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.

Pongo integrates into your existing RAG pipeline to enhance its performance by optimizing search results. It uses advanced semantic filtering techniques to reduce incorrect outputs and improve the overall accuracy and efficiency of searches. Whether you have a vast collection of documents or extensive query requirements, Pongo can handle up to 1 billion documents, making your search process faster and more reliable.