hubian
baf5913bfb
问题：SiliconFlow平台不支持标准tool消息类型，第二轮调用返回参数无效 修复：将tool消息转换为user消息格式 - 收集所有tool消息的内容 - 合并为一个用户消息发送给模型 - 添加明确的提示让模型直接根据结果回答 版本: v3.0.6
642 lines
29 KiB
Python
642 lines
29 KiB
Python
"""
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LLM服务 - 大模型池管理，支持思考功能
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"""
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import httpx
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from typing import List, Dict, AsyncGenerator, Optional, Tuple
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import json
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import logging
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import re
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logger = logging.getLogger(__name__)
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class LLMService:
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"""大模型调用服务，支持思考功能"""
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def __init__(self):
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self.providers_cache = {} # 缓存Provider配置
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def load_provider(self, provider_config: dict):
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"""加载Provider配置"""
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provider_id = provider_config.get('id')
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self.providers_cache[provider_id] = {
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'api_base': provider_config.get('api_base'),
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'api_key': provider_config.get('api_key'),
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'supports_thinking': provider_config.get('supports_thinking', False),
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'thinking_model': provider_config.get('thinking_model'),
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'default_model': provider_config.get('default_model'),
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'max_tokens': provider_config.get('max_tokens', 4096),
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'temperature': provider_config.get('temperature', 0.7)
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}
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async def get_available_models(self, api_base: str, api_key: str) -> List[dict]:
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"""从API获取可用模型列表"""
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if not api_base:
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return []
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try:
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url = f"{api_base}/models"
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headers = {"Authorization": f"Bearer {api_key}"}
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.get(url, headers=headers)
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if response.status_code == 200:
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data = response.json()
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models = []
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for m in data.get('data', []):
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model_id = m.get('id', '')
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if model_id:
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models.append({
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"id": model_id,
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"name": m.get('name', model_id),
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"owned_by": m.get('owned_by', 'unknown')
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})
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return models
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except Exception as e:
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logger.warning(f"获取模型列表失败: {e}")
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return []
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async def test_connection(self, api_base: str, api_key: str, model: str) -> dict:
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"""测试API连接"""
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try:
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url = f"{api_base}/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": "测试连接"}],
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"max_tokens": 50
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}
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async with httpx.AsyncClient(timeout=15.0) as client:
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response = await client.post(url, headers=headers, json=payload)
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if response.status_code == 200:
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data = response.json()
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content = data['choices'][0]['message']['content']
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return {
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"success": True,
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"message": f"连接成功！模型响应: {content[:100]}",
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"model": model
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}
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else:
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return {
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"success": False,
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"message": f"连接失败: HTTP {response.status_code}"
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}
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except httpx.ConnectError:
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return {"success": False, "message": f"无法连接到API地址"}
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except httpx.TimeoutException:
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return {"success": False, "message": "连接超时"}
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except Exception as e:
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return {"success": False, "message": f"连接失败: {str(e)}"}
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async def chat(
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self,
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messages: List[Dict],
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provider_config: dict,
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agent_config: dict,
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enable_thinking: bool = True,
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images: List[Dict] = None # 图片数据列表 [{'name', 'type', 'data': base64}]
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) -> Tuple[str, Optional[str]]:
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"""
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调用AI模型进行对话
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Args:
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messages: 对话历史
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provider_config: LLM Provider配置
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agent_config: Agent配置
