feat: Enhance OpenAI compatibility and introduce unified LLM service data models
- Refactored model fetching logic to support various authentication methods for OpenAI-compatible APIs. - Introduced new data models for LLM service requests and responses to standardize interactions across layers. - Added an adapter base class for unified request execution across different providers. - Implemented utility functions for building OpenAI-compatible client configurations and request overrides.
This commit is contained in:
DrSmoothl
@@ -1,191 +1,492 @@
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"""LLM 服务模块
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"""LLM 服务层。
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提供与 LLM 模型交互的核心功能。
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该模块负责在宿主侧收口统一的 LLM 服务请求模型，并将其转发到
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`src.llm_models` 中的底层请求调度器。
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"""
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from typing import Any, Callable, Dict, List, Optional, Tuple
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from typing import Any, Dict, List, Tuple
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import json
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from src.common.data_models.llm_service_data_models import (
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LLMAudioTranscriptionResult,
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LLMEmbeddingResult,
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LLMGenerationOptions,
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LLMImageOptions,
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LLMResponseResult,
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LLMServiceRequest,
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LLMServiceResult,
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MessageFactory,
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PromptInput,
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PromptMessage,
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)
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from src.common.logger import get_logger
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from src.config.config import config_manager
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from src.config.model_configs import TaskConfig
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from src.llm_models.model_client.base_client import BaseClient
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from src.llm_models.payload_content.message import Message
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from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
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from src.llm_models.payload_content.tool_option import ToolCall
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from src.llm_models.utils_model import LLMRequest
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from src.llm_models.utils_model import LLMOrchestrator
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logger = get_logger("llm_service")
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class LLMServiceClient:
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"""面向上层模块的 LLM 服务对象式门面。
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async def _generate_response(
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model_config: TaskConfig,
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request_type: str,
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prompt: Optional[str] = None,
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message_factory: Optional[Callable[[BaseClient], List[Message]]] = None,
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tool_options: Optional[List[Dict[str, Any]]] = None,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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) -> Tuple[str, str, str, List[ToolCall] | None]:
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llm_request = LLMRequest(model_set=model_config, request_type=request_type)
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当前推荐优先使用以下正式接口：
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- `generate_response`
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- `generate_response_with_messages`
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- `generate_response_for_image`
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- `transcribe_audio`
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- `embed_text`
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"""
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if message_factory is not None:
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response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_with_message_async(
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message_factory=message_factory,
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tools=tool_options,
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temperature=temperature,
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max_tokens=max_tokens,
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def __init__(self, task_name: str, request_type: str = "") -> None:
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"""初始化 LLM 服务门面。
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Args:
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task_name: 任务配置名称，对应 `model_task_config` 下的字段名。
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request_type: 当前请求的业务类型标识。
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"""
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self.task_name = resolve_task_name(task_name)
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self.request_type = request_type
