🎉 News

• [2025.10]🎯📢 🚀 We have released the technical report of RAG-Anything. Access it now to explore our latest research findings.

• [2025.08]🎯📢 🔍 RAG-Anything now features VLM-Enhanced Query mode! When documents include images, the system seamlessly integrates them into VLM for advanced multimodal analysis, combining visual and textual context for deeper insights.

• [2025.07]🎯📢 RAG-Anything now features a context configuration module, enabling intelligent integration of relevant contextual information to enhance multimodal content processing.

• [2025.07]🎯📢 🚀 RAG-Anything now supports multimodal query capabilities, enabling enhanced RAG with seamless processing of text, images, tables, and equations.

• [2025.07]🎯📢 🎉 RAG-Anything has reached 1k🌟 stars on GitHub! Thank you for your incredible support and valuable contributions to the project.

🌟 System Overview

Next-Generation Multimodal Intelligence

Modern documents increasingly contain diverse multimodal content—text, images, tables, equations, charts, and multimedia—that traditional text-focused RAG systems cannot effectively process. RAG-Anything addresses this challenge as a comprehensive All-in-One Multimodal Document Processing RAG system built on LightRAG.

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 single integrated framework. Unlike conventional RAG approaches that struggle with non-textual elements, our all-in-one system delivers comprehensive multimodal retrieval capabilities.

Users can query documents containing interleaved text, visual diagrams, structured tables, and mathematical formulations through one cohesive interface. This consolidated approach makes RAG-Anything particularly valuable for academic research, technical documentation, financial reports, and enterprise knowledge management where rich, mixed-content documents demand a unified processing framework.

🎯 Key Features

• 🔄 End-to-End Multimodal Pipeline - Complete workflow from document ingestion and parsing 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 types

• 🔗 Multimodal Knowledge Graph - Automatic entity extraction and cross-modal relationship discovery for enhanced understanding

• ⚡ Adaptive Processing Modes - Flexible MinerU-based parsing or direct multimodal content injection workflows

• 📋 Direct Content List Insertion - Bypass document parsing by directly inserting pre-parsed content lists from external sources

• 🎯 Hybrid Intelligent Retrieval - Advanced search capabilities spanning textual and multimodal content with contextual understanding

🏗️ Algorithm & Architecture

Core Algorithm

RAG-Anything implements an effective multi-stage multimodal pipeline that fundamentally extends traditional RAG architectures to seamlessly handle diverse content modalities through intelligent orchestration and cross-modal understanding.

📄

Document Parsing

→

🧠

Content Analysis

→

🔍

Knowledge Graph

→

🎯

Intelligent Retrieval

1. Document Parsing Stage

The system provides high-fidelity document extraction through adaptive content decomposition. It intelligently segments heterogeneous elements while preserving contextual relationships. Universal format compatibility is achieved via specialized optimized parsers.

Key Components:

• ⚙️ MinerU Integration: Leverages MinerU for high-fidelity document structure extraction and semantic preservation across complex layouts.

• 🧩 Adaptive Content Decomposition: Automatically segments documents into coherent text blocks, visual elements, structured tables, mathematical equations, and specialized content types while preserving contextual relationships.

• 📁 Universal Format Support: Provides comprehensive handling of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and emerging formats through specialized parsers with format-specific optimization.

2. Multi-Modal Content Understanding & Processing

The system automatically categorizes and routes content through optimized channels. It uses concurrent pipelines for parallel text and multimodal processing. Document hierarchy and relationships are preserved during transformation.

Key Components:

• 🎯 Autonomous Content Categorization and Routing: Automatically identify, categorize, and route different content types through optimized execution channels.

• ⚡ Concurrent Multi-Pipeline Architecture: Implements concurrent execution of textual and multimodal content through dedicated processing pipelines. This approach maximizes throughput efficiency while preserving content integrity.

• 🏗️ Document Hierarchy Extraction: Extracts and preserves original document hierarchy and inter-element relationships during content transformation.