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enable_thinking: 是否启用思考
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images: 图片数据列表（用于多模态模型）
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Returns:
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Tuple[str, Optional[str]]: (回复内容, 思考过程)
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"""
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api_base = provider_config.get('api_base')
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api_key = provider_config.get('api_key')
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model = agent_config.get('model_override') or provider_config.get('default_model', 'auto')
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supports_thinking = provider_config.get('supports_thinking', False)
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thinking_model = provider_config.get('thinking_model')
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max_tokens = provider_config.get('max_tokens', 4096)
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temperature = agent_config.get('temperature_override') or provider_config.get('temperature', 0.7)
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# 构建消息
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final_messages = messages.copy()
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# 添加系统提示
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system_prompt = agent_config.get('system_prompt', '你是一个有用的AI助手。')
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if final_messages and final_messages[0]['role'] != 'system':
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final_messages.insert(0, {"role": "system", "content": system_prompt})
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# 如果有图片，构建多模态消息（只修改最后一条用户消息）
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if images and len(images) > 0:
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# 找到最后一条用户消息
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for i in range(len(final_messages) - 1, -1, -1):
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if final_messages[i]['role'] == 'user':
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original_text = final_messages[i]['content']
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# 构建多模态内容
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multimodal_content = [{"type": "text", "text": original_text if original_text else "请描述这张图片"}]
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for img in images:
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multimodal_content.append({
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"type": "image_url",
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"image_url": {"url": img['data']} # base64 data URL
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})
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final_messages[i]['content'] = multimodal_content
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break
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thinking_content = None
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# 处理思考功能
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if enable_thinking and agent_config.get('enable_thinking', True):
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thinking_prompt = agent_config.get('thinking_prompt')
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thinking_prefix = agent_config.get('thinking_prefix', '')
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thinking_suffix = agent_config.get('thinking_suffix', '')
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if supports_thinking and thinking_model:
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# 使用专门的思考模型
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thinking_messages = final_messages.copy()
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if thinking_prompt:
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thinking_messages.append({"role": "system", "content": thinking_prompt})
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try:
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thinking_result = await self._call_api(
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api_base, api_key, thinking_model, thinking_messages,
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max_tokens=min(max_tokens, 1000),
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temperature=0.3
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)
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thinking_content = thinking_result
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except Exception as e:
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logger.warning(f"思考模型调用失败: {e}")
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elif thinking_prompt:
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# Agent配置了思考提示词，添加到系统提示中
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enhanced_system = f"{system_prompt}\n\n{thinking_prompt}"
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final_messages[0] = {"role": "system", "content": enhanced_system}
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# 调用主模型
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try:
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response = await self._call_api(
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api_base, api_key, model, final_messages,
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max_tokens=max_tokens,
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temperature=temperature
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)
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# 尝试从回复中提取思考内容（支持DeepSeek R1、GLM等模型的思考模式）
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if enable_thinking and agent_config.get('enable_thinking', True):
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thinking_prefix = agent_config.get('thinking_prefix', '')
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thinking_suffix = agent_config.get('thinking_suffix', '')
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# 如果没有配置前缀后缀，使用常见的思考标记
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if not thinking_prefix:
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# 尝试常见的思考标记
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common_thinking_markers = [
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('<think>', '</think>'),
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('【思考】', '【回答】'),
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('Thought:', 'Answer:'),
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('思考：', '回答：'),
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]
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for prefix, suffix in common_thinking_markers:
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if prefix in response and suffix in response:
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thinking_prefix = prefix
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thinking_suffix = suffix
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break
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# 提取思考部分
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if thinking_prefix and thinking_suffix and thinking_prefix in response:
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try:
