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48cbb2bf1d
* ✨ feat(llm): 全面重构LLM服务模块，增强多模态与工具支持 🚀 核心功能增强 - 多模型链式调用：新增 `pipeline_chat` 支持复杂任务流处理 - 扩展提供商支持：新增 ARK(火山方舟)、SiliconFlow(硅基流动) 适配器 - 多模态处理增强：支持URL媒体文件下载转换，提升输入灵活性 - 历史对话支持：AI.analyze 方法支持历史消息上下文和可选 UniMessage 参数 - 文本嵌入功能：新增 `embed`、`analyze_multimodal`、`search_multimodal` 等API - 模型能力系统：新增 `ModelCapabilities` 统一管理模型特性（多模态、工具调用等） 🔧 架构重构与优化 - MCP工具系统重构：配置独立化至 `data/llm/mcp_tools.json`，预置常用工具 - API调用逻辑统一：提取通用 `_perform_api_call` 方法，消除代码重复 - 跨平台兼容：Windows平台MCP工具npx命令自动包装处理 - HTTP客户端增强：兼容不同版本httpx代理配置（0.28+版本适配） 🛠️ API与配置完善 - 统一返回类型：`AI.analyze` 统一返回 `LLMResponse` 类型 - 消息转换工具：新增 `message_to_unimessage` 转换函数 - Gemini适配器增强：URL图片下载编码、动态安全阈值配置 - 缓存管理：新增模型实例缓存和管理功能 - 配置预设：扩展 CommonOverrides 预设配置选项 - 历史管理优化：支持多模态内容占位符替换，提升效率 📚 文档与开发体验 - README全面重写：新增完整使用指南、API参考和架构概览 - 文档内容扩充：补充嵌入模型、缓存管理、工具注册等功能说明 - 日志记录增强：支持详细调试信息输出 - API简化：移除冗余函数，优化接口设计 * 🎨 feat(llm): 统一LLM服务函数文档格式 * ✨ feat(llm): 添加新模型并简化提供者配置加载 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
790 lines
25 KiB
Python
790 lines
25 KiB
Python
"""
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LLM 服务的高级 API 接口
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"""
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import copy
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from dataclasses import dataclass
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from enum import Enum
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from pathlib import Path
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from typing import Any
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from nonebot_plugin_alconna.uniseg import UniMessage
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from zhenxun.services.log import logger
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from .config import CommonOverrides, LLMGenerationConfig
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from .config.providers import get_ai_config
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from .manager import get_global_default_model_name, get_model_instance
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from .tools import tool_registry
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from .types import (
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EmbeddingTaskType,
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LLMContentPart,
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LLMErrorCode,
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LLMException,
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LLMMessage,
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LLMResponse,
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LLMTool,
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ModelName,
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)
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from .utils import create_multimodal_message, unimsg_to_llm_parts
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class TaskType(Enum):
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"""任务类型枚举"""
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CHAT = "chat"
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CODE = "code"
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SEARCH = "search"
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ANALYSIS = "analysis"
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GENERATION = "generation"
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MULTIMODAL = "multimodal"
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@dataclass
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class AIConfig:
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"""AI配置类 - 简化版本"""
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model: ModelName = None
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default_embedding_model: ModelName = None
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temperature: float | None = None
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max_tokens: int | None = None
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enable_cache: bool = False
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enable_code: bool = False
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enable_search: bool = False
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timeout: int | None = None
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enable_gemini_json_mode: bool = False
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enable_gemini_thinking: bool = False
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enable_gemini_safe_mode: bool = False
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enable_gemini_multimodal: bool = False
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enable_gemini_grounding: bool = False
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default_preserve_media_in_history: bool = False
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def __post_init__(self):
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"""初始化后从配置中读取默认值"""
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ai_config = get_ai_config()
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if self.model is None:
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self.model = ai_config.get("default_model_name")
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if self.timeout is None:
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self.timeout = ai_config.get("timeout", 180)
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class AI:
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"""统一的AI服务类 - 平衡设计版本
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提供三层API：
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1. 简单方法：ai.chat(), ai.code(), ai.search()
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2. 标准方法：ai.analyze() 支持复杂参数
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3. 高级方法：通过get_model_instance()直接访问
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"""
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def __init__(
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self, config: AIConfig | None = None, history: list[LLMMessage] | None = None
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):
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"""
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初始化AI服务
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参数:
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config: AI 配置.
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history: 可选的初始对话历史.
