the intro page for the library and the structure of all documentations

docs/source/_static/custom.css

@@ -6,4 +6,21 @@
     background-color: #f0f0f0 !important; /* Light gray color */
     color: #000000 !important; /* Sets text color to black */
 
+}
+.half-width {
+    width: 47%;
+    float: left;
+    padding: 2%;
+    box-sizing: border-box;
+}
+
+.code-comparison {
+    /* display: flex; */
+    width: 100%;
+    justify-content: space-between;
+}
+
+.caption-text {
+    font-size: 14px;  /* Sets the smallest font size */
+    color: gray;     /* Sets the color to gray */
 }

docs/source/conf.py

@@ -13,6 +13,7 @@
 import os
 import sys
 
+
 # sys.path.insert(0, os.path.abspath("."))
 # sys.path.insert(0, os.path.abspath(".."))
 # sys.path.insert(0, os.path.abspath("../.."))
@@ -50,6 +51,7 @@
     # 'recommonmark',
     # 'myst_parser'
 ]
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -65,9 +67,9 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-# html_theme = 'alabaster'
 html_theme = "sphinx_rtd_theme"
 
+
 # These options are for the sphinx_rtd_theme
 html_theme_options = {
     "collapse_navigation": False,
@@ -76,6 +78,8 @@
     "includehidden": True,
     "titles_only": False,
 }
+
+
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".

docs/source/developer_notes/prompt.rst

@@ -77,7 +77,7 @@ For example, your task is to instruct the llm to choose `top_k` from the given c
    :linenos:
 
    from core.prompt_builder import Prompt
-   
+
    task_desc_template = r"""
    Choose the top {{top_k}} from the following choices: {{choices}}
    """

docs/source/index.rst

@@ -9,13 +9,114 @@ LightRAG documentation
 .. .. image:: ../../images/lightrag_structure.png
 ..    :width: 60%
 
-LightRAG is the "PyTorch" library for building large langage model(LLM) applications. It is light, modular and robust like "PyTorch", with essential components for `Retriever`-`Agent`-`Generator` (RAG). 
+LightRAG is the "PyTorch" library for building large langage model(LLM) applications. It is super light, modular and robust like "PyTorch", and offers essential components for `Retriever`-`Agent`-`Generator` (RAG). 
 
-.. LightRAG: 1000 lines of code are all you need. No lock-in to vendors and frameworks, only the best practices of productionable RAG and Agent.
+You have a similar coding experience as PyTorch. Here is a side to side comparison of writing a PyTorch module and a LightRAG component:
 
