Chat with your meeting notes: a CocoIndex knowledge graph and a text-to-SurrealQL agent

A tutorial demonstrates how to convert Markdown meeting notes into a queryable knowledge graph using a CocoIndex pipeline, SurrealDB, and an LLM-based Pydantic AI chat agent. The pipeline incrementally extracts meetings, people, decisions, and tasks, resolves duplicate person names through embeddings and LLM confirmation, and stores records and relationship edges in SurrealDB. Rather than maintaining a static text-to-SQL prompt, the approach generates a live text-to-SurrealQL prompt from database DDL, schema comments, current entity values, and semantically retrieved few-shot examples, helping the model stay aligned as the graph changes. It explains SurrealQL graph traversals, record-ID conventions, schema annotations, value grounding, and examples designed to prevent syntax errors and more dangerous silent query failures. The agent executes generated queries through a read-only database account, can use database errors for limited repair attempts, and exposes the resulting SurrealQL to users for review. Additional safeguards include table allowlists, database permissions, EXPLAIN plans, query timeouts, example-quality controls, and generation logging. Adding or removing notes automatically updates the graph and prompt context, illustrating a broader pattern in which a database can supply the structure, examples, and constraints needed for an agent to query it reliably.

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