from neo4j_graphrag.experimental.pipeline.exceptions import InvalidJSONError from neo4j_graphrag.llm import LLMInterface, LLMResponse @@ -144,16 +145,17 @@ async def test_extractor_llm_ainvoke_failed() -> None: @pytest.mark.asyncio -async def test_extractor_llm_badly_formatted_json() -> None: +async def test_extractor_llm_unfixable_json() -> None: llm = MagicMock(spec=LLMInterface) llm.ainvoke.return_value = LLMResponse( - content='{"nodes": [{"id": "0", "label": "Person", "properties": {}}], "relationships": [}' + content='{"nodes": [{"id": "0", "label": "Person", "properties": {}}], "relationships": }' ) extractor = LLMEntityRelationExtractor( llm=llm, ) chunks = TextChunks(chunks=[TextChunk(text="some text", index=0)]) + with pytest.raises(LLMGenerationError): await extractor.run(chunks=chunks) @@ -177,7 +179,7 @@ async def test_extractor_llm_invalid_json() -> None: @pytest.mark.asyncio -async def test_extractor_llm_badly_formatted_json_do_not_raise() -> None: +async def test_extractor_llm_badly_formatted_json_gets_fixed() -> None: llm = MagicMock(spec=LLMInterface) llm.ainvoke.return_value = LLMResponse( content='{"nodes": [{"id": "0", "label": "Person", "properties": {}}], "relationships": [}' @@ -190,7 +192,11 @@ async def test_extractor_llm_badly_formatted_json_do_not_raise() -> None: ) chunks = TextChunks(chunks=[TextChunk(text="some text", index=0)]) res = await extractor.run(chunks=chunks) - assert res.nodes == [] + + assert len(res.nodes) == 1 + assert res.nodes[0].label == "Person" + assert res.nodes[0].properties == {"chunk_index": 0} + assert res.nodes[0].embedding_properties is None assert res.relationships == [] @@ -205,6 +211,14 @@ async def test_extractor_custom_prompt() -> None: llm.ainvoke.assert_called_once_with("this is my prompt") +def test_fix_invalid_json_empty_result() -> None: + json_string = "invalid json" + + with patch("json_repair.repair_json", return_value=""): + with pytest.raises(InvalidJSONError): + fix_invalid_json(json_string) + + def test_fix_unquoted_keys() -> None: json_string = '{name: "John", age: "30"}' expected_result = '{"name": "John", "age": "30"}' diff --git a/tests/unit/experimental/pipeline/config/__init__.py b/tests/unit/experimental/pipeline/config/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit/experimental/pipeline/config/template_pipeline/__init__.py b/tests/unit/experimental/pipeline/config/template_pipeline/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit/experimental/pipeline/config/template_pipeline/test_base.py b/tests/unit/experimental/pipeline/config/template_pipeline/test_base.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit/experimental/pipeline/config/template_pipeline/test_simple_kg_builder.py b/tests/unit/experimental/pipeline/config/template_pipeline/test_simple_kg_builder.py new file mode 100644 index 00000000..e8d12095 --- /dev/null +++ b/tests/unit/experimental/pipeline/config/template_pipeline/test_simple_kg_builder.py @@ -0,0 +1,301 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import Mock, patch + +import neo4j +import pytest +from neo4j_graphrag.embeddings import Embedder +from neo4j_graphrag.experimental.components.embedder import TextChunkEmbedder +from neo4j_graphrag.experimental.components.entity_relation_extractor import ( + LLMEntityRelationExtractor, + OnError, +) +from neo4j_graphrag.experimental.components.kg_writer import Neo4jWriter +from neo4j_graphrag.experimental.components.pdf_loader import PdfLoader +from neo4j_graphrag.experimental.components.schema import ( + SchemaBuilder, + SchemaEntity, + SchemaRelation, +) +from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import ( + FixedSizeSplitter, +) +from neo4j_graphrag.experimental.pipeline.config.object_config import ComponentConfig +from neo4j_graphrag.experimental.pipeline.config.template_pipeline import ( + SimpleKGPipelineConfig, +) +from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError +from neo4j_graphrag.generation.prompts import ERExtractionTemplate +from neo4j_graphrag.llm import LLMInterface + + +def test_simple_kg_pipeline_config_pdf_loader_from_pdf_is_false() -> None: + config = SimpleKGPipelineConfig(from_pdf=False) + assert config._get_pdf_loader() is None + + +def test_simple_kg_pipeline_config_pdf_loader_from_pdf_is_true() -> None: + config = SimpleKGPipelineConfig(from_pdf=True) + assert isinstance(config._get_pdf_loader(), PdfLoader) + + +def test_simple_kg_pipeline_config_pdf_loader_from_pdf_is_true_class_overwrite() -> ( + None +): + my_pdf_loader = PdfLoader() + config = SimpleKGPipelineConfig(from_pdf=True, pdf_loader=my_pdf_loader) # type: ignore + assert config._get_pdf_loader() == my_pdf_loader + + +def test_simple_kg_pipeline_config_pdf_loader_class_overwrite_but_from_pdf_is_false() -> ( + None +): + my_pdf_loader = PdfLoader() + config = SimpleKGPipelineConfig(from_pdf=False, pdf_loader=my_pdf_loader) # type: ignore + assert config._get_pdf_loader() is None + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.ComponentType.parse") +def test_simple_kg_pipeline_config_pdf_loader_from_pdf_is_true_class_overwrite_from_config( + mock_component_parse: Mock, +) -> None: + my_pdf_loader_config = ComponentConfig( + class_="", + ) + my_pdf_loader = PdfLoader() + mock_component_parse.return_value = my_pdf_loader + config = SimpleKGPipelineConfig( + from_pdf=True, + pdf_loader=my_pdf_loader_config, # type: ignore + ) + assert config._get_pdf_loader() == my_pdf_loader + + +def test_simple_kg_pipeline_config_text_splitter() -> None: + config = SimpleKGPipelineConfig() + assert isinstance(config._get_splitter(), FixedSizeSplitter) + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.ComponentType.parse") +def test_simple_kg_pipeline_config_text_splitter_overwrite( + mock_component_parse: Mock, +) -> None: + my_text_splitter_config = ComponentConfig( + class_="", + ) + my_text_splitter = FixedSizeSplitter() + mock_component_parse.return_value = my_text_splitter + config = SimpleKGPipelineConfig( + text_splitter=my_text_splitter_config, # type: ignore + ) + assert config._get_splitter() == my_text_splitter + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_kg_builder.SimpleKGPipelineConfig.get_default_embedder" +) +def test_simple_kg_pipeline_config_chunk_embedder( + mock_embedder: Mock, embedder: Embedder +) -> None: + mock_embedder.return_value = embedder + config = SimpleKGPipelineConfig() + chunk_embedder = config._get_chunk_embedder() + assert isinstance(chunk_embedder, TextChunkEmbedder) + assert chunk_embedder._embedder == embedder + + +def test_simple_kg_pipeline_config_schema() -> None: + config = SimpleKGPipelineConfig() + assert isinstance(config._get_schema(), SchemaBuilder) + + +def test_simple_kg_pipeline_config_schema_run_params() -> None: + config = SimpleKGPipelineConfig( + entities=["Person"], + relations=["KNOWS"], + potential_schema=[("Person", "KNOWS", "Person")], + ) + assert config._get_run_params_for_schema() == { + "entities": [SchemaEntity(label="Person")], + "relations": [SchemaRelation(label="KNOWS")], + "potential_schema": [ + ("Person", "KNOWS", "Person"), + ], + } + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_kg_builder.SimpleKGPipelineConfig.get_default_llm" +) +def test_simple_kg_pipeline_config_extractor(mock_llm: Mock, llm: LLMInterface) -> None: + mock_llm.return_value = llm + config = SimpleKGPipelineConfig( + on_error="IGNORE", # type: ignore + prompt_template=ERExtractionTemplate(template="my template {text}"), + ) + extractor = config._get_extractor() + assert isinstance(extractor, LLMEntityRelationExtractor) + assert extractor.llm == llm + assert extractor.on_error == OnError.IGNORE + assert extractor.prompt_template.template == "my template {text}" + + +@patch( + "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", + return_value=(5, 23, 0), +) +@patch( + "neo4j_graphrag.experimental.pipeline.config.template_pipeline.simple_kg_builder.SimpleKGPipelineConfig.get_default_neo4j_driver" +) +def test_simple_kg_pipeline_config_writer( + mock_driver: Mock, + _: Mock, + driver: neo4j.Driver, +) -> None: + mock_driver.return_value = driver + config = SimpleKGPipelineConfig( + neo4j_database="my_db", + ) + writer = config._get_writer() + assert isinstance(writer, Neo4jWriter) + assert writer.driver == driver + assert writer.neo4j_database == "my_db" + + +@patch( + "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", + return_value=(5, 23, 0), +) +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.ComponentType.parse") +def test_simple_kg_pipeline_config_writer_overwrite( + mock_component_parse: Mock, + _: Mock, + driver: neo4j.Driver, +) -> None: + my_writer_config = ComponentConfig( + class_="", + ) + my_writer = Neo4jWriter(driver, neo4j_database="my_db") + mock_component_parse.return_value = my_writer + config = SimpleKGPipelineConfig( + kg_writer=my_writer_config, # type: ignore + neo4j_database="my_other_db", + ) + writer: Neo4jWriter = config._get_writer() # type: ignore + assert writer == my_writer + # database not changed: + assert writer.neo4j_database == "my_db" + + +def test_simple_kg_pipeline_config_connections_from_pdf() -> None: + config = SimpleKGPipelineConfig( + from_pdf=True, + perform_entity_resolution=False, + ) + connections = config._get_connections() + assert len(connections) == 5 + expected_connections = [ + ("pdf_loader", "splitter"), + ("schema", "extractor"), + ("splitter", "chunk_embedder"), + ("chunk_embedder", "extractor"), + ("extractor", "writer"), + ] + for actual, expected in zip(connections, expected_connections): + assert (actual.start, actual.end) == expected + + +def test_simple_kg_pipeline_config_connections_from_text() -> None: + config = SimpleKGPipelineConfig( + from_pdf=False, + perform_entity_resolution=False, + ) + connections = config._get_connections() + assert len(connections) == 4 + expected_connections = [ + ("schema", "extractor"), + ("splitter", "chunk_embedder"), + ("chunk_embedder", "extractor"), + ("extractor", "writer"), + ] + for actual, expected in zip(connections, expected_connections): + assert (actual.start, actual.end) == expected + + +def test_simple_kg_pipeline_config_connections_with_er() -> None: + config = SimpleKGPipelineConfig( + from_pdf=True, + perform_entity_resolution=True, + ) + connections = config._get_connections() + assert len(connections) == 6 + expected_connections = [ + ("pdf_loader", "splitter"), + ("schema", "extractor"), + ("splitter", "chunk_embedder"), + ("chunk_embedder", "extractor"), + ("extractor", "writer"), + ("writer", "resolver"), + ] + for actual, expected in zip(connections, expected_connections): + assert (actual.start, actual.end) == expected + + +def test_simple_kg_pipeline_config_run_params_from_pdf_file_path() -> None: + config = SimpleKGPipelineConfig(from_pdf=True) + assert config.get_run_params({"file_path": "my_file"}) == { + "pdf_loader": {"filepath": "my_file"} + } + + +def test_simple_kg_pipeline_config_run_params_from_text_text() -> None: + config = SimpleKGPipelineConfig(from_pdf=False) + assert config.get_run_params({"text": "my text"}) == { + "splitter": {"text": "my text"} + } + + +def test_simple_kg_pipeline_config_run_params_from_pdf_text() -> None: + config = SimpleKGPipelineConfig(from_pdf=True) + with pytest.raises(PipelineDefinitionError) as excinfo: + config.get_run_params({"text": "my text"}) + assert "Expected 'file_path' argument when 'from_pdf' is True" in str(excinfo) + + +def test_simple_kg_pipeline_config_run_params_from_text_file_path() -> None: + config = SimpleKGPipelineConfig(from_pdf=False) + with pytest.raises(PipelineDefinitionError) as