HomeProjectsArticlesPK BoardAbout
Explore Projects

DocsGPT

Active
GitHub Python MIT

Description

A private AI platform for agents, assistants, and enterprise search with built-in agent builder, deep research, document analysis, and multi-model support.

Key Features

  • Wide format support: PDF, DOCX, CSV, XLSX, audio files and more
  • Speech workflows: voice recording, transcription, and searchable meeting notes
  • Agent Builder: visual AI workflow builder with conditional nodes
  • Reliable sourced answers: hallucination-free responses with viewable citations
  • Multi-model support: OpenAI, Google, Anthropic and local models (Ollama, llama_cpp)
  • Pre-built integrations: HTML/React chat widgets, Discord/Telegram bots

Use Cases

💡 Privately deployed enterprise knowledge base Q&A systems
💡 Intelligent assistants and customer service bots based on internal documents
💡 Unified search and analysis across multiple sources (web, GitHub, Reddit)
💡 Enterprise decision support requiring reliable sourcing and audit trails

Strengths & Limitations

✅ Strengths

  • • Actively maintained, recent updates
  • • High community interest (18.2k stars)
  • • Permissive open-source license (MIT)
  • • Established track record (3 years in production)

Tags

document-qa rag knowledge-base enterprise-search python

Categories

📚 RAG Tools

Quick Start

Clone the repository and run ./setup.sh (macOS/Linux) or .\setup.ps1 (Windows), follow the prompts to choose a deployment option, then navigate to http://localhost:5173/
Visit GitHub Visit Website

Related Projects

RAG-Anything

22.9k · Python
Active A+

All-in-one RAG framework supporting text, images, tables, equations and more document formats for retrieval-augmented generation with unified knowledge QA.

ragmultimodalknowledge-base +2
  • · End-to-end multimodal pipeline — Complete workflow from document ingestion to intelligent multimodal query answering
  • · Universal document support — Seamless processing of PDFs, Office documents, images and diverse file formats
  • · Specialized content analysis — Dedicated processors for images, tables, mathematical equations and heterogeneous content
View full details → View on GitHub

RAGFlow

88.6k · Go
Active A+

A leading open-source RAG engine that fuses cutting-edge retrieval-augmented generation with agent capabilities to create a superior context layer for LLMs.

ragdocument-understandingknowledge-base +3
  • · Deep document understanding - Extracts knowledge from complex unstructured data via deepdoc, supporting PDF, Word, PPT, Excel, images, and more
  • · Template-based chunking - Multiple intelligent chunking templates for explainable document segmentation and knowledge organization
  • · Traceable citations with reduced hallucinations - Visualizes text chunks for human intervention, answers include key reference sources
View full details → View on GitHub

QAnything

14.1k · Python
Stale C

QAnything is an open-source local knowledge base Q&A system by NetEase Youdao, supporting any file format with offline RAG capabilities for building private knowledge Q&A.

ragqaknowledge-base +1
  • · Local knowledge base Q&A supporting PDF, Word, PPT, XLS, Markdown, images, CSV, and more
  • · Two-stage vector retrieval with embedding + reranking for scalable accuracy
  • · Full offline operation — supports air-gapped installation with no external network required
View full details → View on GitHub

RAGatouille

4.0k · Python
Stale C

Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

ragpythonembedding +1
  • · ColBERT late-interaction retrieval: wraps advanced ColBERT models into simple APIs, enabling late-interaction retrieval methods in RAG pipelines without deep IR research knowledge
  • · End-to-end training and fine-tuning: built-in RAGTrainer and TrainingDataProcessor with automatic deduplication, positive/negative pairing and hard negative mining, supporting training from scratch or fine-tuning pretrained models
  • · Modular composable architecture: DataProcessor, NegativeMiner and other components can be used independently, with support for custom NegativeMiner integration into training pipelines
View on GitHub