a7a6a91a13
The qwen3-embedding:4b model defaults to 2560-d vectors. Both Ollama (client.embed()) and OpenAI support a dimensions parameter to request a specific output size. This change threads the configured qdrant.vectorSize through the AI provider layer so the model returns vectors matching the Qdrant collection dimensions. - AiApi.embeddings() now accepts optional dimensions parameter - Ollama provider: switched from client.embeddings() to client.embed() - OpenAI provider: passes dimensions to embeddings.create() - VectorStoreService.getEmbedding() passes env.qdrant.vectorSize - Added unit tests for dimension mismatch detection, collection creation, and search guards |
||
|---|---|---|
| .. | ||
| e2e | ||
| fixtures | ||
| helpers | ||
| app.test.ts | ||
| code-session.test.ts | ||
| drone-service.test.ts | ||
| drone-session.test.ts | ||
| project-api.test.ts | ||
| socket-service.test.ts | ||
| vector-store.test.ts | ||