excinfo: + config.get_run_params({"file_path": "my file"}) + assert "Expected 'text' argument when 'from_pdf' is False" in str(excinfo) + + +def test_simple_kg_pipeline_config_run_params_no_file_no_text() -> None: + config = SimpleKGPipelineConfig(from_pdf=False) + with pytest.raises(PipelineDefinitionError) as excinfo: + config.get_run_params({}) + assert ( + "Use either 'text' (when from_pdf=False) or 'file_path' (when from_pdf=True) argument." + in str(excinfo) + ) + + +def test_simple_kg_pipeline_config_run_params_both_file_and_text() -> None: + config = SimpleKGPipelineConfig(from_pdf=False) + with pytest.raises(PipelineDefinitionError) as excinfo: + config.get_run_params({"text": "my text", "file_path": "my file"}) + assert ( + "Use either 'text' (when from_pdf=False) or 'file_path' (when from_pdf=True) argument." + in str(excinfo) + ) diff --git a/tests/unit/experimental/pipeline/config/test_base.py b/tests/unit/experimental/pipeline/config/test_base.py new file mode 100644 index 00000000..20332a79 --- /dev/null +++ b/tests/unit/experimental/pipeline/config/test_base.py @@ -0,0 +1,37 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import patch + +from neo4j_graphrag.experimental.pipeline.config.base import AbstractConfig +from neo4j_graphrag.experimental.pipeline.config.param_resolver import ( + ParamToResolveConfig, +) + + +def test_resolve_param_with_param_to_resolve_object() -> None: + c = AbstractConfig() + with patch( + "neo4j_graphrag.experimental.pipeline.config.param_resolver.ParamToResolveConfig", + spec=ParamToResolveConfig, + ) as mock_param_class: + mock_param = mock_param_class.return_value + mock_param.resolve.return_value = 1 + assert c.resolve_param(mock_param) == 1 + mock_param.resolve.assert_called_once_with({}) + + +def test_resolve_param_with_other_object() -> None: + c = AbstractConfig() + assert c.resolve_param("value") == "value" diff --git a/tests/unit/experimental/pipeline/config/test_object_config.py b/tests/unit/experimental/pipeline/config/test_object_config.py new file mode 100644 index 00000000..baa3c767 --- /dev/null +++ b/tests/unit/experimental/pipeline/config/test_object_config.py @@ -0,0 +1,164 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import patch + +import neo4j +import pytest +from neo4j_graphrag.embeddings import Embedder, OpenAIEmbeddings +from neo4j_graphrag.experimental.pipeline import Pipeline +from neo4j_graphrag.experimental.pipeline.config.object_config import ( + EmbedderConfig, + EmbedderType, + LLMConfig, + LLMType, + Neo4jDriverConfig, + Neo4jDriverType, + ObjectConfig, +) +from neo4j_graphrag.llm import LLMInterface, OpenAILLM + + +def test_get_class_no_optional_module() -> None: + c: ObjectConfig[object] = ObjectConfig() + klass = c._get_class("neo4j_graphrag.experimental.pipeline.Pipeline") + assert klass == Pipeline + + +def test_get_class_optional_module() -> None: + c: ObjectConfig[object] = ObjectConfig() + klass = c._get_class( + "Pipeline", optional_module="neo4j_graphrag.experimental.pipeline" + ) + assert klass == Pipeline + + +def test_get_class_path_and_optional_module() -> None: + c: ObjectConfig[object] = ObjectConfig() + klass = c._get_class( + "pipeline.Pipeline", optional_module="neo4j_graphrag.experimental" + ) + assert klass == Pipeline + + +def test_get_class_wrong_path() -> None: + c: ObjectConfig[object] = ObjectConfig() + with pytest.raises(ValueError): + c._get_class("MyClass") + + +def test_neo4j_driver_config() -> None: + config = Neo4jDriverConfig.model_validate( + { + "params_": { + "uri": "bolt://", + "user": "a user", + "password": "a password", + } + } + ) + assert config.class_ == "not used" + assert config.params_ == { + "uri": "bolt://", + "user": "a user", + "password": "a password", + } + with patch( + "neo4j_graphrag.experimental.pipeline.config.object_config.neo4j.GraphDatabase.driver" + ) as driver_mock: + driver_mock.return_value = "a driver" + d = config.parse() + driver_mock.assert_called_once_with("bolt://", auth=("a user", "a password")) + assert d == "a driver" # type: ignore + + +def test_neo4j_driver_type_with_driver(driver: neo4j.Driver) -> None: + driver_type = Neo4jDriverType(driver) + assert driver_type.parse() == driver + + +def test_neo4j_driver_type_with_config() -> None: + driver_type = Neo4jDriverType( + Neo4jDriverConfig( + params_={ + "uri": "bolt://", + "user": "", + "password": "", + } + ) + ) + driver = driver_type.parse() + assert isinstance(driver, neo4j.Driver) + + +def test_llm_config() -> None: + config = LLMConfig.model_validate( + { + "class_": "OpenAILLM", + "params_": {"model_name": "gpt-4o", "api_key": "my-api-key"}, + } + ) + assert config.class_ == "OpenAILLM" + assert config.get_module() == "neo4j_graphrag.llm" + assert config.get_interface() == LLMInterface + assert config.params_ == {"model_name": "gpt-4o", "api_key": "my-api-key"} + d = config.parse() + assert isinstance(d, OpenAILLM) + + +def test_llm_type_with_driver(llm: LLMInterface) -> None: + llm_type = LLMType(llm) + assert llm_type.parse() == llm + + +def test_llm_type_with_config() -> None: + llm_type = LLMType( + LLMConfig( + class_="OpenAILLM", + params_={"model_name": "gpt-4o", "api_key": "my-api-key"}, + ) + ) + llm = llm_type.parse() + assert isinstance(llm, OpenAILLM) + + +def test_embedder_config() -> None: + config = EmbedderConfig.model_validate( + { + "class_": "OpenAIEmbeddings", + "params_": {"api_key": "my-api-key"}, + } + ) + assert config.class_ == "OpenAIEmbeddings" + assert config.get_module() == "neo4j_graphrag.embeddings" + assert config.get_interface() == Embedder + assert config.params_ == {"api_key": "my-api-key"} + d = config.parse() + assert isinstance(d, OpenAIEmbeddings) + + +def test_embedder_type_with_embedder(embedder: Embedder) -> None: + embedder_type = EmbedderType(embedder) + assert embedder_type.parse() == embedder + + +def test_embedder_type_with_config() -> None: + embedder_type = EmbedderType( + EmbedderConfig( + class_="OpenAIEmbeddings", + params_={"api_key": "my-api-key"}, + ) + ) + embedder = embedder_type.parse() + assert isinstance(embedder, OpenAIEmbeddings) diff --git a/tests/unit/experimental/pipeline/config/test_param_resolver.py b/tests/unit/experimental/pipeline/config/test_param_resolver.py new file mode 100644 index 00000000..efd4d7e9 --- /dev/null +++ b/tests/unit/experimental/pipeline/config/test_param_resolver.py @@ -0,0 +1,56 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +from unittest.mock import patch + +import pytest +from neo4j_graphrag.experimental.pipeline.config.param_resolver import ( + ParamFromEnvConfig, + ParamFromKeyConfig, +) + + +@patch.dict(os.environ, {"MY_KEY": "my_value"}, clear=True) +def test_env_param_config_happy_path() -> None: + resolver = ParamFromEnvConfig(var_="MY_KEY") + assert resolver.resolve({}) == "my_value" + + +@patch.dict(os.environ, {}, clear=True) +def test_env_param_config_missing_env_var() -> None: + resolver = ParamFromEnvConfig(var_="MY_KEY") + assert resolver.resolve({}) is None + + +def test_config_key_param_simple_key() -> None: + resolver = ParamFromKeyConfig(key_="my_key") + assert resolver.resolve({"my_key": "my_value"}) == "my_value" + + +def test_config_key_param_missing_key() -> None: + resolver = ParamFromKeyConfig(key_="my_key") + with pytest.raises(KeyError): + resolver.resolve({}) + + +def test_config_complex_key_param() -> None: + resolver = ParamFromKeyConfig(key_="my_key.my_sub_key") + assert resolver.resolve({"my_key": {"my_sub_key": "value"}}) == "value" + + +def test_config_complex_key_param_missing_subkey() -> None: + resolver = ParamFromKeyConfig(key_="my_key.my_sub_key") + with pytest.raises(KeyError): + resolver.resolve({"my_key": {}}) diff --git a/tests/unit/experimental/pipeline/config/test_pipeline_config.py b/tests/unit/experimental/pipeline/config/test_pipeline_config.py new file mode 100644 index 00000000..4de5874b --- /dev/null +++ b/tests/unit/experimental/pipeline/config/test_pipeline_config.py @@ -0,0 +1,378 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import Mock, patch + +import neo4j +from neo4j_graphrag.embeddings import Embedder +from neo4j_graphrag.experimental.pipeline import Component +from neo4j_graphrag.experimental.pipeline.config.object_config import ( + ComponentConfig, + ComponentType, + Neo4jDriverConfig, + Neo4jDriverType, +) +from neo4j_graphrag.experimental.pipeline.config.param_resolver import ( + ParamFromEnvConfig, + ParamFromKeyConfig, +) +from neo4j_graphrag.experimental.pipeline.config.pipeline_config import ( + AbstractPipelineConfig, +) +from neo4j_graphrag.experimental.pipeline.types import ComponentDefinition +from neo4j_graphrag.llm import LLMInterface + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverConfig.parse" +) +def test_abstract_pipeline_config_neo4j_config_is_a_dict_with_params_( + mock_neo4j_config: Mock, +) -> None: + mock_neo4j_config.return_value = "text" + config = AbstractPipelineConfig.model_validate( + { + "neo4j_config": { + "params_": { + "uri": "bolt://", + "user": "", + "password": "", + } + } + } + ) + assert isinstance(config.neo4j_config, dict) + assert "default" in config.neo4j_config + config.parse() + mock_neo4j_config.assert_called_once() + assert config._global_data["neo4j_config"]["default"] == "text" + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverConfig.parse" +) +def test_abstract_pipeline_config_neo4j_config_is_a_dict_with_names( + mock_neo4j_config: Mock, +) -> None: + mock_neo4j_config.return_value = "text" + config = AbstractPipelineConfig.model_validate( + { + "neo4j_config": { + "my_driver": { + "params_": { + "uri": "bolt://", + "user": "", + "password": "", + } + } + } + } + ) + assert isinstance(config.neo4j_config, dict) + assert "my_driver" in config.neo4j_config + config.parse() + mock_neo4j_config.assert_called_once() + assert config._global_data["neo4j_config"]["my_driver"] == "text" + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverConfig.parse" +) +def test_abstract_pipeline_config_neo4j_config_is_a_dict_with_driver( + mock_neo4j_config: Mock, driver: neo4j.Driver +) -> None: + config = AbstractPipelineConfig.model_validate( + { + "neo4j_config": { + "my_driver": driver, + } + } + ) + assert isinstance(config.neo4j_config, dict) + assert "my_driver" in config.neo4j_config + config.parse() + assert not mock_neo4j_config.called + assert config._global_data["neo4j_config"]["my_driver"] == driver + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverConfig.parse" +) +def test_abstract_pipeline_config_neo4j_config_is_a_driver( + mock_neo4j_config: Mock, driver: neo4j.Driver +) -> None: + config = AbstractPipelineConfig.model_validate( + { + "neo4j_config": driver, + } + ) + assert isinstance(config.neo4j_config, dict) + assert "default" in config.neo4j_config + config.parse() + assert not mock_neo4j_config.called + assert config._global_data["neo4j_config"]["default"] == driver + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.LLMConfig.parse") +def test_abstract_pipeline_config_llm_config_is_a_dict_with_params_( + mock_llm_config: Mock, +) -> None: + mock_llm_config.return_value = "text" + config = AbstractPipelineConfig.model_validate( + {"llm_config": {"class_": "OpenAILLM", "params_": {"model_name": "gpt-4o"}}} + ) + assert isinstance(config.llm_config, dict) + assert "default" in config.llm_config + config.parse() + mock_llm_config.assert_called_once() + assert config._global_data["llm_config"]["default"] == "text" + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.LLMConfig.parse") +def test_abstract_pipeline_config_llm_config_is_a_dict_with_names( + mock_llm_config: