[WIP][Merge main into release]

.github/workflows/documentation.yml

@@ -29,11 +29,17 @@ jobs:
           curl -sSL https://install.python-poetry.org | python3 -
           echo "$HOME/.local/bin" >> $GITHUB_PATH  # Ensure Poetry's bin directory is in PATH
 
+      - name: Install package dependencies using Poetry
+        run: |
+          cd lightrag
+          poetry config virtualenvs.create false  # Avoid creating a virtual environment
+          poetry install --all-extras --verbose  # Install all optional dependencies
+          cd ..  # Change back to the root directory
+
       - name: Install documentation dependencies using Poetry
         run: |
-          cd docs
           poetry config virtualenvs.create false  # Avoid creating a virtual environment
-          poetry install   # Install only the doc dependencies as specified in pyproject.toml
+          poetry install --only doc  # Install only the doc dependencies as specified in pyproject.toml
 
       - name: Build documentation using Makefile
         run: |

.github/workflows/documentation_test.yml

@@ -0,0 +1,81 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - merge-main-into-release # Trigger the workflow when changes are pushed to the release branch
+
+permissions:
+  contents: write
+  actions: read
+
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          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
+
+      - name: Install Poetry
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 -
+          echo "$HOME/.local/bin" >> $GITHUB_PATH  # Ensure Poetry's bin directory is in PATH
+
+      - name: Install documentation dependencies using Poetry
+        run: |
+          cd docs
+          poetry config virtualenvs.create false  # Avoid creating a virtual environment
+          poetry install --with doc  # Install only the doc dependencies as specified in pyproject.toml
+          pip install nest-asyncio # manually install nest-asyncio
+          pip install torch # manually install torch
+          # pip install transformers  # manually install transformers
+          poetry show
+          poetry show lightrag
+
+
+      - name: Build documentation using Makefile
+        run: |
+          echo "Building documentation from: $(pwd)"
+          ls -l  # Debug: List current directory contents
+          poetry run make html  # Run Makefile in docs directory to build HTML docs
+        working-directory: ${{ github.workspace }}/docs
+
+      - name: List built documentation
+        run: |
+          find ./docs/build/ -type f  # List all files in the build directory
+        working-directory: ${{ github.workspace }}
+
+      - name: Create .nojekyll file
+        run: |
+          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 || true
+        working-directory: ${{ github.workspace }}
+
+      - 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
+
+# Uncomment below for debugging purposes
+#      - name: Debug Output
+#        run: |
+#          pwd  # Print the current working directory
+#          ls -l ./build/  # List files in the build directory
+#          cat ./source/conf.py  # Display the Sphinx configuration file
+#        working-directory: ${{ github.workspace }}/docs/build

README.md

@@ -1,21 +1,35 @@
-![LightRAG Logo](https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/LightRAG-logo-doc.jpeg)
+
+<!-- <h4 align="center">
+    <img alt="AdalFlow logo" src="docs/source/_static/images/adalflow-logo.png" style="width: 100%;">
+</h4> -->
+
+<h4 align="center">
+    <img alt="AdalFlow logo" src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/adalflow-logo.png" style="width: 100%;">
+</h4>
+
 
 <p align="center">
     <a href="https://colab.research.google.com/drive/1TKw_JHE42Z_AWo8UuRYZCO2iuMgyslTZ?usp=sharing">
         <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
     </a>
 </p>
 
+<h4 align="center">
+    <p>
+        <a href="https://lightrag.sylph.ai/">Documentation</a> |
+        <a href="https://lightrag.sylph.ai/apis/components/components.model_client.html">Models</a> |
+        <a href="https://lightrag.sylph.ai/apis/components/components.retriever.html">Retrievers</a> |
+        <a href="https://lightrag.sylph.ai/apis/components/components.agent.html">Agents</a>
+    <p>
+</h4>
+
 <p align="center">
     <a href="https://pypi.org/project/lightRAG">
         <img alt="PyPI Version" src="https://img.shields.io/pypi/v/lightRAG?style=flat-square">
     </a>
     <a href="https://star-history.com/#SylphAI-Inc/LightRAG">
         <img alt="GitHub stars" src="https://img.shields.io/github/stars/SylphAI-Inc/LightRAG?style=flat-square">
     </a>
-    <a href="https://pypistats.org/packages/lightrag">
-        <img alt="PyPI Downloads" src="https://img.shields.io/pypi/dm/lightRAG?style=flat-square">
-    </a>
     <a href="https://github.com/SylphAI-Inc/LightRAG/issues">
         <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/SylphAI-Inc/LightRAG?style=flat-square">
     </a>
@@ -33,22 +47,34 @@
         <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
     </a> -->
 
+<!-- <a href="https://pypistats.org/packages/lightrag">
+<img alt="PyPI Downloads" src="https://img.shields.io/pypi/dm/lightRAG?style=flat-square">
+</a> -->
+
+
 
+<h1>
+    <p align="center">
+        AdalFlow: The Library for Large Language Model Applications
+    </p>
+</h1>
 
+AdalFlow helps developers build and optimize LLM task pipelines.
+Embracing similar design pattern to PyTorch, AdalFlow is light, modular, and robust, with a 100% readable codebase.
 
