Local Agentic RAG with LangGraph and Llama 3

LLMs can be empowered with important new capabilities through agents that use planning, memory, and tools to accomplish tasks. This post demonstrates how to build agents capable of tool-calling using LangGraph with Llama 3 and Milvus. Agents can perform actions such as web searching, browsing emails, correcting RAGs, and more. The process involves setting up LangGraph, Ollama & Llama 3, and Milvus Lite. Using these tools, a custom local Llama 3 powered RAG agent is built with different approaches like routing, fallback, and self-correction. Examples of agents include the Hallucination Grader and the Answer Grader. The post concludes by compiling the LangGraph graph and testing it.

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