A Python library that provides an integrated framework to process and query multimodal document content, combining ingestion, specialized analysis, and hybrid vector-graph retrieval.
Project overview
It addresses the inability of conventional text-only retrieval systems to process documents containing mixed visual, tabular, and structural content.
Project type
RAG · Vector Database · Document Processing
Use cases
Documents & Office · Knowledge Q&A
Deployment
Refer to project documentation
License
MIT
Best for
Developers and researchers who need a single library to handle the ingestion, parsing, and hybrid retrieval of diverse multimodal document formats.
Key capabilities
Provides a complete workflow spanning from document ingestion and parsing to multimodal query answering.
Processes PDFs, images, Office documents, and other supported file formats.
Uses dedicated processors to analyze images, tables, mathematical equations, and heterogeneous content.
Retrieves multimodal content by combining vector similarity search with graph traversal.
The documented end-to-end example requires an API key and optionally accepts a base URL for its configured model functions.
The documented example configures gpt-4o-mini as the LLM model, gpt-4o for image processing, and text-embedding-3-large for embeddings.
Processing Office documents requires a separate LibreOffice installation.
Getting started
Basic installation involves a single pip command. Full document-format support may require optional Python extras and a separate LibreOffice installation. The source-install route requires installing uv, cloning the repository, and synchronizing dependencies.
Alternatives and comparisons
Converts PDF, image, and Office inputs into machine-readable formats such as Markdown and JSON for downstream retrieval and extraction.
README: from raganything import RAGAnything, RAGAnythingConfig
README: Modern documents increasingly contain diverse multimodal content—text, images, tables, equations, charts, and multimedia—that traditional text-focused RAG systems cannot effective…
README: As a unified solution, RAG-Anything **eliminates the need for multiple specialized tools**. It provides **seamless processing and querying across all content modalities** within a…
Release: v1.3.1
README: - **🔄 End-to-End Multimodal Pipeline** - Complete workflow from document ingestion and parsing to intelligent multimodal query answering