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Add docstring to Weaviate query_vector()

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ChristianZaccaria 2025-08-29 18:13:45 +01:00
parent 0cd86e696c
commit 65a2f09a6f
1 changed files with 10 additions and 1 deletions
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11
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
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@ -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)