Delete app.py

app.py

@@ -1,311 +0,0 @@
-import os
-import gradio as gr
-import requests
-from langchain_community.document_loaders import TextLoader, DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain_openai import ChatOpenAI
-from langchain.prompts import PromptTemplate
-import numpy as np
-import faiss
-from collections import deque
-from langchain_core.embeddings import Embeddings
-import threading
-import queue
-from langchain_core.messages import HumanMessage, AIMessage
-from sentence_transformers import SentenceTransformer
-import pickle
-import torch
-import time
-from tqdm import tqdm
-import logging
-
-# 设置日志
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# 获取环境变量
-os.environ["OPENROUTER_API_KEY"] = os.getenv("OPENROUTER_API_KEY", "")
-if not os.environ["OPENROUTER_API_KEY"]:
-    raise ValueError("OPENROUTER_API_KEY 未设置")
-SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY")
-if not SILICONFLOW_API_KEY:
-    raise ValueError("SILICONFLOW_API_KEY 未设置")
-
-# SiliconFlow API 配置
-SILICONFLOW_API_URL = "https://api.siliconflow.cn/v1/rerank"
-
-# 自定义嵌入类，优化查询缓存
-class SentenceTransformerEmbeddings(Embeddings):
-    def __init__(self, model_name="BAAI/bge-m3"):
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.model = SentenceTransformer(model_name, device=device)
-        self.batch_size = 32  # 减小批次大小以适应低内存
-        self.query_cache = {}
-        self.cache_lock = threading.Lock()
-
-    def embed_documents(self, texts):
-        embeddings_list = []
-        batch_size = 1000  # 减小批次以降低内存压力
-        total_chunks = len(texts)
-        logger.info(f"生成嵌入，文档数: {total_chunks}")
-        with torch.no_grad():
-            for i in tqdm(range(0, total_chunks, batch_size), desc="生成嵌入"):
-                batch_texts = [text.page_content for text in texts[i:i + batch_size]]
-                batch_emb = self.model.encode(
-                    batch_texts,
-                    normalize_embeddings=True,
-                    batch_size=self.batch_size
-                )
-                embeddings_list.append(batch_emb)
-        embeddings_array = np.vstack(embeddings_list)
-        np.save("embeddings.npy", embeddings_array)
-        return embeddings_array
-
-    def embed_query(self, text):
-        with self.cache_lock:
-            if text in self.query_cache:
-                return self.query_cache[text]
-        with torch.no_grad():
-            emb = self.model.encode([text], normalize_embeddings=True, batch_size=1)[0]
-            with self.cache_lock:
-                self.query_cache[text] = emb
-                if len(self.query_cache) > 1000:  # 限制缓存大小
-                    self.query_cache.pop(next(iter(self.query_cache)))
-        return emb
-
-# 重排序函数
-def rerank_documents(query, documents, top_n=15):
-    try:
-        doc_texts = [(doc.page_content[:2048], doc.metadata.get("book", "未知来源")) for doc in documents[:50]]
-        headers = {"Authorization": f"Bearer {SILICONFLOW_API_KEY}", "Content-Type": "application/json"}
-        payload = {"model": "BAAI/bge-reranker-v2-m3", "query": query, "documents": [text for text, _ in doc_texts], "top_n": top_n}
-        response = requests.post(SILICONFLOW_API_URL, headers=headers, json=payload)
-        response.raise_for_status()
-        result = response.json()
-        reranked_docs = []
-        for res in result["results"]:
-            index = res["index"]
-            score = res["relevance_score"]
-            if index < len(documents):
-                text, book = doc_texts[index]
-                reranked_docs.append((documents[index], score))
-        return sorted(reranked_docs, key=lambda x: x[1], reverse=True)[:top_n]
-    except Exception as e:
-        logger.error(f"重排序失败: {str(e)}")
-        raise
-
-# 构建 HNSW 索引
-def build_hnsw_index(knowledge_base_path, index_path):
-    loader = DirectoryLoader(knowledge_base_path, glob="*.txt", loader_cls=lambda path: TextLoader(path, encoding="utf-8"))
