How to Build a Real-Time RAG-Enabled AI Chatbot with Flink, Elastic, OpenAI, and LangChain
Learn how to build a real-time generative AI chatbot that leverages retrieval-augmented generation (RAG) for accurate and contextually aware responses. We’ll demonstrate this through a real-world use case: Financial services document search and synthesis. Banks spend valuable time researching and summarizing documents—now GenAI makes complex data instantly accessible, helping analysts effectively find and interpret information.
See how this comes to life using Confluent’s data streaming platform with Apache Flink®, Elasticsearch, OpenAI, and LangChain to build a scalable, trustworthy chatbot.
Join experts Gopi Dappili, Senior Solutions Engineer at Confluent, and Jeff Vestal, Principal GenAI Specialist at Elastic, to learn:
- How to do vector embedding and vector search for RAG
- How to build a real-time inference pipeline and workflows
- How to prevent hallucinations, enforce business logic and compliance requirements with LLM outputs
- How to maintain a real-time, contextualized knowledge base using connectors, stream processing, Flink AI Model Inference, and Stream Governance
And get all your questions answered during Q&A.
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