Learn to build a locally-running Multi-Agent system using LangGraph, integrating multiple Large Language Models (LLMs) including Llama 3.2 3B, LLama 3 8B, and DeepSeek R1 1.5B in GGUF int4 format. Explore the implementation of a RAG (Retrieval-Augmented Generation) system utilizing Chroma as a VectorStore with Nomic.ai embeddings. Master the deployment process with LangServe, including the creation of authenticated access points through tokens and authentication headers. Access comprehensive implementation details through the provided GitHub repository, which contains detailed notebooks and code examples. Build upon previous knowledge from related tutorials on building local agents with Llama 3.2 8B, Ollama, and Chroma.
Building Multi-Agent RAG Systems with LangGraph and LangServe - Local LLM Implementation
The Machine Learning Engineer via YouTube
Overview
Syllabus
RAG: LangGraph Múltiples LLM,s in Local. LangServe Authentication #datascience #machinelearning
Taught by
The Machine Learning Engineer
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