3. Multimodal Analysis Engine

The system deploys modality-aware processing units for heterogeneous data modalities:

Specialized Analyzers:

• 🔍 Visual Content Analyzer:

Integrate vision model for image analysis. Generates context-aware descriptive captions based on visual semantics. Extracts spatial relationships and hierarchical structures between visual elements.

• 📊 Structured Data Interpreter:

Performs systematic interpretation of tabular and structured data formats. Implements statistical pattern recognition algorithms for data trend analysis. Identifies semantic relationships and dependencies across multiple tabular datasets.

• 📐 Mathematical Expression Parser:

Parses complex mathematical expressions and formulas with high accuracy. Provides native LaTeX format support for seamless integration with academic workflows. Establishes conceptual mappings between mathematical equations and domain-specific knowledge bases.

• 🔧 Extensible Modality Handler:

Provides configurable processing framework for custom and emerging content types. Enables dynamic integration of new modality processors through plugin architecture. Supports runtime configuration of processing pipelines for specialized use cases.

4. Multimodal Knowledge Graph Index

The multi-modal knowledge graph construction module transforms document content into structured semantic representations. It extracts multimodal entities, establishes cross-modal relationships, and preserves hierarchical organization. The system applies weighted relevance scoring for optimized knowledge retrieval.

Core Functions:

• 🔍 Multi-Modal Entity Extraction: Transforms significant multimodal elements into structured knowledge graph entities. The process includes semantic annotations and metadata preservation.

• 🔗 Cross-Modal Relationship Mapping: Establishes semantic connections and dependencies between textual entities and multimodal components. This is achieved through automated relationship inference algorithms.

• 🏗️ Hierarchical Structure Preservation: Maintains original document organization through "belongs_to" relationship chains. These chains preserve logical content hierarchy and sectional dependencies.

• ⚖️ Weighted Relationship Scoring: Assigns quantitative relevance scores to relationship types. Scoring is based on semantic proximity and contextual significance within the document structure.

5. Modality-Aware Retrieval

The hybrid retrieval system combines vector similarity search with graph traversal algorithms for comprehensive content retrieval. It implements modality-aware ranking mechanisms and maintains relational coherence between retrieved elements to ensure contextually integrated information delivery.

Retrieval Mechanisms:

• 🔀 Vector-Graph Fusion: Integrates vector similarity search with graph traversal algorithms. This approach leverages both semantic embeddings and structural relationships for comprehensive content retrieval.

• 📊 Modality-Aware Ranking: Implements adaptive scoring mechanisms that weight retrieval results based on content type relevance. The system adjusts rankings according to query-specific modality preferences.

• 🔗 Relational Coherence Maintenance: Maintains semantic and structural relationships between retrieved elements. This ensures coherent information delivery and contextual integrity.

🚀 Quick Start

Initialize Your AI Journey

Installation

Option 1: Install from PyPI (Recommended)

With optional dependencies for extended format support:

pip install 'raganything[all]' # All optional features pip install 'raganything[image]' # Image format conversion (BMP, TIFF, GIF, WebP) pip install 'raganything[text]' # Text file processing (TXT, MD) pip install 'raganything[image,text]' # Multiple features`

Option 2: Install from Source

Clone and setup the project with uv

git clone https://github.com/HKUDS/RAG-Anything.git cd RAG-Anything

Install the package and dependencies in a virtual environment

uv sync

If you encounter network timeouts (especially for opencv packages):

UV_HTTP_TIMEOUT=120 uv sync

Run commands directly with uv (recommended approach)

uv run python examples/raganything_example.py --help

Install with optional dependencies

uv sync --extra image --extra text # Specific extras uv sync --all-extras # All optional features`

Optional Dependencies

• [image] - Enables processing of BMP, TIFF, GIF, WebP image formats (requires Pillow)

• [text] - Enables processing of TXT and MD files (requires ReportLab)

• [all] - Includes all Python optional dependencies

⚠️ Office Document Processing Requirements:

• Office documents (.doc, .docx, .ppt, .pptx, .xls, .xlsx) require LibreOffice installation

• Download from LibreOffice official website

• Windows: Download installer from official website

• macOS: brew install --cask libreoffice

• Ubuntu/Debian: sudo apt-get install libreoffice

• CentOS/RHEL: sudo yum install libreoffice

Check MinerU installation:

Check if properly configured

python -c "from raganything import RAGAnything; rag = RAGAnything(); print('✅ MinerU installed properly' if rag.check_parser_installation() else '❌ MinerU installation issue')"`

Models are downloaded automatically on first use. For manual download, refer to MinerU Model Source Configuration.

Usage Examples

1. End-to-End Document Processing

async def main():

Set up API configuration

api_key = "your-api-key" base_url = "your-base-url" # Optional

Create RAGAnything configuration

config = RAGAnythingConfig( working_dir="./rag_storage", parser="mineru", # Parser selection: mineru, docling, or paddleocr parse_method="auto", # Parse method: auto, ocr, or txt enable_image_processing=True, enable_table_processing=True, enable_equation_processing=True, )

Define LLM model function

def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): return openai_complete_if_cache( "gpt-4o-mini", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=api_key, base_url=base_url, **kwargs, )

Define vision model function for image processing

def vision_model_func( prompt, system_prompt=None, history_messages=[], image_data=None, messages=None, kwargs ):

If messages format is provided (for multimodal VLM enhanced query), use it directly

if messages: return openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=messages, api_key=api_key, base_url=base_url, kwargs, )

Traditional single image format

elif image_data: return openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=[ {"role": "system", "content": system_prompt} if system_prompt else None, { "role": "user", "content": [ {"type": "text", "text": prompt}, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{image_data}" }, }, ], } if image_data else {"role": "user", "content": prompt}, ], api_key=api_key, base_url=base_url, kwargs, )

Pure text format

else: return llm_model_func(prompt, system_prompt, history_messages, kwargs)

Define embedding function

embedding_func = EmbeddingFunc( embedding_dim=3072, max_token_size=8192, func=lambda texts: openai_embed.func( texts, model="text-embedding-3-large", api_key=api_key, base_url=base_url, ), )

Initialize RAGAnything

rag = RAGAnything( config=config, llm_model_func=llm_model_func, vision_model_func=vision_model_func, embedding_func=embedding_func, )

Process a document

await rag.process_document_complete( file_path="path/to/your/document.pdf", output_dir="./output", parse_method="auto" )

Query the processed content

Pure text query - for basic knowledge base search

text_result = await rag.aquery( "What are the main findings shown in the figures and tables?", mode="hybrid" ) print("Text query result:", text_result)

Multimodal query with specific multimodal content

multimodal_result = await rag.aquery_with_multimodal( "Explain this formula and its relevance to the document content", multimodal_content=[{ "type": "equation", "latex": "P(d|q) = \frac{P(q|d) \cdot P(d)}{P(q)}", "equation_caption": "Document relevance probability" }], mode="hybrid" ) print("Multimodal query result:", multimodal_result)

if name == "main": asyncio.run(main())`

2. Direct Multimodal Content Processing

async def process_multimodal_content():

Set up API configuration

api_key = "your-api-key" base_url = "your-base-url" # Optional

Initialize LightRAG

rag = LightRAG( working_dir="./rag_storage", llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( "gpt-4o-mini", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=api_key, base_url=base_url, **kwargs, ), embedding_func=EmbeddingFunc( embedding_dim=3072, max_token_size=8192, func=lambda texts: openai_embed.func( texts, model="text-embedding-3-large", api_key=api_key, base_url=base_url, ), ) ) await rag.initialize_storages()

