Embeddings
RAG Pipeline for Local AI: A Practical Guide to Retrieval-Augmented Generation
Build a local RAG pipeline with Ollama, ChromaDB, and your own documents. Chunking strategies, embedding models, vector stores, and the failure modes nobody warns you about.
Ghost Knowledge: When Your RAG System Cites Documents That No Longer Exist
Your RAG system confidently quotes a policy that was updated months ago. The old version is still in the vector database. Nobody notices until the wrong answer costs real money. Here's how to find and fix ghost knowledge.
Obsidian + Local LLM: Build a Private AI Second Brain
Connect Obsidian to a local LLM via Ollama for private AI-powered note search, summaries, and chat. Step-by-step setup with Copilot and Smart Connections.
Embedding Models for RAG: Which to Run Locally
nomic-embed-text is still the default for most local RAG setups — 274MB, 8K context, runs on CPU. But Qwen3-Embedding 0.6B just changed the game. Model picks, VRAM needs, speed numbers, and the chunking mistakes that break retrieval.