BBIT-Kai
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
from langchain_milvus import BM25BuiltInFunction, Milvus
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from config.llm import llmEmbeddings
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URI = "http://10.10.10.9:19530"
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knVectorstore = Milvus(
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embedding_function=llmEmbeddings,
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connection_args={"uri": URI, "token": "root:Milvus", "db_name": "bbit_ai_lab"},
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collection_name="knowledge",
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index_params={"index_type": "FLAT", "metric_type": "L2"},
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consistency_level="Strong",
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auto_id=True,
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primary_field = "id",
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text_field="text",
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vector_field="vector",
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partition_key_field = "kn_id",
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enable_dynamic_field = True,
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drop_old=False, # set to True if seeking to drop the collection with that name if it exists
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)
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memVectorstore = Milvus(
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embedding_function=llmEmbeddings,
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connection_args={"uri": URI, "token": "root:Milvus", "db_name": "bbit_ai_lab"},
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collection_name="memory",
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index_params={"index_type": "FLAT", "metric_type": "L2"},
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consistency_level="Strong",
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auto_id=True,
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primary_field = "id",
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text_field="text",
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vector_field="vector",
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partition_key_field = "ai_id",
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enable_dynamic_field = True,
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drop_old=False, # set to True if seeking to drop the collection with that name if it exists
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) |