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@ -516,6 +516,15 @@ class OpenSearchVectorSearch(VectorStore):
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docs_with_scores = self.similarity_search_with_score(query, k, **kwargs)
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return [doc[0] for doc in docs_with_scores]
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def similarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to the embedding vector."""
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docs_with_scores = self.similarity_search_with_score_by_vector(
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embedding, k, **kwargs
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)
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return [doc[0] for doc in docs_with_scores]
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def similarity_search_with_score(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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@ -534,19 +543,43 @@ class OpenSearchVectorSearch(VectorStore):
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Optional Args:
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same as `similarity_search`
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"""
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embedding = self.embedding_function.embed_query(query)
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return self.similarity_search_with_score_by_vector(embedding, k, **kwargs)
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def similarity_search_with_score_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""Return docs and it's scores most similar to the embedding vector.
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By default, supports Approximate Search.
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Also supports Script Scoring and Painless Scripting.
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Args:
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embedding: Embedding vector to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents along with its scores most similar to the query.
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Optional Args:
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same as `similarity_search`
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"""
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text_field = kwargs.get("text_field", "text")
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metadata_field = kwargs.get("metadata_field", "metadata")
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hits = self._raw_similarity_search_with_score(query=query, k=k, **kwargs)
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hits = self._raw_similarity_search_with_score_by_vector(
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embedding=embedding, k=k, **kwargs
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)
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documents_with_scores = [
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(
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Document(
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page_content=hit["_source"][text_field],
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metadata=hit["_source"]
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if metadata_field == "*" or metadata_field not in hit["_source"]
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else hit["_source"][metadata_field],
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metadata=(
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hit["_source"]
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if metadata_field == "*" or metadata_field not in hit["_source"]
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else hit["_source"][metadata_field]
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),
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),
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hit["_score"],
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)
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@ -554,26 +587,25 @@ class OpenSearchVectorSearch(VectorStore):
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]
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return documents_with_scores
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def _raw_similarity_search_with_score(
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self, query: str, k: int = 4, **kwargs: Any
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def _raw_similarity_search_with_score_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[dict]:
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"""Return raw opensearch documents (dict) including vectors,
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scores most similar to query.
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scores most similar to the embedding vector.
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By default, supports Approximate Search.
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Also supports Script Scoring and Painless Scripting.
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Args:
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query: Text to look up documents similar to.
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embedding: Embedding vector to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of dict with its scores most similar to the query.
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List of dict with its scores most similar to the embedding.
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Optional Args:
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same as `similarity_search`
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"""
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embedding = self.embedding_function.embed_query(query)
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search_type = kwargs.get("search_type", "approximate_search")
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vector_field = kwargs.get("vector_field", "vector_field")
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index_name = kwargs.get("index_name", self.index_name)
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@ -702,7 +734,9 @@ class OpenSearchVectorSearch(VectorStore):
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embedding = self.embedding_function.embed_query(query)
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# Do ANN/KNN search to get top fetch_k results where fetch_k >= k
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results = self._raw_similarity_search_with_score(query, fetch_k, **kwargs)
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results = self._raw_similarity_search_with_score_by_vector(
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embedding, fetch_k, **kwargs
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)
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embeddings = [result["_source"][vector_field] for result in results]
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