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1009 lines (858 loc) · 32.4 KB
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"""
Bot测试Web服务器
提供Web界面测试bot的回复质量、人格系统和RAG功能
"""
from flask import Flask, request, jsonify, render_template_string
from flask_cors import CORS
from datetime import datetime
import sys
import json
import logging
from io import StringIO
from chatbot.utils.config import Config
from chatbot.db.message_db import MessageDB
from chatbot.handlers.ai_interface import AIInterface
from openai import AzureOpenAI
app = Flask(__name__)
CORS(app)
# 自定义日志捕获器
class LogCapture(logging.Handler):
def __init__(self):
super().__init__()
self.logs = []
def emit(self, record):
try:
log_entry = self.format(record)
self.logs.append(log_entry)
except Exception:
pass
def get_logs(self):
return self.logs.copy()
def clear(self):
self.logs.clear()
# 初始化组件
config = Config()
message_db = MessageDB(config)
# 初始化AI客户端
azure_config = config.get_azure_config()
openai_client = AzureOpenAI(
azure_endpoint=azure_config['openai_endpoint'],
api_key=azure_config['openai_key'],
api_version=azure_config['api_version']
)
# 初始化AI接口
ai_interface = AIInterface(config, openai_client, message_db)
# 用于捕获prompt的全局变量
last_prompt_info = {}
# HTML模板
HTML_TEMPLATE = '''
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Bot测试平台 - 带Prompt日志</title>
<style>
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
min-height: 100vh;
padding: 20px;
}
.container {
max-width: 1800px;
margin: 0 auto;
background: white;
border-radius: 20px;
box-shadow: 0 20px 60px rgba(0,0,0,0.3);
overflow: hidden;
}
.header {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 5px;
text-align: center;
}
.header h1 {
font-size: 2.5em;
margin-bottom: 10px;
}
.header p {
font-size: 1.1em;
opacity: 0.9;
}
.main-content {
display: flex;
height: calc(100vh - 200px);
}
.sidebar {
width: 260px;
background: #f8f9fa;
border-right: 1px solid #e9ecef;
padding: 20px;
overflow-y: auto;
}
.sidebar h3 {
color: #495057;
margin-bottom: 15px;
font-size: 1.1em;
}
.persona-list {
list-style: none;
}
.persona-item {
padding: 10px;
margin-bottom: 6px;
background: white;
border-radius: 8px;
cursor: pointer;
transition: all 0.3s;
border: 2px solid transparent;
font-size: 0.9em;
}
.persona-item:hover {
transform: translateX(5px);
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
}
.persona-item.active {
background: #667eea;
color: white;
border-color: #667eea;
}
.persona-emoji {
font-size: 1.3em;
margin-right: 6px;
}
.test-mode {
margin-top: 15px;
padding: 12px;
background: white;
border-radius: 8px;
}
.test-mode label {
display: block;
margin-bottom: 8px;
color: #495057;
font-weight: 500;
font-size: 0.9em;
}
.test-mode select {
width: 100%;
padding: 8px;
border: 1px solid #ced4da;
border-radius: 6px;
font-size: 0.9em;
}
.chat-area {
flex: 1;
display: flex;
flex-direction: column;
padding: 20px;
min-width: 0;
}
.log-area {
width: 450px;
background: #1e1e1e;
border-left: 1px solid #e9ecef;
padding: 20px;
overflow-y: auto;
display: flex;
flex-direction: column;
}
.log-header {
color: #fff;
font-size: 1.2em;
font-weight: bold;
margin-bottom: 15px;
padding-bottom: 10px;
border-bottom: 2px solid #667eea;
}
.log-content {
flex: 1;
overflow-y: auto;
font-family: 'Monaco', 'Menlo', 'Consolas', monospace;
font-size: 0.85em;
line-height: 1.6;
}
.log-entry {
margin-bottom: 20px;
padding: 15px;
background: #2d2d2d;
border-radius: 8px;
border-left: 3px solid #667eea;
animation: slideIn 0.3s ease-out;
}
.log-timestamp {
color: #888;
font-size: 0.9em;
margin-bottom: 8px;
}
.log-section {
margin-bottom: 12px;
}
.log-section-title {
color: #667eea;
font-weight: bold;
margin-bottom: 5px;
text-transform: uppercase;
font-size: 0.85em;
}
.log-section-content {
color: #d4d4d4;
padding: 8px;
background: #1e1e1e;
border-radius: 4px;
white-space: pre-wrap;
word-wrap: break-word;
}
.log-json {
color: #ce9178;
}
.log-key {
color: #9cdcfe;
}
.log-string {
color: #ce9178;
}
.log-number {
color: #b5cea8;
}
.messages {
flex: 1;
overflow-y: auto;
padding: 20px;
background: #f8f9fa;
border-radius: 12px;
margin-bottom: 20px;
}
.message {
margin-bottom: 20px;
animation: slideIn 0.3s ease-out;
}
@keyframes slideIn {
from {
opacity: 0;
transform: translateY(10px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
.message-header {