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self._orchestrator = LLMOrchestrator(task_name=self.task_name, request_type=request_type)
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@staticmethod
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def _normalize_generation_options(options: LLMGenerationOptions | None = None) -> LLMGenerationOptions:
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"""规范化文本生成选项。
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Args:
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options: 原始生成选项。
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Returns:
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LLMGenerationOptions: 可直接用于执行请求的完整选项对象。
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"""
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if options is None:
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return LLMGenerationOptions()
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return options
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@staticmethod
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def _normalize_image_options(options: LLMImageOptions | None = None) -> LLMImageOptions:
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"""规范化图像理解选项。
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Args:
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options: 原始图像理解选项。
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Returns:
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LLMImageOptions: 可直接用于执行请求的完整选项对象。
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"""
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if options is None:
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return LLMImageOptions()
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return options
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async def generate_response(
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self,
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prompt: str,
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options: LLMGenerationOptions | None = None,
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) -> LLMResponseResult:
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"""生成单轮文本响应。
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Args:
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prompt: 文本提示词。
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options: 文本生成选项。
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Returns:
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LLMResponseResult: 统一文本生成结果。
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"""
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active_options = self._normalize_generation_options(options)
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return await self._orchestrator.generate_response_async(
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prompt=prompt,
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temperature=active_options.temperature,
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max_tokens=active_options.max_tokens,
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tools=active_options.tool_options,
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response_format=active_options.response_format,
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raise_when_empty=active_options.raise_when_empty,
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interrupt_flag=active_options.interrupt_flag,
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)
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return response, reasoning_content, model_name, tool_call
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if prompt is None:
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raise ValueError("prompt 与 message_factory 不能同时为空")
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async def generate_response_with_messages(
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self,
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message_factory: MessageFactory,
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options: LLMGenerationOptions | None = None,
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) -> LLMResponseResult:
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"""基于消息工厂生成响应。
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response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_async(
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prompt,
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tools=tool_options,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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return response, reasoning_content, model_name, tool_call
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Args:
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message_factory: 消息工厂，会根据客户端能力构建消息列表。
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options: 文本生成选项。
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Returns:
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LLMResponseResult: 统一文本生成结果。
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"""
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active_options = self._normalize_generation_options(options)
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return await self._orchestrator.generate_response_with_message_async(
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message_factory=message_factory,
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temperature=active_options.temperature,
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max_tokens=active_options.max_tokens,
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tools=active_options.tool_options,
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response_format=active_options.response_format,
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raise_when_empty=active_options.raise_when_empty,
