Initial commit

2026-05-12 09:41:56 +08:00
commit 572283e101
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+from llama_index.core import VectorStoreIndex, Document, Settings
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+# 1. 配置全局嵌入模型
+Settings.embed_model = HuggingFaceEmbedding("BAAI/bge-small-zh-v1.5")
+
+# 2. 创建示例文档
+texts = [
+    "张三是法外狂徒",
+    "LlamaIndex是一个用于构建和查询私有或领域特定数据的框架。",
+    "它提供了数据连接、索引和查询接口等工具。"
+]
+docs = [Document(text=t) for t in texts]
+
+# 3. 创建索引并持久化到本地
+index = VectorStoreIndex.from_documents(docs)
+persist_path = "./llamaindex_index_store"
+index.storage_context.persist(persist_dir=persist_path)
+print(f"LlamaIndex 索引已保存至: {persist_path}")