font-size: 0.9em;
color: #6c757d;
margin-bottom: 5px;
}
.message-content {
padding: 15px;
border-radius: 12px;
line-height: 1.6;
white-space: pre-wrap;
}
.user-message .message-content {
background: #667eea;
color: white;
margin-left: 15%;
}
.bot-message .message-content {
background: white;
color: #212529;
border: 1px solid #e9ecef;
margin-right: 15%;
}
.message-meta {
font-size: 0.85em;
color: #6c757d;
margin-top: 5px;
}
.input-area {
display: flex;
gap: 10px;
}
.input-area input {
flex: 1;
padding: 15px;
border: 2px solid #e9ecef;
border-radius: 12px;
font-size: 1em;
transition: border-color 0.3s;
}
.input-area input:focus {
outline: none;
border-color: #667eea;
}
.input-area button {
padding: 15px 30px;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border: none;
border-radius: 12px;
font-size: 1em;
cursor: pointer;
transition: transform 0.2s;
}
.input-area button:hover {
transform: scale(1.05);
}
.input-area button:disabled {
opacity: 0.6;
cursor: not-allowed;
transform: none;
}
.loading {
display: none;
text-align: center;
padding: 15px;
color: #6c757d;
}
.loading.active {
display: block;
}
.spinner {
border: 3px solid #f3f3f3;
border-top: 3px solid #667eea;
border-radius: 50%;
width: 30px;
height: 30px;
animation: spin 1s linear infinite;
margin: 0 auto 10px;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
.stats {
padding: 12px;
background: #e7f3ff;
border-radius: 8px;
margin-bottom: 15px;
display: none;
font-size: 0.9em;
}
.stats.active {
display: block;
}
.stat-item {
display: inline-block;
margin-right: 15px;
}
.stat-label {
color: #495057;
font-weight: 500;
}
.stat-value {
color: #667eea;
font-weight: bold;
}
.clear-btn {
padding: 6px 12px;
background: #dc3545;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-size: 0.85em;
float: right;
}
.clear-btn:hover {
background: #c82333;
}
.clear-log-btn {
padding: 8px 16px;
background: #6c757d;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-size: 0.9em;
width: 100%;
margin-top: 10px;
}
.clear-log-btn:hover {
background: #5a6268;
}
</style>
</head>
<body>
<div class="container">
<div class="header">
<h1>🤖 Bot测试平台</h1>
</div>
<div class="main-content">
<!-- 左侧：人格选择 -->
<div class="sidebar">
<h3>🎭 选择人格</h3>
<ul class="persona-list" id="personaList">
<li class="persona-item active" data-persona="none">
<span class="persona-emoji">🎯</span>
<span>默认（学者）</span>
</li>
<li class="persona-item" data-persona="学者">
<span class="persona-emoji">🎯</span>
<span>学者</span>
</li>
<li class="persona-item" data-persona="逗比">
<span class="persona-emoji">😄</span>
<span>逗比</span>
</li>
<li class="persona-item" data-persona="阴阳">
<span class="persona-emoji">😈</span>
<span>阴阳师</span>
</li>
<li class="persona-item" data-persona="极简">
<span class="persona-emoji">💬</span>
<span>极简</span>
</li>
<li class="persona-item" data-persona="AI模式">
<span class="persona-emoji">🤖</span>
<span>AI模式</span>
</li>
<li class="persona-item" data-persona="文青">
<span class="persona-emoji">🎨</span>
<span>文青</span>
</li>
<li class="persona-item" data-persona="外语">
<span class="persona-emoji">🌍</span>
<span>外国人</span>
</li>
</ul>
<div class="test-mode">
<label>测试场景</label>
<select id="testMode">
<option value="chat">普通对话</option>
<option value="rag">RAG分析</option>
<option value="search">消息搜索</option>
</select>
</div>
<div class="test-mode" style="margin-top: 10px;">
<button class="clear-btn" onclick="clearMessages()">清空对话</button>
</div>
</div>
<!-- 中间：对话区域 -->
<div class="chat-area">
<div class="stats" id="stats">
<div class="stat-item">
<span class="stat-label">响应:</span>
<span class="stat-value" id="responseTime">-</span>
</div>
<div class="stat-item">
<span class="stat-label">消息:</span>
<span class="stat-value" id="messageCount">0</span>
</div>
<div class="stat-item">
<span class="stat-label">人格:</span>
<span class="stat-value" id="currentPersona">默认</span>
</div>
</div>
<div class="messages" id="messages">
<div class="message bot-message">
<div class="message-header">Bot · 系统消息</div>
<div class="message-content">👋 欢迎使用Bot测试平台！
你可以：
· 左侧选择不同的人格模式
· 测试各种查询场景
· 右侧查看完整的Prompt日志
试试问我：
· "谁是最活跃的用户？"
· "最近讨论了什么话题？"
· 或者随便聊聊天 😊</div>
</div>
</div>
<div class="loading" id="loading">
<div class="spinner"></div>
<div>Bot正在思考中...</div>
</div>
<div class="input-area">
<input
type="text"
id="messageInput"
placeholder="输入测试消息..."