-    documents = loader.load()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    texts = text_splitter.split_documents(documents)
-    for i, doc in enumerate(texts):
-        doc.metadata["book"] = os.path.basename(doc.metadata.get("source", "未知来源")).replace(".txt", "")
-    embeddings_array = embeddings.embed_documents(texts)
-    dimension = embeddings_array.shape[1]
-    index = faiss.IndexHNSWFlat(dimension, 16)
-    index.hnsw.efConstruction = 100
-    index.add(embeddings_array)
-    vector_store = FAISS.from_embeddings([(doc.page_content, embeddings_array[i]) for i, doc in enumerate(texts)], embeddings)
-    vector_store.index = index
-    vector_store.save_local(index_path)
-    with open("chunks.pkl", "wb") as f:
-        pickle.dump(texts, f)
-    return vector_store, texts
-
-# 初始化嵌入模型和索引
-embeddings = SentenceTransformerEmbeddings()
-index_path = "faiss_index_hnsw_new"
-knowledge_base_path = "knowledge_base"
-
-if not os.path.exists(index_path):
-    vector_store, all_documents = build_hnsw_index(knowledge_base_path, index_path)
-else:
-    vector_store = FAISS.load_local(index_path, embeddings=embeddings, allow_dangerous_deserialization=True)
-    vector_store.index.hnsw.efSearch = 200  # 降低 efSearch 以提升速度
-    with open("chunks.pkl", "rb") as f:
-        all_documents = pickle.load(f)
-
-# 初始化 LLM
-llm = ChatOpenAI(
-    model="deepseek/deepseek-r1:free",
-    api_key=os.environ["OPENROUTER_API_KEY"],
-    base_url="https://openrouter.ai/api/v1",
-    timeout=100,
-    temperature=0.3,
-    max_tokens=130000,
-    streaming=True
-)
-
-# 提示词模板
-prompt_template = PromptTemplate(
-    input_variables=["context", "question", "chat_history"],
-    template="""  
-    你是一个研究李敖的专家，根据用户提出的问题{question}、最近7轮对话历史{chat_history}以及从李敖相关书籍和评论中检索的至少10篇文本内容{context}回答问题。  
-    在回答时，请注意以下几点：  
-    - 结合李敖的写作风格和思想，筛选出与问题和对话历史最相关的检索内容，避免无关信息。  
-    - 必须在回答中引用至少10篇不同的文本内容，引用格式为[引用: 文本序号]，例如[引用: 1][引用: 2]，并确保每篇文本在回答中都有明确使用。  
-    - 在回答的末尾，必须以“引用文献”标题列出所有引用的文本序号及其内容摘要（每篇不超过50字）以及具体的书目信息（例如书名和章节），格式为：  
-      - 引用文献：  
-        1. [文本 1] 摘要... 出自：书名，第X页/章节。  
-        2. [文本 2] 摘要... 出自：书名，第X页/章节。  
-        （依此类推，至少10篇）  
-    - 如果问题涉及李敖对某人或某事的评价，优先引用李敖的直接言论或文字，并说明出处。  
-    - 回答应结构化、分段落，确保逻辑清晰，语言生动，类似李敖的犀利风格。  
-    - 如果检索内容和历史不足以直接回答问题，可根据李敖的性格和观点推测其可能的看法，但需说明这是推测。  
-    - 只能基于提供的知识库内容{context}和对话历史{chat_history}回答，不得引入外部信息。  
-    - 对于列举类问题，控制在10个要点以内，并优先提供最相关项。  
-    - 如果回答较长，结构化分段总结，分点作答控制在8个点以内。  
-    - 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。  
-    - 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。  
-    - 你的回答应该综合多个相关知识库内容来回答，不能重复引用一个知识库内容。  
-    - 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
-    """
-)
-
-# 对话历史管理
-class ConversationHistory:
-    def __init__(self, max_length=5):  # 减少历史轮数
-        self.history = deque(maxlen=max_length)
-
-    def add_turn(self, question, answer):
-        self.history.append((question, answer))
-
-    def get_history(self):
-        return [(q, a) for q, a in self.history]
-
-# 用户会话状态
-class UserSession:
-    def __init__(self):
-        self.conversation = ConversationHistory()
-        self.output_queue = queue.Queue()
-        self.stop_flag = threading.Event()
-
-# 生成回答
-def generate_answer_thread(question, session):
-    stop_flag = session.stop_flag
-    output_queue = session.output_queue
-    conversation = session.conversation
-
-    stop_flag.clear()
-    try:
-        # 打印用户问题到控制台
-        logger.info(f"用户问题: {question}")
-
-        history_list = conversation.get_history()
-        history_text = "\n".join([f"问: {q}\n答: {a}" for q, a in history_list[-3:]])  # 只用最后3轮
-        query_with_context = f"{history_text}\n问题: {question}" if history_text else question
-
-        # 异步生成查询嵌入
-        embed_queue = queue.Queue()
-        def embed_task():
-            start = time.time()
-            emb = embeddings.embed_query(query_with_context)
