┌─────────────────────────────────────────────────────────┐
│                   MCP Clients                           │
│  (VS Code, Claude Desktop, Custom Apps, etc.)           │
└─────────────────────────────────────────────────────────┘
                         │
                    MCP Protocol
                         │
┌─────────────────────────────────────────────────────────┐
│              RAG-Anything MCP Server                    │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Document Processing Tools                       │   │
│  │  - upload_document                               │   │
│  │  - process_document                              │   │
│  │  - batch_process                                 │   │
│  └──────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Query Tools                                     │   │
│  │  - query_text                                    │   │
│  │  - query_multimodal                              │   │
│  │  - query_with_context                            │   │
│  └──────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Management Tools                                │   │
│  │  - list_documents                                │   │
│  │  - get_document_info                             │   │
│  │  - delete_document                               │   │
│  └──────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│                  RAG-Anything Core                      │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │   MinerU     │  │   LightRAG   │  │     VLM      │  │
│  │   Parser     │  │  Knowledge   │  │   Analysis   │  │
│  │              │  │    Graph     │  │              │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘

# Clone repository
git clone <your-repo-url>
cd twin-editor-rag

# Install dependencies
pip install -e .

# Or with development dependencies
pip install -e ".[dev]"

# API Configuration
OPENAI_API_KEY=your_openai_api_key
OPENAI_BASE_URL=https://api.openai.com/v1  # Optional

# Model Configuration
LLM_MODEL=gpt-4o-mini
VISION_MODEL=gpt-4o
EMBEDDING_MODEL=text-embedding-3-large
EMBEDDING_DIM=3072

# Model Profile Configuration (optional)
MODEL_PROFILE=openai_default
MODEL_PROFILES_PATH=./model_profiles.json
# Or set MODEL_PROFILES to a JSON object string with profile definitions

# Storage Configuration
RAG_STORAGE_DIR=./rag_storage
UPLOAD_DIR=./uploads
MAX_FILE_SIZE=100  # MB

# Server Configuration
LOG_LEVEL=INFO

# Start MCP server
python src/server.py

# Or use with specific config
python src/server.py --config config.json

{
  "mcp.servers": {
    "rag-anything": {
      "command": "python",
      "args": ["/path/to/twin-editor-rag/src/server.py"],
      "env": {
        "OPENAI_API_KEY": "your_key"
      }
    }
  }
}

{
  "mcpServers": {
    "rag-anything": {
      "command": "python",
      "args": ["/path/to/twin-editor-rag/src/server.py"],
      "env": {
        "OPENAI_API_KEY": "your_key"
      }
    }
  }
}

# Parameters:
- file_path: str - Path to document file
- doc_id: Optional[str] - Custom document ID

# Returns:
- doc_id: str - Document identifier
- status: str - Upload status

# Parameters:
- doc_id: str - Document identifier
- parser: Optional[str] - Parser to use (mineru/docling)
- parse_method: Optional[str] - Parse method (auto/ocr/txt)

# Returns:
- doc_id: str
- status: str
- stats: dict - Processing statistics

# Parameters:
- file_paths: List[str] - List of file paths
- max_concurrent: Optional[int] - Max parallel processing

# Returns:
- results: List[dict] - Processing results

# Parameters:
- query: str - Query text
- mode: str - Query mode (hybrid/local/global/naive)
- top_k: Optional[int] - Number of results

# Returns:
- answer: str - RAG answer
- sources: List[dict] - Source documents

# Parameters:
- query: str - Query text
- multimodal_content: List[dict] - Multimodal content
- mode: str - Query mode

# Returns:
- answer: str - RAG answer with multimodal understanding

# Returns:
- documents: List[dict] - Document list with metadata

# Parameters:
- doc_id: str - Document identifier

# Returns:
- doc_info: dict - Detailed document information

# Parameters:
- doc_id: str - Document identifier

# Returns:
- status: str - Deletion status

# config.json
{
  "rag_config": {
    "enable_image_processing": true,
    "enable_table_processing": true,
    "enable_equation_processing": true,
    "context_window": 2,
    "max_context_tokens": 3000
  },
  "parser_config": {
    "default_parser": "mineru",
    "default_parse_method": "auto"
  }
}

# Using MCP client
import mcp

client = mcp.Client("rag-anything")

# Upload and process
result = await client.call_tool("upload_document", {
    "file_path": "./research_paper.pdf"
})
doc_id = result["doc_id"]

await client.call_tool("process_document", {
    "doc_id": doc_id
})

# Query
answer = await client.call_tool("query_text", {
    "query": "What are the main findings?",
    "mode": "hybrid"
})
print(answer["answer"])

# Query with table data
result = await client.call_tool("query_multimodal", {
    "query": "Compare these metrics with document content",
    "multimodal_content": [{
        "type": "table",
        "table_data": "Method,Accuracy\nRAG,95.2%\nBaseline,87.3%",
        "table_caption": "Performance Comparison"
    }],
    "mode": "hybrid"
})

# Run tests
pytest tests/

# Run with coverage
pytest --cov=src tests/

# Test specific functionality
pytest tests/test_tools.py -v

https://your-app-name.onrender.com/mcp

{
  "servers": {
    "rag-document-mcp": {
      "url": "https://your-app-name.onrender.com/mcp",
      "type": "http"
    }
  }
}