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478 lines
17 KiB
Python
478 lines
17 KiB
Python
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"""
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LLM 适配器基类和通用数据结构
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"""
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any
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from pydantic import BaseModel
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from zhenxun.services.log import logger
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from ..types.exceptions import LLMErrorCode, LLMException
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from ..types.models import LLMToolCall
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if TYPE_CHECKING:
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from ..config.generation import LLMGenerationConfig
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from ..service import LLMModel
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from ..types.content import LLMMessage
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from ..types.enums import EmbeddingTaskType
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class RequestData(BaseModel):
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"""请求数据封装"""
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url: str
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headers: dict[str, str]
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body: dict[str, Any]
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class ResponseData(BaseModel):
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"""响应数据封装 - 支持所有高级功能"""
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text: str
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usage_info: dict[str, Any] | None = None
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raw_response: dict[str, Any] | None = None
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tool_calls: list[LLMToolCall] | None = None
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code_executions: list[Any] | None = None
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grounding_metadata: Any | None = None
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cache_info: Any | None = None
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code_execution_results: list[dict[str, Any]] | None = None
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search_results: list[dict[str, Any]] | None = None
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function_calls: list[dict[str, Any]] | None = None
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safety_ratings: list[dict[str, Any]] | None = None
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citations: list[dict[str, Any]] | None = None
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class BaseAdapter(ABC):
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"""LLM API适配器基类"""
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@property
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@abstractmethod
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def api_type(self) -> str:
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"""API类型标识"""
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pass
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@property
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@abstractmethod
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def supported_api_types(self) -> list[str]:
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"""支持的API类型列表"""
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pass
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def prepare_simple_request(
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self,
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model: "LLMModel",
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api_key: str,
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prompt: str,
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history: list[dict[str, str]] | None = None,
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) -> RequestData:
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"""准备简单文本生成请求
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默认实现：将简单请求转换为高级请求格式
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子类可以重写此方法以提供特定的优化实现
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"""
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from ..types.content import LLMMessage
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messages: list[LLMMessage] = []
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if history:
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for msg in history:
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role = msg.get("role", "user")
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content = msg.get("content", "")
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messages.append(LLMMessage(role=role, content=content))
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messages.append(LLMMessage(role="user", content=prompt))
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config = model._generation_config
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return self.prepare_advanced_request(
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model=model,
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api_key=api_key,
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messages=messages,
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config=config,
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tools=None,
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tool_choice=None,
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)
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@abstractmethod
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def prepare_advanced_request(
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self,
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model: "LLMModel",
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api_key: str,
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messages: list["LLMMessage"],
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config: "LLMGenerationConfig | None" = None,
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tools: list[dict[str, Any]] | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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) -> RequestData:
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"""准备高级请求"""
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pass
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@abstractmethod
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def parse_response(
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self,
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model: "LLMModel",
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response_json: dict[str, Any],
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is_advanced: bool = False,
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) -> ResponseData:
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"""解析API响应"""
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pass
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@abstractmethod
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def prepare_embedding_request(
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self,
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model: "LLMModel",
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api_key: str,
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texts: list[str],
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task_type: "EmbeddingTaskType | str",
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**kwargs: Any,
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) -> RequestData:
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"""准备文本嵌入请求"""
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pass
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@abstractmethod
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def parse_embedding_response(self, response_json: dict[str, Any]) -> list[list[float]]:
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"""解析文本嵌入响应"""
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pass
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def validate_embedding_response(self, response_json: dict[str, Any]) -> None:
