Initial commit

code/C3/02_langchain_faiss.py

@@ -0,0 +1,37 @@
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_core.documents import Document
+
+# 1. 示例文本和嵌入模型
+texts = [
+    "张三是法外狂徒",
+    "FAISS是一个用于高效相似性搜索和密集向量聚类的库。",
+    "LangChain是一个用于开发由语言模型驱动的应用程序的框架。"
+]
+docs = [Document(page_content=t) for t in texts]
+embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5")
+
+# 2. 创建向量存储并保存到本地
+vectorstore = FAISS.from_documents(docs, embeddings)
+
+local_faiss_path = "./faiss_index_store"
+vectorstore.save_local(local_faiss_path)
+
+print(f"FAISS index has been saved to {local_faiss_path}")
+
+# 3. 加载索引并执行查询
+# 加载时需指定相同的嵌入模型，并允许反序列化
+loaded_vectorstore = FAISS.load_local(
+    local_faiss_path,
+    embeddings,
+    allow_dangerous_deserialization=True
+)
+
+# 执行相似性搜索
+query = "FAISS是做什么的？"
+results = loaded_vectorstore.similarity_search(query, k=1)
+
+print(f"\n查询: '{query}'")
+print("相似度最高的文档:")
+for doc in results:
+    print(f"- {doc.page_content}")