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"""
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self.config = config or AIConfig()
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self.history = history or []
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def clear_history(self):
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"""清空当前会话的历史记录"""
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self.history = []
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logger.info("AI session history cleared.")
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def _sanitize_message_for_history(self, message: LLMMessage) -> LLMMessage:
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"""
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净化用于存入历史记录的消息。
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将非文本的多模态内容部分替换为文本占位符，以避免重复处理。
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"""
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if not isinstance(message.content, list):
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return message
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sanitized_message = copy.deepcopy(message)
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content_list = sanitized_message.content
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if not isinstance(content_list, list):
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return sanitized_message
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new_content_parts: list[LLMContentPart] = []
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has_multimodal_content = False
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for part in content_list:
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if isinstance(part, LLMContentPart) and part.type == "text":
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new_content_parts.append(part)
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else:
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has_multimodal_content = True
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if has_multimodal_content:
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placeholder = "[用户发送了媒体文件，内容已在首次分析时处理]"
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text_part_found = False
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for part in new_content_parts:
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if part.type == "text":
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part.text = f"{placeholder} {part.text or ''}".strip()
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text_part_found = True
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break
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if not text_part_found:
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new_content_parts.insert(0, LLMContentPart.text_part(placeholder))
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sanitized_message.content = new_content_parts
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return sanitized_message
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async def chat(
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self,
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message: str | LLMMessage | list[LLMContentPart],
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*,
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model: ModelName = None,
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preserve_media_in_history: bool | None = None,
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**kwargs: Any,
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) -> str:
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"""
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进行一次聊天对话。
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此方法会自动使用和更新会话内的历史记录。
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参数:
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message: 用户输入的消息。
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model: 本次对话要使用的模型。
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preserve_media_in_history: 是否在历史记录中保留原始多模态信息。
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- True: 保留，用于深度多轮媒体分析。
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- False: 不保留，替换为占位符，提高效率。
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- None (默认): 使用AI实例配置的默认值。
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**kwargs: 传递给模型的其他参数。
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返回:
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str: 模型的文本响应。
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"""
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current_message: LLMMessage
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if isinstance(message, str):
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current_message = LLMMessage.user(message)
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elif isinstance(message, list) and all(
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isinstance(part, LLMContentPart) for part in message
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):
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current_message = LLMMessage.user(message)
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elif isinstance(message, LLMMessage):
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current_message = message
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else:
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raise LLMException(
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f"AI.chat 不支持的消息类型: {type(message)}. "
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"请使用 str, LLMMessage, 或 list[LLMContentPart]. "
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"对于更复杂的多模态输入或文件路径，请使用 AI.analyze().",
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code=LLMErrorCode.API_REQUEST_FAILED,
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)
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final_messages = [*self.history, current_message]
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response = await self._execute_generation(
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final_messages, model, "聊天失败", kwargs
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)
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should_preserve = (
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preserve_media_in_history
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if preserve_media_in_history is not None