Process an image

image_processor = ImageModalProcessor( lightrag=rag, modal_caption_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=[ {"role": "system", "content": system_prompt} if system_prompt else None, {"role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}} ]} if image_data else {"role": "user", "content": prompt} ], api_key=api_key, base_url=base_url, **kwargs, ) if image_data else openai_complete_if_cache( "gpt-4o-mini", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=api_key, base_url=base_url, kwargs, ) )

image_content = { "img_path": "path/to/image.jpg", "image_caption": ["Figure 1: Experimental results"], "image_footnote": ["Data collected in 2024"] }

description, entity_info = await image_processor.process_multimodal_content( modal_content=image_content, content_type="image", file_path="research_paper.pdf", entity_name="Experimental Results Figure" )

Process a table

table_processor = TableModalProcessor( lightrag=rag, modal_caption_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( "gpt-4o-mini", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=api_key, base_url=base_url, **kwargs, ) )

table_content = { "table_body": """

description, entity_info = await table_processor.process_multimodal_content( modal_content=table_content, content_type="table", file_path="research_paper.pdf", entity_name="Performance Results Table" )

if name == "main": asyncio.run(process_multimodal_content())`

3. Batch Processing

4. Custom Modal Processors

from raganything.modalprocessors import GenericModalProcessor

class CustomModalProcessor(GenericModalProcessor): async def process_multimodal_content(self, modal_content, content_type, file_path, entity_name):

Your custom processing logic

enhanced_description = await self.analyze_custom_content(modal_content) entity_info = self.create_custom_entity(enhanced_description, entity_name) return await self.create_entity_and_chunk(enhanced_description, entity_info, file_path)`

5. Query Options

RAG-Anything provides three types of query methods:

Pure Text Queries - Direct knowledge base search using LightRAG:

Synchronous version

sync_text_result = rag.query("Your question", mode="hybrid")`

VLM Enhanced Queries - Automatically analyze images in retrieved context using VLM:

vlm_enhanced=True is automatically set when vision_model_func is available

)

Manually control VLM enhancement

vlm_enabled = await rag.aquery( "What do the images show in this document?", mode="hybrid", vlm_enhanced=True # Force enable VLM enhancement )

vlm_disabled = await rag.aquery( "What do the images show in this document?", mode="hybrid", vlm_enhanced=False # Force disable VLM enhancement )

When documents contain images, VLM can see and analyze them directly

The system will automatically:

1. Retrieve relevant context containing image paths

2. Load and encode images as base64

3. Send both text context and images to VLM for comprehensive analysis`

Multimodal Queries - Enhanced queries with specific multimodal content analysis:

Query with equation content

equation_result = await rag.aquery_with_multimodal( "Explain this formula and its relevance to the document content", multimodal_content=[{ "type": "equation", "latex": "P(d|q) = \frac{P(q|d) \cdot P(d)}{P(q)}", "equation_caption": "Document relevance probability" }], mode="hybrid" )`

6. Loading Existing LightRAG Instance

async def load_existing_lightrag():

Set up API configuration

api_key = "your-api-key" base_url = "your-base-url" # Optional

First, create or load existing LightRAG instance

lightrag_working_dir = "./existing_lightrag_storage"

Check if previous LightRAG instance exists

if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir): print("✅ Found existing LightRAG instance, loading...") else: print("❌ No existing LightRAG instance found, will create new one")

Create/load LightRAG instance with your configuration

lightrag_instance = LightRAG( working_dir=lightrag_working_dir, llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( "gpt-4o-mini", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=api_key, base_url=base_url, **kwargs, ), embedding_func=EmbeddingFunc( embedding_dim=3072, max_token_size=8192, func=lambda texts: openai_embed.func( texts, model="text-embedding-3-large", api_key=api_key, base_url=base_url, ), ) )

Initialize storage (this will load existing data if available)

await lightrag_instance.initialize_storages() await initialize_pipeline_status()

Define vision model function for image processing

def vision_model_func( prompt, system_prompt=None, history_messages=[], image_data=None, messages=None, kwargs ):

