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change Reranker to WeightedInMemoryAggregator

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kimbwook 2025-09-11 21:40:21 +09:00
parent 60318b659d
commit 897be1376e
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GPG key ID: 13B032C99CBD373A
6 changed files with 22 additions and 142 deletions
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6
llama_stack/providers/remote/vector_io/chroma/chroma.py
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@ -31,7 +31,7 @@ from llama_stack.providers.utils.memory.vector_store import (
EmbeddingIndex,
VectorDBWithIndex,
)
from llama_stack.providers.utils.vector_io.vector_utils import Reranker
from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator
from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
@ -192,7 +192,9 @@ class ChromaIndex(EmbeddingIndex):
}
# Combine scores using the reranking utility
combined_scores = Reranker.combine_search_results(vector_scores, keyword_scores, reranker_type, reranker_params)
combined_scores = WeightedInMemoryAggregator.combine_search_results(
vector_scores, keyword_scores, reranker_type, reranker_params
)
# Efficient top-k selection because it only tracks the k best candidates it's seen so far
top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1])

1
llama_stack/providers/utils/vector_io/vector_utils.py
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@ -39,7 +39,6 @@ def sanitize_collection_name(name: str, weaviate_format=False) -> str:
return s
class WeightedInMemoryAggregator:
@staticmethod
def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: