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update:weaviate_store format

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This commit is contained in:
aries-ckt 2023-06-15 10:20:08 +08:00
parent f4bc71ad97
commit ba713c6ba9
1 changed files with 30 additions and 19 deletions
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47
pilot/vector_store/weaviate_store.py
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@ -26,9 +26,7 @@ class WeaviateStore(VectorStoreBase):
KNOWLEDGE_UPLOAD_ROOT_PATH, ctx["vector_store_name"] + ".vectordb"
)
self.vector_store_client = weaviate.Client(
self.weaviate_url
)
self.vector_store_client = weaviate.Client(self.weaviate_url)
def similar_search(self, text: str, topk: int) -> None:
"""Perform similar search in Weaviate"""
@ -37,8 +35,13 @@ class WeaviateStore(VectorStoreBase):
"concepts": [text],
"distance": 0.75, # prior to v1.14 use "certainty" instead of "distance"
}
response = (self.vector_store_client.query.get("Document", ["metadata", "text"]).with_near_vector(
{"vector": nearText}).with_limit(topk).with_additional(["distance"]).do())
response = (
self.vector_store_client.query.get("Document", ["metadata", "text"])
.with_near_vector({"vector": nearText})
.with_limit(topk)
.with_additional(["distance"])
.do()
)
return json.dumps(response, indent=2)
@ -61,25 +64,37 @@ class WeaviateStore(VectorStoreBase):
{
"class": "Document",
"description": "A document with metadata and text",
"moduleConfig": {"text2vec-transformers": {"poolingStrategy": "masked_mean", "vectorizeClassName": False}
"moduleConfig": {
"text2vec-transformers": {
"poolingStrategy": "masked_mean",
"vectorizeClassName": False,
}
},
"properties": [
{
"dataType": ["text"],
"moduleConfig": {
"text2vec-transformers": {"skip": False, "vectorizePropertyName": False}},
"text2vec-transformers": {
"skip": False,
"vectorizePropertyName": False,
}
},
"description": "Metadata of the document",
"name": "metadata"
"name": "metadata",
},
{
"dataType": ["text"],
"moduleConfig": {
"text2vec-transformers": {"skip": False, "vectorizePropertyName": False}},
"description": "Text content of the document",
"name": "text"
"text2vec-transformers": {
"skip": False,
"vectorizePropertyName": False,
}
},
"description": "Text content of the document",
"name": "text",
},
],
"vectorizer": "text2vec-transformers"
"vectorizer": "text2vec-transformers",
}
]
}
@ -99,10 +114,6 @@ class WeaviateStore(VectorStoreBase):
# Batch import all documents
for i in range(len(texts)):
properties = {
"metadata": metadatas[i],
"text": texts[i]
}
properties = {"metadata": metadatas[i], "text": texts[i]}
self.vector_store_client.batch.add_data_object(
properties, "Document")
self.vector_store_client.batch.add_data_object(properties, "Document")