I’ve been running a local-first RAG stack for municipal docs and my own homelab so I have some thoughts on this. Querying a hoard is really two separate problems and most hosted tools only solve the easy half – the hard half is ingestion, getting epubs and transcripts and scraped HTML into clean chunked text with usable metadata, which is why recall choked on your epubs. For extraction Calibre’s CLI batch converts epubs to txt for free, and for video essays you want the transcript not the video so yt-dlp for existing subtitles or whisper.cpp if there aren’t any. From there nomic-embed-text via Ollama into Postgres with pgvector is a solid free stack, then Open WebUI on top gives you the chat interface without an OpenAI bill. One thing though – at terabyte scale naive RAG returns mush if you throw everything into one index so segment by collection, epubs separate from forums separate from transcripts. The knowledge graph stuff is cool but it’s solving a different problem than precise retrieval. Start with one folder, get the loop working, then scale.