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42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
from typing import List, Optional
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from uuid import uuid4
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from application.core.settings import settings
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from application.vectorstore.base import BaseVectorStore
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class MilvusStore(BaseVectorStore):
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def __init__(self, source_id: str = "", embeddings_key: str = "embeddings"):
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super().__init__()
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from langchain_milvus import Milvus
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connection_args = {
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"uri": settings.MILVUS_URI,
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"token": settings.MILVUS_TOKEN,
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}
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self._docsearch = Milvus(
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embedding_function=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key),
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collection_name=settings.MILVUS_COLLECTION_NAME,
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connection_args=connection_args,
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)
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self._source_id = source_id
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def search(self, question, k=2, *args, **kwargs):
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# Drop the per-source score_threshold (unsupported here) so it is safely
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# ignored instead of being forwarded into the langchain call.
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kwargs.pop("score_threshold", None)
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expr = f"source_id == '{self._source_id}'"
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return self._docsearch.similarity_search(query=question, k=k, expr=expr, *args, **kwargs)
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def add_texts(self, texts: List[str], metadatas: Optional[List[dict]], *args, **kwargs):
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ids = [str(uuid4()) for _ in range(len(texts))]
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return self._docsearch.add_texts(texts=texts, metadatas=metadatas, ids=ids, *args, **kwargs)
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def save_local(self, *args, **kwargs):
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pass
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def delete_index(self, *args, **kwargs):
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pass
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