Upload app.py

app.py

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+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")
+BYTEZ_API_KEY = os.getenv("BYTEZ_API_KEY")
+if not BYTEZ_API_KEY:
+    raise ValueError("BYTEZ_API_KEY no set")
+
+# SiliconFlow API 配置
+BYTEZ_API_URL = "https://api.bytez.com/models/v2/BAAI/bge-reranker-v2-m3"
+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_with_meta = []
+        for doc in documents[:50]:
+            if isinstance(doc, tuple):
+                actual_doc = doc[0]
+            else:
+                actual_doc = doc
+
+            if hasattr(actual_doc, 'page_content'):
+                text = actual_doc.page_content[:2048]
+                book_meta = actual_doc.metadata.get("book", "unknow source")
+                doc_texts_with_meta.append((text, book_meta))
+            else:
+                logger.warning(f"skip the invalid texts: {type(doc)}")
+
+        headers = {"Authorization": f"Bearer {BYTEZ_API_KEY}", "Content-Type": "application/json"}
+        data = {"query": query, "text": [text for text, _ in doc_texts_with_meta], "top_n": top_n}
+        response = requests.post(BYTEZ_API_URL, headers=headers, json=data)
+        response.raise_for_status()
+        result = response.json()
+
+        '''
+        import json
+        print("---api result---")
+        print(json.dumps(result, indent=2, ensure_ascii=False))
+        print("-----------------------")
+        '''
+
+        reranked_docs = []
+        for res in result["output"]:
+            score = res["score"]
+            pass
+        reranked_results = []
+        for i, res in enumerate(result["output"]):
+            score = res["score"]
+            if i < len(documents):
+                if isinstance(documents[i], tuple):
+                    original_doc = documents[i][0]
+                else:
+                    original_doc = documents[i]
+                reranked_results.append((original_doc, score))
+        return sorted(reranked_results, key=lambda x: x[1], reverse=True)[:top_n]
+    except Exception as e:
+        logger.error(f"重���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="tngtech/deepseek-r1t2-chimera: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���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()
+
+# 自动提问功能：每天触发一次“介绍一下李敖”
+def auto_ask_question():
+    auto_session = UserSession()
+    last_run_time = 0
+    interval = 24 * 60 * 60  # 24小时（单位：秒）
+    
+    while True:
+        current_time = time.time()
+        if current_time - last_run_time >= interval:
+            logger.info("自动触发问题：介绍一下李敖")
+            thread = threading.Thread(target=generate_answer_thread, args=("介绍一下李敖", auto_session))
+            thread.start()
+            thread.join()  # 等待回答生成完成
+            last_run_time = current_time
+        time.sleep(60)  # 每分钟检查一次，避免占用过多资源
+
+# Gradio 界面
+with gr.Blocks(title="AI李敖助手") as interface:
+    gr.Markdown("## AI李敖助手")
+    gr.Markdown("### 作者：爱华山樱")
+    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。")
+    gr.Markdown("提问之后红框存在期间表示正在生成回答��如果红框消失之后答案没出来，说明生成有问题（偶尔会这样），重来一次即可。")
+    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__":
+    # 启动自动提问线程
+    auto_thread = threading.Thread(target=auto_ask_question, daemon=True)
+    auto_thread.start()
+    # 启动 Gradio 界面
+    interface.launch(share=True)