Spike: scope natural language querying

[kmcginnes]

Goal

Investigate and scope the natural language querying feature before any implementation begins. Questions to resolve:

• UI placement — where does the NL query experience live? Search sidebar, query editor mode, or a dedicated surface?
• Interaction model — how does it function? One-shot NL → query translation the user can inspect/edit, a conversational flow, or direct NL → results?
• Deployment — how does a user deploy this? Graph Explorer is a client-side app with a thin proxy; does inference go through the proxy server, a sidecar, or directly from the browser?
• Bring-your-own backend — can users supply their own model endpoint and credentials (Bedrock, Anthropic, OpenAI-compatible, local)? What's the configuration and credential-storage story given all persistence is client-side?
• MCP story — is there an MCP server exposing schema/connection/query tools so external agents can drive Graph Explorer, and/or does Graph Explorer act as an MCP client?
• Context strategy — what schema/sample context does the model need to generate correct Gremlin/openCypher/SPARQL, and how does it fit within existing schema sync?

Related Issues

• Part of [Roadmap] Natural Language Queries #690
• Related to [Roadmap] Query Editor Enhancements #810 — query editor enhancements; a likely UI surface
• Related to Detect database capabilities using probe queries #1596 — database capability detection; NL generation needs to know dialect/version constraints

Important

Internal only — this issue is maintained by the core team and is not accepting external contributions.