92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
from typing import List, Optional
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from llama_index.core import Document, VectorStoreIndex
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from llama_index.vector_stores.milvus import MilvusVectorStore
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from app.config.env import env
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from app.utils.llm_utils import create_embeddings, create_llama_index_llm
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class MilvusService:
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def __init__(self):
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milvus_vector_store: Optional[MilvusVectorStore] = None
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self._milvus_vector_store = milvus_vector_store
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self.embeddings = create_embeddings()
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# 获取一个MilvusVectorStore实例，如果不存在就异步创建
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async def get_vector_store(self):
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if not self._milvus_vector_store:
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print("create new_vector_store...")
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self._milvus_vector_store = MilvusVectorStore(
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uri=env.milvus_uri,
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dim=env.milvus_dimension,
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collection_name=env.milvus_collection,
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db_name=env.milvus_database,
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embedding_field="embedding",
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id_field="id",
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similarity_metric="COSINE",
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consistency_level="Strong",
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overwrite=False,
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)
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return self._milvus_vector_store
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# 检查Milvus连接是否正常
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async def check_milvus_connection(self):
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retrieve_result = await self.async_search("hello")
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print("✅ Milvus connection successful：", f"{env.milvus_uri}:{env.milvus_database}/{env.milvus_collection}", f"[{retrieve_result}]")
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async def async_create_index_from_documents(self, documents: List[Document]) -> VectorStoreIndex:
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vector_store = await self.get_vector_store()
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"""异步创建向量索引并插入文档"""
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vector_index: VectorStoreIndex = VectorStoreIndex.from_vector_store(
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vector_store=vector_store,
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embed_model=self.embeddings,
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)
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from llama_index.core.node_parser import HierarchicalNodeParser
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parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
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nodes = parser.get_nodes_from_documents(documents)
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await vector_index.ainsert_nodes(nodes)
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return vector_index
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# 检索milvus文档
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async def async_search(self, query: str, top_k: int = 5) -> List[dict]:
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"""异步向量搜索"""
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# 创建查询引擎，设置top_k参数
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vector_index = VectorStoreIndex.from_vector_store(
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vector_store=await self.get_vector_store(),
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embed_model=self.embeddings,
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)
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query_engine = vector_index.as_query_engine(
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streaming=False,
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similarity_top_k=top_k, # 这里正确使用top_k参数
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llm=create_llama_index_llm()
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)
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# 异步执行查询
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response = await query_engine.aquery(query)
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return {
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"answer": str(response),
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"sources": [
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{
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"text": node.node.text,
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"metadata": node.node.metadata,
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"score": node.score
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}
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for node in response.source_nodes
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]
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}
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async def async_delete(self, doc_id: str):
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vector_index = VectorStoreIndex.from_vector_store(
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vector_store=await self.get_vector_store(),
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embed_model=self.embeddings,
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)
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await vector_index.adelete_ref_doc(doc_id)
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return None
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# 全局实例
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milvus_service = MilvusService()
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