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nomic-embeddings

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RAG-Agents-with-Llama3.1

Shreyash-Gaur / RAG-Agents-with-Llama3.1

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This notebook demonstrates a fully local implementation of Retrieval-Augmented Generation (RAG) using the Llama 3.1 model. It integrates LangChain for document retrieval, grading, and response generation, with optional web search capabilities via Tavily. The workflow ensures data privacy and efficient local inference.

jupyter-notebook ai-agents rag vector-search generative-ai langchain ollama langgraph tavily llama3 agentic-ai nomic-embeddings
  • Updated Mar 19, 2025
  • Jupyter Notebook

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