-            embed_queue.put((emb, time.time() - start))
-        embed_thread = threading.Thread(target=embed_task)
-        embed_thread.start()
-        embed_thread.join()
-        query_embedding, embed_time = embed_queue.get()
-
-        if stop_flag.is_set():
-            output_queue.put("生成已停止")
-            return
-
-        # 初始检索
-        start = time.time()
-        docs_with_scores = vector_store.similarity_search_with_score_by_vector(query_embedding, k=50)
-        search_time = time.time() - start
-
-        if stop_flag.is_set():
-            output_queue.put("生成已停止")
-            return
-
-        # 重排序
-        initial_docs = [doc for doc, _ in docs_with_scores]
-        start = time.time()
-        reranked_docs_with_scores = rerank_documents(query_with_context, initial_docs)
-        rerank_time = time.time() - start
-        final_docs = [doc for doc, _ in reranked_docs_with_scores][:10]
-
-        # 打印重排序结果到控制台
-        logger.info("重排序结果（最终保留的片段及其得分）：")
-        for i, (doc, score) in enumerate(reranked_docs_with_scores[:10], 1):
-            logger.info(f"片段 {i}:")
-            logger.info(f"  内容: {doc.page_content[:100]}...")
-            logger.info(f"  来源: {doc.metadata.get('book', '未知来源')}")
-            logger.info(f"  得分: {score:.4f}")
-
-        context = "\n".join([f"[文本 {i+1}] {doc.page_content} (出处: {doc.metadata.get('book')})" for i, doc in enumerate(final_docs)])
-        prompt = prompt_template.format(context=context, question=question, chat_history=history_text)
-
-        # 将时间信息加入回答开头
-        timing_info = (
-            f"处理时间统计：\n"
-            f"- 嵌入时间: {embed_time:.2f} 秒\n"
-            f"- 检索时间: {search_time:.2f} 秒\n"
-            f"- 重排序时间: {rerank_time:.2f} 秒\n\n"
-        )
-
-        answer = timing_info
-        output_queue.put(answer)  # 先显示时间信息
-
-        # LLM 生成回答
-        start = time.time()
-        for chunk in llm.stream([HumanMessage(content=prompt)]):
-            if stop_flag.is_set():
-                output_queue.put(answer + "\n(生成已停止)")
-                return
-            answer += chunk.content
-            output_queue.put(answer)
-        llm_time = time.time() - start
-        answer += f"\n\n生成耗时: {llm_time:.2f} 秒"
-        output_queue.put(answer)
-
-        conversation.add_turn(question, answer)
-        output_queue.put(answer)
-
-    except Exception as e:
-        output_queue.put(f"Error: {str(e)}")
-
-# Gradio 接口
-def answer_question(question, session_state):
-    if session_state is None:
-        session_state = UserSession()
-    
-    thread = threading.Thread(target=generate_answer_thread, args=(question, session_state))
-    thread.start()
-    
-    while thread.is_alive() or not session_state.output_queue.empty():
-        try:
-            output = session_state.output_queue.get(timeout=0.1)
-            yield output, session_state
-        except queue.Empty:
-            continue
-
-def stop_generation(session_state):
-    if session_state:
-        session_state.stop_flag.set()
-    return "生成已停止"
-
-def clear_conversation():
-    return "对话已清空", UserSession()
-
-# Gradio 界面
-with gr.Blocks(title="AI李敖助手") as interface:
-    gr.Markdown("### AI李敖助手")
-    gr.Markdown("基于李敖163本相关书籍构建的知识库，支持上下文关联，记住最近5轮对话，输入问题以获取李敖风格的回答。")
-    session_state = gr.State(value=None)
-    question_input = gr.Textbox(label="问题")
-    submit_button = gr.Button("提交")
-    clear_button = gr.Button("新建对话")
-    stop_button = gr.Button("停止生成")
-    output_text = gr.Textbox(label="回答", interactive=False)
-
-    submit_button.click(fn=answer_question, inputs=[question_input, session_state], outputs=[output_text, session_state])
-    clear_button.click(fn=clear_conversation, inputs=None, outputs=[output_text, session_state])
-    stop_button.click(fn=stop_generation, inputs=[session_state], outputs=output_text)
-
-if __name__ == "__main__":
-    interface.launch(share=True)