Delete app.py

app.py

@@ -1,419 +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
-from langchain_core.documents import Document
-import time
-from tqdm import tqdm
-
-# 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，请在环境变量中配置或在 .env 文件中添加")
-SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY")
-if not SILICONFLOW_API_KEY:
-    raise ValueError("SILICONFLOW_API_KEY 未设置，请在 Hugging Face Spaces 的 Settings > Secrets 中添加 SILICONFLOW_API_KEY")
-
-# SiliconFlow API 配置
-SILICONFLOW_API_URL = "https://api.siliconflow.cn/v1/rerank"  # 需根据实际文档确认
-
-# 自定义 APIEmbeddings 类（使用 Hugging Face API 调用 BAAI/bge-m3）
-class APIEmbeddings(Embeddings):
-    def __init__(self, model_name="BAAI/bge-m3"):
-        self.model_name = model_name
-        self.api_key = os.getenv("HUGGINGFACE_API_KEY")
-        if not self.api_key:
-            raise ValueError("HUGGINGFACE_API_KEY 未设置，请在环境变量中配置或在 .env 文件中添加")
-
-    def embed_documents(self, texts):
-        embeddings = []
-        batch_size = 1000  # 根据需要调整批次大小
-
-        for i in tqdm(range(0, len(texts), batch_size), desc="生成嵌入进度"):
-            batch_texts = texts[i:i + batch_size]
-            batch_embeddings = self._request_embeddings(batch_texts)
-            embeddings.extend(batch_embeddings)
-
-        return embeddings
-
-    def embed_query(self, text):
-        query_embeddings = self._request_embeddings([text])
-        return query_embeddings[0]
-
-    def _request_embeddings(self, texts):
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json"
-        }
-        payload = {
-            "inputs": texts,
-            "model": self.model_name
-        }
-
-        response = requests.post("https://api-inference.huggingface.co/models/BAAI/bge-m3", headers=headers, json=payload)
-        response.raise_for_status()
-
-        return response.json()[0]["embedding"]
-
-# 重排序函数，使用 SiliconFlow API 调用 BAAI/bge-reranker-v2-m3
-def rerank_documents(query, documents, top_n=15):
-    try:
-        if not documents or not query:
-            raise ValueError("查询或文档列表为空")
-        
-        # 提取文档内容和元数据，限制长度为 2048 字符
-        doc_texts = [(doc.page_content[:2048].replace("\n", " ").strip(), doc.metadata.get("book", "未知来源")) for doc in documents[:50]]
-        print(f"Query: {query[:100]}... (长度: {len(query)})")
-        print(f"文档数量 (前50个): {len(doc_texts)}")
-        for i, (doc, book) in enumerate(doc_texts[:5]):  # 仅打印前5个用于调试
-            print(f"  Doc {i}: {doc[:100]}... (来源: {book})")
-        
-        # 构造 SiliconFlow API 请求
-        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
-        }
-        
-        start_time = time.time()
-        response = requests.post(SILICONFLOW_API_URL, headers=headers, json=payload)
-        response.raise_for_status()  # 检查请求是否成功
-        rerank_time = time.time() - start_time
-        print(f"重排序耗时: {rerank_time:.2f} 秒")
-        
-        # 解析 SiliconFlow API 响应
-        result = response.json()
-        print(f"SiliconFlow API 响应: {result}")
-        
-        # 验证返回结果
-        if "results" not in result or not isinstance(result["results"], list):
-            raise ValueError(f"SiliconFlow API 返回格式错误: {result}")
-        
-        # 构建重排序结果，修正键名为 "relevance_score"
-        reranked_docs = []
-        for res in result["results"]:
-            if "index" not in res or "relevance_score" not in res:
-                raise ValueError(f"SiliconFlow API 返回的条目格式错误: {res}")
-            index = res["index"]
-            score = res["relevance_score"]
-            if index < len(documents):
-                text, book = doc_texts[index]
-                reranked_docs.append((Document(page_content=text, metadata={"book": book}), score))
-        
-        # 按得分排序并截取 top_n
-        reranked_docs = sorted(reranked_docs, key=lambda x: x[1], reverse=True)[:top_n]
-        print(f"重排序结果 (数量: {len(reranked_docs)}):")
-        for i, (doc, score) in enumerate(reranked_docs):
-            print(f"  Doc {i}: {doc.page_content[:100]}... (来源: {doc.metadata.get('book', '未知来源')}, 得分: {score:.4f})")
-        
-        return reranked_docs
-    except Exception as e:
-        error_msg = str(e)
-        print(f"错误详情: {error_msg}")
-        raise Exception(f"重排序失败: {error_msg}")
-
-# 构建 HNSW 索引
-def build_hnsw_index(knowledge_base_path, index_path):
-    print("开始加载文档...")
-    start_time = time.time()
-    loader = DirectoryLoader(knowledge_base_path, glob="*.txt", loader_cls=lambda path: TextLoader(path, encoding="utf-8"), use_multithreading=False)
-    documents = loader.load()
-    load_time = time.time() - start_time
-    print(f"加载完成，共 {len(documents)} 个文档，耗时 {load_time:.2f} 秒")
-    
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    if not os.path.exists("chunks.pkl"):
-        print("开始分片...")
