Semantic Search (RAG)
rag_searchRead-onlyIdempotent
Search indexed documents by meaning to retrieve relevant passages with source files, grounding answers in your own data.
Instructions
Search previously indexed documents by meaning and return the most relevant passages with their source files. Use this to ground answers in the user's own files/docs that you indexed with rag_index.
Requires an embedding server (LM Studio or llama.cpp) running with an embedding model loaded.
Args:
query (string): What to look for (a question or topic).
top_k (number): Number of passages to return, 1-20 (default 5).
Returns ranked { source, score, text } passages.
Example: { "query": "how is authentication handled", "top_k": 5 }
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| top_k | No | Passages to return |