If messages format is provided (for multimodal VLM enhanced query), use it directly

if messages: return openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=messages, api_key=api_key, base_url=base_url, kwargs, )

Traditional single image format

elif image_data: return openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=[ {"role": "system", "content": system_prompt} if system_prompt else None, { "role": "user", "content": [ {"type": "text", "text": prompt}, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{image_data}" }, }, ], } if image_data else {"role": "user", "content": prompt}, ], api_key=api_key, base_url=base_url, kwargs, )

Pure text format

else: return lightrag_instance.llm_model_func(prompt, system_prompt, history_messages, kwargs)

Now use existing LightRAG instance to initialize RAGAnything

rag = RAGAnything( lightrag=lightrag_instance, # Pass existing LightRAG instance vision_model_func=vision_model_func,

Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance

)

Query existing knowledge base

result = await rag.aquery( "What data has been processed in this LightRAG instance?", mode="hybrid" ) print("Query result:", result)

Add new multimodal document to existing LightRAG instance

await rag.process_document_complete( file_path="path/to/new/multimodal_document.pdf", output_dir="./output" )

if name == "main": asyncio.run(load_existing_lightrag())`

7. Direct Content List Insertion

For scenarios where you already have a pre-parsed content list (e.g., from external parsers or previous processing), you can directly insert it into RAGAnything without document parsing:

async def insert_content_list_example():

Set up API configuration

api_key = "your-api-key" base_url = "your-base-url" # Optional

Create RAGAnything configuration

config = RAGAnythingConfig( working_dir="./rag_storage", enable_image_processing=True, enable_table_processing=True, enable_equation_processing=True, )

Define model functions

def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): return openai_complete_if_cache( "gpt-4o-mini", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=api_key, base_url=base_url, **kwargs, )

def vision_model_func(prompt, system_prompt=None, history_messages=[], image_data=None, messages=None, kwargs):

If messages format is provided (for multimodal VLM enhanced query), use it directly

if messages: return openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=messages, api_key=api_key, base_url=base_url, kwargs, )

Traditional single image format

elif image_data: return openai_complete_if_cache( "gpt-4o", "", system_prompt=None, history_messages=[], messages=[ {"role": "system", "content": system_prompt} if system_prompt else None, { "role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}} ], } if image_data else {"role": "user", "content": prompt}, ], api_key=api_key, base_url=base_url, kwargs, )

Pure text format

else: return llm_model_func(prompt, system_prompt, history_messages, kwargs)

embedding_func = EmbeddingFunc( embedding_dim=3072, max_token_size=8192, func=lambda texts: openai_embed.func( texts, model="text-embedding-3-large", api_key=api_key, base_url=base_url, ), )

Initialize RAGAnything

rag = RAGAnything( config=config, llm_model_func=llm_model_func, vision_model_func=vision_model_func, embedding_func=embedding_func, )

Example: Pre-parsed content list from external source

content_list = [ { "type": "text", "text": "This is the introduction section of our research paper.", "page_idx": 0 # Page number where this content appears }, { "type": "image", "img_path": "/absolute/path/to/figure1.jpg", # IMPORTANT: Use absolute path "image_caption": ["Figure 1: System Architecture"], "image_footnote": ["Source: Authors' original design"], "page_idx": 1 # Page number where this image appears }, { "type": "table", "table_body": "| Method | Accuracy | F1-Score |\n|--------|----------|----------|\n| Ours | 95.2% | 0.94 |\n| Baseline | 87.3% | 0.85 |", "table_caption": ["Table 1: Performance Comparison"], "table_footnote": ["Results on test dataset"], "page_idx": 2 # Page number where this table appears }, { "type": "equation", "latex": "P(d|q) = \frac{P(q|d) \cdot P(d)}{P(q)}", "text": "Document relevance probability formula", "page_idx": 3 # Page number where this equation appears }, { "type": "text", "text": "In conclusion, our method demonstrates superior performance across all metrics.", "page_idx": 4 # Page number where this content appears } ]