-        start_time = time.time()
-        texts = []
-        total_chars = 0
-        total_bytes = 0
-        for i, doc in enumerate(documents):
-            doc_chunks = text_splitter.split_documents([doc])
-            for chunk in doc_chunks:
-                content = chunk.page_content
-                file_path = chunk.metadata.get("source", "")
-                book_name = os.path.basename(file_path).replace(".txt", "").replace("_", "·")
-                texts.append(Document(page_content=content, metadata={"book": book_name or "未知来源"}))
-                total_chars += len(content)
-                total_bytes += len(content.encode('utf-8'))
-            if i < 5:
-                print(f"文件 {i} 字符数: {len(doc.page_content)}, 字节数: {len(doc.page_content.encode('utf-8'))}, 来源: {file_path}")
-            if (i + 1) % 10 == 0:
-                print(f"分片进度: 已处理 {i + 1}/{len(documents)} 个文件，当前分片总数: {len(texts)}")
-        with open("chunks.pkl", "wb") as f:
-            pickle.dump(texts, f)
-        split_time = time.time() - start_time
-        print(f"分片完成，共 {len(texts)} 个 chunk，总字符数: {total_chars}，总字节数: {total_bytes}，耗时 {split_time:.2f} 秒")
-    else:
-        with open("chunks.pkl", "rb") as f:
-            texts = pickle.load(f)
-        print(f"加载已有分片，共 {len(texts)} 个 chunk")
-    
-    if not os.path.exists("embeddings.npy"):
-        print("开始生成嵌入（使用 BAAI/bge-m3 API，分批处理）...")
-        embeddings = APIEmbeddings()
-        texts_content = [text.page_content for text in texts]
-        embeddings_array = embeddings.embed_documents(texts_content)
-        if os.path.exists("embeddings_temp.npy"):
-            os.remove("embeddings_temp.npy")
-        print(f"嵌入生成完成，维度: {embeddings_array.shape}")
-    else:
-        embeddings_array = np.load("embeddings.npy")
-        print(f"加载已有嵌入，维度: {embeddings_array.shape}")
-    
-    dimension = embeddings_array.shape[1]
-    index = faiss.IndexHNSWFlat(dimension, 16)
-    index.hnsw.efConstruction = 100
-    print("开始构建 HNSW 索引...")
-    
-    batch_size = 5000
-    total_vectors = embeddings_array.shape[0]
-    for i in range(0, total_vectors, batch_size):
-        batch = embeddings_array[i:i + batch_size]
-        index.add(batch)
-        print(f"索引构建进度: {min(i + batch_size, total_vectors)} / {total_vectors}")
-    
-    text_embeddings = [(text.page_content, embeddings_array[i]) for i, text in enumerate(texts)]
-    vector_store = FAISS.from_embeddings(text_embeddings, embeddings, normalize_L2=True)
-    vector_store.index = index
-    vector_store.docstore._dict.clear()
-    vector_store.index_to_docstore_id.clear()
-    
-    for i, text in enumerate(texts):
-        doc_id = str(i)
-        vector_store.docstore._dict[doc_id] = text
-        vector_store.index_to_docstore_id[i] = doc_id
-    
-    print("开始保存索引...")
-    vector_store.save_local(index_path)
-    print(f"HNSW 索引已生成并保存到 '{index_path}'")
-    return vector_store, texts
-
-# 初始化嵌入模型
-embeddings = APIEmbeddings(model_name="BAAI/bge-m3")
-print("已初始化 BAAI/bge-m3 嵌入模型，通过 API 调用")
-
-# 加载或生成索引
-index_path = "faiss_index_hnsw_new"
-knowledge_base_path = "knowledge_base"
-
-if not os.path.exists(index_path):
-    if os.path.exists(knowledge_base_path):
-        print("检测到 knowledge_base，正在生成 HNSW 索引...")
