app.py

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=7):  # 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[-4:]])  # 只用最后5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7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")
    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)