RAGFlow Claude MCP Server

A Model Context Protocol (MCP) server that provides seamless integration between tools like Claude Desktop and RAGFlow's REST API for knowledge base querying and document management. It enriches the context of the LLMs.

This is a personal-use software, which I create for my own RnD. It's not bug-free, and certainly not high quality code. But it gets the job done. :)

Features

• Direct Document Retrieval: Access raw document chunks with similarity scores using RAGFlow's retrieval API

• DSPy Query Deepening: Intelligent query refinement using DSPy for iterative search improvement

• Reranking Support: Optional reranking for improved result quality (Currently disabled due to API protocol issues)

• Enhanced Result Control: Default 10 results per query with configurable page_size and similarity_threshold parameters

• Document Filtering: Limit search results to specific documents within a dataset (supports fuzzy matching)

• Dataset Name Lookup: Query knowledge bases using familiar names instead of cryptic IDs

• Fuzzy Matching: Find datasets with partial name matches (case-insensitive)

• Pagination Support: Retrieve results in manageable batches with full pagination control

• Source References: Each chunk includes document ID, similarity scores, and highlighted matches

• Enhanced Error Handling: Clear error messages and dataset suggestions

• Multiple Query Methods: Support for both ID-based and name-based queries

Installation

1. Clone the repository:

   git clone https://github.com/norandom/ragflow-claude-desktop-local-mcp
   cd ragflow-claude-desktop-local-mcp

2. Install dependencies:

   # On macOS, install DSPy first to avoid build issues:
   pip install git+https://github.com/stanfordnlp/dspy.git
   
   # Then install all dependencies
   uv install

3. Configure the server: Create a config.json file by copying the config.json.sample file:

   cp config.json.sample config.json

   Then, edit config.json with your server details:

   • RAGFLOW_BASE_URL: The URL of your RAGFlow instance (e.g., "http://your-ragflow-server:port").

   • RAGFLOW_API_KEY: Your RAGFlow API key.

   • RAGFLOW_DEFAULT_RERANK: Default rerank model (default: "rerank-multilingual-v3.0").

   • CF_ACCESS_CLIENT_ID: (Optional) Cloudflare Zero Trust Access Client ID.

   • CF_ACCESS_CLIENT_SECRET: (Optional) Cloudflare Zero Trust Access Client Secret.

   • DSPY_MODEL: DSPy language model for query refinement (default: "openai/gpt-4o-mini").

   • OPENAI_API_KEY: OpenAI API key (required for DSPy query deepening).

Cloudflare Zero Trust Configuration (Optional)

If your RAGFlow instance is protected by Cloudflare Zero Trust, you can configure authentication:

1. Obtain your Service Token credentials from your Cloudflare Zero Trust dashboard

2. Add the following to your config.json:

   {
     "CF_ACCESS_CLIENT_ID": "your-client-id.access",
     "CF_ACCESS_CLIENT_SECRET": "your-client-secret",
     ...
   }

The server will automatically include the necessary CF-Access-Client-Id and CF-Access-Client-Secret headers in all API requests when these credentials are configured.

Claude Desktop Configuration

Add the following to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "ragflow": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/ragflow-claude-desktop-local-mcp",
        "ragflow-claude-mcp"
      ]
    }
  }
}

Available Tools

1. ragflow_retrieval_by_name ⭐ Recommended

Retrieve document chunks by dataset names using the retrieval API. Returns raw chunks with similarity scores.

Parameters:

• dataset_names (required): List of names of the datasets/knowledge bases to search (e.g., ["BASF", "Quant Literature"])

• query (required): Search query or question

• document_name (optional): Name of document to filter results to specific document (supports partial matching)

• top_k (optional): Number of chunks for vector cosine computation. Defaults to 1024.

• similarity_threshold (optional): Minimum similarity score for chunks (0.0 to 1.0). Defaults to 0.2.

• page (optional): Page number for pagination. Defaults to 1.

