The repository provides a step-by-step tutorial and reference implementation for creating a ReAct-based AI agent that uses DeepSeek for high-dimensional vector retrieval. It covers environment setup, dependency installation, and configuration of vector stores for custom data. The agent employs the ReAct pattern to combine reasoning traces with external knowledge searches, resulting in transparent and explainable responses. Users can extend the system by integrating additional document loaders, fine-tuning prompt templates, or swapping vector databases. This flexible framework enables developers and researchers to prototype powerful conversational agents that reason, retrieve, and interact seamlessly with various knowledge sources in a few lines of Python code.

TunaCode is designed to streamline the entire software development lifecycle by translating plain-English project descriptions into production-ready code. Leveraging advanced machine learning algorithms trained on millions of code repositories, TunaCode supports popular frameworks such as React, Vue, Express, and Django. It can generate UI components with responsive layouts, define RESTful APIs, configure database models for SQL or NoSQL stores, and scaffold automated unit tests. A built-in real-time editor allows users to refine generated code snippets instantly, while version control integration ensures seamless collaboration. Additionally, TunaCode can produce detailed documentation and deployment scripts for cloud platforms like AWS, Azure, or Heroku. This comprehensive AI agent eliminates repetitive boilerplate tasks and empowers developers to focus on innovation and complex problem-solving.