📚 RAG Knowledge Base

What it is

A Retrieval-Augmented Generation pipeline that serves as persistent long-term memory. Instead of relying solely on model parameters or session context, it stores and retrieves relevant information from a large indexed corpus — so nothing important gets forgotten between conversations.

How it works

• Vector embeddings — documents are chunked, embedded, and stored as vectors
• Semantic search — queries are embedded and matched against the corpus using cosine similarity
• Top-k retrieval — the most relevant chunks are returned and injected as context before the model responds
• First-line recall — RAG is checked before going to the internet for answers

Stack

Why it matters

AI agents wake up fresh every session. Without external memory, they lose everything — preferences, corrections, project context, lessons learned. This RAG pipeline is the brain that persists across sessions, so the same mistakes aren't repeated and useful knowledge compounds over time.