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Overview

search runs a semantic vector search over all decisions in the workspace. It finds decisions by meaning — not just keyword matching — so “how do we handle auth?” returns JWT decisions even if “authentication” wasn’t the exact word used. Decisions are embedded with OpenAI’s text-embedding-3-small (1536 dimensions) and indexed in pgvector for fast approximate nearest-neighbor search.

Parameters

Response

Response Fields

Examples

Find auth decisions

Find database choices

Check for existing patterns

  • Before implementing something — “did we already decide on this?”
  • When unfamiliar with part of the codebase — “what’s the rationale for how this works?”
  • Before picking a library — “have we already adopted something for this use case?”
  • When debugging — “has anyone dealt with this type of issue before?”
Use natural language queries. The semantic search understands intent — “how do we validate user input?” will find Zod decisions even if you don’t mention Zod.