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53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
from langchain.vectorstores import Milvus
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from pymilvus import Collection,utility
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from pymilvus import connections, DataType, FieldSchema, CollectionSchema
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from pilot.source_embedding.Text2Vectors import Text2Vectors
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# milvus = connections.connect(
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# alias="default",
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# host='localhost',
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# port="19530"
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# )
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# collection = Collection("book")
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# Get an existing collection.
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# collection.load()
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#
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# search_params = {"metric_type": "L2", "params": {}, "offset": 5}
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#
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# results = collection.search(
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# data=[[0.1, 0.2]],
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# anns_field="book_intro",
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# param=search_params,
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# limit=10,
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# expr=None,
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# output_fields=['book_id'],
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# consistency_level="Strong"
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# )
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#
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# # get the IDs of all returned hits
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# results[0].ids
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#
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# # get the distances to the query vector from all returned hits
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# results[0].distances
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#
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# # get the value of an output field specified in the search request.
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# # vector fields are not supported yet.
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# hit = results[0][0]
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# hit.entity.get('title')
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milvus = connections.connect(
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alias="default",
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host='localhost',
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port="19530"
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)
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data = ["aaa", "bbb"]
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text_embeddings = Text2Vectors()
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mivuls = Milvus(collection_name='document', embedding_function= text_embeddings, connection_args={"host": "127.0.0.1", "port": "19530", "alias":"default"}, text_field="")
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mivuls.from_texts(texts=data, embedding=text_embeddings)
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# docs,
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# embedding=embeddings,
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# connection_args={"host": "127.0.0.1", "port": "19530", "alias": "default"}
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# ) |