WeKnora/README.md

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    <img src="./docs/images/logo.png" alt="WeKnora Logo" height="120"/>
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    <a href="https://weknora.weixin.qq.com" target="_blank">
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| <b>English</b> | <a href="./README_CN.md"><b>简体中文</b></a> | <a href="./README_JA.md"><b>日本語</b></a> |
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<p align="center">
  <h4 align="center">

  [Overview](#-overview) • [Architecture](#-architecture) • [Key Features](#-key-features) • [Getting Started](#-getting-started) • [API Reference](#-api-reference) • [Developer Guide](#-developer-guide)
  
  </h4>
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# 💡 WeKnora - LLM-Powered Document Understanding & Retrieval Framework

## 📌 Overview

[**WeKnora**](https://weknora.weixin.qq.com) 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

## 🔒 Security Notice

**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

## 🏗️ Architecture

![weknora-pipeline.png](./docs/images/pipeline.jpg)

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.

## 🎯 Key Features

- **🔍 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

## 📊 Application Scenarios

| Scenario | Applications | Core Value |
|---------|----------|----------|
| **Enterprise Knowledge Management** | Internal document retrieval, policy Q&A, operation manual search | Improve knowledge discovery efficiency, reduce training costs |
| **Academic Research Analysis** | Paper retrieval, research report analysis, scholarly material organization | Accelerate literature review, assist research decisions |
| **Product Technical Support** | Product manual Q&A, technical documentation search, troubleshooting | Enhance customer service quality, reduce support burden |
| **Legal & Compliance Review** | Contract clause retrieval, regulatory policy search, case analysis | Improve compliance efficiency, reduce legal risks |
| **Medical Knowledge Assistance** | Medical literature retrieval, treatment guideline search, case analysis | Support clinical decisions, improve diagnosis quality |

## 🧩 Feature Matrix

| Module | Support | Description |
|---------|---------|------|
| Document Formats | ✅ PDF / Word / Txt / Markdown / Images (with OCR / Caption) | Support for structured and unstructured documents with text extraction from images |
| Embedding Models | ✅ Local models, BGE / GTE APIs, etc. | Customizable embedding models, compatible with local deployment and cloud vector generation APIs |
| Vector DB Integration | ✅ PostgreSQL (pgvector), Elasticsearch | Support for mainstream vector index backends, flexible switching for different retrieval scenarios |
| Retrieval Strategies | ✅ BM25 / Dense Retrieval / GraphRAG | Support for sparse/dense recall and knowledge graph-enhanced retrieval with customizable retrieve-rerank-generate pipelines |
| LLM Integration | ✅ Support for Qwen, DeepSeek, etc., with thinking/non-thinking mode switching | Compatible with local models (e.g., via Ollama) or external API services with flexible inference configuration |
| QA Capabilities | ✅ Context-aware, multi-turn dialogue, prompt templates | Support for complex semantic modeling, instruction control and chain-of-thought Q&A with configurable prompts and context windows |
| E2E Testing | ✅ Retrieval+generation process visualization and metric evaluation | End-to-end testing tools for evaluating recall hit rates, answer coverage, BLEU/ROUGE and other metrics |
| Deployment Modes | ✅ Support for local deployment / Docker images | Meets private, offline deployment and flexible operation requirements |
| User Interfaces | ✅ Web UI + RESTful API | Interactive interface and standard API endpoints, suitable for both developers and business users |

## 🚀 Getting Started

### 🛠 Prerequisites

Make sure the following tools are installed on your system:

* [Docker](https://www.docker.com/)
* [Docker Compose](https://docs.docker.com/compose/)
* [Git](https://git-scm.com/)

### 📦 Installation

#### ① Clone the repository

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

#### ② Configure environment variables

```bash
# Copy example env file
cp .env.example .env

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

#### ③ Start the services (include Ollama)

Check the images that need to be started in the .env file.

