Metis · Librarian — Search Pdf Knowledge
search_pdf_knowledgeRetrieve relevant PDF chunks from your knowledge base using semantic search. Query with natural language to find cited answers from indexed documents.
Instructions
Semantic search across one or more knowledge database layers.
Meaning-based (vector) search over your knowledge-base PDF chunks — the RAG
retrieval tool behind grounded, cited answers. For exact keyword matches use
search_fulltext; for reference metadata use search_library; for your own
notes/memory (not documents) use semantic_search.
Searches indexed PDF chunks using 768-dim nomic-embed vector similarity.
You can search a single layer or combine layers (e.g. PH background + HAT specialist).
Args:
query: Natural language question or keyword phrase to embed and match.
databases: List of database slugs to search; pass None (the default) to
search all indexed databases. Examples: ['ph-background'],
['hat-specialist', 'epi-methods'].
top_k: Number of results to return (default 8).
Returns:
A single TextContent listing the top-ranked PDF chunks (title, similarity
score, layer, domain, page, source file, and an excerpt), or a message if
nothing is indexed yet or no chunks match the requested databases.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No | ||
| databases | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |