A step-by-step guide to building a fully local RAG (Retrieval-Augmented Generation) document question-answering system using Ollama, ChromaDB, LangChain, and Sentence Transformers. The setup keeps all data on-machine with no cloud APIs required. Covers installing Ollama and pulling Mistral or Phi-3, creating a Python environment with pinned dependencies, loading and chunking documents with RecursiveCharacterTextSplitter, generating embeddings with all-MiniLM-L6-v2, persisting vectors in ChromaDB, and building a RetrievalQA chain. Also addresses tuning chunk size and k-retrieval parameters, common troubleshooting issues, security/privacy hardening, and potential extensions like Gradio UI or FastAPI wrapping.
Table of contents
How to Set Up Local RAG for Private Document AITable of ContentsWhy Go Local with RAG?Architecture Overview and Component SelectionPrerequisites and Environment SetupIngesting and Chunking Your DocumentsGenerating Embeddings and Storing Vectors LocallyIngestion ScriptQuerying Your Documents: The RAG ChainTesting, Tuning, and TroubleshootingSecurity and Privacy ConsiderationsWhere to Go Next2 Comments
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