search
Retrieve relevant text chunks from a research knowledge base using natural language queries. Each result includes exact page-level citations from PDF documents.
Instructions
Semantic search over the corpus. Returns chunks with page-level citations.
Args: query: natural-language question or topic. top_k: number of results (default 6). doc_id: restrict to one document (from list_documents), "" = all. note_type: which payload types to search — "raw" (verbatim paper text, ground truth), "claude_note" (Claude's reading notes), "hyde" (generated HyDE bridges only), "all" (raw + claude_note, the default), or "everything" (raw + claude_note + hyde). hypothesis: HyDE fallback. When the plain query misses (low scores), pass a short hypothetical answer paragraph here. It is embedded and averaged with the query (Gao et al., 2023) to retrieve REAL corpus chunks via the hypothesis. Each hit is tagged retrieved_via="hyde". "" = ordinary query-vector search (retrieved_via="direct").
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_k | No | ||
| doc_id | No | ||
| note_type | No | all | |
| hypothesis | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |