BBIT-Kai
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
from langchain_milvus import Milvus
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from config.llm import llmEmbeddings
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from utils.GlobalVariable import LOCAL_IP
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URI = "http://" + LOCAL_IP + ":19530"
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_knVectorstore = None
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_memVectorstore = None
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def get_kn_vectorstore():
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global _knVectorstore
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if _knVectorstore is None:
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try:
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_knVectorstore = Milvus(
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embedding_function=llmEmbeddings,
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connection_args={
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"uri": URI,
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"token": "root:Milvus",
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"db_name": "bbit_ai_lab",
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},
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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,
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)
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except Exception as e:
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print(f"[Milvus Init Failed] knowledge: {e}")
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_knVectorstore = None
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return _knVectorstore
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def get_mem_vectorstore():
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global _memVectorstore
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if _memVectorstore is None:
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try:
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_memVectorstore = Milvus(...)
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except Exception as e:
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print(f"[Milvus Init Failed] memory: {e}")
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_memVectorstore = None
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return _memVectorstore
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