From 65a2f09a6fb72e9c84d151017ab1567f5fe503c8 Mon Sep 17 00:00:00 2001 From: ChristianZaccaria Date: Fri, 29 Aug 2025 18:13:45 +0100 Subject: [PATCH] Add docstring to Weaviate query_vector() --- .../providers/remote/vector_io/weaviate/weaviate.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py index 792a6eb12..f17646d77 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py +++ b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py @@ -89,6 +89,15 @@ class WeaviateIndex(EmbeddingIndex): collection.data.delete_many(where=Filter.by_property("chunk_id").contains_any(chunk_ids)) async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + """ + Performs vector search using Weaviate's built-in vector search. + Args: + embedding: The query embedding vector + k: Limit of number of results to return + score_threshold: Minimum similarity score threshold + Returns: + QueryChunksResponse with chunks and scores + """ log.info( f"WEAVIATE VECTOR SEARCH CALLED: embedding_shape={embedding.shape}, k={k}, threshold={score_threshold}" ) @@ -151,7 +160,7 @@ class WeaviateIndex(EmbeddingIndex): k: Limit of number of results to return score_threshold: Minimum similarity score threshold Returns: - QueryChunksResponse with combined results + QueryChunksResponse with chunks and scores """ log.info(f"WEAVIATE KEYWORD SEARCH CALLED: query='{query_string}', k={k}, threshold={score_threshold}") sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)