• page_size (optional): Number of chunks per page. Defaults to 10.

• use_rerank (optional): Enable reranking for improved quality. Defaults to false.

• deepening_level (optional): DSPy query refinement level (0-3). Defaults to 0.

2. ragflow_retrieval

Retrieve document chunks directly from RAGFlow datasets using the retrieval API. Returns raw chunks with similarity scores.

Parameters:

• dataset_ids (required): List of IDs of the datasets/knowledge bases to search (e.g., ["id1", "id2"])

• query (required): Search query or question

• document_name (optional): Name of document to filter results to specific document (supports partial matching)

• top_k (optional): Number of chunks for vector cosine computation. Defaults to 1024.

• similarity_threshold (optional): Minimum similarity score for chunks (0.0 to 1.0). Defaults to 0.2.

• page (optional): Page number for pagination. Defaults to 1.

• page_size (optional): Number of chunks per page. Defaults to 10.

• use_rerank (optional): Enable reranking for improved quality. Defaults to false.

• deepening_level (optional): DSPy query refinement level (0-3). Defaults to 0.

3. ragflow_list_datasets

List all available knowledge bases in your RAGFlow instance.

Parameters: None

Multi-Knowledge Base Search

You can now search across multiple knowledge bases (datasets) in a single query. When doing so, ensure that the selected knowledge bases use compatible embedding models for optimal retrieval quality.

Please use the ragflow_retrieval_by_name tool with dataset_names ["Finance Reports", "Legal Documents"] and query "Summarize the key financial risks and compliance requirements for new market entry."

4. ragflow_list_documents

List documents within a specific dataset.

Parameters:

• dataset_id (required): ID of the dataset

5. ragflow_get_chunks

Get document chunks with references from a specific document.

Parameters:

• dataset_id (required): ID of the dataset

• document_id (required): ID of the document

6. ragflow_list_sessions

Show active chat sessions for all datasets.

Parameters: None

7. ragflow_list_documents_by_name

List documents in a dataset by dataset name.

Parameters:

• dataset_name (required): Name of the dataset/knowledge base to list documents from

8. ragflow_reset_session

Reset/clear the chat session for a specific dataset.

Parameters:

• dataset_id (required): ID of the dataset to reset session for

Result Control and Filtering

Enhanced Retrieval Parameters

The retrieval tools support fine-tuned control over query results:

• page_size: Number of chunks returned per page (default: 10, previously 30)

• similarity_threshold: Filters chunks based on relevance score (default: 0.2, range: 0.0-1.0)

• top_k: Number of chunks for vector computation (default: 1024)

Result Optimization Tips

• For broader results: Use page_size=15, similarity_threshold=0.15, and use_rerank=true

• For precise results: Use page_size=5, similarity_threshold=0.4, and use_rerank=true

• For comprehensive analysis: Use page_size=20, similarity_threshold=0.1, use_rerank=true, and deepening_level=1

• For complex queries: Use deepening_level=2 with use_rerank=true for intelligent refinement

• For maximum quality: Use deepening_level=2, use_rerank=true, and similarity_threshold=0.3

• For speed: Keep use_rerank=false and deepening_level=0 (default behavior)

Usage Examples

Basic Retrieval by Dataset Name

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF" and query "What is BASF's latest income statement? Please provide the revenue, operating income, net income, and other key financial figures."

Document-Specific Search

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", document_name "annual_report_2023", and query "What were the key financial highlights for 2023?" to search only within the specific annual report document.

Note: Document names support fuzzy matching - you can use partial names like "annual" to match "annual_report_2023.pdf". When multiple documents match (e.g., "annual" matches both "annual_report_2023.pdf" and "annual_report_2024.pdf"), the system uses the most recent document by default and provides information about all matches in the response metadata for user choice.

DSPy Query Deepening for Complex Queries

Please use the ragflow_retrieval_by_name tool with dataset_name "Quant Literature", query "what is a volatility clock", deepening_level 2, and use_rerank false for intelligent query refinement and best quality results.

