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Knowledge GPT

by Community

Accurate answers and instant citations for your documents.

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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