Mock, +) -> None: + mock_llm_config.return_value = "text" + config = AbstractPipelineConfig.model_validate( + { + "llm_config": { + "my_llm": {"class_": "OpenAILLM", "params_": {"model_name": "gpt-4o"}} + } + } + ) + assert isinstance(config.llm_config, dict) + assert "my_llm" in config.llm_config + config.parse() + mock_llm_config.assert_called_once() + assert config._global_data["llm_config"]["my_llm"] == "text" + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.LLMConfig.parse") +def test_abstract_pipeline_config_llm_config_is_a_dict_with_llm( + mock_llm_config: Mock, llm: LLMInterface +) -> None: + config = AbstractPipelineConfig.model_validate( + { + "llm_config": { + "my_llm": llm, + } + } + ) + assert isinstance(config.llm_config, dict) + assert "my_llm" in config.llm_config + config.parse() + assert not mock_llm_config.called + assert config._global_data["llm_config"]["my_llm"] == llm + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.LLMConfig.parse") +def test_abstract_pipeline_config_llm_config_is_a_llm( + mock_llm_config: Mock, llm: LLMInterface +) -> None: + config = AbstractPipelineConfig.model_validate( + { + "llm_config": llm, + } + ) + assert isinstance(config.llm_config, dict) + assert "default" in config.llm_config + config.parse() + assert not mock_llm_config.called + assert config._global_data["llm_config"]["default"] == llm + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.EmbedderConfig.parse") +def test_abstract_pipeline_config_embedder_config_is_a_dict_with_params_( + mock_embedder_config: Mock, +) -> None: + mock_embedder_config.return_value = "text" + config = AbstractPipelineConfig.model_validate( + {"embedder_config": {"class_": "OpenAIEmbeddings", "params_": {}}} + ) + assert isinstance(config.embedder_config, dict) + assert "default" in config.embedder_config + config.parse() + mock_embedder_config.assert_called_once() + assert config._global_data["embedder_config"]["default"] == "text" + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.EmbedderConfig.parse") +def test_abstract_pipeline_config_embedder_config_is_a_dict_with_names( + mock_embedder_config: Mock, +) -> None: + mock_embedder_config.return_value = "text" + config = AbstractPipelineConfig.model_validate( + { + "embedder_config": { + "my_embedder": {"class_": "OpenAIEmbeddings", "params_": {}} + } + } + ) + assert isinstance(config.embedder_config, dict) + assert "my_embedder" in config.embedder_config + config.parse() + mock_embedder_config.assert_called_once() + assert config._global_data["embedder_config"]["my_embedder"] == "text" + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.EmbedderConfig.parse") +def test_abstract_pipeline_config_embedder_config_is_a_dict_with_llm( + mock_embedder_config: Mock, embedder: Embedder +) -> None: + config = AbstractPipelineConfig.model_validate( + { + "embedder_config": { + "my_embedder": embedder, + } + } + ) + assert isinstance(config.embedder_config, dict) + assert "my_embedder" in config.embedder_config + config.parse() + assert not mock_embedder_config.called + assert config._global_data["embedder_config"]["my_embedder"] == embedder + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.EmbedderConfig.parse") +def test_abstract_pipeline_config_embedder_config_is_an_embedder( + mock_embedder_config: Mock, embedder: Embedder +) -> None: + config = AbstractPipelineConfig.model_validate( + { + "embedder_config": embedder, + } + ) + assert isinstance(config.embedder_config, dict) + assert "default" in config.embedder_config + config.parse() + assert not mock_embedder_config.called + assert config._global_data["embedder_config"]["default"] == embedder + + +def test_abstract_pipeline_config_parse_global_data_no_extras(driver: Mock) -> None: + config = AbstractPipelineConfig( + neo4j_config={"my_driver": Neo4jDriverType(driver)}, + ) + gd = config._parse_global_data() + assert gd == { + "extras": {}, + "neo4j_config": { + "my_driver": driver, + }, + "llm_config": {}, + "embedder_config": {}, + } + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.param_resolver.ParamFromEnvConfig.resolve" +) +def test_abstract_pipeline_config_parse_global_data_extras( + mock_param_resolver: Mock, +) -> None: + mock_param_resolver.return_value = "my value" + config = AbstractPipelineConfig( + extras={"my_extra_var": ParamFromEnvConfig(var_="some key")}, + ) + gd = config._parse_global_data() + assert gd == { + "extras": {"my_extra_var": "my value"}, + "neo4j_config": {}, + "llm_config": {}, + "embedder_config": {}, + } + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.param_resolver.ParamFromEnvConfig.resolve" +) +@patch( + "neo4j_graphrag.experimental.pipeline.config.object_config.Neo4jDriverType.parse" +) +def test_abstract_pipeline_config_parse_global_data_use_extras_in_other_config( + mock_neo4j_parser: Mock, + mock_param_resolver: Mock, +) -> None: + """Parser is able to read variables in the 'extras' section of config + to instantiate another object (neo4j.Driver in this test case) + """ + mock_param_resolver.side_effect = ["bolt://myhost", "myuser", "mypwd"] + mock_neo4j_parser.return_value = "my driver" + config = AbstractPipelineConfig( + extras={ + "my_extra_uri": ParamFromEnvConfig(var_="some key"), + "my_extra_user": ParamFromEnvConfig(var_="some key"), + "my_extra_pwd": ParamFromEnvConfig(var_="some key"), + }, + neo4j_config={ + "my_driver": Neo4jDriverType( + Neo4jDriverConfig( + params_=dict( + uri=ParamFromKeyConfig(key_="extras.my_extra_uri"), + user=ParamFromKeyConfig(key_="extras.my_extra_user"), + password=ParamFromKeyConfig(key_="extras.my_extra_pwd"), + ) + ) + ) + }, + ) + gd = config._parse_global_data() + expected_extras = { + "my_extra_uri": "bolt://myhost", + "my_extra_user": "myuser", + "my_extra_pwd": "mypwd", + } + assert