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else self.config.default_preserve_media_in_history
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)
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if should_preserve:
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logger.debug("深度分析模式：在历史记录中保留原始多模态消息。")
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self.history.append(current_message)
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else:
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logger.debug("高效模式：净化历史记录中的多模态消息。")
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sanitized_user_message = self._sanitize_message_for_history(current_message)
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self.history.append(sanitized_user_message)
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self.history.append(LLMMessage.assistant_text_response(response.text))
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return response.text
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async def code(
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self,
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prompt: str,
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*,
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model: ModelName = None,
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timeout: int | None = None,
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**kwargs: Any,
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) -> dict[str, Any]:
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"""
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代码执行
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参数:
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prompt: 代码执行的提示词。
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model: 要使用的模型名称。
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timeout: 代码执行超时时间（秒）。
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**kwargs: 传递给模型的其他参数。
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返回:
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dict[str, Any]: 包含执行结果的字典，包含text、code_executions和success字段。
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"""
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resolved_model = model or self.config.model or "Gemini/gemini-2.0-flash"
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config = CommonOverrides.gemini_code_execution()
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if timeout:
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config.custom_params = config.custom_params or {}
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config.custom_params["code_execution_timeout"] = timeout
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messages = [LLMMessage.user(prompt)]
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response = await self._execute_generation(
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messages, resolved_model, "代码执行失败", kwargs, base_config=config
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)
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return {
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"text": response.text,
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"code_executions": response.code_executions or [],
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"success": True,
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}
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async def search(
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self,
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query: str | UniMessage,
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*,
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model: ModelName = None,
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instruction: str = "",
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**kwargs: Any,
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) -> dict[str, Any]:
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"""
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信息搜索 - 支持多模态输入
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参数:
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query: 搜索查询内容，支持文本或多模态消息。
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model: 要使用的模型名称。
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instruction: 搜索指令。
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**kwargs: 传递给模型的其他参数。
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返回:
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dict[str, Any]: 包含搜索结果的字典，包含text、sources、queries和success字段
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"""
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resolved_model = model or self.config.model or "Gemini/gemini-2.0-flash"
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config = CommonOverrides.gemini_grounding()
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if isinstance(query, str):
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messages = [LLMMessage.user(query)]
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elif isinstance(query, UniMessage):
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content_parts = await unimsg_to_llm_parts(query)
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final_messages: list[LLMMessage] = []
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if instruction:
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final_messages.append(LLMMessage.system(instruction))
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if not content_parts:
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if instruction:
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final_messages.append(LLMMessage.user(instruction))
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else:
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raise LLMException(
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"搜索内容为空或无法处理。", code=LLMErrorCode.API_REQUEST_FAILED
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)
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else:
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final_messages.append(LLMMessage.user(content_parts))
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messages = final_messages
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else:
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raise LLMException(
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f"不支持的搜索输入类型: {type(query)}. 请使用 str 或 UniMessage.",
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code=LLMErrorCode.API_REQUEST_FAILED,
