Building Enterprise AI Agents with Flink SQL: Connecting LLMs to Internal Data

This article explores building enterprise AI agents using Flink SQL, connecting Large Language Models (LLMs) with internal data and resources. For structured data, Flink SQL's SQL join semantics easily integrate external database data with LLM input. For unstructured data, the article proposes Retrieval-Augmented Generation (RAG), encoding data into vectors stored in a vector database, then querying and integrating via Flink SQL's vector type support. Using the example of summarizing research papers and incorporating internal research, the article demonstrates building an AI agent system with two Flink SQL jobs: one updates the vector store, the other queries and invokes the LLM. Finally, it mentions using Process Table Functions (PTFs) to integrate Anthropic's MCP standard for more flexible AI agent construction.