gd["extras"] == expected_extras + assert gd["neo4j_config"] == {"my_driver": "my driver"} + mock_neo4j_parser.assert_called_once_with({"extras": expected_extras}) + + +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.ComponentType.parse") +def test_abstract_pipeline_config_resolve_component_definition_no_run_params( + mock_component_parse: Mock, + component: Component, +) -> None: + mock_component_parse.return_value = component + config = AbstractPipelineConfig() + component_type = ComponentType(component) + component_definition = config._resolve_component_definition("name", component_type) + assert isinstance(component_definition, ComponentDefinition) + mock_component_parse.assert_called_once_with({}) + assert component_definition.name == "name" + assert component_definition.component == component + assert component_definition.run_params == {} + + +@patch( + "neo4j_graphrag.experimental.pipeline.config.pipeline_config.AbstractPipelineConfig.resolve_params" +) +@patch("neo4j_graphrag.experimental.pipeline.config.object_config.ComponentType.parse") +def test_abstract_pipeline_config_resolve_component_definition_with_run_params( + mock_component_parse: Mock, + mock_resolve_params: Mock, + component: Component, +) -> None: + mock_component_parse.return_value = component + mock_resolve_params.return_value = {"param": "resolver param result"} + config = AbstractPipelineConfig() + component_type = ComponentType( + ComponentConfig(class_="", params_={}, run_params_={"param1": "value1"}) + ) + component_definition = config._resolve_component_definition("name", component_type) + assert isinstance(component_definition, ComponentDefinition) + mock_component_parse.assert_called_once_with({}) + assert component_definition.name == "name" + assert component_definition.component == component + assert component_definition.run_params == {"param": "resolver param result"} + mock_resolve_params.assert_called_once_with({"param1": "value1"}) diff --git a/tests/unit/experimental/pipeline/config/test_runner.py b/tests/unit/experimental/pipeline/config/test_runner.py new file mode 100644 index 00000000..327b5221 --- /dev/null +++ b/tests/unit/experimental/pipeline/config/test_runner.py @@ -0,0 +1,56 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import Mock, patch + +from neo4j_graphrag.experimental.pipeline import Pipeline +from neo4j_graphrag.experimental.pipeline.config.pipeline_config import PipelineConfig +from neo4j_graphrag.experimental.pipeline.config.runner import PipelineRunner +from neo4j_graphrag.experimental.pipeline.types import PipelineDefinition + + +@patch("neo4j_graphrag.experimental.pipeline.pipeline.Pipeline.from_definition") +def test_pipeline_runner_from_def_empty(mock_from_definition: Mock) -> None: + mock_from_definition.return_value = Pipeline() + runner = PipelineRunner( + pipeline_definition=PipelineDefinition(components=[], connections=[]) + ) + assert runner.config is None + assert runner.pipeline is not None + assert runner.pipeline._nodes == {} + assert runner.pipeline._edges == [] + assert runner.run_params == {} + mock_from_definition.assert_called_once() + + +def test_pipeline_runner_from_config() -> None: + config = PipelineConfig(component_config={}, connection_config=[]) + runner = PipelineRunner.from_config(config) + assert runner.config is not None + assert runner.pipeline is not None + assert runner.pipeline._nodes == {} + assert runner.pipeline._edges == [] + assert runner.run_params == {} + + +@patch("neo4j_graphrag.experimental.pipeline.config.runner.PipelineRunner.from_config") +@patch("neo4j_graphrag.experimental.pipeline.config.config_reader.ConfigReader.read") +def test_pipeline_runner_from_config_file( + mock_read: Mock, mock_from_config: Mock +) -> None: + mock_read.return_value = {"dict": "with data"} + PipelineRunner.from_config_file("file.yaml") + + mock_read.assert_called_once_with("file.yaml") + mock_from_config.assert_called_once_with({"dict": "with data"}, do_cleaning=True) diff --git a/tests/unit/experimental/pipeline/test_kg_builder.py b/tests/unit/experimental/pipeline/test_kg_builder.py index b1b29151..bddbac50 100644 --- a/tests/unit/experimental/pipeline/test_kg_builder.py +++ b/tests/unit/experimental/pipeline/test_kg_builder.py @@ -18,10 +18,8 @@ import neo4j import pytest from neo4j_graphrag.embeddings import Embedder -from neo4j_graphrag.experimental.components.entity_relation_extractor import OnError from neo4j_graphrag.experimental.components.schema import ( SchemaEntity, - SchemaProperty, SchemaRelation, ) from neo4j_graphrag.experimental.components.types import LexicalGraphConfig @@ -31,136 +29,6 @@ from neo4j_graphrag.llm.base import LLMInterface -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -@pytest.mark.asyncio -async def test_knowledge_graph_builder_init_with_text(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - from_pdf=False, - ) - - assert kg_builder.llm == llm - assert kg_builder.driver == driver - assert kg_builder.embedder == embedder - assert kg_builder.from_pdf is False - assert kg_builder.entities == [] - assert kg_builder.relations == [] - assert kg_builder.potential_schema == [] - assert "pdf_loader" not in kg_builder.pipeline - - text_input = "May thy knife chip and shatter." - - with patch.object( - kg_builder.pipeline, - "run", - return_value=PipelineResult(run_id="test_run", result=None), - ) as mock_run: - await kg_builder.run_async(text=text_input) - mock_run.assert_called_once() - pipe_inputs = mock_run.call_args[0][0] - assert pipe_inputs["splitter"]["text"] == text_input - - -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -@pytest.mark.asyncio -async def test_knowledge_graph_builder_init_with_file_path(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - from_pdf=True, - ) - - assert kg_builder.llm == llm - assert kg_builder.driver == driver - assert kg_builder.from_pdf is True - assert kg_builder.entities == [] - assert kg_builder.relations == [] - assert kg_builder.potential_schema == [] - assert "pdf_loader" in kg_builder.pipeline - - file_path = "path/to/test.pdf" - - with patch.object( - kg_builder.pipeline, - "run", - return_value=PipelineResult(run_id="test_run", result=None), - ) as mock_run: - await kg_builder.run_async(file_path=file_path) - mock_run.assert_called_once() - pipe_inputs = mock_run.call_args[0][0] - assert pipe_inputs["pdf_loader"]["filepath"] == file_path - - -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -@pytest.mark.asyncio -async def test_knowledge_graph_builder_run_with_both_inputs(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - from_pdf=True, - ) - - text_input = "May thy knife chip and shatter." - file_path = "path/to/test.pdf" - - with pytest.raises(PipelineDefinitionError) as exc_info: - await kg_builder.run_async(file_path=file_path, text=text_input) - - assert "Expected 'file_path' argument when 'from_pdf' is True." in str( - exc_info.value - ) or "Expected 'text' argument when 'from_pdf' is False." in str(exc_info.value) - - -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -@pytest.mark.asyncio -async def test_knowledge_graph_builder_run_with_no_inputs(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - from_pdf=True, - ) - - with pytest.raises(PipelineDefinitionError) as exc_info: - await kg_builder.run_async() - - assert "Expected 'file_path' argument when 'from_pdf' is True." in str( - exc_info.value - ) or "Expected 'text' argument when 'from_pdf' is False." in str(exc_info.value) - - @mock.patch( "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", return_value=(5, 23, 0), @@ -181,13 +49,13 @@ async def test_knowledge_graph_builder_document_info_with_file(_: Mock) -> None: file_path = "path/to/test.pdf" with patch.object( - kg_builder.pipeline, + kg_builder.runner.pipeline, "run", return_value=PipelineResult(run_id="test_run", result=None), ) as mock_run: await kg_builder.run_async(file_path=file_path) - pipe_inputs = mock_run.call_args[0][0] + pipe_inputs = mock_run.call_args[1]["data"] assert "pdf_loader" in pipe_inputs assert pipe_inputs["pdf_loader"] == {"filepath": file_path} assert "extractor" not in pipe_inputs @@ -213,13 +81,13 @@ async def test_knowledge_graph_builder_document_info_with_text(_: Mock) -> None: text_input = "May thy knife chip and shatter." with patch.object( - kg_builder.pipeline, + kg_builder.runner.pipeline, "run", return_value=PipelineResult(run_id="test_run", result=None), ) as mock_run: await kg_builder.run_async(text=text_input) - pipe_inputs = mock_run.call_args[0][0] + pipe_inputs = mock_run.call_args[1]["data"] assert "splitter" in pipe_inputs assert pipe_inputs["splitter"] == {"text": text_input} @@ -248,51 +116,33 @@ async def test_knowledge_graph_builder_with_entities_and_file(_: Mock) -> None: from_pdf=True, ) - internal_entities = [SchemaEntity(label=label) for label in entities] - internal_relations = [SchemaRelation(label=label) for label in relations] - assert kg_builder.entities == internal_entities - assert kg_builder.relations == internal_relations - assert kg_builder.potential_schema == potential_schema + # assert kg_builder.entities == entities + # assert kg_builder.relations == relations + # assert kg_builder.potential_schema == potential_schema file_path = "path/to/test.pdf" + internal_entities = [SchemaEntity(label=label) for label in entities] + internal_relations = [SchemaRelation(label=label) for label in relations] + with patch.object( - kg_builder.pipeline, + kg_builder.runner.pipeline, "run", return_value=PipelineResult(run_id="test_run", result=None), ) as mock_run: await kg_builder.run_async(file_path=file_path) - pipe_inputs = mock_run.call_args[0][0] + pipe_inputs = mock_run.call_args[1]["data"] assert pipe_inputs["schema"]["entities"] == internal_entities assert pipe_inputs["schema"]["relations"] == internal_relations assert pipe_inputs["schema"]["potential_schema"] == potential_schema -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -def test_simple_kg_pipeline_on_error_conversion(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - on_error="RAISE", - ) - - assert kg_builder.on_error == OnError.RAISE - - def test_simple_kg_pipeline_on_error_invalid_value() -> None: llm = MagicMock(spec=LLMInterface) driver = MagicMock(spec=neo4j.Driver) embedder = MagicMock(spec=Embedder) - with pytest.raises(PipelineDefinitionError) as exc_info: + with pytest.raises(PipelineDefinitionError): SimpleKGPipeline( llm=llm, driver=driver, @@ -300,50 +150,6 @@ def test_simple_kg_pipeline_on_error_invalid_value() -> None: on_error="INVALID_VALUE", ) - assert "Expected one of ['RAISE', 'IGNORE']" in str(exc_info.value) - - -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -def test_simple_kg_pipeline_no_entity_resolution(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - on_error="IGNORE", - perform_entity_resolution=False, - ) - - assert "resolver" not in kg_builder.pipeline - - -@mock.patch( - "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", - return_value=(5, 23, 0), -) -@pytest.mark.asyncio -def test_simple_kg_pipeline_lexical_graph_config_attribute(_: Mock) -> None: - llm = MagicMock(spec=LLMInterface) - driver = MagicMock(spec=neo4j.Driver) - embedder = MagicMock(spec=Embedder) - - lexical_graph_config = LexicalGraphConfig() - kg_builder = SimpleKGPipeline( - llm=llm, - driver=driver, - embedder=embedder, - on_error="IGNORE", - lexical_graph_config=lexical_graph_config, - ) - - assert kg_builder.lexical_graph_config == lexical_graph_config - @mock.patch( "neo4j_graphrag.experimental.components.kg_writer.Neo4jWriter._get_version", @@ -372,58 +178,14 @@ async def test_knowledge_graph_builder_with_lexical_graph_config(_: Mock) -> Non text_input = "May thy knife chip and shatter." with patch.object( - kg_builder.pipeline, + kg_builder.runner.pipeline, "run", return_value=PipelineResult(run_id="test_run", result=None), ) as mock_run: await kg_builder.run_async(text=text_input) - pipe_inputs = mock_run.call_args[0][0] + pipe_inputs = mock_run.call_args[1]["data"] assert "extractor" in pipe_inputs assert pipe_inputs["extractor"] == { "lexical_graph_config": lexical_graph_config } - - -def test_knowledge_graph_builder_to_schema_entity_method() -> None: - assert SimpleKGPipeline.to_schema_entity("EntityType") == SchemaEntity( - label="EntityType" - ) - assert SimpleKGPipeline.to_schema_entity({"label": "EntityType"}) == SchemaEntity( - label="EntityType" - ) - assert SimpleKGPipeline.to_schema_entity( - {"label": "EntityType", "description": "A special entity"} - ) == SchemaEntity(label="EntityType", description="A special entity") - assert SimpleKGPipeline.to_schema_entity( - {"label": "EntityType", "properties": []} - ) == SchemaEntity(label="EntityType") - assert SimpleKGPipeline.to_schema_entity( - { - "label": "EntityType", - "properties": [{"name": "entityProperty", "type": "DATE"}], - } - ) == SchemaEntity( - label="EntityType", - properties=[SchemaProperty(name="entityProperty", type="DATE")], - ) - - -def test_knowledge_graph_builder_to_schema_relation_method() -> None: - assert SimpleKGPipeline.to_schema_relation("REL_TYPE") == SchemaRelation( - label="REL_TYPE" - ) - assert SimpleKGPipeline.to_schema_relation({"label": "REL_TYPE"}) == SchemaRelation( - label="REL_TYPE" - ) - assert SimpleKGPipeline.to_schema_relation( - {"label": "REL_TYPE", "description": "A rel type"} - ) == SchemaRelation(label="REL_TYPE", description="A rel type") - assert SimpleKGPipeline.to_schema_relation( - {"label": "REL_TYPE", "properties": []} - ) == SchemaRelation(label="REL_TYPE") - assert SimpleKGPipeline.to_schema_relation( - {"label": "REL_TYPE", "properties": [{"name": "relProperty", "type": "DATE"}]} - ) == SchemaRelation( - label="REL_TYPE", properties=[SchemaProperty(name="relProperty", type="DATE")] - ) diff --git a/tests/unit/llm/test_ollama_llm.py b/tests/unit/llm/test_ollama_llm.py new file mode 100644 index 00000000..f7be308b --- /dev/null +++ b/tests/unit/llm/test_ollama_llm.py @@ -0,0 +1,46 @@ +# Copyright (c) "Neo4j" +# Neo4j Sweden AB [https://neo4j.com] +# # +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# # +# https://www.apache.org/licenses/LICENSE-2.0 +# # +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from unittest.mock import MagicMock, Mock, patch + +import ollama +import pytest +from neo4j_graphrag.llm import LLMResponse +from neo4j_graphrag.llm.ollama_llm import OllamaLLM + + +def get_mock_ollama() -> MagicMock: + mock = MagicMock() + mock.ResponseError = ollama.ResponseError + return mock + + +@patch("builtins.__import__", side_effect=ImportError) +def test_ollama_llm_missing_dependency(mock_import: Mock) -> None: + with pytest.raises(ImportError): + OllamaLLM(model_name="gpt-4o") + + +@patch("builtins.__import__") +def test_ollama_llm_happy_path(mock_import: Mock) -> None: + mock_ollama = get_mock_ollama() + mock_import.return_value = mock_ollama + mock_ollama.Client.return_value.chat.return_value = MagicMock( + message=MagicMock(content="ollama chat response"), + ) + llm = OllamaLLM(model_name="gpt") + + res = llm.invoke("my text") + assert isinstance(res, LLMResponse) + assert res.content == "ollama chat response" diff --git a/tests/unit/retrievers/test_text2cypher.py b/tests/unit/retrievers/test_text2cypher.py index 4d110c8e..05b1e545 100644 --- a/tests/unit/retrievers/test_text2cypher.py +++ b/tests/unit/retrievers/test_text2cypher.py @@ -19,6 +19,7 @@ from neo4j.exceptions import CypherSyntaxError, Neo4jError from neo4j_graphrag.exceptions import ( RetrieverInitializationError, + SchemaFetchError, SearchValidationError, Text2CypherRetrievalError, ) @@ -39,8 +40,8 @@ def test_t2c_retriever_initialization(driver: MagicMock, llm: MagicMock) -> None @patch("neo4j_graphrag.retrievers.base.Retriever._verify_version") @patch("neo4j_graphrag.retrievers.text2cypher.get_schema") def test_t2c_retriever_schema_retrieval( - _verify_version_mock: MagicMock, get_schema_mock: MagicMock, + _verify_version_mock: MagicMock, driver: MagicMock, llm: MagicMock, ) -> None: @@ -51,13 +52,13 @@ def test_t2c_retriever_schema_retrieval( @patch("neo4j_graphrag.retrievers.base.Retriever._verify_version") @patch("neo4j_graphrag.retrievers.text2cypher.get_schema") def test_t2c_retriever_schema_retrieval_failure( - _verify_version_mock: MagicMock, get_schema_mock: MagicMock, + _verify_version_mock: MagicMock, driver: MagicMock, llm: MagicMock, ) -> None: get_schema_mock.side_effect = Neo4jError - with pytest.raises(Neo4jError): + with pytest.raises(SchemaFetchError): Text2CypherRetriever(driver, llm) @@ -310,3 +311,31 @@ def test_t2c_retriever_with_custom_prompt_bad_prompt_params( llm.invoke.assert_called_once_with( """This is a custom prompt. test ['example A', 'example B']""" ) + + +@patch("neo4j_graphrag.retrievers.base.Retriever._verify_version") +@patch("neo4j_graphrag.retrievers.text2cypher.get_schema") +def test_t2c_retriever_with_custom_prompt_and_schema( + get_schema_mock: MagicMock, + _verify_version_mock: MagicMock, + driver: MagicMock, + llm: MagicMock, + neo4j_record: MagicMock, +) -> None: + prompt = "This is a custom prompt. {query_text} {schema}" + query = "test" + + driver.execute_query.return_value = ( + [neo4j_record], + None, + None, + ) + + retriever = Text2CypherRetriever(driver=driver, llm=llm, custom_prompt=prompt) + retriever.search( + query_text=query, + prompt_params={}, + ) + + get_schema_mock.assert_not_called() + llm.invoke.assert_called_once_with("""This is a custom prompt. test """)