Precise Retrieval with High Similarity Threshold

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", query "What is BASF's exact revenue for Q4 2023?", page_size 5, similarity_threshold 0.4, and use_rerank true for highly relevant results only.

Multi-Page Retrieval

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", query "BASF business segments", page_size 10, and page 2 to get the next set of results.

List Available Datasets

Please use the ragflow_list_datasets tool to show me all available knowledge bases.

Get Document Details

Please use the ragflow_list_documents tool with dataset_id "43066ee0599411f089787a39c10de57b" to see what BASF documents are available.

List Documents by Dataset Name

Please use the ragflow_list_documents_by_name tool with dataset_name "BASF" to see all available documents in the BASF knowledge base.

Get Specific Document Chunks

Please use the ragflow_get_chunks tool with dataset_id "43066ee0599411f089787a39c10de57b" and document_id "d74a1c105a3311f09fc94a0fcd8b7722" to get chunks from the BASF annual report.

Sample Prompts for Claude Desktop

Comprehensive Financial Analysis with Enhanced Features

I need to analyze BASF's financial performance. Please help me by:

1. First, use the ragflow_retrieval_by_name tool to search the "BASF" knowledge base for their latest income statement. Ask for revenue, operating income, net income, and key financial figures. Use page_size 15, similarity_threshold 0.15, use_rerank true, and deepening_level 1 for comprehensive and refined results.

2. Then, use the ragflow_retrieval_by_name tool again with query about their cash flow statement, page_size 10, similarity_threshold 0.2, and use_rerank true.

3. Finally, use the ragflow_retrieval_by_name tool to find information about significant changes in their financial performance compared to the previous year using page_size 12, similarity_threshold 0.18, and use_rerank true.

Advanced Query with DSPy Deepening

I need to understand complex financial concepts. Please use the ragflow_retrieval_by_name tool with dataset_name "finance_kb", query "what is a volatility clock", deepening_level 2, use_rerank true, and page_size 10. This will use intelligent query refinement to find better results about this complex topic.

Precision Search with Maximum Quality

I need to find specific financial metrics for BASF. Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", query "What were BASF's exact Q4 2023 revenue figures and operating margin?", page_size 5, similarity_threshold 0.45, and use_rerank true to get only the most relevant and precise results with highest quality ranking.

Multi-Page Comprehensive Research with Reranking

Please conduct comprehensive research about BASF's sustainability initiatives. Use ragflow_retrieval_by_name with dataset_name "BASF", query "Find information about BASF's environmental sustainability programs, carbon reduction targets, and green chemistry initiatives", page_size 20, similarity_threshold 0.12, use_rerank true, and deepening_level 1 for broad coverage with intelligent refinement. If needed, use page 2 and page 3 for additional results.

Comparative Analysis with Enhanced Quality

I need to compare BASF's performance across different business segments. Please use ragflow_retrieval_by_name with dataset_name "BASF", query "Compare the financial performance of BASF's different business segments including Chemicals, Materials, Industrial Solutions, Surface Technologies, Nutrition & Health, and Agricultural Solutions", page_size 18, similarity_threshold 0.16, use_rerank true, and deepening_level 1 for comprehensive segment data with enhanced quality.

Enhanced Single Query with All Features

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", query "What is BASF's latest income statement? Please provide detailed information about revenue, operating income, net income, gross margin, operating margin, and any other key financial metrics.", page_size 15, similarity_threshold 0.2, use_rerank true, and deepening_level 1 for the best possible results with intelligent query refinement and reranking.