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"""验证嵌入API响应"""
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if "error" in response_json:
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error_info = response_json["error"]
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msg = (
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error_info.get("message", str(error_info))
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if isinstance(error_info, dict)
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else str(error_info)
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)
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raise LLMException(
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f"嵌入API错误: {msg}", code=LLMErrorCode.EMBEDDING_FAILED, details=response_json
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)
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def get_api_url(self, model: "LLMModel", endpoint: str) -> str:
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"""构建API URL"""
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if not model.api_base:
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raise LLMException(
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f"模型 {model.model_name} 的 api_base 未设置",
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code=LLMErrorCode.CONFIGURATION_ERROR,
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)
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return f"{model.api_base.rstrip('/')}{endpoint}"
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def get_base_headers(self, api_key: str) -> dict[str, str]:
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"""获取基础请求头"""
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from zhenxun.utils.user_agent import get_user_agent
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headers = get_user_agent()
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headers.update(
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{
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}",
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}
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)
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return headers
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def convert_messages_to_openai_format(self, messages: list["LLMMessage"]) -> list[dict[str, Any]]:
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"""将LLMMessage转换为OpenAI格式 - 通用方法"""
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openai_messages: list[dict[str, Any]] = []
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for msg in messages:
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openai_msg: dict[str, Any] = {"role": msg.role}
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if msg.role == "tool":
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openai_msg["tool_call_id"] = msg.tool_call_id
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openai_msg["name"] = msg.name
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openai_msg["content"] = msg.content
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else:
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if isinstance(msg.content, str):
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openai_msg["content"] = msg.content
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else:
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content_parts = []
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for part in msg.content:
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if part.type == "text":
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content_parts.append({"type": "text", "text": part.text})
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elif part.type == "image":
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content_parts.append(
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{"type": "image_url", "image_url": {"url": part.image_source}}
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)
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openai_msg["content"] = content_parts
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if msg.role == "assistant" and msg.tool_calls:
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assistant_tool_calls = []
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for call in msg.tool_calls:
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assistant_tool_calls.append(
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{
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"id": call.id,
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"type": "function",
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"function": {
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"name": call.function.name,
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"arguments": call.function.arguments,
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},
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}
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)
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openai_msg["tool_calls"] = assistant_tool_calls
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if msg.name and msg.role != "tool":
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openai_msg["name"] = msg.name
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openai_messages.append(openai_msg)
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return openai_messages
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def parse_openai_response(self, response_json: dict[str, Any]) -> ResponseData:
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"""解析OpenAI格式的响应 - 通用方法"""
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self.validate_response(response_json)
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try:
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choices = response_json.get("choices", [])
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if not choices:
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logger.debug("OpenAI响应中没有choices，可能为空回复或流结束。")
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return ResponseData(text="", raw_response=response_json)
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choice = choices[0]
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message = choice.get("message", {})
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content = message.get("content", "")
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parsed_tool_calls: list[LLMToolCall] | None = None
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if message_tool_calls := message.get("tool_calls"):
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from ..types.models import LLMToolFunction
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parsed_tool_calls = []
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for tc_data in message_tool_calls:
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try:
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if tc_data.get("type") == "function":
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parsed_tool_calls.append(
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LLMToolCall(
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id=tc_data["id"],
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function=LLMToolFunction(
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name=tc_data["function"]["name"],
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arguments=tc_data["function"]["arguments"],
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),
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)
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)
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except KeyError as e:
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logger.warning(f"解析OpenAI工具调用数据时缺少键: {tc_data}, 错误: {e}")
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except Exception as e:
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logger.warning(f"解析OpenAI工具调用数据时出错: {tc_data}, 错误: {e}")
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if not parsed_tool_calls:
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parsed_tool_calls = None
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final_text = content if content is not None else ""