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interrupt_flag=active_options.interrupt_flag,
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)
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async def generate_response_for_image(
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self,
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prompt: str,
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image_base64: str,
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image_format: str,
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options: LLMImageOptions | None = None,
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) -> LLMResponseResult:
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"""为图像内容生成响应。
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Args:
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prompt: 文本提示词。
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image_base64: 图像的 Base64 编码字符串。
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image_format: 图像格式，例如 ``png``、``jpeg``。
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options: 图像理解选项。
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Returns:
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LLMResponseResult: 统一文本生成结果。
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"""
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active_options = self._normalize_image_options(options)
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return await self._orchestrator.generate_response_for_image(
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prompt=prompt,
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image_base64=image_base64,
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image_format=image_format,
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temperature=active_options.temperature,
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max_tokens=active_options.max_tokens,
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interrupt_flag=active_options.interrupt_flag,
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)
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async def transcribe_audio(self, voice_base64: str) -> LLMAudioTranscriptionResult:
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"""执行音频转写请求。
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Args:
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voice_base64: 音频的 Base64 编码字符串。
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Returns:
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LLMAudioTranscriptionResult: 音频转写结果对象。
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"""
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return await self._orchestrator.generate_response_for_voice(voice_base64)
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async def embed_text(self, embedding_input: str) -> LLMEmbeddingResult:
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"""生成文本嵌入向量。
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Args:
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embedding_input: 待编码的文本。
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Returns:
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LLMEmbeddingResult: 向量生成结果对象。
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"""
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return await self._orchestrator.get_embedding(embedding_input)
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def get_available_models() -> Dict[str, TaskConfig]:
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"""获取所有可用的模型配置
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"""获取所有可用模型配置。
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Returns:
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Dict[str, Any]: 模型配置字典，key为模型名称，value为模型配置
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Dict[str, TaskConfig]: 以模型任务名为键的配置映射。
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"""
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try:
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models = config_manager.get_model_config().model_task_config
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attrs = dir(models)
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rets: Dict[str, TaskConfig] = {}
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for attr in attrs:
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if not attr.startswith("__"):
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try:
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value = getattr(models, attr)
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if not callable(value) and isinstance(value, TaskConfig):
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rets[attr] = value
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except Exception as e:
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logger.debug(f"[LLMService] 获取属性 {attr} 失败: {e}")
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continue
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return rets
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except Exception as e:
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logger.error(f"[LLMService] 获取可用模型失败: {e}")
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available_models: Dict[str, TaskConfig] = {}
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for attr_name in dir(models):
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if attr_name.startswith("__"):
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continue
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try:
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attr_value = getattr(models, attr_name)
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except Exception as exc:
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logger.debug(f"[LLMService] 获取属性 {attr_name} 失败: {exc}")
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continue
