Local RAG
What this article covers
- What RAG is and why it matters.
- Each step of the process: chunking, embedding, storage, retrieval.
- Links to all RAG articles.
Introduction
RAG - Retrieval-Augmented Generation - lets you feed your own documents into language models. Instead of relying on general knowledge, the model retrieves information from uploaded content. Run it locally and your data stays within your own network.
Content
- RAG Fundamentals - Architecture, workflow, and typical use cases.
- Chunking - Breaking documents into meaningful pieces.
- Embedding Models - Converting text into vectors.
- Vector Databases - Storage solutions like Chroma and Qdrant.
- RAG vs. Context Window - When RAG makes sense and when a large context window is enough.
- Preparing Documents - Formats, cleaning, and structuring for quality RAG data.
- PDFs and OCR - Making scanned documents usable with Tesseract and layout detection.
- Reranking - Cross-encoders and rerankers for better results.
- Hybrid Search - Combining semantic and lexical search.
- Source Attribution - Traceable answers with metadata and citations.
- Testing RAG Quality - Metrics like recall, precision, and RAGAS.
- Maintaining Knowledge - Managing, updating, and versioning RAG data.
- RAG Tutorials - Step-by-step guides with Python code.
Key terms
| Term | Meaning |
|---|---|
| Chunking | Breaking text into searchable pieces |
| Embedding | Vector representation of text |
| Vector database | Storage for embeddings with similarity search |
| Retrieval | Finding matching document sections |
FAQ
Can I run RAG entirely locally?
Yes. Ollama for models, Chroma or Qdrant for vector databases, and local documents give you a fully local RAG stack.
What does local RAG cost?
Mostly hardware and power. The software is open source. No ongoing API charges.