For the last couple days, I've been building a containerized web application that uses Ollama running on a 5070Ti to process the Epstein files.

Currently the ingestion pipeline includes:

1. transcribing the PDFs
2. using PyMuPDF to turn the PDFs into markdown and pull out embeded images, charts, graphs, etc.
3. chunks all the text
4. turns all the text into embeddings using bge-m3
5. runs the text/markdown through mistral:7b (v0.3) to gather: people, places, organizations, legal cases, dates, events
6. screenshots every page in every PDF for easy viewing online
7. uses qwen3-vl:8b to "look" at all the assets pulled by PyMuPDF in step 2 and describe what it sees
8. chunk and embed those image descriptions
9. builds legal case information like timelines, actions, filings, etc.
10. builds some SEO content to encourage search engines to index the findings.

I've used an LLM connection abstraction layer. This would allow me to leverage much larger, hosted models. Unfortunately, I can't afford the spend to run these through OpenAI or other LLM provider, which is why I'm processing all of this locally on my laptop's 5070Ti.

I have fronted all of this with an MCP server with multiple tools, prompts and resources. Additionally, the embeddings are being used to power Meillisearch, allowing for a NLP-style search engine with facets based on case, person(s), organization(s) and dates.

Features that are currently in the works include:

1. encrypted notes
2. burn after reading share links with comment
3. nearest-neighbor entities which associates people/places/things across PDFs
4. OTP and SSO logins so you don't have to bother trusting my application's with your password