onkeypress="handleKeyPress(event)"
/>
<button onclick="sendMessage()" id="sendBtn">发送</button>
</div>
</div>
<!-- 右侧：Prompt日志 -->
<div class="log-area">
<div class="log-header">
📝 Prompt日志
</div>
<div class="log-content" id="logContent">
<div class="log-entry">
<div class="log-timestamp">等待第一次请求...</div>
<div class="log-section-content">
发送消息后，这里将显示完整的：
· System Prompt
· User Message
· 工具调用信息
· 响应时间统计
</div>
</div>
</div>
<button class="clear-log-btn" onclick="clearLogs()">清空日志</button>
</div>
</div>
</div>
<script>
let selectedPersona = 'none';
let messageCount = 0;
// 人格选择
document.querySelectorAll('.persona-item').forEach(item => {
item.addEventListener('click', function() {
document.querySelectorAll('.persona-item').forEach(i => i.classList.remove('active'));
this.classList.add('active');
selectedPersona = this.dataset.persona;
document.getElementById('currentPersona').textContent =
this.querySelector('span:last-child').textContent;
});
});
// 发送消息
function sendMessage() {
const input = document.getElementById('messageInput');
const message = input.value.trim();
if (!message) return;
// 构建带人格的消息
let fullMessage = message;
if (selectedPersona !== 'none') {
fullMessage = `@bot ${selectedPersona} ${message}`;
} else {
fullMessage = `@bot ${message}`;
}
// 显示用户消息
addMessage('user', message, selectedPersona);
input.value = '';
// 显示加载状态
document.getElementById('loading').classList.add('active');
document.getElementById('sendBtn').disabled = true;
const startTime = Date.now();
try {
const response = fetch('/api/chat', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
message: fullMessage,
user: 'test_user'
})
});
const data = response.json();
const endTime = Date.now();
const responseTime = ((endTime - startTime) / 1000).toFixed(2);
// 隐藏加载状态
document.getElementById('loading').classList.remove('active');
document.getElementById('sendBtn').disabled = false;
// 显示Bot响应
addMessage('bot', data.response, null, responseTime);
// 显示Prompt日志
if (data.prompt_info) {
addPromptLog(data.prompt_info, responseTime);
}
// 更新统计
document.getElementById('responseTime').textContent = responseTime + 's';
document.getElementById('stats').classList.add('active');
} catch (error) {
document.getElementById('loading').classList.remove('active');
document.getElementById('sendBtn').disabled = false;
addMessage('bot', '❌ 错误: ' + error.message, null);
}
}
// 添加消息到界面
function addMessage(type, content, persona, responseTime) {
const messagesDiv = document.getElementById('messages');
const messageDiv = document.createElement('div');
messageDiv.className = `message ${type}-message`;
const now = new Date().toLocaleTimeString('zh-CN');
const header = type === 'user'
? `测试用户 · ${now}${persona && persona !== 'none' ? ' · ' + persona : ''}`
: `Bot · ${now}`;
let meta = '';
if (responseTime) {
meta = `<div class="message-meta">⏱️ 响应时间: ${responseTime}秒</div>`;
}
messageDiv.innerHTML = `
<div class="message-header">${header}</div>
<div class="message-content">${escapeHtml(content)}</div>
${meta}
`;
messagesDiv.appendChild(messageDiv);
messagesDiv.scrollTop = messagesDiv.scrollHeight;
messageCount++;
document.getElementById('messageCount').textContent = messageCount;
}
// 添加Prompt日志
function addPromptLog(promptInfo, responseTime) {
const logContent = document.getElementById('logContent');
const logEntry = document.createElement('div');
logEntry.className = 'log-entry';
const now = new Date().toLocaleTimeString('zh-CN');
let html = `<div class="log-timestamp">🕐 ${now} (${responseTime}s)</div>`;
// System Prompt
if (promptInfo.system_prompt) {
html += `
<div class="log-section">
