semantic_search
Search your personal memory layers by combining vector similarity with keyword matching to retrieve relevant episodic, semantic, and procedural memories.
Instructions
Search across memory layers using vector similarity + keyword RRF fusion.
Searches your personal MEMORY layers (episodic/semantic/procedural — things
you or Metis recorded), NOT your document library. For documents/PDFs use
search_pdf_knowledge; for reference metadata use search_library.
Retrieval pipeline (M5.8):
1. Embed the query.
2. Run vector similarity search on each requested layer.
3. Run keyword LIKE search on each layer.
4. Fuse results using Reciprocal Rank Fusion (RRF, k=60).
5. Return top_k deduplicated results ranked by fused score.
Args:
query: Natural language search query to embed and keyword-match.
layers: Comma-separated memory layers to search, drawn from 'episodic',
'semantic', and 'procedural'.
top_k: Number of fused results to return (default 5).
Returns:
A single TextContent listing the top fused results (layer, title, score,
timestamp, and a content preview), or a "no results" / error message.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| layers | No | episodic,semantic,procedural | |
| top_k | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |