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