Insert the content list directly

await rag.insert_content_list( content_list=content_list, file_path="research_paper.pdf", # Reference file name for citation split_by_character=None, # Optional text splitting split_by_character_only=False, # Optional text splitting mode doc_id=None, # Optional custom document ID (will be auto-generated if not provided) display_stats=True # Show content statistics )

Query the inserted content

result = await rag.aquery( "What are the key findings and performance metrics mentioned in the research?", mode="hybrid" ) print("Query result:", result)

You can also insert multiple content lists with different document IDs

another_content_list = [ { "type": "text", "text": "This is content from another document.", "page_idx": 0 # Page number where this content appears }, { "type": "table", "table_body": "| Feature | Value |\n|---------|-------|\n| Speed | Fast |\n| Accuracy | High |", "table_caption": ["Feature Comparison"], "page_idx": 1 # Page number where this table appears } ]

await rag.insert_content_list( content_list=another_content_list, file_path="another_document.pdf", doc_id="custom-doc-id-123" # Custom document ID )

if name == "main": asyncio.run(insert_content_list_example())`

Content List Format:

The content_list should follow the standard format with each item being a dictionary containing:

• Text content: {"type": "text", "text": "content text", "page_idx": 0}

• Image content: {"type": "image", "img_path": "/absolute/path/to/image.jpg", "image_caption": ["caption"], "image_footnote": ["note"], "page_idx": 1}

• Table content: {"type": "table", "table_body": "markdown table", "table_caption": ["caption"], "table_footnote": ["note"], "page_idx": 2}

• Equation content: {"type": "equation", "latex": "LaTeX formula", "text": "description", "page_idx": 3}

• Generic content: {"type": "custom_type", "content": "any content", "page_idx": 4}

Important Notes:

• img_path: Must be an absolute path to the image file (e.g., /home/user/images/chart.jpg or C:\Users\user\images\chart.jpg)

• page_idx: Represents the page number where the content appears in the original document (0-based indexing)

• Content ordering: Items are processed in the order they appear in the list

This method is particularly useful when:

• You have content from external parsers (non-MinerU/Docling)

• You want to process programmatically generated content

• You need to insert content from multiple sources into a single knowledge base

• You have cached parsing results that you want to reuse

🛠️ Examples

Practical Implementation Demos

The examples/ directory contains comprehensive usage examples:

• raganything_example.py: End-to-end document processing with MinerU

• modalprocessors_example.py: Direct multimodal content processing

• office_document_test.py: Office document parsing test with MinerU (no API key required)

• image_format_test.py: Image format parsing test with MinerU (no API key required)

• text_format_test.py: Text format parsing test with MinerU (no API key required)

Run examples:

Direct modal processing

python examples/modalprocessors_example.py --api-key YOUR_API_KEY

Office document parsing test (MinerU only)

python examples/office_document_test.py --file path/to/document.docx

Image format parsing test (MinerU only)

python examples/image_format_test.py --file path/to/image.bmp

Text format parsing test (MinerU only)

python examples/text_format_test.py --file path/to/document.md

Check LibreOffice installation

python examples/office_document_test.py --check-libreoffice --file dummy

Check PIL/Pillow installation

python examples/image_format_test.py --check-pillow --file dummy

Check ReportLab installation

python examples/text_format_test.py --check-reportlab --file dummy`

🔧 Configuration

System Optimization Parameters

Environment Variables

Create a .env file (refer to .env.example):

Note: For backward compatibility, legacy environment variable names are still supported:

• MINERU_PARSE_METHOD is deprecated, please use PARSE_METHOD

Note: API keys are only required for full RAG processing with LLM integration. The parsing test files (office_document_test.py and image_format_test.py) only test parser functionality and do not require API keys.