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if not callable(attr_value) and isinstance(attr_value, TaskConfig):
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available_models[attr_name] = attr_value
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return available_models
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except Exception as exc:
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logger.error(f"[LLMService] 获取可用模型失败: {exc}")
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return {}
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async def generate_with_model(
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prompt: str,
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model_config: TaskConfig,
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request_type: str = "plugin.generate",
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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) -> Tuple[bool, str, str, str]:
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"""使用指定模型生成内容
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def resolve_task_name(task_name: str = "") -> str:
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"""根据名称解析任务配置名。
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Args:
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prompt: 提示词
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model_config: 模型配置（从 get_available_models 获取的模型配置）
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request_type: 请求类型标识
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task_name: 目标任务配置名；为空时返回首个可用任务名。
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Returns:
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Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称)
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str: 解析得到的任务配置名。
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Raises:
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RuntimeError: 当前没有任何可用模型配置。
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ValueError: 指定名称不存在时抛出。
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"""
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try:
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logger.debug(f"[LLMService] 完整提示词: {prompt}")
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response, reasoning_content, model_name, _ = await _generate_response(
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model_config=model_config,
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request_type=request_type,
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prompt=prompt,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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return True, response, reasoning_content, model_name
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except Exception as e:
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error_msg = f"生成内容时出错: {str(e)}"
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logger.error(f"[LLMService] {error_msg}")
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return False, error_msg, "", ""
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models = get_available_models()
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if not models:
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raise RuntimeError("没有可用的模型配置")
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normalized_task_name = task_name.strip()
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if not normalized_task_name:
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return next(iter(models.keys()))
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if normalized_task_name not in models:
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raise ValueError(f"未找到名为 `{normalized_task_name}` 的模型配置")
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return normalized_task_name
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async def generate_with_model_with_tools(
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prompt: str,
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model_config: TaskConfig,
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tool_options: List[Dict[str, Any]] | None = None,
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request_type: str = "plugin.generate",
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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) -> Tuple[bool, str, str, str, List[ToolCall] | None]:
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"""使用指定模型和工具生成内容
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def _normalize_role(role_name: str) -> RoleType:
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"""将原始角色字符串转换为内部角色枚举。
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Args:
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prompt: 提示词
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model_config: 模型配置（从 get_available_models 获取的模型配置）
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tool_options: 工具选项列表
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request_type: 请求类型标识
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temperature: 温度参数
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max_tokens: 最大token数
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role_name: 原始角色名称。
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Returns:
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Tuple[bool, str, str, str, List[ToolCall] | None]: (是否成功, 生成的内容, 推理过程, 模型名称, 工具调用列表)
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RoleType: 规范化后的角色枚举。
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Raises:
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ValueError: 角色类型不受支持时抛出。
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"""
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normalized_role_name = role_name.strip().lower()
