PAI-RAG is an open-source framework designed for modular Retrieval-Augmented Generation (RAG) systems. It targets businesses and developers seeking a production-ready RAG workflow, enabling truthful question-answering by integrating LLMs with flexible, configurable RAG modules.

How It Works

PAI-RAG employs a modular architecture, allowing customization of individual RAG components. It supports advanced RAG capabilities like multi-modal RAG, agentic RAG, and NL2SQL, built upon community open-source projects. An integrated multi-dimensional evaluation system aids in performance monitoring, complemented by LLM-based application tracing and visualization tools for iterative tuning.

Quick Start & Requirements

• Docker: Clone the repository, copy .env.example to .env (configure API keys/storage if needed), and run docker compose up -d. Access the UI at http://localhost:8680. Initial model download may take ~20 minutes.
• Local: Refer to the local development guide for setup.
• Dependencies: Requires Docker or a local Python environment. Specific LLM and vector store configurations may require additional dependencies or API keys (e.g., DashScope, OSS).
• Docs: API Specification, MultiModal RAG, Agentic RAG, Data Analysis.

Highlighted Details

• Supports multi-modal RAG (images) and agentic RAG with function calling.
• Includes data analysis capabilities for databases and spreadsheets.
• Offers an interactive UI and API for easy tuning and deployment.
• Supports a wide range of file types including .txt, .pdf, .docx, .csv, .jsonl, and image formats.

Maintenance & Community

• The project is hosted on GitHub under aigc-apps/PAI-RAG. Community links (Discord/Slack) are not explicitly mentioned in the README.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

• .doc and .ppt files require conversion to .docx and .pptx respectively before upload. The project's specific LLM and vector store dependencies are not fully detailed in the README.