Parser Configuration

RAGAnything now supports multiple parsers, each with specific advantages:

MinerU Parser

• Supports PDF, images, Office documents, and more formats

• Powerful OCR and table extraction capabilities

• GPU acceleration support

Docling Parser

• Optimized for Office documents and HTML files

• Better document structure preservation

• Native support for multiple Office formats

PaddleOCR Parser

• OCR-focused parser for images and PDFs

• Produces text blocks compatible with existing content_list processing

• Supports optional Office/TXT/MD parsing by converting to PDF first

Install PaddleOCR parser extras:

pip install -e ".[paddleocr]"

or

uv sync --extra paddleocr`

Note: PaddleOCR also requires paddlepaddle (CPU/GPU package varies by platform). Install it with the official guide: https://www.paddlepaddle.org.cn/install/quick

MinerU Configuration

# MinerU 2.0 uses command-line parameters instead of config files

Check available options:

mineru --help

Common configurations:

mineru -p input.pdf -o output_dir -m auto # Automatic parsing mode mineru -p input.pdf -o output_dir -m ocr # OCR-focused parsing mineru -p input.pdf -o output_dir -b pipeline --device cuda # GPU acceleration`

You can also configure parsing through RAGAnything parameters:

Advanced parsing configuration with special parameters

await rag.process_document_complete( file_path="document.pdf", output_dir="./output/", parse_method="auto", # Parsing method: "auto", "ocr", "txt" parser="mineru", # Parser selection: "mineru", "docling", or "paddleocr"

MinerU special parameters - all supported kwargs:

lang="ch", # Document language for OCR optimization (e.g., "ch", "en", "ja") device="cuda:0", # Inference device: "cpu", "cuda", "cuda:0", "npu", "mps" start_page=0, # Starting page number (0-based, for PDF) end_page=10, # Ending page number (0-based, for PDF) formula=True, # Enable formula parsing table=True, # Enable table parsing backend="pipeline", # Parsing backend: pipeline|hybrid-auto-engine|hybrid-http-client|vlm-auto-engine|vlm-http-client. source="huggingface", # Model source: "huggingface", "modelscope", "local"

vlm_url="http://127.0.0.1:3000" # Service address when using backend=vlm-http-client

Standard RAGAnything parameters

display_stats=True, # Display content statistics split_by_character=None, # Optional character to split text by doc_id=None # Optional document ID )`

Note: MinerU 2.0 no longer uses the magic-pdf.json configuration file. All settings are now passed as command-line parameters or function arguments. RAG-Anything supports multiple document parsers, including MinerU, Docling, and PaddleOCR.

Processing Requirements

Different content types require specific optional dependencies:

• Office Documents (.doc, .docx, .ppt, .pptx, .xls, .xlsx): Install LibreOffice

• Extended Image Formats (.bmp, .tiff, .gif, .webp): Install with pip install raganything[image]

• Text Files (.txt, .md): Install with pip install raganything[text]

• PaddleOCR Parser (parser="paddleocr"): Install with pip install raganything[paddleocr], then install paddlepaddle for your platform

📋 Quick Install: Use pip install raganything[all] to enable all format support (Python dependencies only - LibreOffice still needs separate installation)

🧪 Supported Content Types

Document Formats

• PDFs - Research papers, reports, presentations

• Office Documents - DOC, DOCX, PPT, PPTX, XLS, XLSX

• Images - JPG, PNG, BMP, TIFF, GIF, WebP

• Text Files - TXT, MD

Multimodal Elements

• Images - Photographs, diagrams, charts, screenshots

• Tables - Data tables, comparison charts, statistical summaries

• Equations - Mathematical formulas in LaTeX format

• Generic Content - Custom content types via extensible processors

For installation of format-specific dependencies, see the Configuration section.

📖 Citation

Academic Reference

📖

If you find RAG-Anything useful in your research, please cite our paper:

🔗 Related Projects

Ecosystem & Extensions

⭐ Star History

Community Growth Trajectory

🤝 Contribution

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We thank all our contributors for their valuable contributions.

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Building the Future of Multimodal AI