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start_idx = response.find(thinking_prefix)
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end_idx = response.find(thinking_suffix, start_idx)
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if end_idx > start_idx:
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thinking_content = response[start_idx + len(thinking_prefix):end_idx].strip()
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# 移除思考部分，只保留回复
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response = response[end_idx + len(thinking_suffix):].strip()
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except Exception as e:
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logger.warning(f"提取思考内容失败: {e}")
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return response, thinking_content
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except Exception as e:
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logger.error(f"LLM调用失败: {e}")
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raise
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async def _call_api(
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self,
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api_base: str,
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api_key: str,
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model: str,
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messages: List[Dict],
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max_tokens: int = 4096,
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temperature: float = 0.7
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) -> str:
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"""调用API"""
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url = f"{api_base.rstrip('/')}/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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# 打印请求详情（调试）
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logger.info(f"调用LLM: url={url}, model={model}")
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logger.info(f"消息数量: {len(messages)}, 第一条消息类型: {type(messages[0].get('content'))}")
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.post(url, headers=headers, json=payload)
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# 检查HTTP状态
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if response.status_code != 200:
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logger.error(f"API返回错误: status={response.status_code}, body={response.text[:500]}")
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response.raise_for_status()
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data = response.json()
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# 检查响应格式
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if 'choices' not in data or len(data['choices']) == 0:
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logger.error(f"API响应格式错误: {data}")
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raise ValueError("API响应格式错误：缺少choices")
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return data['choices'][0]['message']['content']
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except httpx.HTTPStatusError as e:
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logger.error(f"HTTP错误: {e.response.status_code}, {e.response.text}")
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raise
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except Exception as e:
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logger.error(f"API调用异常: {type(e).__name__}: {e}")
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raise
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async def chat_stream(
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self,
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messages: List[Dict],
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provider_config: dict,
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agent_config: dict,
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enable_thinking: bool = True
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) -> AsyncGenerator[dict, None]:
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"""
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流式调用AI模型
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Yields:
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dict: {"type": "thinking"|"content", "text": "..."}
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"""
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api_base = provider_config.get('api_base')
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api_key = provider_config.get('api_key')
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model = agent_config.get('model_override') or provider_config.get('default_model', 'auto')
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max_tokens = provider_config.get('max_tokens', 4096)
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temperature = agent_config.get('temperature_override') or provider_config.get('temperature', 0.7)
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# 构建消息
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final_messages = messages.copy()
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system_prompt = agent_config.get('system_prompt', '你是一个有用的AI助手。')
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if final_messages and final_messages[0]['role'] != 'system':
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final_messages.insert(0, {"role": "system", "content": system_prompt})
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# 如果启用思考但模型不支持
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if enable_thinking and agent_config.get('enable_thinking', True):
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supports_thinking = provider_config.get('supports_thinking', False)
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thinking_prompt = agent_config.get('thinking_prompt')
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if not supports_thinking and thinking_prompt:
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thinking_prefix = agent_config.get('thinking_prefix', '')
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thinking_suffix = agent_config.get('thinking_suffix', '')
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enhanced_system = f"{system_prompt}\n\n在回答之前，请先思考问题。思考过程请用{thinking_prefix}和{thinking_suffix}包裹，然后再给出正式回答。"
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if thinking_prompt:
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enhanced_system += f"\n思考指导：{thinking_prompt}"
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final_messages[0] = {"role": "system", "content": enhanced_system}
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url = f"{api_base}/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": final_messages,
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"stream": True,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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thinking_prefix = agent_config.get('thinking_prefix', '')
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thinking_suffix = agent_config.get('thinking_suffix', '')
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buffer = "" # 用于累积和检测思考部分
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async with httpx.AsyncClient(timeout=60.0) as client:
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async with client.stream("POST", url, headers=headers, json=payload) as response:
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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data_str = line[6:]
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if data_str == "[DONE]":
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break
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try:
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data = json.loads(data_str)
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if 'choices' in data and len(data['choices']) > 0:
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delta = data['choices'][0].get('delta', {})
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if 'content' in delta:
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text = delta['content']
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buffer += text
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# 检测思考部分（简化逻辑）
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if thinking_prefix and thinking_suffix and thinking_prefix in buffer:
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# 尝试解析思考部分
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try:
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start_idx = buffer.find(thinking_prefix)
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if start_idx >= 0:
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# 找到思考开始，继续找结束
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end_idx = buffer.find(thinking_suffix, start_idx)
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if end_idx > start_idx:
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# 思考部分完整，发送思考然后发送内容
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thinking = buffer[start_idx + len(thinking_prefix):end_idx]
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yield {"type": "thinking", "text": thinking}
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# 发送思考后的内容
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remaining = buffer[end_idx + len(thinking_suffix):]
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if remaining:
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yield {"type": "content", "text": remaining}
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buffer = ""
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else:
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# 思考部分还没结束，先发送之前的内容
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if start_idx > 0:
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yield {"type": "content", "text": buffer[:start_idx]}
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# 等待更多数据来完成思考部分
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buffer = buffer[start_idx:]
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else:
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# 没有思考标记，直接发送内容
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yield {"type": "content", "text": text}
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buffer = ""
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except:
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yield {"type": "content", "text": text}
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else:
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# 没有思考标记配置，直接发送内容
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yield {"type": "content", "text": text}
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except json.JSONDecodeError:
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continue
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# 处理剩余buffer
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if buffer:
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yield {"type": "content", "text": buffer}
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async def chat_with_tools(
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self,
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messages: List[Dict],
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provider_config: dict,
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agent_config: dict,
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tools: List[Dict] = None,
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enable_thinking: bool = True,
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images: List[Dict] = None
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) -> Tuple[str, Optional[str], Optional[List[Dict]]]:
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"""
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支持Function Calling的对话
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Args:
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messages: 对话历史
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provider_config: LLM Provider配置
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agent_config: Agent配置
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tools: 工具定义列表（OpenAI Function Calling格式）
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enable_thinking: 是否启用思考
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images: 图片数据列表
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Returns:
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Tuple[str, Optional[str], Optional[List[Dict]]]: (回复内容, 思考过程, 工具调用记录)
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"""
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api_base = provider_config.get('api_base')
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api_key = provider_config.get('api_key')
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model = agent_config.get('model_override') or provider_config.get('default_model', 'auto')
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supports_function_calling = provider_config.get('supports_function_calling', False)
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max_tokens = provider_config.get('max_tokens', 4096)
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temperature = agent_config.get('temperature_override') or provider_config.get('temperature', 0.7)
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# 如果不支持Function Calling，直接调用普通chat
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if not supports_function_calling or not tools:
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response, thinking = await self.chat(messages, provider_config, agent_config, enable_thinking, images)
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return response, thinking, None
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# 构建消息
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final_messages = messages.copy()
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system_prompt = agent_config.get('system_prompt', '你是一个有用的AI助手。')
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if final_messages and final_messages[0]['role'] != 'system':
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final_messages.insert(0, {"role": "system", "content": system_prompt})
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# 处理图片（多模态）
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if images and len(images) > 0:
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for i in range(len(final_messages) - 1, -1, -1):
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if final_messages[i]['role'] == 'user':
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original_text = final_messages[i]['content']
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multimodal_content = [{"type": "text", "text": original_text if original_text else "请描述这张图片"}]
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for img in images:
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multimodal_content.append({
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"type": "image_url",
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"image_url": {"url": img['data']}
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})
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final_messages[i]['content'] = multimodal_content
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break
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# 第一次调用：让LLM决定是否调用工具
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url = f"{api_base.rstrip('/')}/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": final_messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"tools": tools # 传入工具定义
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}
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logger.info(f"Function Calling调用: url={url}, model={model}, tools={len(tools)}")
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tool_calls_record = [] # 记录工具调用
|
||
|
||
try:
|
||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||
response = await client.post(url, headers=headers, json=payload)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"API返回错误: status={response.status_code}, body={response.text[:500]}")
|
||
response.raise_for_status()
|
||
|
||
data = response.json()
|
||
|
||
if 'choices' not in data or len(data['choices']) == 0:
|
||
raise ValueError("API响应格式错误：缺少choices")
|
||
|
||
message = data['choices'][0]['message']
|
||
|
||
# 检查是否有工具调用
|
||
if 'tool_calls' in message and message['tool_calls']:
|
||
logger.info(f"LLM请求调用工具: {len(message['tool_calls'])} 个")
|
||
|
||
# 将LLM的工具调用消息添加到历史
|
||
final_messages.append({
|
||
"role": "assistant",
|
||
"content": None,
|
||
"tool_calls": message['tool_calls']
|
||
})
|
||
|
||
# 记录工具调用
|
||
for tc in message['tool_calls']:
|
||
tool_calls_record.append({
|
||
"id": tc['id'],
|
||
"name": tc['function']['name'],
|
||
"arguments": json.loads(tc['function']['arguments'])
|
||
})
|
||
|
||
# 返回工具调用记录，由调用方执行工具
|
||
return None, None, tool_calls_record
|
||
|
||
# 没有工具调用，直接返回内容
|
||
content = message.get('content', '')
|
||
|
||
# 处理思考内容（如果有）
|
||
thinking_content = None
|
||
# 这里可以添加思考内容提取逻辑
|
||
|
||
return content, thinking_content, None
|
||
|
||
except httpx.HTTPStatusError as e:
|
||
logger.error(f"HTTP错误: {e.response.status_code}, {e.response.text}")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Function Calling调用异常: {type(e).__name__}: {e}")
|
||
raise
|
||
|
||
async def chat_with_tool_results(
|
||
self,
|
||
messages: List[Dict],
|
||
provider_config: dict,
|
||
agent_config: dict,
|
||
enable_thinking: bool = True
|
||
) -> Tuple[str, Optional[str]]:
|
||
"""
|
||
第二阶段调用：使用包含工具调用和结果的完整消息历史
|
||
|
||
注意：SiliconFlow 等平台不支持标准 tool 消息类型，
|
||
需要将工具结果转换为普通用户消息格式。
|
||
|
||
Args:
|
||
messages: 已包含assistant tool_calls和tool结果的完整消息历史
|
||
provider_config: LLM Provider配置
|
||
agent_config: Agent配置
|
||
|
||
Returns:
|
||
Tuple[str, Optional[str]]: (回复内容, 思考过程)
|
||
"""
|
||
api_base = provider_config.get('api_base')
|
||
api_key = provider_config.get('api_key')
|
||
model = agent_config.get('model_override') or provider_config.get('default_model', 'auto')
|
||
max_tokens = provider_config.get('max_tokens', 4096)
|
||
temperature = agent_config.get('temperature_override') or provider_config.get('temperature', 0.7)
|
||
|
||
# 转换消息格式：将 tool 相关消息转为普通消息格式
|
||
# 因为很多 API 平台（如 SiliconFlow）不支持 tool 消息类型
|
||
converted_messages = []
|
||
tool_results_content = []
|
||
|
||
for msg in messages:
|
||
role = msg.get('role')
|
||
|
||
if role == 'system':
|
||
converted_messages.append(msg)
|
||
elif role == 'user':
|
||
converted_messages.append(msg)
|
||
elif role == 'assistant':
|
||
# 如果有 tool_calls，跳过这个消息（不发送给不支持的平台）
|
||
if msg.get('tool_calls'):
|
||
# 记录工具调用信息（可选）
|
||
pass
|
||
else:
|
||
converted_messages.append(msg)
|
||
elif role == 'tool':
|
||
# 收集工具结果，后面合并成一个用户消息
|
||
tool_results_content.append(msg.get('content', ''))
|
||
|
||
# 如果有工具结果，添加为一个特殊的用户消息
|
||
if tool_results_content:
|
||
combined_results = "\n\n".join([
|
||
f"【搜索结果 {i+1}】\n{result}"
|
||
for i, result in enumerate(tool_results_content)
|
||
])
|
||
|
||
# 添加工具结果作为用户消息
|
||
converted_messages.append({
|
||
"role": "user",
|
||
"content": f"以下是搜索工具返回的结果：\n\n{combined_results}\n\n请根据以上搜索结果回答我之前的问题，不要再说\"让我搜索一下\"，直接给出回答。如果搜索结果不足以回答，请说明。"
|
||
})
|
||
|
||
final_messages = converted_messages
|
||
|
||
# 调用LLM生成最终回复
|
||
url = f"{api_base.rstrip('/')}/chat/completions"
|
||
headers = {
|
||
"Authorization": f"Bearer {api_key}",
|
||
"Content-Type": "application/json"
|
||
}
|
||
payload = {
|
||
"model": model,
|
||
"messages": final_messages,
|
||
"temperature": temperature,
|
||
"max_tokens": max_tokens
|
||
}
|
||
|
||
logger.info(f"工具结果返回LLM: url={url}, model={model}, 消息数={len(final_messages)}")
|
||
# 打印消息内容（调试）
|
||
for i, msg in enumerate(final_messages):
|
||
role = msg.get('role')
|
||
content_preview = str(msg.get('content', ''))[:100] if msg.get('content') else 'None'
|
||
if role == 'tool':
|
||
logger.info(f"消息[{i}] role={role}, tool_call_id={msg.get('tool_call_id')}, content长度={len(msg.get('content',''))}")
|
||
elif role == 'assistant' and msg.get('tool_calls'):
|
||
logger.info(f"消息[{i}] role={role}, tool_calls={len(msg['tool_calls'])}")
|
||
else:
|
||
logger.info(f"消息[{i}] role={role}, content={content_preview}...")
|
||
|
||
try:
|
||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||
response = await client.post(url, headers=headers, json=payload)
|
||
|
||
if response.status_code != 200:
|
||
logger.error(f"API返回错误: status={response.status_code}, body={response.text[:500]}")
|
||
response.raise_for_status()
|
||
|
||
data = response.json()
|
||
content = data['choices'][0]['message']['content']
|
||
|
||
# 过滤掉伪工具调用格式（某些模型如Kimi会输出这种内部格式）
|
||
# 模式：<|tool_calls_section_begin|>...<|tool_calls_section_end|>
|
||
import re
|
||
tool_pattern = r'<\|tool_calls_section_begin\|>.*?<\|tool_calls_section_end\|>'
|
||
content = re.sub(tool_pattern, '', content, flags=re.DOTALL)
|
||
|
||
# 也过滤单个 tool_call 格式
|
||
tool_call_pattern = r'<\|tool_call_begin\|>.*?<\|tool_call_end\|>'
|
||
content = re.sub(tool_call_pattern, '', content, flags=re.DOTALL)
|
||
|
||
# 清理可能残留的格式标记
|
||
content = content.replace('<|tool_calls_section_begin|>', '')
|
||
content = content.replace('<|tool_calls_section_end|>', '')
|
||
content = content.replace('<|tool_call_begin|>', '')
|
||
content = content.replace('<|tool_call_end|>', '')
|
||
content = content.replace('<|tool_call_argument_begin|>', '')
|
||
content = content.replace('<|tool_call_argument_end|>', '')
|
||
|
||
# 清理多余空行
|
||
content = re.sub(r'\n{3,}', '\n\n', content).strip()
|
||
|
||
return content, None
|
||
|
||
except Exception as e:
|
||
logger.error(f"工具结果调用异常: {e}")
|
||
raise
|
||
|
||
|
||
# 全局实例
|
||
llm_service = LLMService() |