<div class="log-section-title">📤 System Prompt (发送给LLM)</div>
<div class="log-section-content">${escapeHtml(promptInfo.system_prompt)}</div>
</div>
`;
}
// User Message
if (promptInfo.user_message) {
html += `
<div class="log-section">
<div class="log-section-title">📤 User Message (发送给LLM)</div>
<div class="log-section-content">${escapeHtml(promptInfo.user_message)}</div>
</div>
`;
}
// 人格信息
if (promptInfo.persona_info) {
html += `
<div class="log-section">
<div class="log-section-title">🎯 人格信息</div>
<div class="log-section-content">${escapeHtml(JSON.stringify(promptInfo.persona_info, null, 2))}</div>
</div>
`;
}
// 工具信息
if (promptInfo.tools) {
html += `
<div class="log-section">
<div class="log-section-title">🔧 可用工具</div>
<div class="log-section-content">${promptInfo.tools.length} 个工具</div>
</div>
`;
}
// 温度和tokens
if (promptInfo.temperature !== undefined) {
html += `
<div class="log-section">
<div class="log-section-title">⚙️ 模型参数</div>
<div class="log-section-content">Temperature: ${promptInfo.temperature}, Max Tokens: ${promptInfo.max_tokens || 1000}</div>
</div>
`;
}
// 执行日志
if (promptInfo.execution_logs && promptInfo.execution_logs.length > 0) {
// 先对每条日志进行HTML转义，然后用<br>连接
const escapedLogs = promptInfo.execution_logs.map(log => escapeHtml(log));
const logsText = escapedLogs.join('<br>');
html += `
<div class="log-section">
<div class="log-section-title">🔍 后端执行日志</div>
<div class="log-section-content">${logsText}</div>
</div>
`;
}
// LLM返回值
if (promptInfo.llm_response) {
const llmResp = promptInfo.llm_response;
const statusIcon = llmResp.success ? '✅' : '❌';
const statusText = llmResp.success ? '成功' : '失败';
const toolCallsText = llmResp.tool_calls ?
'<br><br>工具调用:<br>' + escapeHtml(JSON.stringify(llmResp.tool_calls, null, 2)) : '';
html += `
<div class="log-section">
<div class="log-section-title">📥 LLM返回值</div>
<div class="log-section-content">状态: ${statusIcon} ${statusText}<br>处理时间: ${llmResp.processing_time}<br><br>回复内容:<br>${escapeHtml(llmResp.response_text)}${toolCallsText}</div>
</div>
`;
}
logEntry.innerHTML = html;
logContent.insertBefore(logEntry, logContent.firstChild);
logContent.scrollTop = 0;
}
// HTML转义
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
// 按Enter发送
function handleKeyPress(event) {
if (event.key === 'Enter') {
sendMessage();
}
}
// 清空对话
function clearMessages() {
const messagesDiv = document.getElementById('messages');
messagesDiv.innerHTML = `
<div class="message bot-message">
<div class="message-header">Bot · 系统消息</div>
<div class="message-content">对话已清空，可以开始新的测试！</div>
</div>
`;
messageCount = 0;
document.getElementById('messageCount').textContent = '0';
document.getElementById('responseTime').textContent = '-';
}
// 清空日志
function clearLogs() {
const logContent = document.getElementById('logContent');
logContent.innerHTML = `
<div class="log-entry">
<div class="log-timestamp">日志已清空</div>
<div class="log-section-content">发送新消息查看Prompt日志...</div>
</div>
`;
}
</script>
</body>
</html>
'''
@app.route('/')
def index():
"""主页"""
return render_template_string(HTML_TEMPLATE)
@app.route('/api/chat', methods=['POST'])
def chat():
"""处理聊天请求，并返回prompt信息"""
try:
data = request.json
message = data.get('message', '')
user = data.get('user', 'test_user')
# 创建日志捕获器
log_capture = LogCapture()
log_capture.setLevel(logging.INFO)
log_capture.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
# 添加到所有相关的logger
loggers_to_capture = [
'chatbot.handlers.ai_interface',
'chatbot.handlers.persona_manager',
'chatbot.analytics.enhanced_rag_analyzer',
'chatbot.analytics.intelligent_query_processor',
'chatbot.analytics.query_templates',
'chatbot.search.semantic_search',
