amscotti/local-LLM-with-RAG
Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)
Implements agentic RAG using Pydantic AI for tool calling, enabling models to autonomously decide when and how to search documents rather than following fixed pipelines. Embeddings are generated locally via Ollama's nomic-embed-text and indexed in LanceDB for vector search, with document parsing handled by MarkItDown to support PDFs, Office files, and multiple formats. Requires tool-calling-capable models (qwen3:8b+ recommended) and provides a Streamlit interface for interactive document querying.
271 stars.
Stars
271
Forks
52
Language
Python
License
MIT
Category
Last pushed
Jan 02, 2026
Commits (30d)
0
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