fix weaviate hybrid search issue (#1600)

Co-authored-by: jyong <jyong@dify.ai>
This commit is contained in:
Jyong 2023-11-22 16:41:20 +08:00 committed by GitHub
parent 9587479b76
commit b930716745
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5 changed files with 11 additions and 10 deletions

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@ -111,7 +111,7 @@ class WeaviateVectorIndex(BaseVectorIndex):
if self._vector_store: if self._vector_store:
return self._vector_store return self._vector_store
attributes = ['doc_id', 'dataset_id', 'document_id'] attributes = ['doc_id', 'dataset_id', 'document_id', 'doc_hash']
if self._is_origin(): if self._is_origin():
attributes = ['doc_id'] attributes = ['doc_id']

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@ -60,7 +60,7 @@ def _create_weaviate_client(**kwargs: Any) -> Any:
def _default_score_normalizer(val: float) -> float: def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val)) return 1 - val
def _json_serializable(value: Any) -> Any: def _json_serializable(value: Any) -> Any:
@ -243,7 +243,8 @@ class Weaviate(VectorStore):
query_obj = query_obj.with_where(kwargs.get("where_filter")) query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("additional"): if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional")) query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_bm25(query=content).with_limit(k).do() properties = ['text', 'dataset_id', 'doc_hash', 'doc_id', 'document_id']
result = query_obj.with_bm25(query=query, properties=properties).with_limit(k).do()
if "errors" in result: if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}") raise ValueError(f"Error during query: {result['errors']}")
docs = [] docs = []
@ -380,14 +381,14 @@ class Weaviate(VectorStore):
result = ( result = (
query_obj.with_near_vector(vector) query_obj.with_near_vector(vector)
.with_limit(k) .with_limit(k)
.with_additional("vector") .with_additional(["vector", "distance"])
.do() .do()
) )
else: else:
result = ( result = (
query_obj.with_near_text(content) query_obj.with_near_text(content)
.with_limit(k) .with_limit(k)
.with_additional("vector") .with_additional(["vector", "distance"])
.do() .do()
) )
@ -397,7 +398,7 @@ 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(res["_additional"]["vector"], embedded_query) score = res["_additional"]["distance"]
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

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@ -1,4 +1,4 @@
from langchain.vectorstores import Weaviate from core.vector_store.vector.weaviate import Weaviate
class WeaviateVectorStore(Weaviate): class WeaviateVectorStore(Weaviate):

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@ -30,7 +30,7 @@ services:
# The Weaviate vector store. # The Weaviate vector store.
weaviate: weaviate:
image: semitechnologies/weaviate:1.18.4 image: semitechnologies/weaviate:1.19.0
restart: always restart: always
volumes: volumes:
# Mount the Weaviate data directory to the container. # Mount the Weaviate data directory to the container.

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@ -253,7 +253,7 @@ services:
# The Weaviate vector store. # The Weaviate vector store.
weaviate: weaviate:
image: semitechnologies/weaviate:1.18.4 image: semitechnologies/weaviate:1.19.0
restart: always restart: always
volumes: volumes:
# Mount the Weaviate data directory to the container. # Mount the Weaviate data directory to the container.