```bash
./scripts/start_all.sh
```

or

```bash
make start-all
```

#### ③.0 Start ollama services (Optional)

```bash
ollama serve > /dev/null 2>&1 &
```

#### ③.1 Activate different combinations of features

- Minimum core services
```bash
docker compose up -d
```

- All features enabled
```bash
docker-compose --profile full up -d
```

- Tracing logs required
```bash
docker-compose --profile jaeger up -d
```

- Neo4j knowledge graph required
```bash
docker-compose --profile neo4j up -d
```

- Minio file storage service required
```bash
docker-compose --profile minio up -d
```

- Multiple options combination
```bash
docker-compose --profile neo4j --profile minio up -d
```

#### ④ Stop the services

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

### 🌐 Access Services

Once started, services will be available at:

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

### 🔌 Using WeChat Dialog Open Platform

WeKnora serves as the core technology framework for the [WeChat Dialog Open Platform](https://chatbot.weixin.qq.com), 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

### 🔗 Access WeKnora via MCP Server

#### 1️⃣ Clone the repository
```
git clone https://github.com/Tencent/WeKnora
```

#### 2️⃣ Configure MCP Server
> It is recommended to directly refer to the [MCP Configuration Guide](./mcp-server/MCP_CONFIG.md) for configuration.

Configure the MCP client to connect to the server:
```json
{
  "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
```

## 🔧 Initialization Configuration Guide

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.

### ① Stop the services

```bash
./scripts/start_all.sh --stop
```

### ② Clear existing data tables (recommended when no important data exists)

```bash
make clean-db
```

### ③ Compile and start services

```bash
./scripts/start_all.sh
```

### ④ Access Web UI

http://localhost

On your first visit, you will be automatically redirected to the registration/login page. After completing registration, please create a new knowledge base and finish the relevant settings on its configuration page.

## 📱 Interface Showcase

### Web UI Interface

<table>
  <tr>
    <td><b>Knowledge Upload</b><br/><img src="./docs/images/knowledges.png" alt="Knowledge Upload Interface"></td>
    <td><b>Q&A Entry</b><br/><img src="./docs/images/qa.png" alt="Q&A Entry Interface"></td>
  </tr>
  <tr>
    <td colspan="2"><b>Rich Text & Image Responses</b><br/><img src="./docs/images/answer.png" alt="Rich Answer Interface"></td>
  </tr>
</table>

**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.

### Document Knowledge Graph

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.

For detailed configuration, please refer to the [Knowledge Graph Configuration Guide](./docs/KnowledgeGraph.md).

### MCP Server

Please refer to the [MCP Configuration Guide](./mcp-server/MCP_CONFIG.md) for the necessary setup.

## 📘 API Reference

Troubleshooting FAQ: [Troubleshooting FAQ](./docs/QA.md)

Detailed API documentation is available at: [API Docs](./docs/API.md)

## 🧭 Developer Guide

### 📁 Directory Structure

```
WeKnora/
├── client/      # go client
├── cmd/         # Main entry point
├── config/      # Configuration files
├── docker/      # docker images files
├── docreader/   # Document parsing app
├── docs/        # Project documentation
├── frontend/    # Frontend app
├── internal/    # Core business logic
├── mcp-server/  # MCP server
├── migrations/  # DB migration scripts
└── scripts/     # Shell scripts
```

## 🤝 Contributing

We welcome community contributions! For suggestions, bugs, or feature requests, please submit an [Issue](https://github.com/Tencent/WeKnora/issues) or directly create a Pull Request.

### 🎯 How to Contribute

- 🐛 **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

### 📋 Contribution Process

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

### 🎨 Code Standards

- Follow [Go Code Review Comments](https://github.com/golang/go/wiki/CodeReviewComments)
- Format code using `gofmt`
- Add necessary unit tests
- Update relevant documentation

### 📝 Commit Guidelines

Use [Conventional Commits](https://www.conventionalcommits.org/) 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
```

## 👥 Contributors

Thanks to these excellent contributors:

[![Contributors](https://contrib.rocks/image?repo=Tencent/WeKnora)](https://github.com/Tencent/WeKnora/graphs/contributors)

## 📄 License

This project is licensed under the [MIT License](./LICENSE).
You are free to use, modify, and distribute the code with proper attribution.

## 📈 Project Statistics

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