Document Exploration Workflow

Please help me explore the BASF dataset by:
1. First using ragflow_list_documents_by_name with dataset_name "BASF" to see available documents
2. Then using ragflow_retrieval_by_name with dataset_name "BASF", document_name "sustainability_report_2023", query "carbon reduction targets", page_size 10
3. Finally using ragflow_get_chunks for specific document analysis if needed

Document-Filtered Research with Advanced Features

Please conduct targeted research on BASF's sustainability initiatives using document filtering:
1. First, use ragflow_list_documents_by_name with dataset_name "BASF" to see available documents
2. Then, use ragflow_retrieval_by_name with dataset_name "BASF", document_name "sustainability_report", query "carbon neutrality goals and timeline", page_size 15, similarity_threshold 0.2, use_rerank true, deepening_level 1
3. Follow up with ragflow_retrieval_by_name using document_name "annual_report_2023" and query "environmental investments and green chemistry initiatives"

Handling Multiple Document Matches

When using document_name "annual", the system might find multiple matches:
- "annual_report_2024.pdf" (most recent, used by default)
- "annual_report_2023.pdf" 
- "annual_financial_summary_2023.pdf"

The response metadata will show all matches. To target a specific document, use more specific names like "annual_report_2023" or check the response metadata for exact document names.

Technical Details

DSPy Query Deepening

• Intelligent Query Refinement: Uses DSPy to analyze search results and generate improved queries

• Iterative Improvement: Deepening levels 1-3 perform multiple refinement cycles

• Gap Analysis: Identifies missing information in initial results and targets specific improvements

• Query Evolution: Tracks original query → refined queries → final results with full metadata

• Multilingual Support: Automatically handles queries in German, English, and other languages

Multilingual Queries

The DSPy query deepening system supports multilingual queries and will intelligently handle different languages during refinement:

English Queries

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", query "What are BASF's main business segments?", deepening_level 2, use_rerank false

German Queries

Please use the ragflow_retrieval_by_name tool with dataset_name "BASF", query "Was sind die wichtigsten Geschäftsbereiche von BASF?", deepening_level 2, use_rerank false

Mixed Language Research

Please use the ragflow_retrieval_by_name tool with dataset_name "Finance KB", query "Volatility clustering und Markteffizienz", deepening_level 2, use_rerank false

Key Features:

• Automatic Language Detection: DSPy automatically detects the query language

• Intelligent Translation: When needed, queries are refined in the most appropriate language

• Cross-Language Results: Can find relevant content regardless of document language

• Enhanced with Deepening: Use deepening_level 1-3 for best multilingual performance

Requirements:

• DSPY_MODEL: "openai/gpt-4o-mini" (configured in config.json)

• OPENAI_API_KEY: Required for multilingual DSPy functionality

Reranking Support

• Status: Currently unavailable due to an upstream API issue (see Known Issues).

• Optional Enhancement: Reranking disabled by default for speed, enabled via use_rerank: true

• Server-Side Configuration: Uses rerank-multilingual-v3.0 model configured in config.json

• Quality Improvement: Typically 10-30% better relevance scores when enabled

• Performance Trade-off: Significantly increases response time but improves result quality

Result Control and Optimization

• Enhanced Retrieval Performance: Default page_size set to 10 chunks for optimal response size

• Similarity Filtering: similarity_threshold parameter filters chunks based on relevance scores (0.0-1.0)

• Pagination Support: Use page parameter to retrieve additional results beyond the first 10

• Vector Search Control: top_k parameter controls the number of chunks for vector computation (default: 1024)

Retrieval Features

• Direct Document Access: Raw document chunks with exact text from source files

• Similarity Scoring: Each chunk includes relevance scores for quality assessment

• Source References: Full document and chunk location information provided

• Flexible Pagination: Retrieve results in manageable batches with full control

Session Management

• Session management tools are available for workflow compatibility

• ragflow_list_sessions shows active chat sessions

• ragflow_reset_session clears problematic sessions

• Note: Retrieval tools (ragflow_retrieval and ragflow_retrieval_by_name) don't require session management

