37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
from langchain_community.vectorstores import FAISS
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_core.documents import Document
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# 1. 示例文本和嵌入模型
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texts = [
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"张三是法外狂徒",
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"FAISS是一个用于高效相似性搜索和密集向量聚类的库。",
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"LangChain是一个用于开发由语言模型驱动的应用程序的框架。"
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]
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docs = [Document(page_content=t) for t in texts]
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embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5")
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# 2. 创建向量存储并保存到本地
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vectorstore = FAISS.from_documents(docs, embeddings)
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local_faiss_path = "./faiss_index_store"
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vectorstore.save_local(local_faiss_path)
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print(f"FAISS index has been saved to {local_faiss_path}")
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# 3. 加载索引并执行查询
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# 加载时需指定相同的嵌入模型，并允许反序列化
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loaded_vectorstore = FAISS.load_local(
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local_faiss_path,
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embeddings,
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allow_dangerous_deserialization=True
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)
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# 执行相似性搜索
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query = "FAISS是做什么的？"
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results = loaded_vectorstore.similarity_search(query, k=1)
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print(f"\n查询: '{query}'")
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print("相似度最高的文档:")
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for doc in results:
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print(f"- {doc.page_content}") |