-        vector_store, all_documents = build_hnsw_index(knowledge_base_path, index_path)
-    else:
-        raise FileNotFoundError("未找到 'knowledge_base'，请提供知识库数据")
-else:
-    vector_store = FAISS.load_local(index_path, embeddings=embeddings, allow_dangerous_deserialization=True)
-    vector_store.index.hnsw.efSearch = 300
-    print("已加载 HNSW 索引 'faiss_index_hnsw_new'，efSearch 设置为 300")
-    with open("chunks.pkl", "rb") as f:
-        all_documents = pickle.load(f)
-    book_counts = {}
-    for doc in all_documents:
-        book = doc.metadata.get("book", "未知来源")
-        book_counts[book] = book_counts.get(book, 0) + 1
-    print(f"all_documents 书籍分布: {book_counts}")
-
-# 初始化 ChatOpenAI
-llm = ChatOpenAI(
-    model="deepseek/deepseek-r1:free",
-    api_key=os.environ["OPENROUTER_API_KEY"],
-    base_url="https://openrouter.ai/api/v1",
-    timeout=60,
-    temperature=0.3,
-    max_tokens=130000,
-    streaming=True
-)
-
-# 定义提示词模板
-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10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。
-    """
-)
-
-# 对话历史管理类
-class ConversationHistory:
-    def __init__(self, max_length=10):
-        self.history = deque(maxlen=max_length)
-
-    def add_turn(self, question, answer):
-        self.history.append((question, answer))
-
-    def get_history(self):
-        return [(turn[0], turn[1]) for turn in self.history]
-
-    def clear(self):
-        self.history.clear()
-
-# 用户会话状态类
-class UserSession:
-    def __init__(self):
-        self.conversation = ConversationHistory()
-        self.output_queue = queue.Queue()
-        self.stop_flag = threading.Event()
-
-# 生成回答的线程函数
-def generate_answer_thread(question, session):
-    stop_flag = session.stop_flag
-    output_queue = session.output_queue
-    conversation = session.conversation
-    
-    stop_flag.clear()
-    try:
-        history_list = conversation.get_history()
-        history_text = "\n".join([f"问: {q}\n答: {a}" for q, a in history_list]) if history_list else ""
-        query_with_context = f"{history_text}\n当前问题: {question}" if history_text else question
-        
-        # 1. 使用 BAAI/bge-m3 API 生成查询嵌入
-        start_time = time.time()
-        embeddings = APIEmbeddings()
-        query_embedding = embeddings.embed_query(query_with_context)
-        embed_time = time.time() - start_time
-        output_queue.put(f"嵌入耗时 (BAAI/bge-m3 API): {embed_time:.2f} 秒\n")
-        
-        if stop_flag.is_set():
-            output_queue.put("生成已停止")
-            return
-        
-        # 2. 使用 FAISS HNSW 索引进行初始检索
-        start_time = time.time()
-        initial_docs_with_scores = vector_store.similarity_search_with_score(query_with_context, k=50)
-        search_time = time.time() - start_time
-        output_queue.put(f"初始检索数量: {len(initial_docs_with_scores)}\n检索耗时: {search_time:.2f} 秒\n")
-        
-        if stop_flag.is_set():
-            output_queue.put("生成已停止")
-            return
-        
-        initial_docs = [doc for doc, _ in initial_docs_with_scores]
-        
-        # 3. 使用 SiliconFlow 的 BAAI/bge-reranker-v2-m3 进行重排序
-        start_time = time.time()
-        reranked_docs_with_scores = rerank_documents(query_with_context, initial_docs, top_n=15)
-        rerank_time = time.time() - start_time
-        output_queue.put(f"重排序耗时 (BAAI/bge-reranker-v2-m3): {rerank_time:.2f} 秒\n")
-        
-        if stop_flag.is_set():
-            output_queue.put("生成已停止")
-            return
-        
-        # 调整 final_docs 数量，取前 10 篇
-        final_docs = [doc for doc, _ in reranked_docs_with_scores][:10]
-        if len(final_docs) < 10:
-            output_queue.put(f"警告：仅检索到 {len(final_docs)} 篇文本，可能无法满足引用 10 篇的要求")
-        
-        # 构造 context，包含文本内容和书目信息
-        context = "\n\n".join([f"[文本 {i+1}] {doc.page_content} (出处: {doc.metadata.get('book', '未知来源')})" for i, doc in enumerate(final_docs)])
-        chat_history = [HumanMessage(content=q) if i % 2 == 0 else AIMessage(content=a) 
-                        for i, (q, a) in enumerate(history_list)]
-        prompt = prompt_template.format(context=context, question=question, chat_history=history_text)
-        
-        # 4. 使用 LLM 生成回答
-        answer = ""
-        start_time = time.time()
-        for chunk in llm.stream([HumanMessage(content=prompt)]):
-            if stop_flag.is_set():
-                output_queue.put(answer + "\n\n(生成已停止)")
-                return
-            answer += chunk.content
-            output_queue.put(answer)
-        llm_time = time.time() - start_time
-        output_queue.put(f"\nLLM 生成耗时: {llm_time:.2f} 秒")
-        
-        conversation.add_turn(question, answer)
-        output_queue.put(answer)
-
-    except Exception as e:
-        output_queue.put(f"Error: {str(e)}")
-
-# Gradio 接口函数
-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
-    
-    while not session_state.output_queue.empty():
-        yield session_state.output_queue.get(), session_state
-
-def stop_generation(session_state):
-    if session_state is not None:
-        session_state.stop_flag.set()
-    return "生成已停止，正在中止..."
-
-def clear_conversation():
-    return "对话历史已清空，请开始新的对话。", UserSession()
-
-# 创建 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10轮对话，输入问题以获取李敖风格的回答。")
-    
-    session_state = gr.State(value=None)
-    
-    with gr.Row():
-        with gr.Column(scale=3):
-            question_input = gr.Textbox(label="请输入您的问题", placeholder="输入您的问题...")
-            submit_button = gr.Button("提交")
-        with gr.Column(scale=1):
-            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)
-
-# 启动应用
-if __name__ == "__main__":
-    interface.launch(share=True)