Building a Multi Source RAG Agent with LangGraph: Routing Between SQL and Vector Search
📰 Medium · LLM
Learn to build a multi-source RAG agent using LangGraph, routing between SQL and vector search, and improve your skills in AI and software engineering
Action Steps
- Build a RAG agent using LangGraph and FastAPI
- Configure routing between SQL and vector search
- Integrate multiple data sources into the RAG agent
- Test the agent's performance and accuracy
- Apply the agent to real-world applications and refine its capabilities
Who Needs to Know This
Data scientists, software engineers, and AI researchers can benefit from this article to improve their skills in building multi-source RAG agents and integrating different data sources
Key Insight
💡 Integrating multiple data sources and routing between different search methods can improve the performance and accuracy of a RAG agent
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🤖 Build a multi-source RAG agent with LangGraph and route between SQL and vector search! 🚀
Key Takeaways
Learn to build a multi-source RAG agent using LangGraph, routing between SQL and vector search, and improve your skills in AI and software engineering
Full Article
A behind the scenes walkthrough of OmniSource, the multi source RAG agent I built with LangGraph, FastAPI, and two very different… Continue reading on Medium »
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