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add integrations and githubchat in docs
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| .. _tutorials-rag_with_memory: | ||
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| RAG with Memory | ||
| ============== | ||
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| This guide demonstrates how to implement a RAG system with conversation memory using AdalFlow, based on our `github_chat <https://github.com/SylphAI-Inc/github_chat>`_ reference implementation. | ||
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| Overview | ||
| -------- | ||
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| The github_chat project is a practical RAG implementation that allows you to chat with GitHub repositories while maintaining conversation context. It demonstrates: | ||
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| - Code-aware responses using RAG | ||
| - Memory management for conversation context | ||
| - Support for multiple programming languages | ||
| - Both web and command-line interfaces | ||
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| Architecture | ||
| ----------- | ||
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| The system is built with several key components: | ||
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| Data Pipeline | ||
| ^^^^^^^^^^^^ | ||
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| .. code-block:: text | ||
| Input Documents → Text Splitter → Embedder → Vector Database | ||
| The data pipeline processes repository content through: | ||
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| 1. Document reading and preprocessing | ||
| 2. Text splitting for optimal chunk sizes | ||
| 3. Embedding generation | ||
| 4. Storage in vector database | ||
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| RAG System | ||
| ^^^^^^^^^^ | ||
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| .. code-block:: text | ||
| User Query → RAG Component → [FAISS Retriever, Generator, Memory] | ||
| ↓ | ||
| Response | ||
| The RAG system includes: | ||
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| - FAISS-based retrieval for efficient similarity search | ||
| - LLM-based response generation | ||
| - Memory component for conversation history | ||
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| Memory Management | ||
| --------------- | ||
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| The memory system maintains conversation context through: | ||
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| 1. Dialog turn tracking | ||
| 2. Context preservation | ||
| 3. Dynamic memory updates | ||
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| This enables: | ||
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| - Follow-up questions | ||
| - Reference to previous context | ||
| - More coherent conversations | ||
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| Quick Start | ||
| ---------- | ||
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| 1. Installation: | ||
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| .. code-block:: bash | ||
| git clone https://github.com/SylphAI-Inc/github_chat | ||
| cd github_chat | ||
| poetry install | ||
| 2. Set up your OpenAI API key: | ||
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| .. code-block:: bash | ||
| mkdir -p .streamlit | ||
| echo 'OPENAI_API_KEY = "your-key-here"' > .streamlit/secrets.toml | ||
| 3. Run the application: | ||
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| .. code-block:: bash | ||
| # Web interface | ||
| poetry run streamlit run app.py | ||
| # Repository analysis | ||
| poetry run streamlit run app_repo.py | ||
| Example Usage | ||
| ----------- | ||
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| Here are some example queries you can try: | ||
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| .. code-block:: text | ||
| "What does the RAG class do?" | ||
| "Can you explain how the memory system works?" | ||
| "Show me the implementation of text splitting" | ||
| "How is the conversation context maintained?" | ||
| Implementation Details | ||
| ------------------- | ||
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| The system uses AdalFlow's components: | ||
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| - :class:`core.embedder.Embedder` for document embedding | ||
| - :class:`core.retriever.Retriever` for similarity search | ||
| - :class:`core.generator.Generator` for response generation | ||
| - Custom memory management for conversation tracking | ||
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| For detailed implementation examples, check out the `github_chat repository <https://github.com/SylphAI-Inc/github_chat>`_. |