What file types and content modes does RAG Anything support?

It supports PDF, DOCX, PPTX, TXT, and MD files, with full multimodal content extraction for images, tables, equations, charts, and text using advanced AI models like GPT-4V.

What is RAG Anything?

RAG Anything is an MCP server offering comprehensive multimodal Retrieval-Augmented Generation (RAG) capabilities to process and query entire directories of documents, including text, images, tables, and equations.

How does RAG Anything allow me to query my documents?

It offers advanced querying with multiple modes (e.g., hybrid, local, global, mix) for both pure text and multimodal-enhanced queries, leveraging persistently stored RAG instances for efficient access.

Can I process large collections of documents with RAG Anything?

Yes, RAG Anything is built for batch processing entire directories of documents, enabling efficient indexing and querying of vast datasets with options for concurrent processing (max_workers).

RAG Anything

Name: RAG Anything
Author: jesse-merhi

byjesse-merhi

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Content Management

Data Science & ML

Learning & Documentation

Processes and queries directories of documents using multimodal Retrieval-Augmented Generation (RAG) capabilities.

RAG Anything provides a robust Model Context Protocol (MCP) server designed to deliver comprehensive Retrieval-Augmented Generation (RAG) functionalities. It excels at processing and querying large collections of documents, supporting full multimodal content extraction including images, tables, and equations. This server enables batch processing of entire directories, offers advanced querying with various modes (hybrid, local, global, naive, mix, bypass), and ensures persistent storage of RAG instances for efficient and repeatable information retrieval from diverse file types like PDFs, DOCX, and TXT.

Key Features

01Advanced Querying with Multiple Modes (Hybrid, Local, Global, Naive, Mix, Bypass)

02End-to-End Document Processing with Multimodal Content Extraction

03Persistent Storage for Processed RAG Instances

04Comprehensive Multimodal RAG (Images, Tables, Equations, Charts)

05Batch Processing of Entire Directories

061 GitHub stars

Use Cases

01Performing complex queries that combine pure text input with external multimodal content for enhanced analytical insights.

02Building a searchable knowledge base from a collection of diverse documents, including research papers and corporate archives.

03Automating the extraction, indexing, and querying of information across large volumes of structured and unstructured documents.