semantic_search
Perform intelligent semantic search on research content using natural language queries, with optional tech stack filtering for relevant results.
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 |