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@ -8,6 +8,8 @@ from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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ClassVar,
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Collection,
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Dict,
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Iterable,
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List,
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@ -519,6 +521,17 @@ class AzureSearch(VectorStore):
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]
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return docs
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def hybrid_search_with_relevance_scores(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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score_threshold = kwargs.pop("score_threshold", None)
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result = self.hybrid_search_with_score(query, k=k, **kwargs)
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return (
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result
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if score_threshold is None
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else [r for r in result if r[1] >= score_threshold]
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)
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def semantic_hybrid_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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@ -687,9 +700,16 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
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"""Azure Search instance used to find similar documents."""
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search_type: str = "hybrid"
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"""Type of search to perform. Options are "similarity", "hybrid",
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"semantic_hybrid"."""
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"semantic_hybrid", "similarity_score_threshold", "hybrid_score_threshold"."""
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k: int = 4
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"""Number of documents to return."""
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allowed_search_types: ClassVar[Collection[str]] = (
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"similarity",
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"similarity_score_threshold",
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"hybrid",
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"hybrid_score_threshold",
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"semantic_hybrid",
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)
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class Config:
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"""Configuration for this pydantic object."""
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@ -701,17 +721,10 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
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"""Validate search type."""
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if "search_type" in values:
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search_type = values["search_type"]
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if search_type not in (
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allowed_search_types := (
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"similarity",
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"similarity_score_threshold",
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"hybrid",
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"semantic_hybrid",
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)
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):
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if search_type not in cls.allowed_search_types:
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raise ValueError(
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f"search_type of {search_type} not allowed. Valid values are: "
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f"{allowed_search_types}"
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f"{cls.allowed_search_types}"
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)
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return values
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@ -732,6 +745,13 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
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]
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elif self.search_type == "hybrid":
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docs = self.vectorstore.hybrid_search(query, k=self.k, **kwargs)
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elif self.search_type == "hybrid_score_threshold":
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docs = [
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doc
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for doc, _ in self.vectorstore.hybrid_search_with_relevance_scores(
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query, k=self.k, **kwargs
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)
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]
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elif self.search_type == "semantic_hybrid":
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docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **kwargs)
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else:
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