CogniVault Backend Explained, Part 3 · How a Question Becomes a Cited Answer
Hybrid retrieval, a six-tool agent, and a stream that shows the model think before it answers. A beginner's walk through the heart of CogniVault: the RAG loop.
CogniVault Backend Explained, Part 2 · From File to Searchable Knowledge
How does a 1,000-page scanned PDF become something an AI can search in seconds? A beginner's walk through CogniVault's ingestion pipeline: extraction, OCR, chunking, embeddings, …
CogniVault Backend Explained, Part 1 · Meet the Backend: Three Processes, Four Layers
New to RAG, vector databases, or local AI? This four-part beginner series walks through the CogniVault backend from the ground up. Part 1: the big picture, the full tech stack, and …
Part 1 · CogniVault Architecture: Why Standard RAG Isn't Enough (Hybrid Search)
Standard vector search fails on exact keywords. Here is how CogniVault uses Hybrid Search and an agent loop to actually find what you are looking for.
Gemma CogniVault
A fully local, privacy-first AI Study Companion — Gemma 4 + FAISS + BM25 + Strands Agents, running entirely on your machine.
Part 4 · Crash-Resumable Ingestion: DBOS, SHA-256, and Surviving a kill -9
Re-embedding a 200-page PDF every time you tweak one paragraph is a tax nobody wants to pay. Here's how CogniVault uses DBOS workflows and content hashing to ingest only what …
Part 2 · Hybrid Retrieval in Practice: FAISS + BM25, Fused with RRF
Dense vectors are smart but forgetful. Keyword search is dumb but loyal. Here's how I combined FAISS, BM25, and Reciprocal Rank Fusion in CogniVault — and why pure semantic search …
Part 1 · Why I Built a Local-First RAG
Cloud AI assistants are powerful — but for trainers, researchers, and anyone handling sensitive material, they're also a leaky abstraction. Here's why I built a 100% local …