82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
import os
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import faiss
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from typing import List
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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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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
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from scripts.permissions import get_user_allowed_indexes
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USER_INDEX_PATH = "index_data"
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USER_DOC_PATH = "docs"
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def build_user_index(user_id: str):
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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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documents = SimpleDirectoryReader(doc_dir).load_data()
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embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
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service_context = ServiceContext.from_defaults(embed_model=embed_model)
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# 构建向量索引
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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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vector_store=FaissVectorStore()
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)
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# 保存为用户专属 .index 文件
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index_path = os.path.join(USER_INDEX_PATH, f"{user_id}.index")
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faiss.write_index(index.vector_store.index, index_path)
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print(f"[BUILD] 为用户 {user_id} 构建并保存了索引 → {index_path}")
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def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
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index_path = os.path.join(USER_INDEX_PATH, f"{user_id}.index")
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if not os.path.exists(index_path):
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raise FileNotFoundError(f"[ERROR] 用户 {user_id} 的索引不存在")
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embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
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service_context = ServiceContext.from_defaults(embed_model=embed_model)
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# 加载主索引
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vector_store = FaissVectorStore.from_persist_path(index_path)
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index = VectorStoreIndex.from_vector_store(vector_store, service_context=service_context)
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nodes = index.as_retriever(similarity_top_k=top_k).retrieve(question)
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# 加载权限范围内的共享索引
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shared_indexes = get_user_allowed_indexes(user_id)
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for shared_name in shared_indexes:
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shared_path = os.path.join(USER_INDEX_PATH, shared_name)
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if os.path.exists(shared_path):
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shared_store = FaissVectorStore.from_persist_path(shared_path)
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shared_index = VectorStoreIndex.from_vector_store(shared_store, service_context=service_context)
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nodes += shared_index.as_retriever(similarity_top_k=top_k).retrieve(question)
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# 构造 Prompt
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context_str = "\n\n".join([n.get_content() for n in nodes])
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prompt_template = PromptTemplate(
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"请根据以下内容回答用户问题：\n\n{context}\n\n问题：{query}"
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)
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final_prompt = prompt_template.format(
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context=context_str,
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query=question,
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)
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print("[PROMPT构建完成]")
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return final_prompt
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# 示例：
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if __name__ == "__main__":
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uid = "user_001"
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build_user_index(uid)
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prompt = query_user_rag(uid, "这份资料中提到了哪些关键点？")
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print("\n------ 最终构建的 Prompt 给 LLM 使用 ------\n")
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print(prompt)
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