149 lines
5.6 KiB
Python
149 lines
5.6 KiB
Python
import asyncio
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from typing import List, Optional
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from llama_index.core import Document, VectorStoreIndex
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from llama_index.core.vector_stores import MetadataFilters, MetadataFilter, FilterOperator
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from llama_index.vector_stores.milvus import MilvusVectorStore
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from pydantic import BaseModel, Field
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from app.config.env import env
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from app.utils.llm_utils import create_embeddings, create_llm
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from app.utils.nltk_utils import load_nltk
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class KnowledgeQueryParam(BaseModel):
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question: str = Field(..., description="问题文本")
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kb_code: str = Field(..., description="知识库编码")
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top_k: int = Field(default=5, description="返回结果数量")
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class MilvusSearchNode(BaseModel):
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text: str = Field(..., description="文档内容")
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metadata: dict = Field(..., description="文档元信息")
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score: float = Field(..., description="相似度分数")
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class MilvusSearchResponse(BaseModel):
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answer: str = Field(..., description="根据用户问题从检索结果中提取的内容")
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nodes: List[MilvusSearchNode] = Field(..., description="搜索结果")
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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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user=env.milvus_username,
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password=env.milvus_password,
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db_name=env.llama_index_database,
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collection_name=env.llama_index_collection,
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dim=env.llama_index_dimension,
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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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await self.async_search(KnowledgeQueryParam(question="hello", kb_code=""))
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print("✅ Milvus connection successful：", f"Milvus://{env.milvus_username}:{env.milvus_password}@{env.milvus_uri}/{env.llama_index_database}/{env.llama_index_collection}")
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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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# 预先加载NLTK语料库以避免多线程环境中的竞争条件
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# 否则在多线程环境下，parser.get_nodes_from_documents 可能会出现报错信息：'WordListCorpusReader' object has no attribute '_LazyCorpusLoader__args'
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load_nltk()
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from llama_index.core.node_parser import SimpleNodeParser
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# parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
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parser = SimpleNodeParser()
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# nodes = await parser.aget_nodes_from_documents(documents)
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# 使用同步方法并包装在 asyncio.to_thread 中
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nodes = await asyncio.to_thread(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, param: KnowledgeQueryParam) -> MilvusSearchResponse:
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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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filters = MetadataFilters(filters=[MetadataFilter(key="parent_code", value=param.kb_code, operator=FilterOperator.EQ)])
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retriever = vector_index.as_retriever(filters=filters, similarity_top_k=param.top_k)
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result_nodes = await retriever.aretrieve(param.question)
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chain = ChatPromptTemplate.from_template("""
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你将收到一个用户问题和一组来自LlamaIndex的检索结果。你的任务是：
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1. 分析检索结果内容
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2. 提取与用户问题直接相关的信息片段
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3. 基于这些相关信息生成准确、简洁且有帮助的回答
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用户问题如下<question/>标签中的内容所示：
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<question>
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{question}
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</question>
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检索结果如下<context/>标签中的内容所示：
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<context>
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{context}
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</context>
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处理要求：
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- 仅关注检索结果中与用户问题直接相关的内容
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- 忽略任何无关或关联性较弱的信息
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- 如果检索结果中没有相关信息，请明确回答："在提供的资料中未找到相关信息"
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- 不要添加检索结果之外的额外知识
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- 回答时优先使用检索结果中的原文表述
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""") | create_llm() | StrOutputParser()
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chain_input = {"context": "\n".join([node.node.text for node in result_nodes]), "question": param.question}
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relative_content = await chain.ainvoke(chain_input)
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return MilvusSearchResponse(
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answer=relative_content,
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nodes=[
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MilvusSearchNode(text=node.node.text, metadata=node.node.metadata, score=node.score)
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for node in result_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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