Query documents by similarity search.
Supports semantic (vector), lexical (text), or hybrid search modes.
Args: request: Query parameters including text, filters, and reranking options vector_store_name: The unique name of the vector store vector_store_use_case: Injected vector store use case
Returns: Matching documents with similarity scores and query metadata
The name of the vector store
Request to query documents.
Text query for automatic embedding (required)
Query type: semantic, lexical, or hybrid
semantic, lexical, hybrid Number of search results to return
x >= 1Metadata filter expression
When to apply filter: 'post' (after kNN, faster) or 'pre' (before kNN, exact)
post, pre Enable reranking of search results
Reranking model to use (uses system default if not specified)
Number of results after reranking (defaults to top_k)
Include embedding vectors in response