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)
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response = await self._execute_generation(
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messages, resolved_model, "信息搜索失败", kwargs, base_config=config
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)
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result = {
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"text": response.text,
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"sources": [],
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"queries": [],
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"success": True,
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}
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if response.grounding_metadata:
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result["sources"] = response.grounding_metadata.grounding_attributions or []
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result["queries"] = response.grounding_metadata.web_search_queries or []
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return result
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async def analyze(
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self,
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message: UniMessage | None,
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*,
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instruction: str = "",
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model: ModelName = None,
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use_tools: list[str] | None = None,
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tool_config: dict[str, Any] | None = None,
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activated_tools: list[LLMTool] | None = None,
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history: list[LLMMessage] | None = None,
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**kwargs: Any,
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) -> LLMResponse:
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"""
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内容分析 - 接收 UniMessage 物件进行多模态分析和工具呼叫。
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参数:
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message: 要分析的消息内容（支持多模态）。
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instruction: 分析指令。
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model: 要使用的模型名称。
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use_tools: 要使用的工具名称列表。
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tool_config: 工具配置。
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activated_tools: 已激活的工具列表。
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history: 对话历史记录。
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**kwargs: 传递给模型的其他参数。
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返回:
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LLMResponse: 模型的完整响应结果。
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"""
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content_parts = await unimsg_to_llm_parts(message or UniMessage())
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final_messages: list[LLMMessage] = []
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if history:
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final_messages.extend(history)
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if instruction:
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if not any(msg.role == "system" for msg in final_messages):
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final_messages.insert(0, LLMMessage.system(instruction))
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if not content_parts:
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if instruction and not history:
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final_messages.append(LLMMessage.user(instruction))
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elif not history:
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raise LLMException(
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"分析内容为空或无法处理。", code=LLMErrorCode.API_REQUEST_FAILED
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)
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else:
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final_messages.append(LLMMessage.user(content_parts))
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llm_tools: list[LLMTool] | None = activated_tools
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if not llm_tools and use_tools:
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try:
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llm_tools = tool_registry.get_tools(use_tools)
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logger.debug(f"已从注册表加载工具定义: {use_tools}")
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except ValueError as e:
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raise LLMException(
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f"加载工具定义失败: {e}",
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code=LLMErrorCode.CONFIGURATION_ERROR,
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cause=e,
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)
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tool_choice = None
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if tool_config:
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mode = tool_config.get("mode", "auto")
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if mode in ["auto", "any", "none"]:
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tool_choice = mode
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response = await self._execute_generation(
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final_messages,
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model,
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"内容分析失败",
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kwargs,
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llm_tools=llm_tools,
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tool_choice=tool_choice,
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)
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return response
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async def _execute_generation(
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self,
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messages: list[LLMMessage],
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model_name: ModelName,