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try:
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model_name_list = model_config.model_list
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logger.info(f"使用模型{model_name_list}生成内容")
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logger.debug(f"完整提示词: {prompt}")
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response, reasoning_content, model_name, tool_call = await _generate_response(
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model_config=model_config,
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request_type=request_type,
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prompt=prompt,
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tool_options=tool_options,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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return True, response, reasoning_content, model_name, tool_call
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except Exception as e:
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error_msg = f"生成内容时出错: {str(e)}"
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logger.error(f"[LLMService] {error_msg}")
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return False, error_msg, "", "", None
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return RoleType(normalized_role_name)
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except ValueError as exc:
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raise ValueError(f"不支持的消息角色: {role_name}") from exc
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async def generate_with_model_with_tools_by_message_factory(
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message_factory: Callable[[BaseClient], List[Message]],
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model_config: TaskConfig,
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tool_options: List[Dict[str, Any]] | None = None,
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request_type: str = "plugin.generate",
|
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
|
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) -> Tuple[bool, str, str, str, List[ToolCall] | None]:
|
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"""使用指定模型和工具生成内容（通过消息工厂构建消息列表）
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def _parse_data_url_image(image_url: str) -> Tuple[str, str]:
|
||||
"""解析 Data URL 形式的图片内容。
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||||
|
||||
Args:
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message_factory: 消息工厂函数
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||||
model_config: 模型配置
|
||||
tool_options: 工具选项列表
|
||||
request_type: 请求类型标识
|
||||
temperature: 温度参数
|
||||
max_tokens: 最大token数
|
||||
image_url: 图片 URL。
|
||||
|
||||
Returns:
|
||||
Tuple[bool, str, str, str, List[ToolCall] | None]: (是否成功, 生成的内容, 推理过程, 模型名称, 工具调用列表)
|
||||
Tuple[str, str]: `(图片格式, Base64 数据)`。
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||||
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||||
Raises:
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||||
ValueError: 输入不是受支持的 Data URL 时抛出。
|
||||
"""
|
||||
try:
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||||
model_name_list = model_config.model_list
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||||
logger.info(f"使用模型 {model_name_list} 生成内容")
|
||||
if not image_url.startswith("data:image/") or ";base64," not in image_url:
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raise ValueError("仅支持 Data URL 形式的图片输入")
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prefix, image_base64 = image_url.split(";base64,", maxsplit=1)
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||||
image_format = prefix.removeprefix("data:image/")
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||||
if not image_format or not image_base64:
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||||
raise ValueError("图片 Data URL 不完整")
|
||||
return image_format, image_base64
|
||||
|
||||
response, reasoning_content, model_name, tool_call = await _generate_response(
|
||||
model_config=model_config,
|
||||
request_type=request_type,
|
||||
message_factory=message_factory,
|
||||
tool_options=tool_options,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
|
||||
def _append_content_parts(message_builder: MessageBuilder, content: Any) -> None:
|
||||
"""将原始消息内容追加到内部消息构建器。
|
||||
|
||||
Args:
|
||||
message_builder: 目标消息构建器。
|
||||
content: 原始消息内容。
|
||||
|
||||
Raises:
|
||||
ValueError: 消息内容结构不受支持时抛出。
|
||||
"""
|
||||
if isinstance(content, str):
|
||||
message_builder.add_text_content(content)
|
||||
return
|
||||
|
||||
content_items: List[Any]
|
||||
if isinstance(content, list):
|
||||
content_items = content
|
||||
elif isinstance(content, dict):
|
||||
content_items = [content]
|
||||
else:
|
||||
raise ValueError("消息内容必须为字符串、字典或列表")
|
||||
|
||||
for content_item in content_items:
|
||||
if isinstance(content_item, str):
|
||||
message_builder.add_text_content(content_item)
|
||||
continue
|
||||
if not isinstance(content_item, dict):
|
||||
raise ValueError("消息内容列表中仅支持字符串或字典片段")
|
||||
|
||||
part_type = str(content_item.get("type", "text")).strip().lower()
|
||||
if part_type == "text":
|
||||
text_content = content_item.get("text")
|
||||
if not isinstance(text_content, str):
|
||||
raise ValueError("文本片段缺少 `text` 字段")
|
||||
message_builder.add_text_content(text_content)
|
||||
continue
|
||||
|
||||
if part_type in {"image", "image_url", "input_image"}:
|
||||
image_url = content_item.get("image_url")
|
||||
if isinstance(image_url, dict):
|
||||
image_url = image_url.get("url")
|
||||
if isinstance(image_url, str):
|
||||
image_format, image_base64 = _parse_data_url_image(image_url)
|
||||
message_builder.add_image_content(image_format=image_format, image_base64=image_base64)
|
||||
continue
|
||||
|
||||
image_format = content_item.get("image_format")
|
||||
image_base64 = content_item.get("image_base64")
|
||||
if isinstance(image_format, str) and isinstance(image_base64, str):
|
||||
message_builder.add_image_content(image_format=image_format, image_base64=image_base64)
|
||||
continue
|
||||
raise ValueError("图片片段缺少可识别的图片数据")
|
||||
|
||||
raise ValueError(f"不支持的消息片段类型: {part_type}")
|
||||
|
||||
|
||||
def _normalize_tool_arguments(arguments: Any) -> Dict[str, Any] | None:
|
||||
"""将原始工具参数规范化为字典。
|
||||
|
||||
Args:
|
||||
arguments: 原始工具参数。
|
||||
|
||||
Returns:
|
||||
Dict[str, Any] | None: 规范化后的参数字典。
|
||||
"""
|
||||
if arguments is None:
|
||||
return None
|
||||
if isinstance(arguments, dict):
|
||||
return arguments
|
||||
if isinstance(arguments, str):
|
||||
stripped_arguments = arguments.strip()
|
||||
if not stripped_arguments:
|
||||
return {}
|
||||
try:
|
||||
parsed_arguments = json.loads(stripped_arguments)
|
||||
except json.JSONDecodeError:
|
||||
return {"raw_arguments": arguments}
|
||||
if isinstance(parsed_arguments, dict):
|
||||
return parsed_arguments
|
||||
return {"value": parsed_arguments}
|
||||
return {"value": arguments}
|
||||
|
||||
|
||||
def _build_tool_calls(raw_tool_calls: Any) -> List[ToolCall] | None:
|
||||
"""从原始消息中提取工具调用列表。
|
||||
|
||||
Args:
|
||||
raw_tool_calls: 原始工具调用结构。
|
||||
|
||||
Returns:
|
||||
List[ToolCall] | None: 规范化后的工具调用列表。
|
||||
|
||||
Raises:
|
||||
ValueError: 工具调用结构缺失必要字段时抛出。
|
||||
"""
|
||||
if raw_tool_calls is None:
|
||||
return None
|
||||
if not isinstance(raw_tool_calls, list):
|
||||
raise ValueError("`tool_calls` 必须为列表")
|
||||
|
||||
tool_calls: List[ToolCall] = []
|
||||
for raw_tool_call in raw_tool_calls:
|
||||
if not isinstance(raw_tool_call, dict):
|
||||
raise ValueError("工具调用项必须为字典")
|
||||
|
||||
function_info = raw_tool_call.get("function")
|
||||
if isinstance(function_info, dict):
|
||||
func_name = function_info.get("name")
|
||||
arguments = function_info.get("arguments")
|
||||
else:
|
||||
func_name = raw_tool_call.get("name") or raw_tool_call.get("func_name")
|
||||
arguments = raw_tool_call.get("arguments") or raw_tool_call.get("args")
|
||||
|
||||
call_id = raw_tool_call.get("id") or raw_tool_call.get("call_id")
|
||||
if not isinstance(call_id, str) or not isinstance(func_name, str):
|
||||
raise ValueError("工具调用缺少 `id` 或函数名称")
|
||||
|
||||
tool_calls.append(
|
||||
ToolCall(
|
||||
call_id=call_id,
|
||||
func_name=func_name,
|
||||
args=_normalize_tool_arguments(arguments),
|
||||
)
|
||||
)
|
||||
return True, response, reasoning_content, model_name, tool_call
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"生成内容时出错: {str(e)}"
|
||||
logger.error(f"[LLMService] {error_msg}")
|
||||
return False, error_msg, "", "", None
|
||||
return tool_calls or None
|
||||
|
||||
|
||||
def _build_message_from_dict(raw_message: PromptMessage) -> Message:
|
||||
"""将原始消息字典转换为内部消息对象。
|
||||
|
||||
Args:
|
||||
raw_message: 原始消息字典。
|
||||
|
||||
Returns:
|
||||
Message: 规范化后的消息对象。
|
||||
|
||||
Raises:
|
||||
ValueError: 原始消息结构不合法时抛出。
|
||||
"""
|
||||
raw_role = raw_message.get("role")
|
||||
if not isinstance(raw_role, str):
|
||||
raise ValueError("消息缺少字符串类型的 `role` 字段")
|
||||
|
||||
role = _normalize_role(raw_role)
|
||||
message_builder = MessageBuilder().set_role(role)
|
||||
|
||||
tool_calls = _build_tool_calls(raw_message.get("tool_calls"))
|
||||
if tool_calls is not None:
|
||||
message_builder.set_tool_calls(tool_calls)
|
||||
|
||||
tool_call_id = raw_message.get("tool_call_id")
|
||||
if isinstance(tool_call_id, str) and role == RoleType.Tool:
|
||||
message_builder.set_tool_call_id(tool_call_id)
|
||||
|
||||
if "content" in raw_message and raw_message["content"] not in (None, "", []):
|
||||
_append_content_parts(message_builder, raw_message["content"])
|
||||
|
||||
return message_builder.build()
|
||||
|
||||
|
||||
def _build_prompt_message_factory(prompt: PromptInput) -> MessageFactory:
|
||||
"""将统一提示输入转换为消息工厂。
|
||||
|
||||
Args:
|
||||
prompt: 原始提示输入。
|
||||
|
||||
Returns:
|
||||
MessageFactory: 惰性构建消息列表的工厂函数。
|
||||
"""
|
||||
if isinstance(prompt, str):
|
||||
def build_messages(_: BaseClient) -> List[Message]:
|
||||
"""构建单条用户消息。"""
|
||||
message_builder = MessageBuilder()
|
||||
message_builder.add_text_content(prompt)
|
||||
return [message_builder.build()]
|
||||
|
||||
return build_messages
|
||||
|
||||
def build_messages(_: BaseClient) -> List[Message]:
|
||||
"""构建多消息对话输入。"""
|
||||
return [_build_message_from_dict(raw_message) for raw_message in prompt]
|
||||
|
||||
return build_messages
|
||||
|
||||
|
||||
async def generate(request: LLMServiceRequest) -> LLMServiceResult:
|
||||
"""执行统一的 LLM 服务请求。
|
||||
|
||||
Args:
|
||||
request: 服务层统一请求对象。
|
||||
|
||||
Returns:
|
||||
LLMServiceResult: 统一响应对象；失败时 `success=False`。
|
||||
"""
|
||||
llm_client = LLMServiceClient(task_name=request.task_name, request_type=request.request_type)
|
||||
if request.message_factory is not None:
|
||||
active_message_factory = request.message_factory
|
||||
else:
|
||||
prompt = request.prompt
|
||||
if prompt is None:
|
||||
raise ValueError("`prompt` 与 `message_factory` 必须且只能提供一个")
|
||||
active_message_factory = _build_prompt_message_factory(prompt)
|
||||
|
||||
try:
|
||||
generation_result = await llm_client.generate_response_with_messages(
|
||||
message_factory=active_message_factory,
|
||||
options=LLMGenerationOptions(
|
||||
temperature=request.temperature,
|
||||
max_tokens=request.max_tokens,
|
||||
tool_options=request.tool_options,
|
||||
response_format=request.response_format,
|
||||
interrupt_flag=request.interrupt_flag,
|
||||
),
|
||||
)
|
||||
return LLMServiceResult.from_response_result(generation_result)
|
||||
except Exception as exc:
|
||||
error_message = f"生成内容时出错: {exc}"
|
||||
logger.error(f"[LLMService] {error_message}")
|
||||
return LLMServiceResult.from_error(error_message, str(exc))
|
||||
|
||||
Reference in New Issue
Block a user