Epstein emails graph relationship extraction and visualizer
Posted by madmax_br5@reddit | LocalLLaMA | View on Reddit | 5 comments
I built this visualizer with the help of claude code: https://github.com/maxandrews/Epstein-doc-explorer
There is a hosted version linked in the repo, I can't paste it here because reddit inexplicably banned the link sitewide (see my post history for details if you're interested).
It uses the claude agents framework (so you can use your MAX plan inference budget if you have one) to extract relationships triple, tags, and other metadata from the documents, then clusters tags with qwen instruct embeddings, dedupes actor names into an alias table, and serves it all in a nice UI. If you don't have a max plan, you can fork and refactor to use any other capable LLM.
Analysis Pipeline Features
- AI-Powered Extraction: Uses Claude to extract entities, relationships, and events from documents
- Semantic Tagging: Automatically tags triples with contextual metadata (legal, financial, travel, etc.)
- Tag Clustering: Groups 28,000+ tags into 30 semantic clusters using K-means for better filtering
- Entity Deduplication: Merges duplicate entities using LLM-based similarity detection
- Incremental Processing: Supports analyzing new documents without reprocessing everything
- Top-3 Cluster Assignment: Each relationship is assigned to its 3 most relevant tag clusters
Visualization Features
- Interactive Network Graph: Force-directed graph with 15,000+ relationships
- Actor-Centric Views: Click any actor to see their specific relationships
- Smart Filtering: Filter by 30 content categories (Legal, Financial, Travel, etc.)
- Timeline View: Chronological relationship browser with document links
- Document Viewer: Full-text document display with highlighting
- Responsive Design: Works on desktop and mobile devices
- Performance Optimized: Uses materialized database columns for fast filtering
AlwaysLateToThaParty@reddit
I wonder how long it's going to take AI tools to unredact the names from the source material.
Jumpy_Maple@reddit
I have local LLMs on my Mac Studio. Like 512 ram. How do I do this ? I use LLM Studio
Chromix_@reddit
Highly political topic with some strong interests tied to it. Yet you're not taking a stance here, but merely offer a tool that others can use to inform themselves about it. It should be allowed, given that the general discussing around the topic is allowed in different places here.
Your project isn't "local" though, as the analysis pipeline is tied to Claude. You might get some more points with this on r/dataisbeautiful
madmax_br5@reddit (OP)
I do however use local qwen3 embeddings for the tag clustering, so it's partly local! Tell you what, I'll make it easier to substitute the claude pipeline with other providers or local models, I really just did it this way so I could use my existing claude max budget effectively and because it was in the interest of speed.
Chromix_@reddit
It makes sense for these kind of burst workloads where time-to-result matters. Mistral 7B was used in the other extraction approach. A reasoning model at least 5 times that size will likely achieve nicer results. Running that would take a while though.