Adds vector-based semantic search across all chat sessions using Qdrant.
When a ChatTurn finishes, its content is chunked, embedded, and upserted
to a Qdrant collection. A search API and UI components enable searching
at user, project, and session scope.
Phase 1 — Configuration & Dependencies
- Add port/apiKey to GadgetCodeConfig.qdrant type
- Uncomment and update qdrant section in YAML config example
- Add qdrant config passthrough in env.ts
- Add @qdrant/js-client-rest dependency
Phase 2 — AI Embedding API (@gadget/ai)
- Add IAiEmbeddingResponse interface and abstract embeddings() to AiApi
- Implement embeddings() in OllamaAiApi (client.embeddings)
- Implement embeddings() in OpenAiApi (client.embeddings.create)
- Export IAiEmbeddingResponse from package index
Phase 3 — Backend Vector Store Service
- Create VectorStoreService (ingestTurn, search, removeTurnPoints)
- Hook fire-and-forget ingest after turn.save() in drone-session
- Register VectorStoreService in service startup/shutdown
Phase 4 — Backend Search API
- Create POST /api/v1/search controller with userId enforcement
- Batch-hydrate results from MongoDB (user, project, session, turn)
- Register search route in v1 API router
Phase 5 — Frontend Search Components
- SearchInput: debounced input with lucide-react icons
- ChatSearchResults: modal with score badges, metadata, loading states
- DroneSelectionModal: drone picker for sessions without a drone
- Add searchApi and ISearchResult to API client
- Add search to Home (global), ProjectManager (project), ChatSessionView (session)
- Add id=turn-{turnId} to ChatTurn for scroll targeting
- Scroll-to-turn from search result selection and router state
- Show DroneSelectionModal when no drone available
- Add Select Drone button in ChatSessionView sidebar
Moved all Mongoose model interfaces to @gadget/api to commonize the data
structures being passed around the system as JSON objects via HTTP and
Socket.IO.
