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feat(community): support semantic hybrid score threshold in Azure AI Search (#21527)

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Support semantic hybrid search with a score threshold -- similar to what
we do for similarity search and for hybrid search (#20907).
This commit is contained in:
Massimiliano Pronesti 2024-05-16 21:54:32 +02:00 committed by GitHub
parent 5e445a7e4e
commit 0c0db7c5db
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1 changed files with 33 additions and 4 deletions
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37
libs/community/langchain_community/vectorstores/azuresearch.py
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@ -13,6 +13,7 @@ from typing import (
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Type,
@ -567,7 +568,11 @@ class AzureSearch(VectorStore):
return [doc for doc, _, _ in docs_and_scores]
def semantic_hybrid_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
self,
query: str,
k: int = 4,
score_type: Literal["score", "reranker_score"] = "score",
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Returns the most similar indexed documents to the query text.
@ -575,14 +580,29 @@ class AzureSearch(VectorStore):
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_type: Must either be "score" or "reranker_score".
Defaulted to "score".
Returns:
List[Document]: A list of documents that are most similar to the query text.
List[Tuple[Document, float]]: A list of documents and their
corresponding scores.
"""
score_threshold = kwargs.pop("score_threshold", None)
docs_and_scores = self.semantic_hybrid_search_with_score_and_rerank(
query, k=k, filters=kwargs.get("filters", None)
)
return [(doc, score) for doc, score, _ in docs_and_scores]
if score_type == "score":
return [
(doc, score)
for doc, score, _ in docs_and_scores
if score_threshold is None or score >= score_threshold
]
elif score_type == "reranker_score":
return [
(doc, reranker_score)
for doc, _, reranker_score in docs_and_scores
if score_threshold is None or reranker_score >= score_threshold
]
def semantic_hybrid_search_with_score_and_rerank(
self, query: str, k: int = 4, filters: Optional[str] = None
@ -716,7 +736,8 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
"""Azure Search instance used to find similar documents."""
search_type: str = "hybrid"
"""Type of search to perform. Options are "similarity", "hybrid",
"semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold"."""
"semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold",
or "semantic_hybrid_score_threshold"."""
k: int = 4
"""Number of documents to return."""
allowed_search_types: ClassVar[Collection[str]] = (
@ -725,6 +746,7 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
"hybrid",
"hybrid_score_threshold",
"semantic_hybrid",
"semantic_hybrid_score_threshold",
)
class Config:
@ -770,6 +792,13 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
]
elif self.search_type == "semantic_hybrid":
docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **kwargs)
elif self.search_type == "semantic_hybrid_score_threshold":
docs = [
doc
for doc, _ in self.vectorstore.semantic_hybrid_search_with_score(
query, k=self.k, **kwargs
)
]
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs