Autonomous RAG | The next evolution of RAG AI Assistants
AI Summary
Summary: Building an Autonomous Rag Assistant with AutoRag
- Introduction
- Presenter: Aspr
- Topic: Creating an autonomous rag assistant using AutoRag
- Features of AutoRag
- Long-term memory via a database
- Knowledge base powered by Vector DB
- Web search, database queries, and API call capabilities
- Functionality
- LLM decides how to answer user queries
- Searches chat history, knowledge base, or uses tools as needed
- Demonstration
- Knowledge base includes blog posts
- Example queries:
- “What did Meta release?” uses knowledge base search
- ”What’s happening in France?” uses web search
- ”Summarize our conversation” uses chat history
- Extensibility
- Can handle date/time specific queries
- Example: “What did I do yesterday?”
- Code Availability
- Open source, using OpenAI’s LLM GPT-4
- Memory backed by PostgreSQL database
- Knowledge base includes PDFs and websites
- Search web tool included, with options to add more
- Repository and Setup
- Code found in F-dat repo, under cookbooks folder, AutoRag example
- Instructions for setting up in a code editor
- Use of Python virtual environment for dependency management
- Running PG Vector with Docker
- Running the Streamlit application
- Application Configuration
- Assistant and app structure in the code
- Assistant uses OpenAI chat LLM, PostgreSQL, and PG Vector
- Instructions for the assistant to prioritize knowledge base
- Use of default and custom tools for functionality
- Documentation available for additional tools
- Usage
- Assistant can format responses in Markdown
- Tunable chat history inclusion
- Conclusion
- AutoRag assistant adapts to user queries using memory, knowledge, and tools
- Encouragement to check out the code and join the community for support
- Contact
- GitHub issues or Discord for questions and support