82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
import os
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import logging
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from typing import List
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import faiss
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from llama_index import (
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SimpleDirectoryReader,
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VectorStoreIndex,
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ServiceContext,
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StorageContext,
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PromptTemplate
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)
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.vector_stores.faiss import FaissVectorStore
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from app.core.config import settings # 确保配置导入
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from scripts.permissions import get_user_allowed_indexes # 确保权限导入
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USER_INDEX_PATH = "index_data"
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USER_DOC_PATH = "docs"
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# ✅ 自动加前缀的 BGE-m3 embedding 封装类
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class BGEEmbedding(HuggingFaceEmbedding):
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def _get_query_embedding(self, query: str) -> List[float]:
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prefix = "Represent this sentence for searching relevant passages: "
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return super()._get_query_embedding(prefix + query)
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def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]:
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prefix = "Represent this sentence for searching relevant passages: "
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return super()._get_query_embeddings([prefix + q for q in queries])
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def build_user_index(user_id: str):
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# 设置日志
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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logger.info(f"开始为用户 {user_id} 构建索引...")
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doc_dir = os.path.join(USER_DOC_PATH, user_id)
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if not os.path.exists(doc_dir):
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raise FileNotFoundError(f"文档目录不存在: {doc_dir}")
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logger.info(f"发现文档目录: {doc_dir}")
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documents = SimpleDirectoryReader(doc_dir).load_data()
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logger.info(f"载入文档数量: {len(documents)}")
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embed_model = BGEEmbedding(model_name=settings.MODEL_NAME)
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logger.info(f"使用模型: {settings.MODEL_NAME}")
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service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=None)
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faiss_index = faiss.IndexFlatL2(1024)
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vector_store = FaissVectorStore(faiss_index=faiss_index)
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persist_dir = os.path.join(USER_INDEX_PATH, user_id)
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os.makedirs(persist_dir, exist_ok=True)
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logger.info(f"索引保存路径: {persist_dir}")
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# 检查目录中是否存在 index_store.json 文件
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index_store_path = os.path.join(persist_dir, "index_store.json")
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if not os.path.exists(index_store_path):
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logger.info(f"未找到 index_store.json，准备创建")
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else:
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logger.info(f"已找到 index_store.json，跳过创建")
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storage_context = StorageContext.from_defaults(
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persist_dir=persist_dir,
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vector_store=vector_store,
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)
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try:
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# 构建索引
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index = VectorStoreIndex.from_documents(
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documents,
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service_context=service_context,
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storage_context=storage_context
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)
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index.persist(persist_dir=persist_dir)
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logger.info(f"索引已保存到 {persist_dir}")
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except Exception as e:
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logger.error(f"索引构建失败: {e}")
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raise HTTPException(status_code=500, detail="索引构建失败")
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