RAG Knowledge Engine

My own knowledge base and agent workflows: over 5M words across hundreds of mostly Markdown files, plus textbooks in PDF and YouTube content (transcripts and comments). All of it needed to be queryable locally by whatever agents I was experimenting with.

Vanilla RAG stacks with static chunking and a single generic embedding could not handle the mix. Long essays, structured SOPs, noisy transcripts, and comment threads all got chopped poorly. Retrieval felt random, and nothing integrated cleanly with locally run agent tooling.

I built on the open-source Archon platform and replaced the default pipelines with document-type-specific ones. For each source (Markdown, PDF, YouTube), I used a cluster-semantic chunker tuned to how I actually write and read. Then I leaned on Google Gemini Embedding 001 task types: cluster for visualizing and organizing the embedding space, semantic for meaning-level similarity, and the dedicated question-answering and document-query task types for retrieval. The whole thing is exposed through a locally run MCP so any agent in my IDE can talk to it as a first-class tool.

• Archon
• Gemini Embedding 001 (cluster, semantic, QA, query task types)
• Python stack (Pydantic, FastAPI, Logfire, Hypothesis)
• Local MCP integration
• Vector indexing inside Archon

Instead of a vague "chat with your docs" toy, I have a targeted retrieval engine that can pull high-signal passages from my own writing, external textbooks, and YouTube data on demand. Agents can now draft, fact-check, and explore ideas using the actual corpus I care about, with far fewer nonsense hops.

This local engine quietly became the backbone of my content systems and agent experiments. It's the difference between "LLM with vibes" and a serious, evolving knowledge infrastructure I control.

Document-type-specific chunking and task-typed embeddings matter as much as the base model. If you treat RAG as an afterthought, everything built on top of it inherits the sloppiness.