aigc-apps/PAI-RAG

What it solves

PAI-RAG is an open-source Agentic RAG framework designed for enterprise-level complex knowledge processing and intelligent Q&A. It moves beyond traditional RAG by integrating knowledge base management, multimodal understanding, and an agentic reasoning loop to handle complex tasks that simple retrieval cannot solve.

How it works

The system uses a ReAct (Reasoning and Acting) agent engine that follows a "think-act-observe" loop, allowing it to plan tasks and call tools in parallel. It combines multiple retrieval strategies—including vector, full-text, and hybrid search with reranking—to fetch information from knowledge bases supporting over 20 document formats. It also integrates the Model Context Protocol (MCP) to extend its toolset and supports multimodal inputs (images and video) by extracting descriptions and indexing them.

Who it’s for

It is built for developers and enterprises who need to build sophisticated AI agents capable of data analysis, multimodal document understanding, and real-time web searching without requiring deep machine learning expertise.

Highlights

• Multimodal Capabilities: Extracts and indexes images from documents and supports video parsing to generate descriptions.
• Agentic Reasoning: Native support for Chain-of-Thought models (like DeepSeek-R1) and task planning for multi-step queries.
• Enterprise-Ready: Features multi-tenancy, RBAC (Role-Based Access Control) down to the chunk level, and OpenTelemetry for observability.
• Extensible Tooling: Includes a built-in Python code sandbox, Text-to-SQL for database queries, and deep MCP integration for external tool access.
• Broad Compatibility: Supports seven different vector database backends and any OpenAI-compatible LLM.