]
for logger_name in loggers_to_capture:
logger = logging.getLogger(logger_name)
logger.addHandler(log_capture)
execution_logs = []
def log_execution(msg):
"""记录执行日志"""
import datetime as dt
timestamp = dt.datetime.now().strftime('%H:%M:%S.%f')[:-3]
execution_logs.append(f"[{timestamp}] {msg}")
log_execution("🚀 开始处理请求")
# 为每个请求创建新的数据库连接（解决SQLite线程安全问题）
log_execution("📦 初始化数据库连接")
thread_message_db = MessageDB(config)
thread_ai_interface = AIInterface(config, openai_client, thread_message_db)
# 获取prompt信息（在调用之前）
log_execution("🎭 检测人格并生成System Prompt")
prompt_info = extract_prompt_info(message, user, thread_ai_interface)
log_execution(f" 检测到人格: {prompt_info.get('persona_info', {}).get('name', '未知')}")
# 调用AI接口处理消息
# 记录开始时间
import time
start_time = time.time()
log_execution("💬 发送请求到AI接口")
result = thread_ai_interface.ai_process_message(message, user)
# 记录结束时间
end_time = time.time()
processing_time = end_time - start_time
log_execution(f"✅ AI处理完成，耗时 {processing_time:.2f}s")
# 检查是否有工具调用
if result.get('tool_calls'):
log_execution(f"🔧 执行了工具调用")
# 获取捕获的日志
captured_logs = log_capture.get_logs()
# 移除日志处理器
for logger_name in loggers_to_capture:
logger = logging.getLogger(logger_name)
logger.removeHandler(log_capture)
# 添加LLM返回信息到prompt_info
prompt_info['llm_response'] = {
'success': result.get('success', False),
'response_text': result.get('response', '处理失败'),
'processing_time': f"{processing_time:.2f}s",
'tool_calls': result.get('tool_calls', None)
}
# 合并执行日志和捕获的日志
all_logs = execution_logs + captured_logs
prompt_info['execution_logs'] = all_logs
log_execution("📤 返回响应给前端")
return jsonify({
'success': result.get('success', False),
'response': result.get('response', '处理失败'),
'prompt_info': prompt_info
})
except Exception as e:
import traceback
error_detail = traceback.format_exc()
print(f"Error in chat endpoint: {error_detail}")
return jsonify({
'success': False,
'response': f'服务器错误: {str(e)}'
}), 500
def extract_prompt_info(message, user, ai_interface_instance):
"""提取prompt相关信息"""
try:
# 检测人格
persona_key, cleaned_message = ai_interface_instance.persona_manager.detect_persona(message)
persona_info = ai_interface_instance.persona_manager.get_persona_info(persona_key)
# 获取system prompt
system_prompt = ai_interface_instance.persona_manager.get_system_prompt(
persona_key, user_id=user
)
# 获取工具列表
tools = ai_interface_instance.tools
# 构建prompt信息
prompt_info = {
'system_prompt': system_prompt,
'user_message': f"用户 '{user}' 通过@你说: '{cleaned_message}'\n请针对这句话回复。",
'persona_info': {
'name': persona_info.get('name'),
'emoji': persona_info.get('emoji'),
'description': persona_info.get('description')
},
'tools': tools,
'temperature': 1.3,
'max_tokens': 1000
}
return prompt_info
except Exception as e:
import traceback
error_detail = traceback.format_exc()
print(f"Error in extract_prompt_info: {error_detail}")
return {
'error': str(e),
'system_prompt': '获取失败',
'user_message': message
}
@app.route('/api/personas', methods=['GET'])
def get_personas():
"""获取可用人格列表"""
personas = [
{'key': 'none', 'name': '默认（学者）', 'emoji': '🎯'},
{'key': '学者', 'name': '学者模式', 'emoji': '🎯'},
{'key': '逗比', 'name': '逗比模式', 'emoji': '😄'},
{'key': '阴阳', 'name': '阴阳师模式', 'emoji': '😈'},
{'key': '极简', 'name': '极简模式', 'emoji': '💬'},
{'key': 'AI模式', 'name': 'AI模式', 'emoji': '🤖'},
{'key': '文青', 'name': '文青模式', 'emoji': '🎨'},
{'key': '外语', 'name': '外国人模式', 'emoji': '🌍'},
]
return jsonify(personas)
if __name__ == '__main__':
print("\n" + "="*60)
print("🚀 Bot测试服务器启动中...")
print("="*60)
print(f"\n📍 访问地址: http://localhost:5050")
print(f"⏰ 启动时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print("\n💡 使用说明:")