126 lines
4.7 KiB
Python
126 lines
4.7 KiB
Python
# pip install pydantic requests
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from langchain_core.documents import Document
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from langchain_core.vectorstores import InMemoryVectorStore
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from langchain_openai import OpenAIEmbeddings
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from pydantic import SecretStr
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import requests
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import json
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from Config.Config import (
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EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY,
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RERANK_MODEL, RERANK_BASE_URL, RERANK_BINDING_API_KEY
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)
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class VectorDBUtil:
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"""向量数据库工具类，提供文本向量化存储和查询功能"""
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def __init__(self):
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"""初始化向量数据库工具"""
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# 初始化嵌入模型
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self.embeddings = OpenAIEmbeddings(
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model=EMBED_MODEL_NAME,
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base_url=EMBED_BASE_URL,
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api_key=SecretStr(EMBED_API_KEY) # 包装成 SecretStr 类型
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)
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# 初始化向量存储
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self.vector_store = None
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def text_to_vector_db(self, text: str, chunk_size: int = 200, chunk_overlap: int = 0) -> tuple:
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"""
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将文本存入向量数据库
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参数:
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text: 要入库的文本
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chunk_size: 文本分割块大小
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chunk_overlap: 文本块重叠大小
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返回:
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tuple: (向量存储对象, 文档数量, 分割后的文档块数量)
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"""
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# 创建文档对象
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docs = [Document(page_content=text, metadata={"source": "simulated_document"})]
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doc_count = len(docs)
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print(f"文档数量：{doc_count} 个")
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# 切割文档
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size, chunk_overlap=chunk_overlap, add_start_index=True
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)
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all_splits = text_splitter.split_documents(docs)
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split_count = len(all_splits)
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print(f"切割后的文档块数量：{split_count}")
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# 向量存储
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self.vector_store = InMemoryVectorStore(self.embeddings)
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ids = self.vector_store.add_documents(documents=all_splits)
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return self.vector_store, doc_count, split_count
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def query_vector_db(self, query: str, k: int = 4) -> list:
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"""
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从向量数据库查询文本
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参数:
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query: 查询字符串
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k: 要返回的结果数量
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返回:
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list: 重排后的结果列表，每个元素是(文档对象, 可信度分数)的元组
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"""
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if not self.vector_store:
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print("错误: 向量数据库未初始化，请先调用text_to_vector_db方法")
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return []
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# 向量查询 - 获取更多结果用于重排
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results = self.vector_store.similarity_search(query, k=k)
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print(f"向量搜索结果数量：{len(results)}")
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# 存储重排后的文档和分数
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reranked_docs_with_scores = []
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# 调用重排模型
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if len(results) > 1:
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# 准备重排请求数据
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rerank_data = {
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"model": RERANK_MODEL,
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"query": query,
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"documents": [doc.page_content for doc in results],
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"top_n": len(results)
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}
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# 调用SiliconFlow API进行重排
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {RERANK_BINDING_API_KEY}"
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}
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try:
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response = requests.post(RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data))
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response.raise_for_status() # 检查请求是否成功
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rerank_result = response.json()
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# 处理重排结果，提取relevance_score
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if "results" in rerank_result:
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for item in rerank_result["results"]:
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doc_idx = item.get("index")
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score = item.get("relevance_score", 0.0)
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if 0 <= doc_idx < len(results):
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reranked_docs_with_scores.append((results[doc_idx], score))
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else:
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print("警告: 无法识别重排API响应格式")
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reranked_docs_with_scores = [(doc, 0.0) for doc in results]
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print(f"重排后结果数量：{len(reranked_docs_with_scores)}")
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except Exception as e:
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print(f"重排模型调用失败: {e}")
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print("将使用原始搜索结果")
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reranked_docs_with_scores = [(doc, 0.0) for doc in results]
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else:
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# 只有一个结果，无需重排
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reranked_docs_with_scores = [(doc, 1.0) for doc in results] # 单个结果可信度设为1.0
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return reranked_docs_with_scores
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