DocuMind
An AI assistant for technical documents, with semantic search and source-grounded answers.
DocuMind is a project in development: an AI assistant meant to answer questions about technical documentation, grounded in the actual source text rather than the model's guesses.
The problem
Finding an answer in large technical documentation takes time, and a plain LLM chat with no document grounding can give a plausible but wrong answer.
The solution
DocuMind is aimed at semantic search inside uploaded documents and building an answer tied to the source fragments it found — showing where the answer came from, instead of just the model's guess.
Key features
- 01Document ingestion and processing (in development)
- 02Semantic search over document contents (in development)
- 03Answers grounded in retrieved sources — RAG (in development)
- 04Citations/source links displayed (planned)
- 05Basic prompt-injection defenses (planned)
Interface
Project interface will be added once materials are ready.
Architecture
Planned architecture: documents are split into chunks and turned into embeddings, stored in a vector database (pgvector); the answer is built through RAG — retrieving relevant chunks and generating a response grounded in them.
Limitations of this demo
- — The project is under active development: the features listed describe direction, not a confirmed finished result.
- — Live Demo, video, and verification data aren't available yet.
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