# Dump-ster Diver
## Dump-ster Diver
[](https://www.docker.com/) [](https://www.python.org/) [](https://neo4j.com/) [](https://flask.palletsprojects.com/)
**A Knowledge Graph Forensics System for Document Analysis**
Dump-ster Diver is an AI-powered document analysis system that extracts entities, relationships, and insights from large document collections. This tool transforms unstructured documents into an interconnected knowledge graph, making it easier to discover patterns, connections, and key information across thousands of files - whether you're analyzing legal documents, corporate archives, email dumps, or any other large-scale document repository.
[View on Github](https://github.com/thebriandurham/dump-ster-diver)
### Features
#### 🎯 Processing Mode
**Simple Processing Mode** (Recommended for Large Collections)
* Generates concise 1-2 sentence summaries for each document
* Automatic document type classification (email, memo, legal-document, chat, etc.)
* Intelligent tag extraction for easy filtering and search
* Faster processing ideal for initial document review
* Review status tracking and flagging system
#### 📄 Supported Document Types
* **Text Files**: `.txt`
* **Images**: `.jpg`, `.jpeg`, `.png`, `.gif`, `.tif` (OCR via vision models)
#### 🎨 Interactive Web Interface
* **Windows 95-inspired retro UI** for nostalgia and clarity
* Filter documents by type, tags, review status, and flags
* Document detail viewer with inline text/image display
* Real-time processing progress monitoring
* Dark/light mode toggle
#### 🤖 Configurable AI Models
* Note: these are just what I used while testing this project
* **Text Models**: llama3.2:3b, qwen3:4b-instruct, qwen3:8b, qwen3:30b
* **Vision Models**: gemma3:4b, gemma3:12b, qwen3-vl:8b, qwen3-vl:32b
* Switch models on-the-fly through the UI
* Powered by [Ollama](https://ollama.ai/) for local, privacy-focused AI
#### 💾 Graph Database Storage
* Neo4j 5.13 with APOC plugins for advanced graph operations
* Efficient querying of entity relationships
* Built-in similarity relationships between documents
* Persistent storage with Docker volumes
***
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# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.bdurham.dev/r-and-d/dump-ster-diver.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.