More model support, including OpenAI and Claude, with corresponding updates to the README documentation.

utils/ai_analyzer.py

@@ -1,4 +1,5 @@
 import openai
+import anthropic
 import json
 from typing import List, Dict
 import os
@@ -8,11 +9,34 @@ from utils.logger import app_logger as logging
 class AIAnalyzer:
     def __init__(self):
         # 从环境变量获取API密钥
-        self.api_key = os.getenv('OPENAI_API_KEY')
-        if not self.api_key:
-            raise ValueError("请设置OPENAI_API_KEY环境变量")
+        self.openai_key = os.getenv('OPENAI_API_KEY')
+        self.claude_key = os.getenv('ANTHROPIC_API_KEY')
         
-        openai.api_key = self.api_key
+        if not self.openai_key and not self.claude_key:
+            raise ValueError("请至少设置一个API密钥 (OPENAI_API_KEY 或 ANTHROPIC_API_KEY)")
+        
+        if self.openai_key:
+            openai.api_key = self.openai_key
+        if self.claude_key:
+            self.claude_client = anthropic.Anthropic(api_key=self.claude_key)
+        
+        # 支持的模型列表
+        self.supported_models = {
+            # OpenAI 模型
+            'gpt-3.5-turbo': {'provider': 'openai', 'max_tokens': 2000, 'cost_per_1k': 0.0015},
+            'gpt-3.5-turbo-16k': {'provider': 'openai', 'max_tokens': 16000, 'cost_per_1k': 0.003},
+            'gpt-4': {'provider': 'openai', 'max_tokens': 8000, 'cost_per_1k': 0.03},
+            'gpt-4-32k': {'provider': 'openai', 'max_tokens': 32000, 'cost_per_1k': 0.06},
+            'gpt-4-turbo-preview': {'provider': 'openai', 'max_tokens': 128000, 'cost_per_1k': 0.01},
+            
+            # Claude 模型
+            'claude-3-opus-20240229': {'provider': 'anthropic', 'max_tokens': 4000, 'cost_per_1k': 0.015},
+            'claude-3-sonnet-20240229': {'provider': 'anthropic', 'max_tokens': 3000, 'cost_per_1k': 0.003},
+            'claude-3-haiku-20240307': {'provider': 'anthropic', 'max_tokens': 2000, 'cost_per_1k': 0.0025},
+            'claude-2.1': {'provider': 'anthropic', 'max_tokens': 100000, 'cost_per_1k': 0.008},
+            'claude-2.0': {'provider': 'anthropic', 'max_tokens': 100000, 'cost_per_1k': 0.008},
+            'claude-instant-1.2': {'provider': 'anthropic', 'max_tokens': 100000, 'cost_per_1k': 0.0015}
+        }
         
         # 不同深度的分析提示词
         self.prompt_templates = {
@@ -73,48 +97,144 @@ class AIAnalyzer:
                              analysis_depth: str = "standard") -> List[Dict]:
         """分析一批消息并返回分析结果"""
         try:
+            if model_type not in self.supported_models:
+                raise ValueError(f"不支持的模型类型: {model_type}")
+            
+            model_info = self.supported_models[model_type]
+            provider = model_info['provider']
+            max_tokens = model_info['max_tokens']
+            
+            # 根据模型类型调整批处理大小
+            adjusted_batch_size = min(batch_size, self._get_optimal_batch_size(model_type))
+            if adjusted_batch_size != batch_size:
+                logging.info(f"已将批处理大小从 {batch_size} 调整为 {adjusted_batch_size}")
+            
             all_results = []
+            total_cost = 0
             
             # 分批处理消息
-            for i in range(0, len(messages), batch_size):
-                batch = messages[i:i + batch_size]
+            for i in range(0, len(messages), adjusted_batch_size):
+                batch = messages[i:i + adjusted_batch_size]
                 formatted_messages = []
                 for msg in batch:
                     formatted_messages.append(f"消息ID: {msg['id']}\n内容: {msg['content']}")
                 
                 messages_text = "\n---\n".join(formatted_messages)
-                
-                # 获取对应深度的提示词
                 system_prompt = self.prompt_templates.get(analysis_depth, self.prompt_templates['standard'])
                 
