How to Build RAG Using Confluent with Flink AI Model Inference and MongoDB
How to Build RAG Using Confluent with Flink AI Model Inference and MongoDB
Retrieval-augmented generation (RAG) is a pattern in GenAI designed to enhance the accuracy and relevance of responses generated by Large Language Models (LLMs), helping reduce hallucinations. RAG retrieves external data from a vector database at prompt time. To ensure that the data retrieved is always current, the vector database needs to be continuously updated with real-time information.
How do you build RAG with real-time data?
Join experts Britton LaRoche, Staff Solutions Engineer at Confluent, and Vasanth Kumar, Principal Architect at MongoDB, as they walk through a RAG tutorial using Confluent data streaming platform and MongoDB Atlas. Register now to learn:
- How to implement RAG in 4 key steps: data augmentation, inference, workflows, and post-processing
- How to use data streaming, Flink stream processing and AI Model Inference, and semantic vector search with a vector database like MongoDB Atlas
- Step-by-step walkthrough of vector embedding for RAG
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