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fix(vector-io): unify score calculation to use cosine and normalize to [0,1]

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This commit is contained in:
ChristianZaccaria 2025-09-04 13:03:59 +01:00
parent 9618adba89
commit a0e0c7030b
9 changed files with 166 additions and 42 deletions
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15
llama_stack/providers/remote/vector_io/milvus/milvus.py
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@ -153,6 +153,9 @@ class MilvusIndex(EmbeddingIndex):
raise e
async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
logger.info(
f"MILVUS VECTOR SEARCH CALLED: embedding_shape={embedding.shape}, k={k}, threshold={score_threshold}"
)
search_res = await asyncio.to_thread(
self.client.search,
collection_name=self.collection_name,
@ -162,8 +165,16 @@ class MilvusIndex(EmbeddingIndex):
output_fields=["*"],
search_params={"params": {"radius": score_threshold}},
)
chunks = [Chunk(**res["entity"]["chunk_content"]) for res in search_res[0]]
scores = [res["distance"] for res in search_res[0]]
chunks, scores = [], []
for res in search_res[0]:
score = float(res["distance"] + 1.0) / 2.0 # rescale to [0,1]
if score < score_threshold:
continue
chunks.append(Chunk(**res["entity"]["chunk_content"]))
scores.append(score)
logger.info(f"MILVUS VECTOR SEARCH RESULTS: Found {len(chunks)} chunks with scores {scores}")
return QueryChunksResponse(chunks=chunks, scores=scores)
async def query_keyword(