Knowledge GPT
by Community
Accurate answers and instant citations for your documents.
OSS
Knowledge GPT
Added 1 June 2026
Overview
Knowledge GPT is an open-source Python tool that enables users to query their documents and receive accurate answers with instant citations. It works by indexing uploaded files and using a language model to retrieve relevant passages, making document-based question answering straightforward.
Best for
Best for
Developers and researchers who need a lightweight RAG solution for document Q&A with citation transparency.
Use cases
- Extract specific answers from research papers or reports
- Quickly review legal contracts for key clauses
- Build a custom Q&A system for internal knowledge bases
Notes
Knowledge GPT is an open-source Python tool that enables users to query their documents and receive accurate answers with instant citations. It works by indexing uploaded files and using a language model to retrieve relevant passages, making document-based question answering straightforward.
1,645 stars on GitHub. Last updated 2024-05-29. Licensed MIT.
Use cases
- Extract specific answers from research papers or reports
- Quickly review legal contracts for key clauses
- Build a custom Q&A system for internal knowledge bases
Pros
- Simple setup and clear documentation for quick deployment
- Provides source citations with every answer for verifiability
- Free and open-source with active community support
Cons
- Limited to text-based document formats (PDF, TXT) without image or table parsing
- No built-in support for handling very large document collections efficiently
- Requires local or cloud LLM setup which can be resource-intensive
Indexed from awesome-langchain and enriched against its public facts.
Pros
- Simple setup and clear documentation for quick deployment
- Provides source citations with every answer for verifiability
- Free and open-source with active community support
Cons
- Limited to text-based document formats (PDF, TXT) without image or table parsing
- No built-in support for handling very large document collections efficiently
- Requires local or cloud LLM setup which can be resource-intensive
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