100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
import os
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from typing import List
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import asyncio
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import faiss
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from sentence_transformers import SentenceTransformer
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from llama_index import (
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SimpleDirectoryReader,
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VectorStoreIndex,
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PromptHelper,
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PromptTemplate,
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ServiceContext,
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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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# ✅ BGE-m3 模型嵌入类，加前缀
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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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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 = BGEEmbedding(model_name=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) # ✅ bge-m3 是 1024维
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vector_store = FaissVectorStore(faiss_index=faiss_index)
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index = VectorStoreIndex.from_documents(
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documents,
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vector_store=vector_store,
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service_context=service_context
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)
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index_path = os.path.join(USER_INDEX_PATH, f"{user_id}.index")
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faiss.write_index(vector_store.index, index_path) # ✅ 用传入的 vector_store
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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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embed_model = BGEEmbedding(model_name=settings.MODEL_NAME)
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service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=None)
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all_nodes = []
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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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user_store = FaissVectorStore.from_persist_path(index_path)
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user_index = VectorStoreIndex.from_vector_store(user_store, service_context=service_context)
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all_nodes += user_index.as_retriever(similarity_top_k=top_k).retrieve(question)
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shared_indexes = get_user_allowed_indexes(user_id)
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if shared_indexes:
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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) and shared_path != index_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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all_nodes += shared_index.as_retriever(similarity_top_k=top_k).retrieve(question)
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else:
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print(f"[INFO] 用户 {user_id} 没有共享索引权限")
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sorted_nodes = sorted(all_nodes, key=lambda n: -(n.score or 0))
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top_nodes = sorted_nodes[:top_k]
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context_str = "\n\n".join([n.get_text() for n in top_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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