LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.

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WeKnora is an LLM-powered framework designed for deep document understanding and semantic retrieval, especially for handling complex, heterogeneous documents.

It adopts a modular architecture that combines multimodal preprocessing, semantic vector indexing, intelligent retrieval, and large language model inference. At its core, WeKnora follows the RAG (Retrieval-Augmented Generation) paradigm, enabling high-quality, context-aware answers by combining relevant document chunks with model reasoning.

Website: https://weknora.weixin.qq.com

Important: Starting from v0.1.3, WeKnora includes login authentication functionality to enhance system security. For production deployments, we strongly recommend:

• Deploy WeKnora services in internal/private network environments rather than public internet
• Avoid exposing the service directly to public networks to prevent potential information leakage
• Configure proper firewall rules and access controls for your deployment environment
• Regularly update to the latest version for security patches and improvements

WeKnora employs a modern modular design to build a complete document understanding and retrieval pipeline. The system primarily includes document parsing, vector processing, retrieval engine, and large model inference as core modules, with each component being flexibly configurable and extendable.

• 🔍 Precise Understanding: Structured content extraction from PDFs, Word documents, images and more into unified semantic views
• 🧠 Intelligent Reasoning: Leverages LLMs to understand document context and user intent for accurate Q&A and multi-turn conversations
• 🔧 Flexible Extension: All components from parsing and embedding to retrieval and generation are decoupled for easy customization
• ⚡ Efficient Retrieval: Hybrid retrieval strategies combining keywords, vectors, and knowledge graphs
• 🎯 User-Friendly: Intuitive web interface and standardized APIs for zero technical barriers
• 🔒 Secure & Controlled: Support for local deployment and private cloud, ensuring complete data sovereignty

Make sure the following tools are installed on your system:

• Docker
• Docker Compose
• Git

# Clone the main repository
git clone https://github.com/Tencent/WeKnora.git
cd WeKnora

# Copy example env file
cp .env.example .env

# Edit .env and set required values
# All variables are documented in the .env.example comments

# Start all services (Ollama + backend containers)
./scripts/start_all.sh
# Or
make start-all

# Start ollama services (Optional)
ollama serve > /dev/null 2>&1 &

# Start the service
docker compose up -d

./scripts/start_all.sh --stop
# Or
make stop-all

Once started, services will be available at:

• Web UI: http://localhost
• Backend API: http://localhost:8080
• Jaeger Tracing: http://localhost:16686

WeKnora serves as the core technology framework for the WeChat Dialog Open Platform, providing a more convenient usage approach:

• Zero-code Deployment: Simply upload knowledge to quickly deploy intelligent Q&A services within the WeChat ecosystem, achieving an "ask and answer" experience
• Efficient Question Management: Support for categorized management of high-frequency questions, with rich data tools to ensure accurate, reliable, and easily maintainable answers
• WeChat Ecosystem Integration: Through the WeChat Dialog Open Platform, WeKnora's intelligent Q&A capabilities can be seamlessly integrated into WeChat Official Accounts, Mini Programs, and other WeChat scenarios, enhancing user interaction experiences

git clone https://github.com/Tencent/WeKnora

It is recommended to directly refer to the MCP Configuration Guide for configuration.

Configure the MCP client to connect to the server:

{
  "mcpServers": {
    "weknora": {
      "args": [
        "path/to/WeKnora/mcp-server/run_server.py"
      ],
      "command": "python",
      "env":{
        "WEKNORA_API_KEY":"Enter your WeKnora instance, open developer tools, check the request header x-api-key starting with sk",
        "WEKNORA_BASE_URL":"http(s)://your-weknora-address/api/v1"
      }
    }
  }
}

Run directly using stdio command:

pip install weknora-mcp-server
python -m weknora-mcp-server

To help users quickly configure various models and reduce trial-and-error costs, we've improved the original configuration file initialization method by adding a Web UI interface for model configuration. Before using, please ensure the code is updated to the latest version. The specific steps are as follows: If this is your first time using this project, you can skip steps ①② and go directly to steps ③④.

./scripts/start_all.sh --stop

make clean-db

./scripts/start_all.sh

http://localhost

On first access, it will automatically redirect to the initialization configuration page. After configuration is complete, it will automatically redirect to the knowledge base page. Please follow the page instructions to complete model configuration.

Knowledge Upload	Q&A Entry
Rich Text & Image Responses

Knowledge Base Management: Support for dragging and dropping various documents, automatically identifying document structures and extracting core knowledge to establish indexes. The system clearly displays processing progress and document status, achieving efficient knowledge base management.

WeKnora supports transforming documents into knowledge graphs, displaying the relationships between different sections of the documents. Once the knowledge graph feature is enabled, the system analyzes and constructs an internal semantic association network that not only helps users understand document content but also provides structured support for indexing and retrieval, enhancing the relevance and breadth of search results.

Troubleshooting FAQ: Troubleshooting FAQ

Detailed API documentation is available at: API Docs

WeKnora/
├── cmd/         # Main entry point
├── internal/    # Core business logic
├── config/      # Configuration files
├── migrations/  # DB migration scripts
├── scripts/     # Shell scripts
├── services/    # Microservice logic
├── frontend/    # Frontend app
└── docs/        # Project documentation

# Wipe all data from DB (use with caution)
make clean-db

We welcome community contributions! For suggestions, bugs, or feature requests, please submit an Issue or directly create a Pull Request.

• 🐛 Bug Fixes: Discover and fix system defects
• ✨ New Features: Propose and implement new capabilities
• 📚 Documentation: Improve project documentation
• 🧪 Test Cases: Write unit and integration tests
• 🎨 UI/UX Enhancements: Improve user interface and experience

1. Fork the project to your GitHub account
2. Create a feature branch git checkout -b feature/amazing-feature
3. Commit changes git commit -m 'Add amazing feature'
4. Push branch git push origin feature/amazing-feature
5. Create a Pull Request with detailed description of changes

• Follow Go Code Review Comments
• Format code using gofmt
• Add necessary unit tests
• Update relevant documentation

Use Conventional Commits standard:

feat: Add document batch upload functionality
fix: Resolve vector retrieval precision issue
docs: Update API documentation
test: Add retrieval engine test cases
refactor: Restructure document parsing module

This project is licensed under the MIT License. You are free to use, modify, and distribute the code with proper attribution.