Giving AI Fresh Context with Cocoindex & FalkorDB's GraphRAG

Q&A stars at 44:00 👉🏻 Webinar TL;DR Learn how to combine CocoIndex's incremental data transformation capabilities with FalkorDB's graph and vector search to build scalable, low-latency GraphRAG systems that keep AI models grounded in fresh, structured knowledge. ​AI engineers, data engineers, developers building LLM applications who need to keep knowledge graphs in sync with changing source data, GraphRAG practitioners, and anyone interested in incremental data pipelines for AI. ​ ☑️ 3 main takeaways: 1. Source data changes constantly, without incremental processing, you'd re-run the entire pipeline every time a file changes. CocoIndex tracks what changed and reprocesses only that, turning unstructured data (PDFs, code, meeting notes) into structured graph data ready for FalkorDB. 2. ​How FalkorDB stores and queries this graph data with vector and full-text indexes for fast retrieval in AI workflows. 3. Best practices for building end-to-end GraphRAG pipelines that update incrementally as source data changes, grounding LLM responses in structured graph context instead of raw text chunks. Support our work, drop a star! ⭐️ GraphRAG-SDK: https://github.com/FalkorDB/GraphRAG-SDK/ Coocindex: https://github.com/cocoindex-io/cocoindex