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@ -88,6 +88,7 @@ class Weaviate(VectorStore):
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relevance_score_fn: Optional[
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Callable[[float], float]
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] = _default_score_normalizer,
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by_text: bool = True,
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):
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"""Initialize with Weaviate client."""
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try:
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@ -107,6 +108,7 @@ class Weaviate(VectorStore):
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self._text_key = text_key
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self._query_attrs = [self._text_key]
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self._relevance_score_fn = relevance_score_fn
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self._by_text = by_text
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if attributes is not None:
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self._query_attrs.extend(attributes)
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@ -163,6 +165,29 @@ class Weaviate(VectorStore):
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text 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 most similar to the query.
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"""
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if self._by_text:
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return self.similarity_search_by_text(query, k, **kwargs)
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else:
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if self._embedding is None:
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raise ValueError(
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"_embedding cannot be None for similarity_search when "
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"_by_text=False"
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)
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embedding = self._embedding.embed_query(query)
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return self.similarity_search_by_vector(embedding, k, **kwargs)
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def similarity_search_by_text(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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@ -291,6 +316,10 @@ class Weaviate(VectorStore):
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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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if self._embedding is None:
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raise ValueError(
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"_embedding cannot be None for similarity_search_with_score"
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)
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content: Dict[str, Any] = {"concepts": [query]}
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if kwargs.get("search_distance"):
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content["certainty"] = kwargs.get("search_distance")
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@ -305,10 +334,6 @@ class Weaviate(VectorStore):
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raise ValueError(f"Error during query: {result['errors']}")
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docs_and_scores = []
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if self._embedding is None:
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raise ValueError(
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"_embedding cannot be None for similarity_search_with_score"
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)
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for res in result["data"]["Get"][self._index_name]:
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text = res.pop(self._text_key)
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score = np.dot(
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@ -332,7 +357,7 @@ class Weaviate(VectorStore):
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"relevance_score_fn must be provided to"
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" Weaviate constructor to normalize scores"
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)
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docs_and_scores = self.similarity_search_with_score(query, k=k)
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docs_and_scores = self.similarity_search_with_score(query, k=k, **kwargs)
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return [
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(doc, self._relevance_score_fn(score)) for doc, score in docs_and_scores
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]
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@ -413,4 +438,6 @@ class Weaviate(VectorStore):
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batch.flush()
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return cls(client, index_name, text_key, embedding, attributes)
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return cls(
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client, index_name, text_key, embedding=embedding, attributes=attributes
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)
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