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Pin weaviate-client version

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This commit is contained in:
ChristianZaccaria 2025-09-08 09:35:50 +01:00
parent 980c7c244d
commit 920c2a3e12
5 changed files with 18 additions and 25 deletions
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2
llama_stack/providers/registry/vector_io.py
View file

@ -500,7 +500,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
api=Api.vector_io,
adapter_type="weaviate",
provider_type="remote::weaviate",
pip_packages=["weaviate-client"],
pip_packages=["weaviate-client>=4.16.5"],
module="llama_stack.providers.remote.vector_io.weaviate",
config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",

17
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
View file

@ -96,9 +96,9 @@ class WeaviateIndex(EmbeddingIndex):
k: Limit of number of results to return
score_threshold: Minimum similarity score threshold
Returns:
QueryChunksResponse with chunks and scores
QueryChunksResponse with chunks and scores.
"""
log.info(
log.debug(
f"WEAVIATE VECTOR SEARCH CALLED: embedding_shape={embedding.shape}, k={k}, threshold={score_threshold}"
)
sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
@ -135,7 +135,7 @@ class WeaviateIndex(EmbeddingIndex):
chunks.append(chunk)
scores.append(score)
log.info(f"WEAVIATE VECTOR SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
log.debug(f"WEAVIATE VECTOR SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
return QueryChunksResponse(chunks=chunks, scores=scores)
async def delete(self, chunk_ids: list[str] | None = None) -> None:
@ -166,7 +166,7 @@ class WeaviateIndex(EmbeddingIndex):
Returns:
QueryChunksResponse with chunks and scores
"""
log.info(f"WEAVIATE KEYWORD SEARCH CALLED: query='{query_string}', k={k}, threshold={score_threshold}")
log.debug(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)
collection = self.client.collections.get(sanitized_collection_name)
@ -199,7 +199,7 @@ class WeaviateIndex(EmbeddingIndex):
chunks.append(chunk)
scores.append(score)
log.info(f"WEAVIATE KEYWORD SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}.")
log.debug(f"WEAVIATE KEYWORD SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}.")
return QueryChunksResponse(chunks=chunks, scores=scores)
async def query_hybrid(
@ -223,7 +223,7 @@ class WeaviateIndex(EmbeddingIndex):
Returns:
QueryChunksResponse with combined results
"""
log.info(
log.debug(
f"WEAVIATE HYBRID SEARCH CALLED: query='{query_string}', embedding_shape={embedding.shape}, k={k}, threshold={score_threshold}, reranker={reranker_type}"
)
sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
@ -265,11 +265,10 @@ class WeaviateIndex(EmbeddingIndex):
if score < score_threshold:
continue
log.info(f"Document {chunk.metadata.get('document_id')} has score {score}")
chunks.append(chunk)
scores.append(score)
log.info(f"WEAVIATE HYBRID SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
log.debug(f"WEAVIATE HYBRID SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
return QueryChunksResponse(chunks=chunks, scores=scores)
@ -297,7 +296,7 @@ class WeaviateVectorIOAdapter(
def _get_client(self) -> weaviate.WeaviateClient:
if "localhost" in self.config.weaviate_cluster_url:
log.info("using Weaviate locally in container")
log.info("Using Weaviate locally in container")
host, port = self.config.weaviate_cluster_url.split(":")
key = "local_test"
client = weaviate.connect_to_local(