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Remove Weaviate unit tests

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This commit is contained in:
ChristianZaccaria 2025-09-03 10:34:48 +01:00
parent c630a646e3
commit 980c7c244d
4 changed files with 34 additions and 292 deletions
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54
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
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@ -104,11 +104,15 @@ class WeaviateIndex(EmbeddingIndex):
sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
collection = self.client.collections.get(sanitized_collection_name)
results = collection.query.near_vector(
near_vector=embedding.tolist(),
limit=k,
return_metadata=wvc.query.MetadataQuery(distance=True),
)
try:
results = collection.query.near_vector(
near_vector=embedding.tolist(),
limit=k,
return_metadata=wvc.query.MetadataQuery(distance=True),
)
except Exception as e:
log.error(f"Weaviate client vector search failed: {e}")
raise
chunks = []
scores = []
@ -123,8 +127,8 @@ class WeaviateIndex(EmbeddingIndex):
if doc.metadata.distance is None:
continue
# Convert cosine distance ∈ [0,2] → cosine similarity ∈ [-1,1]
score = 1.0 - float(doc.metadata.distance)
# Convert cosine distance ∈ [0,2] -> normalized cosine similarity ∈ [0,1]
score = 1.0 - (float(doc.metadata.distance) / 2.0)
if score < score_threshold:
continue
@ -167,11 +171,15 @@ class WeaviateIndex(EmbeddingIndex):
collection = self.client.collections.get(sanitized_collection_name)
# Perform BM25 keyword search on chunk_content field
results = collection.query.bm25(
query=query_string,
limit=k,
return_metadata=wvc.query.MetadataQuery(score=True),
)
try:
results = collection.query.bm25(
query=query_string,
limit=k,
return_metadata=wvc.query.MetadataQuery(score=True),
)
except Exception as e:
log.error(f"Weaviate client keyword search failed: {e}")
raise
chunks = []
scores = []
@ -229,14 +237,18 @@ class WeaviateIndex(EmbeddingIndex):
rerank = HybridFusion.RELATIVE_SCORE
# Perform hybrid search using Weaviate's native hybrid search
results = collection.query.hybrid(
query=query_string,
alpha=0.5, # Range <0, 1>, where 0.5 will equally favor vector and keyword search
vector=embedding.tolist(),
limit=k,
fusion_type=rerank,
return_metadata=wvc.query.MetadataQuery(score=True),
)
try:
results = collection.query.hybrid(
query=query_string,
alpha=0.5, # Range <0, 1>, where 0.5 will equally favor vector and keyword search
vector=embedding.tolist(),
limit=k,
fusion_type=rerank,
return_metadata=wvc.query.MetadataQuery(score=True),
)
except Exception as e:
log.error(f"Weaviate client hybrid search failed: {e}")
raise
chunks = []
scores = []
@ -283,7 +295,7 @@ class WeaviateVectorIOAdapter(
self.openai_vector_stores: dict[str, dict[str, Any]] = {}
self.metadata_collection_name = "openai_vector_stores_metadata"
def _get_client(self) -> weaviate.Client:
def _get_client(self) -> weaviate.WeaviateClient:
if "localhost" in self.config.weaviate_cluster_url:
log.info("using Weaviate locally in container")
host, port = self.config.weaviate_cluster_url.split(":")