semantic_search
Find relevant research content using natural language queries with semantic understanding, filtering by technology stacks when needed.
Instructions
Semantic search using LanceDB vectors (Gemini embeddings). More intelligent than keyword search.
Args: query: Natural language query (e.g., 'how to implement RAG pipelines') top_k: Number of results (default: 5) stack: Optional stack filter, comma-separated (e.g. 'python,fastapi'). Results mentioning these are boosted.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No | ||
| stack | No |