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error_message: str,
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config_overrides: dict[str, Any],
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llm_tools: list[LLMTool] | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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base_config: LLMGenerationConfig | None = None,
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) -> LLMResponse:
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"""通用的生成执行方法，封装模型获取和单次API调用"""
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try:
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resolved_model_name = self._resolve_model_name(
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model_name or self.config.model
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)
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final_config_dict = self._merge_config(
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config_overrides, base_config=base_config
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)
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async with await get_model_instance(
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resolved_model_name, override_config=final_config_dict
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) as model_instance:
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return await model_instance.generate_response(
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messages,
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tools=llm_tools,
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tool_choice=tool_choice,
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)
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except LLMException:
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raise
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except Exception as e:
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logger.error(f"{error_message}: {e}", e=e)
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raise LLMException(f"{error_message}: {e}", cause=e)
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def _resolve_model_name(self, model_name: ModelName) -> str:
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"""解析模型名称"""
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if model_name:
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return model_name
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default_model = get_global_default_model_name()
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if default_model:
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return default_model
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raise LLMException(
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"未指定模型名称且未设置全局默认模型",
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code=LLMErrorCode.MODEL_NOT_FOUND,
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)
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def _merge_config(
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self,
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user_config: dict[str, Any],
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base_config: LLMGenerationConfig | None = None,
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) -> dict[str, Any]:
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"""合并配置"""
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final_config = {}
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if base_config:
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final_config.update(base_config.to_dict())
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if self.config.temperature is not None:
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final_config["temperature"] = self.config.temperature
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if self.config.max_tokens is not None:
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final_config["max_tokens"] = self.config.max_tokens
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if self.config.enable_cache:
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final_config["enable_caching"] = True
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if self.config.enable_code:
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final_config["enable_code_execution"] = True
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if self.config.enable_search:
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final_config["enable_grounding"] = True
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|
||
if self.config.enable_gemini_json_mode:
|
||
final_config["response_mime_type"] = "application/json"
|
||
if self.config.enable_gemini_thinking:
|
||
final_config["thinking_budget"] = 0.8
|
||
if self.config.enable_gemini_safe_mode:
|
||
final_config["safety_settings"] = (
|
||
CommonOverrides.gemini_safe().safety_settings
|
||
)
|
||
if self.config.enable_gemini_multimodal:
|
||
final_config.update(CommonOverrides.gemini_multimodal().to_dict())
|
||
if self.config.enable_gemini_grounding:
|
||
final_config["enable_grounding"] = True
|
||
|
||
final_config.update(user_config)
|
||
|
||
return final_config
|
||
|
||
async def embed(
|
||
self,
|
||
texts: list[str] | str,
|
||
*,
|
||
model: ModelName = None,
|
||
task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
|
||
**kwargs: Any,
|
||
) -> list[list[float]]:
|
||
"""
|
||
生成文本嵌入向量
|
||
|
||
参数:
|
||
texts: 要生成嵌入向量的文本或文本列表。
|
||
model: 要使用的嵌入模型名称。
|
||
task_type: 嵌入任务类型。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
list[list[float]]: 文本的嵌入向量列表。
|
||
"""
|
||
if isinstance(texts, str):
|
||
texts = [texts]
|
||
if not texts:
|
||
return []
|
||
|
||
try:
|
||
resolved_model_str = (
|
||
model or self.config.default_embedding_model or self.config.model
|
||
)
|
||
if not resolved_model_str:
|
||
raise LLMException(
|
||
"使用 embed 功能时必须指定嵌入模型名称，"
|
||
"或在 AIConfig 中配置 default_embedding_model。",
|
||
code=LLMErrorCode.MODEL_NOT_FOUND,
|
||
)
|
||
resolved_model_str = self._resolve_model_name(resolved_model_str)
|
||
|
||
async with await get_model_instance(
|
||
resolved_model_str,
|
||
override_config=None,
|
||
) as embedding_model_instance:
|
||
return await embedding_model_instance.generate_embeddings(
|
||
texts, task_type=task_type, **kwargs
|
||
)
|
||
except LLMException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"文本嵌入失败: {e}", e=e)
|
||
raise LLMException(
|
||
f"文本嵌入失败: {e}", code=LLMErrorCode.EMBEDDING_FAILED, cause=e
|
||
)
|
||
|
||
|
||
async def chat(
|
||
