Overview
DocMind is a Retrieval-Augmented Generation (RAG) system that answers questions from technical PDFs. It uses semantic retrieval, reranking, and source-grounded generation to produce answers. The project explores whether a RAG pipeline can provide accurate answers while explicitly showing the document passages used to generate each response.
The objective is to build a question-answering system that retrieves relevant document chunks for a user query, generates an answer using an LLM, provides citations to the retrieved sources, and minimizes unsupported or hallucinated responses.
Key features include semantic document retrieval, hybrid search, reranking of retrieved chunks, citation-aware responses, streaming LLM responses, and hallucination detection.
Pricing
Free
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Use Cases
- Teams use DocMind to retrieve relevant document chunks for a user query.
- Teams use DocMind to generate answers using an LLM.
- Teams use DocMind to provide citations to retrieved sources.
- Teams use DocMind to minimize unsupported or hallucinated responses.
- Teams use DocMind to perform semantic document retrieval.
- Teams use DocMind to perform hybrid search.
Features
AI assistant
Natural language interface
Workflow automation
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