Jerry Yan
0e0782a452
实现功能： - 创建 kg_extraction/interface.py（FastAPI 路由） - 实现 POST /api/kg/extract（单条文本抽取） - 实现 POST /api/kg/extract/batch（批量抽取，最多 50 条） - 集成到 FastAPI 主路由（/api/kg/ 前缀） 技术实现： - 配置管理：从环境变量读取 LLM 配置（API Key、Base URL、Model、Temperature） - 安全性： - API Key 使用 SecretStr 保护 - 错误信息脱敏（使用 trace_id，不暴露原始异常） - 请求文本不写入日志（使用 SHA-256 hash） - 强制要求 X-User-Id 头（鉴权边界） - 超时控制： - kg_llm_timeout_seconds（60秒） - kg_llm_max_retries（2次） - 输入校验： - graph_id 和 source_id 使用 UUID pattern - source_type 使用 Enum（4个值） - allowed_nodes/relationships 元素使用正则约束（ASCII，1-50字符） - 审计日志：记录 caller、trace_id、text_hash 代码审查： - 经过 3 轮 Codex 审查和 2 轮 Claude 修复 - 所有问题已解决（5个 P1/P2 + 3个 P3） - 语法检查通过 API 端点： - POST /api/kg/extract：单条文本抽取 - POST /api/kg/extract/batch：批量抽取（最多 50 条） 配置环境变量： - KG_LLM_API_KEY：LLM API 密钥 - KG_LLM_BASE_URL：自定义端点（可选） - KG_LLM_MODEL：模型名称（默认 gpt-4o-mini） - KG_LLM_TEMPERATURE：生成温度（默认 0.0） - KG_LLM_TIMEOUT_SECONDS：超时时间（默认 60） - KG_LLM_MAX_RETRIES：重试次数（默认 2）
DataMate All-in-One Data Work Platform
DataMate is an enterprise-level data processing platform for model fine-tuning and RAG retrieval, supporting core functions such as data collection, data management, operator marketplace, data cleaning, data synthesis, data annotation, data evaluation, and knowledge generation.
If you like this project, please give it a Star⭐️!
🌟 Core Features
- Core Modules: Data Collection, Data Management, Operator Marketplace, Data Cleaning, Data Synthesis, Data Annotation, Data Evaluation, Knowledge Generation.
- Visual Orchestration: Drag-and-drop data processing workflow design.
- Operator Ecosystem: Rich built-in operators and support for custom operators.
🚀 Quick Start
Prerequisites
- Git (for pulling source code)
- Make (for building and installing)
- Docker (for building images and deploying services)
- Docker-Compose (for service deployment - Docker method)
- Kubernetes (for service deployment - k8s method)
- Helm (for service deployment - k8s method)
This project supports deployment via two methods: docker-compose and helm. After executing the command, please enter the corresponding number for the deployment method. The command echo is as follows:
Choose a deployment method:
1. Docker/Docker-Compose
2. Kubernetes/Helm
Enter choice:
Clone the Code
git clone git@github.com:ModelEngine-Group/DataMate.git
cd DataMate
Deploy the basic services
make install
If the machine you are using does not have make installed, please run the following command to deploy it:
# Windows
set REGISTRY=ghcr.io/modelengine-group/
docker compose -f ./deployment/docker/datamate/docker-compose.yml up -d
docker compose -f ./deployment/docker/milvus/docker-compose.yml up -d
# Linux/Mac
export REGISTRY=ghcr.io/modelengine-group/
docker compose -f ./deployment/docker/datamate/docker-compose.yml up -d
docker compose -f ./deployment/docker/milvus/docker-compose.yml up -d
Once the container is running, access http://localhost:30000 in a browser to view the front-end interface.
To list all available Make targets, flags and help text, run:
make help
Build and deploy Mineru Enhanced PDF Processing
make build-mineru
make install-mineru
Deploy the DeerFlow service
make install-deer-flow
Local Development and Deployment
After modifying the local code, please execute the following commands to build the image and deploy using the local image.
make build
make install dev=true
Uninstall
make uninstall
When running make uninstall, the installer will prompt once whether to delete volumes; that single choice is applied to all components. The uninstall order is: milvus -> label-studio -> datamate, which ensures the datamate network is removed cleanly after services that use it have stopped.
🤝 Contribution Guidelines
Thank you for your interest in this project! We warmly welcome contributions from the community. Whether it's submitting bug reports, suggesting new features, or directly participating in code development, all forms of help make the project better.
• 📮 GitHub Issues: Submit bugs or feature suggestions.
• 🔧 GitHub Pull Requests: Contribute code improvements.
📄 License
DataMate is open source under the MIT license. You are free to use, modify, and distribute the code of this project in compliance with the license terms.