-What is LightRAG?
-=================
-LightRAG comes from the best of the AI research and engineering. Fundamentally, we ask ourselves: what kind of system that combines the best of research(such as LLM), engineering (such as 'jinja') to build the best applications? We are not a framework. We do not want you to directly install the package. We want you to carefully decide to take modules and structures from here to build your own library and applications. This is a cookbook organized uniquely for easy understanding: you can read the 1000 lines of code to see a typical RAG end-to-end without jumping between files and going through multi-level class inheritance. If we build our system expanding from light_rag.py, we as a community will share the same RAG languages, and share other building blocks and use cases easily without depending on a complex framework.
+#TODO: make it side to side comparison
+
+**PyTorch:**
+
+.. code-block:: python
+
+   import torch
+   import torch.nn as nn
+
+   class Net(nn.Module):
+      def __init__(self):
+         super(Net, self).__init__()
+         self.conv1 = nn.Conv2d(1, 32, 3, 1)
+         self.conv2 = nn.Conv2d(32, 64, 3, 1)
+         self.dropout1 = nn.Dropout2d(0.25)
+         self.dropout2 = nn.Dropout2d(0.5)
+         self.fc1 = nn.Linear(9216, 128)
+         self.fc2 = nn.Linear(128, 10)
+
+      def forward(self, x):
+         x = self.conv1(x)
+         x = self.conv2(x)
+         x = self.dropout1(x)
+         x = self.dropout2(x)
+         x = self.fc1(x)
+         return self.fc2(x)
+
+   my_nn = Net()
+   print(my_nn)
+
+**LightRAG:**
+
+.. code-block:: python
+
+   from core.component import Component
+   from core.generator import Generator
+   from components.api_client import OpenAIClient
+
+   class SimpleQA(Component):
+      def __init__(self):
+         super().__init__()
+         self.generator = Generator(
+            model_client=OpenAIClient,
+            model_kwargs={'model_name': 'gpt-3.5-turbo'}
+         )
+
+      def call(self, query):
+         return self.generator.call(query)
+
+      async def acall(self, query):
+         return await self.generator.acall(query)
+
+   qa = SimpleQA()
+   print(qa)
+
+
+**Why LightRAG?**
+
+
+1. **Clarity and Simplicity**
+
+   We understand that developers building real-world Large Language Model (LLM) applications are the real heroes. Just like AI researchers and engineers who build models on top of PyTorch, developers require **Maximum Flexibility and Customizability**: Each developer has unique data needs to build their own models/components, experiment with In-context Learning (ICL) or model finetuning, and deploy the LLM applications to production. This means the library must provide fundamental lower-level building blocks and strive for clarity and simplicity:
+
+   - We maintain no more than two levels of subclasses.
+   - Each core abstract class is designed to be robust and flexible.
+   - We use 10X less code than other libraries to achieve 10X more robustness and flexibility.
+
+
+2. **Control and Transparency**
+
+   Coming from a deep AI research background, we understand that the more control and transparency developers have over their prompts, the better. In default:
+
+   - LightRAG simplifies what developers need to send to LLM proprietary APIs to just two messages each time: a `system message` and a `user message`. This minimizes reliance on and manipulation by API providers.
+   - LightRAG provides advanced tooling for developers to build `agents`, `tools/function calls`, etc., without relying on any proprietary API provider's 'advanced' features such as `OpenAI` assistant, tools, and JSON format.
+
+3. **Suitted for Both Researchers and Production Engineers**
+
+   We start with understanding the frontier of AI research and LLM productionalization, and our design is grounded on our understanding of all dataflow in LLM applications, and 
+   the process of 
+
+
+**LightRAG vs other LLM libraries:**
+
+
+**LightRAG library structures as follows:**
+
+#TODO: One diagram to make people understand lightrag faster
+
+* `core` - Base abstractions, core functions, and core components like `Generator` and `Embedder` to support more advanced components.
+* `components` - Components that are built on top of the core directive. Users will install relevant depencides on their own for some components.
+
+
+**LightRAG documentation is divided into two parts:**
+
+* **Developer Documentation**: This documentation explains how LightRAG is designed in more depth and is especially useful
+ for developers who want to contribute to LightRAG.
+
+* **User Documentation**: This documentation is for users who want to use LightRAG to build their applications.
+
+We encourage all users to at least skim through the developer documentation. Different from "PyTorch" where a normal user does not have to customize a building module for neural network, 
+LLM applications have much bigger scope and varies even more to different product environments, so developers customizing components on their own is much more common.
+
+Developer documentation
+=======================
 
 .. toctree::
    :glob:
@@ -42,36 +143,40 @@ LightRAG comes from the best of the AI research and engineering. Fundamentally,
    developer_notes/retriever
    developer_notes/data_classes
 
+
 .. toctree::
-   :glob:
    :maxdepth: 1
-   :caption: Get Started
-
-   get_started/installation
-   get_started/introduction
+   :caption: API Reference
 
+   apis/core/core
+   apis/components/components
 
 
 .. toctree::
+   :glob:
    :maxdepth: 1
-   :caption: Tutorials
+   :caption: Resources
+
+   resources/resources
+   resources/contributing
 
-   tutorials/simpleQA
 
+User documentation
+=======================
 
 .. toctree::
+   :glob:
    :maxdepth: 1
-   :caption: API Reference
+   :caption: Get Started
+
+   get_started/installation
+   get_started/introduction
 
-   apis/core/core
-   apis/components/components
 
 
 .. toctree::
-   :glob:
    :maxdepth: 1
-   :caption: Resources
-
-   resources/resources
-   resources/contributing
+   :caption: Tutorials
+
+   tutorials/simpleQA
 

pyproject.toml

@@ -40,6 +40,7 @@ datasets = "^2.19.1"
 sphinx = "^7.3.7"
 sphinx-rtd-theme = "^2.0.0"
 
+
 [tool.poetry.group.dev.dependencies]
 langchain = "^0.1.16"
 llama-index = "^0.10.30"