message: str | LLMMessage | list[LLMContentPart],
|
||
*,
|
||
model: ModelName = None,
|
||
**kwargs: Any,
|
||
) -> str:
|
||
"""
|
||
聊天对话便捷函数
|
||
|
||
参数:
|
||
message: 用户输入的消息。
|
||
model: 要使用的模型名称。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
str: 模型的文本响应。
|
||
"""
|
||
ai = AI()
|
||
return await ai.chat(message, model=model, **kwargs)
|
||
|
||
|
||
async def code(
|
||
prompt: str,
|
||
*,
|
||
model: ModelName = None,
|
||
timeout: int | None = None,
|
||
**kwargs: Any,
|
||
) -> dict[str, Any]:
|
||
"""
|
||
代码执行便捷函数
|
||
|
||
参数:
|
||
prompt: 代码执行的提示词。
|
||
model: 要使用的模型名称。
|
||
timeout: 代码执行超时时间（秒）。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
dict[str, Any]: 包含执行结果的字典。
|
||
"""
|
||
ai = AI()
|
||
return await ai.code(prompt, model=model, timeout=timeout, **kwargs)
|
||
|
||
|
||
async def search(
|
||
query: str | UniMessage,
|
||
*,
|
||
model: ModelName = None,
|
||
instruction: str = "",
|
||
**kwargs: Any,
|
||
) -> dict[str, Any]:
|
||
"""
|
||
信息搜索便捷函数
|
||
|
||
参数:
|
||
query: 搜索查询内容。
|
||
model: 要使用的模型名称。
|
||
instruction: 搜索指令。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
dict[str, Any]: 包含搜索结果的字典。
|
||
"""
|
||
ai = AI()
|
||
return await ai.search(query, model=model, instruction=instruction, **kwargs)
|
||
|
||
|
||
async def analyze(
|
||
message: UniMessage | None,
|
||
*,
|
||
instruction: str = "",
|
||
model: ModelName = None,
|
||
use_tools: list[str] | None = None,
|
||
tool_config: dict[str, Any] | None = None,
|
||
**kwargs: Any,
|
||
) -> str | LLMResponse:
|
||
"""
|
||
内容分析便捷函数
|
||
|
||
参数:
|
||
message: 要分析的消息内容。
|
||
instruction: 分析指令。
|
||
model: 要使用的模型名称。
|
||
use_tools: 要使用的工具名称列表。
|
||
tool_config: 工具配置。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
str | LLMResponse: 分析结果。
|
||
"""
|
||
ai = AI()
|
||
return await ai.analyze(
|
||
message,
|
||
instruction=instruction,
|
||
model=model,
|
||
use_tools=use_tools,
|
||
tool_config=tool_config,
|
||
**kwargs,
|
||
)
|
||
|
||
|
||
async def analyze_multimodal(
|
||
text: str | None = None,
|
||
images: list[str | Path | bytes] | str | Path | bytes | None = None,
|
||
videos: list[str | Path | bytes] | str | Path | bytes | None = None,
|
||
audios: list[str | Path | bytes] | str | Path | bytes | None = None,
|
||
*,
|
||
instruction: str = "",
|
||
model: ModelName = None,
|
||
**kwargs: Any,
|
||
) -> str | LLMResponse:
|
||
"""
|
||
多模态分析便捷函数
|
||
|
||
参数:
|
||
text: 文本内容。
|
||
images: 图片文件路径、字节数据或列表。
|
||
videos: 视频文件路径、字节数据或列表。
|
||
audios: 音频文件路径、字节数据或列表。
|
||
instruction: 分析指令。
|
||
model: 要使用的模型名称。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
str | LLMResponse: 分析结果。
|
||
"""
|
||
message = create_multimodal_message(
|
||
text=text, images=images, videos=videos, audios=audios
|
||
)
|
||
return await analyze(message, instruction=instruction, model=model, **kwargs)
|
||
|
||
|
||
async def search_multimodal(
|
||
text: str | None = None,
|
||
images: list[str | Path | bytes] | str | Path | bytes | None = None,
|
||
videos: list[str | Path | bytes] | str | Path | bytes | None = None,
|
||
audios: list[str | Path | bytes] | str | Path | bytes | None = None,
|
||
*,
|
||
instruction: str = "",
|
||
model: ModelName = None,
|
||
**kwargs: Any,
|
||
) -> dict[str, Any]:
|
||
"""
|
||
多模态搜索便捷函数
|
||
|
||
参数:
|
||
text: 文本内容。
|
||
images: 图片文件路径、字节数据或列表。
|
||
videos: 视频文件路径、字节数据或列表。
|
||
audios: 音频文件路径、字节数据或列表。
|
||
instruction: 搜索指令。
|
||
model: 要使用的模型名称。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
dict[str, Any]: 包含搜索结果的字典。
|
||
"""
|
||
message = create_multimodal_message(
|
||
text=text, images=images, videos=videos, audios=audios
|
||
)
|
||
ai = AI()
|
||
return await ai.search(message, model=model, instruction=instruction, **kwargs)
|
||
|
||
|
||
async def embed(
|
||
texts: list[str] | str,
|
||
*,
|
||
model: ModelName = None,
|
||
task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
|
||
**kwargs: Any,
|
||
) -> list[list[float]]:
|
||
"""
|
||
文本嵌入便捷函数
|
||
|
||
参数:
|
||
texts: 要生成嵌入向量的文本或文本列表。
|
||
model: 要使用的嵌入模型名称。
|
||
task_type: 嵌入任务类型。
|
||
**kwargs: 传递给模型的其他参数。
|
||
|
||
返回:
|
||
list[list[float]]: 文本的嵌入向量列表。
|
||
"""
|
||
ai = AI()
|
||
return await ai.embed(texts, model=model, task_type=task_type, **kwargs)
|
||
|
||
|
||
async def pipeline_chat(
|
||
message: UniMessage | str | list[LLMContentPart],
|
||
model_chain: list[ModelName],
|
||
*,
|
||
initial_instruction: str = "",
|
||
final_instruction: str = "",
|
||
**kwargs: Any,
|
||
) -> LLMResponse:
|
||
"""
|
||
AI模型链式调用，前一个模型的输出作为下一个模型的输入。
|
||
|
||
参数:
|
||
message: 初始输入消息（支持多模态）
|
||
model_chain: 模型名称列表
|
||
initial_instruction: 第一个模型的系统指令
|
||
final_instruction: 最后一个模型的系统指令
|
||
**kwargs: 传递给模型实例的其他参数
|
||
|
||
返回:
|
||
LLMResponse: 最后一个模型的响应结果
|
||
"""
|
||
if not model_chain:
|
||
raise ValueError("模型链`model_chain`不能为空。")
|
||
|
||
current_content: str | list[LLMContentPart]
|
||
if isinstance(message, str):
|
||
current_content = message
|
||
elif isinstance(message, list):
|
||
current_content = message
|
||
else:
|
||
current_content = await unimsg_to_llm_parts(message)
|
||
|
||
final_response: LLMResponse | None = None
|
||
|
||
for i, model_name in enumerate(model_chain):
|
||
if not model_name:
|
||
raise ValueError(f"模型链中第 {i + 1} 个模型名称为空。")
|
||
|
||
is_first_step = i == 0
|
||
is_last_step = i == len(model_chain) - 1
|
||
|
||
messages_for_step: list[LLMMessage] = []
|
||
instruction_for_step = ""
|
||
if is_first_step and initial_instruction:
|
||
instruction_for_step = initial_instruction
|
||
elif is_last_step and final_instruction:
|
||
instruction_for_step = final_instruction
|
||
|
||
if instruction_for_step:
|
||
messages_for_step.append(LLMMessage.system(instruction_for_step))
|
||
|
||
messages_for_step.append(LLMMessage.user(current_content))
|
||
|
||
logger.info(
|
||
f"Pipeline Step [{i + 1}/{len(model_chain)}]: "
|
||
f"使用模型 '{model_name}' 进行处理..."
|
||
)
|
||
try:
|
||
async with await get_model_instance(model_name, **kwargs) as model:
|
||
response = await model.generate_response(messages_for_step)
|
||
final_response = response
|
||
current_content = response.text.strip()
|
||
if not current_content and not is_last_step:
|
||
logger.warning(
|
||
f"模型 '{model_name}' 在中间步骤返回了空内容，流水线可能无法继续。"
|
||
)
|
||
break
|
||
|
||
except Exception as e:
|
||
logger.error(f"在模型链的第 {i + 1} 步 ('{model_name}') 出错: {e}", e=e)
|
||
raise LLMException(
|
||
f"流水线在模型 '{model_name}' 处执行失败: {e}",
|
||
code=LLMErrorCode.GENERATION_FAILED,
|
||
cause=e,
|
||
)
|
||
|
||
if final_response is None:
|
||
raise LLMException(
|
||
"AI流水线未能产生任何响应。", code=LLMErrorCode.GENERATION_FAILED
|
||
)
|
||
|
||
return final_response
|