A multi-line description of two to three lines explaining what the post covers: what an embedding represents, dense vs sparse vs hybrid retrieval, HNSW and IVF-PQ ANN indexing, and where ranking quality actually lives.
Rag
-
Embedding Models and Vector Search, Honestly -
RAG Beyond Toy Demos: Chunking, Reranking, and the Evaluation Problem Nobody Solved The gap between a notebook RAG demo and a system that ships: why chunking is the retrieval you never tuned, query rewriting and HyDE, the retrieve-wide rank-narrow pipeline, citation faithfulness and grounding, and the evaluation problem that quietly decides whether any of it works.
-
Local Vector Search for Homelab RAG: pgvector vs Qdrant vs Chroma A practical comparison of Chroma, pgvector, and Qdrant for fully self-hosted vector search — covering setup, embedding generation on local hardware, and wiring each into an offline RAG stack that never phones home.
-
Preserving a Voice: Fine-Tuning a Local LLM on a Loved One's Writing A careful, end-to-end guide to building a private, local language model that writes in a late parent's voice — from recovering and cleaning years of blog posts, to QLoRA fine-tuning on an RTX 5090, to grounding it with retrieval, packaging it as a GGUF, and sharing it with siblings over Tailscale. Includes the honest limits and the ethics that should shape every decision.
-
Personal Knowledge Management with AI Second brain methodology, Obsidian with AI plugins for semantic search, building a personal knowledge graph, RAG over your own notes with local embeddings, and the honest failure modes of PKM systems — including when note-taking becomes procrastination.
-
Vector Databases in Production: Pinecone, Weaviate, Qdrant, and pgvector A practical guide to vector databases for production — how vector search works, indexing algorithms (HNSW, IVF, DiskANN), choosing between Pinecone, Weaviate, Qdrant, and pgvector, and operational patterns for scaling and reliability.
-
RAG from Scratch: Building a Retrieval-Augmented Chatbot Over Your Own Documents A complete technical walkthrough of building a retrieval-augmented generation (RAG) system from scratch — covering embeddings, vector databases, chunking strategy, retrieval quality, and a fully runnable Python implementation using Ollama and ChromaDB.
-
Running a Local Knowledge Base: Obsidian, Logseq, and AI-Powered Search Over Your Notes A practical guide to building a personal knowledge base that you actually own — using Obsidian or Logseq for capture and organization, syncing without the cloud, and wiring in local AI so you can search and query your notes with natural language.