Merge remote-tracking branch 'origin/main' into release

SylphAI-Inc · Jul 4, 2024 · 101b2b9 · 101b2b9
2 parents 978c331 + 8ff2ef1
commit 101b2b9
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diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml
@@ -3,7 +3,7 @@ name: Documentation
 on:
   push:
     branches:
-      - release  # Trigger the workflow when changes are pushed to the release branch
+      - release # Trigger the workflow when changes are pushed to the release branch
 
 permissions:
   contents: write
@@ -17,12 +17,12 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
         with:
-          fetch-depth: 0  # Fetch all history for all branches and tags
+          fetch-depth: 0 # Fetch all history for all branches and tags
 
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.11'  # Ensure the Python version is correct
+          python-version: '3.11' # Ensure the Python version is correct
 
       - name: Install Poetry
         run: |
@@ -51,15 +51,18 @@ jobs:
           touch .nojekyll  # Prevent GitHub Pages from ignoring files that start with an underscore
         working-directory: ${{ github.workspace }}/docs/build
 
+      - name: Copy CNAME file
+        run: |
+          cp ${{ github.workspace }}/CNAME ${{ github.workspace }}/docs/build/CNAME
+
       - name: Deploy to GitHub Pages
         uses: peaceiris/actions-gh-pages@v3
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}  # GitHub token for authentication
-          publish_branch: gh-pages  # Target branch for GitHub Pages deployment
-          publish_dir: ./docs/build/  # Directory containing the built documentation
-          user_name: github-actions[bot]  # Username for the commit
-          user_email: github-actions[bot]@users.noreply.github.com  # Email for the commit
-          force_orphan: true  # Force the use of an orphan branch, removing all previous history
+          github_token: ${{ secrets.GITHUB_TOKEN }} # GitHub token for authentication
+          publish_branch: gh-pages # Target branch for GitHub Pages deployment
+          publish_dir: ./docs/build/ # Directory containing the built documentation
+          user_name: github-actions[bot] # Username for the commit
+          user_email: github-actions[bot]@users.noreply.github.com # Email for the commit
 
 # Uncomment below for debugging purposes
 #      - name: Debug Output

diff --git a/.github/workflows/documentation_li.yml b/.github/workflows/documentation_li.yml
@@ -51,6 +51,10 @@ jobs:
           touch .nojekyll
         working-directory: ${{ github.workspace }}/docs/build
 
+      - name: Copy CNAME file
+        run: |
+          cp ${{ github.workspace }}/CNAME ${{ github.workspace }}/docs/build/CNAME
+
       - name: Deploy to GitHub Pages
         uses: peaceiris/actions-gh-pages@v3
         with:

diff --git a/CNAME b/CNAME
@@ -0,0 +1 @@
+lightrag.sylph.ai
diff --git a/README.md b/README.md
@@ -1,7 +1,11 @@
-# Introduction
+![LightRAG Logo](https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/LightRAG-logo-doc.jpeg)
+
+## ⚡⚡⚡ The PyTorch Library for Large language Model (LLM) Applications ⚡⚡⚡
+
+*LightRAG* helps developers with both building and optimizing *Retriever-Agent-Generator (RAG)* pipelines.
+It is *light*, *modular*, and *robust*.
+
 
-LightRAG is the `PyTorch` library for building large language model (LLM) applications. We help developers with both building and optimizing `Retriever`-`Agent`-`Generator` (RAG) pipelines.
-It is light, modular, and robust.
 
 **PyTorch**
 
@@ -34,7 +38,6 @@ class Net(nn.Module):
 
 from lightrag.core import Component, Generator
 from lightrag.components.model_client import GroqAPIClient
-from lightrag.utils import setup_env #noqa
 
 class SimpleQA(Component):
    def __init__(self):
@@ -58,46 +61,45 @@ class SimpleQA(Component):
       return await self.generator.acall({"input_str": query})
 ```
 
-## Simplicity
+## Quick Install
+
+Install LightRAG with pip:
 
-Developers who are building real-world Large Language Model (LLM) applications are the real heroes.
-As a library, we provide them with the fundamental building blocks with 100% clarity and simplicity.
+```bash
+pip install lightrag
+```
 
-* Two fundamental and powerful base classes: Component for the pipeline and DataClass for data interaction with LLMs.
-* We end up with less than two levels of subclasses. Class Hierarchy Visualization.
-* The result is a library with bare minimum abstraction, providing developers with maximum customizability.
+Please refer to the [full installation guide](https://lightrag.sylph.ai/get_started/installation.html) for more details.
 
-Similar to the PyTorch module, our Component provides excellent visualization of the pipeline structure.
 
-```
-SimpleQA(
-   (generator): Generator(
-      model_kwargs={'model': 'llama3-8b-8192'},
-      (prompt): Prompt(
-         template: <SYS>
-               You are a helpful assistant.
-               </SYS>
-               User: {{input_str}}
-               You:
-               , prompt_variables: ['input_str']
-      )
-      (model_client): GroqAPIClient()
-   )
-)
-```
 
-## Controllability
 
-Our simplicity did not come from doing 'less'.
-On the contrary, we have to do 'more' and go 'deeper' and 'wider' on any topic to offer developers maximum control and robustness.
+# Documentation
 
-* LLMs are sensitive to the prompt. We allow developers full control over their prompts without relying on API features such as tools and JSON format with components like Prompt, OutputParser, FunctionTool, and ToolManager.
-* Our goal is not to optimize for integration, but to provide a robust abstraction with representative examples. See this in ModelClient and Retriever.
-* All integrations, such as different API SDKs, are formed as optional packages but all within the same library. You can easily switch to any models from different providers that we officially support.
+LightRAG full documentation available at [lightrag.sylph.ai](https://lightrag.sylph.ai/):
 
-## Future of LLM Applications
+- [Introduction](https://lightrag.sylph.ai/)
+- [Full installation guide](https://lightrag.sylph.ai/get_started/installation.html)
+- [Design philosophy](https://lightrag.sylph.ai/developer_notes/lightrag_design_philosophy.html): Design based on three principles: Simplicity over complexity, Quality over quantity, and Optimizing over building.
+- [Class hierarchy](https://lightrag.sylph.ai/developer_notes/class_hierarchy.html): We have no more than two levels of subclasses. The bare minimum abstraction will developers with maximum customizability and simplicity.
+- [Tutorials](https://lightrag.sylph.ai/developer_notes/index.html): Learn the `why` and `how-to` (customize and integrate) behind each core part within the `LightRAG` library.
+- [API reference](https://lightrag.sylph.ai/apis/index.html)
 
-On top of the easiness to use, we in particular optimize the configurability of components for researchers to build their solutions and to benchmark existing solutions.
-Like how PyTorch has united both researchers and production teams, it enables smooth transition from research to production.
-With researchers building on LightRAG, production engineers can easily take over the method and test and iterate on their production data.
-Researchers will want their code to be adapted into more products too.
+
+
+
+## Contributors
+
+[![contributors](https://contrib.rocks/image?repo=SylphAI-Inc/LightRAG&max=2000)](https://github.com/SylphAI-Inc/LightRAG/graphs/contributors)
+
+# Citation
+
+```bibtex
+@software{Yin-LightRAG-2024,
+  author = {Yin, Li},
+  title = {{LightRAG: The PyTorch Library for Large language Model (LLM) Applications}},
+  month = {7},
+  year = {2024},
+  url = {https://github.com/SylphAI-Inc/LightRAG}
+}
+```