-                # 调用OpenAI API
-                response = await openai.ChatCompletion.acreate(
-                    model=model_type,
-                    messages=[
-                        {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": f"请分析以下消息:\n{messages_text}"}
-                    ],
-                    temperature=0.3,  # 降低随机性
-                    max_tokens=2000 if analysis_depth != 'deep' else 3000,
-                    n=1
-                )
-                
-                try:
-                    result = json.loads(response.choices[0].message.content)
-                    if isinstance(result, dict) and 'analysis_results' in result:
-                        all_results.extend(result['analysis_results'])
-                    else:
-                        logging.error(f"API返回格式不正确: {response.choices[0].message.content}")
-                except json.JSONDecodeError as e:
-                    logging.error(f"JSON解析失败: {e}")
-                    continue
+                if provider == 'openai':
+                    result = await self._analyze_with_openai(
+                        messages_text, 
+                        system_prompt, 
+                        model_type, 
+                        max_tokens
+                    )
+                else:  # anthropic
+                    result = await self._analyze_with_claude(
+                        messages_text, 
+                        system_prompt, 
+                        model_type, 
+                        max_tokens
+                    )
                 
+                if result:
+                    all_results.extend(result)
+                    # 计算本批次成本
+                    batch_cost = self._calculate_cost(len(messages_text), model_type)
+                    total_cost += batch_cost
+                    logging.info(f"批次处理完成,成本: ${batch_cost:.4f}")
+            
+            logging.info(f"分析完成,总成本: ${total_cost:.4f}")
             return all_results
                 
         except Exception as e:
             logging.error(f"AI分析过程出错: {e}")
             return []
     
+    def _get_optimal_batch_size(self, model_type: str) -> int:
+        """根据模型类型获取最优批处理大小"""
+        model_info = self.supported_models[model_type]
+        max_tokens = model_info['max_tokens']
+        
+        # 估算每条消息的平均token数(假设为200)
+        avg_tokens_per_message = 200
+        
+        # 预留20%的token用于系统提示词和响应
+        available_tokens = int(max_tokens * 0.8)
+        
+        # 计算最优批处理大小
+        optimal_batch_size = max(1, min(100, available_tokens // avg_tokens_per_message))
+        
+        return optimal_batch_size
+    
+    def _calculate_cost(self, input_length: int, model_type: str) -> float:
+        """计算API调用成本"""
+        model_info = self.supported_models[model_type]
+        cost_per_1k = model_info['cost_per_1k']
+        
+        # 估算token数(假设每4个字符约等于1个token)
+        estimated_tokens = input_length // 4
+        
+        # 计算成本(美元)
+        cost = (estimated_tokens / 1000) * cost_per_1k
+        
+        return cost
+    
+    async def _analyze_with_openai(self, messages_text: str, system_prompt: str, 
+                                 model: str, max_tokens: int) -> List[Dict]:
+        """使用OpenAI API进行分析"""
+        try:
+            response = await openai.ChatCompletion.acreate(
+                model=model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": f"请分析以下消息:\n{messages_text}"}
+                ],
+                temperature=0.3,
+                max_tokens=max_tokens,
+                n=1,
+                response_format={"type": "json_object"}  # 强制JSON响应格式
+            )
+            
+            result = json.loads(response.choices[0].message.content)
+            if isinstance(result, dict) and 'analysis_results' in result:
+                return result['analysis_results']
+            else:
+                logging.error(f"OpenAI API返回格式不正确: {response.choices[0].message.content}")
+                return []
+                
+        except Exception as e:
+            logging.error(f"OpenAI API调用失败: {e}")
+            return []
+    
+    async def _analyze_with_claude(self, messages_text: str, system_prompt: str, 
+                                 model: str, max_tokens: int) -> List[Dict]:
+        """使用Claude API进行分析"""
+        try:
+            response = await self.claude_client.messages.create(
+                model=model,
+                max_tokens=max_tokens,
+                temperature=0.3,
+                system=system_prompt,
+                messages=[
+                    {
+                        "role": "user",
+                        "content": f"请分析以下消息:\n{messages_text}"
+                    }
+                ]
+            )
+            
+            result = json.loads(response.content[0].text)
+            if isinstance(result, dict) and 'analysis_results' in result:
+                return result['analysis_results']
+            else:
+                logging.error(f"Claude API返回格式不正确: {response.content[0].text}")
+                return []
+                
+        except Exception as e:
+            logging.error(f"Claude API调用失败: {e}")
+            return []
+    
     def format_analysis_for_display(self, analysis: Dict) -> Dict:
         """将分析结果格式化为前端显示格式"""
         base_result = {