PyDebeziumAI: Real-time CDC Integration for LangChain & LangGraph using Debezium

Modern AI systems relying on Retrieval-Augmented Generation (RAG) often suffer from stale data due to batch-based updates. This project, PyDebeziumAI, aims to solve this by enabling real-time synchronization between databases and LLM pipelines using Debezium Change Data Capture (CDC). The project will extend pydbzengine to stream CDC events directly into LangChain and LangGraph by transforming database changes into semantic Document objects and updating vector stores in real time. Key deliverables include: - A Python library integrating Debezium with LangChain/LangGraph - Real-time vector store synchronization (Chroma, PGVector, Milvus) - Pluggable document transformation and ID strategies - End-to-end examples (RAG chatbot, reactive agent, dashboard) - Full documentation, testing, and PyPI release This will provide a production-ready bridge between CDC systems and AI frameworks, ensuring always up-to-date LLM context.