Dataset Lookup

• Case-insensitive name matching

• Fuzzy matching for partial names

• Automatic caching of dataset information

• Clear error messages with available dataset suggestions

Document Matching & Ranking

• Intelligent Ranking: Exact matches > starts with > contains > partial matches

• Recency Priority: Most recent documents (by update time) ranked higher

• Multiple Match Handling: When multiple documents match, uses best-ranked document and provides all options in response metadata

• Smart Keywords: Documents with "2024", "2023", "latest", "current", "new" get ranking boost

• User Choice: Response metadata includes all matching documents for user selection

Error Handling

• Comprehensive error messages for API failures

• Dataset availability checks

• Automatic session ownership recovery

• Session reset capability for problematic sessions

• Network connectivity issues

• Graceful fallback for session creation failures

Environment Variables

• RAGFLOW_BASE_URL: Base URL of your RAGFlow instance (default: http://192.168.122.93:9380)

• RAGFLOW_API_KEY: Your RAGFlow API key (required)

Development

Running the Server

To run the server directly:

uv run ragflow-claude-mcp

The server will start and listen for MCP requests via stdio.

Development Dependencies

For development and testing, install the optional development dependencies:

uv install --extra dev

This includes:

• pytest - Test framework

• pytest-asyncio - Async test support

• pytest-mock - Mocking utilities

• pytest-cov - Coverage reporting

Running Tests

Run the test suite:

# Run all tests
uv run pytest

# Run with coverage report
uv run pytest --cov=src --cov-report=html --cov-report=term

# Run specific test file
uv run pytest tests/test_server.py

# Run with verbose output
uv run pytest -v

Test Coverage

The project maintains comprehensive test coverage for:

• Server initialization and configuration

• RAGFlow API integration

• DSPy query deepening functionality

• OpenRouter and OpenAI configuration

• Error handling and edge cases

• Configuration file loading

Current coverage: 44% with 22/23 tests passing (1 test skipped due to intermittent CI issues).

Code Quality

The test suite includes:

• Unit tests for core functionality

• Integration tests for external API calls

• Configuration validation tests

• Mock-based testing for external dependencies

• Async test support for asynchronous operations

Implementation Notes

RAGFlow API Approach

• Primary Tools: Use ragflow_retrieval_by_name and ragflow_retrieval for document search

• Direct Access: Retrieval API provides raw document chunks without LLM processing

• Better Control: Full pagination and similarity filtering control

• No Dependencies: Works independently of server-side configurations

Troubleshooting

1. "Dataset not found" errors: Use ragflow_list_datasets to see available datasets

2. Connection errors: Verify your RAGFLOW_BASE_URL and RAGFLOW_API_KEY are correct

3. Server won't start: Check that all dependencies are installed with uv install

4. Need raw document access: Use ragflow_retrieval_by_name or ragflow_retrieval for direct document chunk access

5. Session issues: If using session tools, check ragflow_list_sessions and use ragflow_reset_session if needed

Known Issues

Rerank Functionality Protocol Error

• Issue: Using use_rerank=true parameter causes "UnsupportedProtocol: Request URL is missing an 'http://' or 'https://' protocol" error

• Status: Known defect mentioned by RAGFlow developers

• Workaround: Use use_rerank=false (default) for standard vector similarity retrieval

• Impact: Reranking feature currently unavailable, but standard retrieval works normally

• Follow-up: Monitor RAGFlow GitHub issues for resolution

Contributing

We welcome contributions! However, please note that all changes must be submitted via Pull Requests (PRs) as the main branch is protected.

How to Contribute

1. Fork the repository

2. Create a new branch for your feature/fix: git checkout -b feature/your-feature-name

3. Make your changes and commit them with clear, descriptive messages

4. Push to your fork: git push origin feature/your-feature-name

5. Open a Pull Request against the main branch

PR Requirements

• All PRs are automatically scanned for exposed credentials using TruffleHog

• Ensure your code doesn't contain any API keys, tokens, or secrets

• Include a clear description of what your PR does and why it's needed

• Update documentation if you're adding new features

For detailed contribution guidelines, see CONTRIBUTING.md.