-### ⚡ The Lightning Library for Large Language Model Applications ⚡
+Note: We are in the progress of renaming to adalflow from lightrag.
 
-*LightRAG* helps developers build and optimize *Retriever-Agent-Generator* pipelines.
-Embracing similar design pattern to *PyTorch*, LightRAG is *light*, *modular*, and *robust*, with a 100% readable codebase.
+# AdalFlow: A Tribute to Ada Lovelace
 
+AdalFlow is named in honor of [Ada Lovelace](https://en.wikipedia.org/wiki/Ada_Lovelace), the pioneering female mathematician who first recognized that machines could do more than just calculations. As a female-led team, we aim to inspire more women to enter the AI field.
 
-# Why LightRAG?
+# Why AdalFlow?
 
 LLMs are like water; they can be shaped into anything, from GenAI applications such as chatbots, translation, summarization, code generation, and autonomous agents to classical NLP tasks like text classification and named entity recognition. They interact with the world beyond the model’s internal knowledge via retrievers, memory, and tools (function calls). Each use case is unique in its data, business logic, and user experience.
 
 Because of this, no library can provide out-of-the-box solutions. Users must build towards their own use case. This requires the library to be modular, robust, and have a clean, readable codebase. The only code you should put into production is code you either 100% trust or are 100% clear about how to customize and iterate.
 
-<!-- This is what LightRAG is: light, modular, and robust, with a 100% readable codebase. -->
+<!-- This is what AdalFlow is: light, modular, and robust, with a 100% readable codebase. -->
 
 
 Further reading: [How We Started](https://www.linkedin.com/posts/li-yin-ai_both-ai-research-and-engineering-use-pytorch-activity-7189366364694892544-Uk1U?utm_source=share&utm_medium=member_desktop),
@@ -81,7 +107,7 @@ class Net(nn.Module):
       x = self.fc1(x)
       return self.fc2(x)
 ``` -->
-# LightRAG Task Pipeline
+# AdalFlow Task Pipeline
 
 We will ask the model to respond with ``explanation`` and ``example`` of a concept. To achieve this, we will build a simple pipeline to get the structured output as ``QAOutput``.
 
@@ -254,7 +280,7 @@ self.generator = Generator(
 
 # Quick Install
 
-Install LightRAG with pip:
+Install AdalFlow with pip:
 
 ```bash
 pip install lightrag
@@ -267,13 +293,15 @@ Please refer to the [full installation guide](https://lightrag.sylph.ai/get_star
 
 # Documentation
 
-LightRAG full documentation available at [lightrag.sylph.ai](https://lightrag.sylph.ai/):
+AdalFlow full documentation available at [lightrag.sylph.ai](https://lightrag.sylph.ai/):
 - [How We Started](https://www.linkedin.com/posts/li-yin-ai_both-ai-research-and-engineering-use-pytorch-activity-7189366364694892544-Uk1U?utm_source=share&utm_medium=member_desktop)
 - [Introduction](https://lightrag.sylph.ai/)
 - [Full installation guide](https://lightrag.sylph.ai/get_started/installation.html)
 - [Design philosophy](https://lightrag.sylph.ai/tutorials/lightrag_design_philosophy.html)
 - [Class hierarchy](https://lightrag.sylph.ai/tutorials/class_hierarchy.html)
 - [Tutorials](https://lightrag.sylph.ai/tutorials/index.html)
+- [Supported Models](https://lightrag.sylph.ai/apis/components/components.model_client.html)
+- [Supported Retrievers](https://lightrag.sylph.ai/apis/components/components.retriever.html)
 - [API reference](https://lightrag.sylph.ai/apis/index.html)
 
 
@@ -286,9 +314,9 @@ LightRAG full documentation available at [lightrag.sylph.ai](https://lightrag.sy
 # Citation
 
 ```bibtex
-@software{Yin2024LightRAG,
+@software{Yin2024AdalFlow,
   author = {Li Yin},
-  title = {{LightRAG: The Lightning Library for Large Language Model (LLM) Applications}},
+  title = {{AdalFlow: The Library for Large Language Model (LLM) Applications}},
   month = {7},
   year = {2024},
   doi = {10.5281/zenodo.12639531},