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Avoid re-computation of embedding in weaviate similarity search (#8284)

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Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Rohit Gupta 2023-07-27 02:01:55 +05:30 committed by GitHub
parent 01a9b06400
commit e5dba8978a
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1 changed files with 3 additions and 5 deletions
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8
libs/langchain/langchain/vectorstores/weaviate.py
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@ -345,9 +345,9 @@ class Weaviate(VectorStore):
content["certainty"] = kwargs.get("search_distance") content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs) query_obj = self._client.query.get(self._index_name, self._query_attrs)
embedded_query = self._embedding.embed_query(query)
if not self._by_text: if not self._by_text:
embedding = self._embedding.embed_query(query) vector = {"vector": embedded_query}
vector = {"vector": embedding}
result = ( result = (
query_obj.with_near_vector(vector) query_obj.with_near_vector(vector)
.with_limit(k) .with_limit(k)
@ -368,9 +368,7 @@ class Weaviate(VectorStore):
docs_and_scores = [] docs_and_scores = []
for res in result["data"]["Get"][self._index_name]: for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key) text = res.pop(self._text_key)
score = np.dot( score = np.dot(res["_additional"]["vector"], embedded_query)
res["_additional"]["vector"], self._embedding.embed_query(query)
)
docs_and_scores.append((Document(page_content=text, metadata=res), score)) docs_and_scores.append((Document(page_content=text, metadata=res), score))
return docs_and_scores return docs_and_scores