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if not final_text and parsed_tool_calls:
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final_text = f"请求调用 {len(parsed_tool_calls)} 个工具。"
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usage_info = response_json.get("usage")
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return ResponseData(
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text=final_text,
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tool_calls=parsed_tool_calls,
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usage_info=usage_info,
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raw_response=response_json,
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)
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except Exception as e:
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logger.error(f"解析OpenAI格式响应失败: {e}", e=e)
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raise LLMException(f"解析API响应失败: {e}", code=LLMErrorCode.RESPONSE_PARSE_ERROR, cause=e)
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def validate_response(self, response_json: dict[str, Any]) -> None:
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"""验证API响应，解析不同API的错误结构"""
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if "error" in response_json:
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error_info = response_json["error"]
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if isinstance(error_info, dict):
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error_message = error_info.get("message", "未知错误")
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error_code = error_info.get("code", "unknown")
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error_type = error_info.get("type", "api_error")
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error_code_mapping = {
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"invalid_api_key": LLMErrorCode.API_KEY_INVALID,
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"authentication_failed": LLMErrorCode.API_KEY_INVALID,
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"rate_limit_exceeded": LLMErrorCode.API_RATE_LIMITED,
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"quota_exceeded": LLMErrorCode.API_RATE_LIMITED,
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"model_not_found": LLMErrorCode.MODEL_NOT_FOUND,
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"invalid_model": LLMErrorCode.MODEL_NOT_FOUND,
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"context_length_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
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"max_tokens_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
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}
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llm_error_code = error_code_mapping.get(error_code, LLMErrorCode.API_RESPONSE_INVALID)
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logger.error(f"API返回错误: {error_message} (代码: {error_code}, 类型: {error_type})")
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else:
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error_message = str(error_info)
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error_code = "unknown"
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llm_error_code = LLMErrorCode.API_RESPONSE_INVALID
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logger.error(f"API返回错误: {error_message}")
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raise LLMException(
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f"API请求失败: {error_message}",
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code=llm_error_code,
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details={"api_error": error_info, "error_code": error_code},
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)
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if "candidates" in response_json:
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candidates = response_json.get("candidates", [])
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if candidates:
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candidate = candidates[0]
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finish_reason = candidate.get("finishReason")
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if finish_reason in ["SAFETY", "RECITATION"]:
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safety_ratings = candidate.get("safetyRatings", [])
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logger.warning(f"Gemini内容被安全过滤: {finish_reason}, 安全评级: {safety_ratings}")
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raise LLMException(
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f"内容被安全过滤: {finish_reason}",
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code=LLMErrorCode.CONTENT_FILTERED,
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details={
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"finish_reason": finish_reason,
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"safety_ratings": safety_ratings,
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},
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)
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if not response_json:
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logger.error("API返回空响应")
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raise LLMException(
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"API返回空响应",
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code=LLMErrorCode.API_RESPONSE_INVALID,
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details={"response": response_json},
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)
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def _apply_generation_config(
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self,
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model: "LLMModel",
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config: "LLMGenerationConfig | None" = None,
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) -> dict[str, Any]:
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"""通用的配置应用逻辑"""
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if config is not None:
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return config.to_api_params(model.api_type, model.model_name)
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if model._generation_config is not None:
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return model._generation_config.to_api_params(model.api_type, model.model_name)
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base_config = {}
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|
|
if model.temperature is not None:
|
|||
|
|
base_config["temperature"] = model.temperature
|
|||
|
|
if model.max_tokens is not None:
|
|||
|
|
if model.api_type in ["gemini", "gemini_native"]:
|
|||
|
|
base_config["maxOutputTokens"] = model.max_tokens
|
|||
|
|
else:
|
|||
|
|
base_config["max_tokens"] = model.max_tokens
|
|||
|
|
|
|||
|
|
return base_config
|
|||
|
|
|
|||
|
|
def apply_config_override(
|
|||
|
|
self,
|
|||
|
|
model: "LLMModel",
|
|||
|
|
body: dict[str, Any],
|
|||
|
|
config: "LLMGenerationConfig | None" = None,
|
|||
|
|
) -> dict[str, Any]:
|
|||
|
|
"""应用配置覆盖"""
|
|||
|
|
config_params = self._apply_generation_config(model, config)
|
|||
|
|
body.update(config_params)
|
|||
|
|
return body
|
|||
|
|
|
|||
|
|
|
|||
|
|
class OpenAICompatAdapter(BaseAdapter):
|
|||
|
|
"""
|
|||
|
|
处理所有 OpenAI 兼容 API 的通用适配器。
|
|||
|
|
消除 OpenAIAdapter 和 ZhipuAdapter 之间的代码重复。
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
@abstractmethod
|
|||
|
|
def get_chat_endpoint(self) -> str:
|
|||
|
|
"""子类必须实现，返回 chat completions 的端点"""
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
@abstractmethod
|
|||
|
|
def get_embedding_endpoint(self) -> str:
|
|||
|
|
"""子类必须实现，返回 embeddings 的端点"""
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
def prepare_advanced_request(
|
|||
|
|
self,
|
|||
|
|
model: "LLMModel",
|
|||
|
|
api_key: str,
|
|||
|
|
messages: list["LLMMessage"],
|
|||
|
|
config: "LLMGenerationConfig | None" = None,
|
|||
|
|
tools: list[dict[str, Any]] | None = None,
|
|||
|
|
tool_choice: str | dict[str, Any] | None = None,
|
|||
|
|
) -> RequestData:
|
|||
|
|
"""准备高级请求 - OpenAI兼容格式"""
|
|||
|
|
url = self.get_api_url(model, self.get_chat_endpoint())
|
|||
|
|
headers = self.get_base_headers(api_key)
|
|||
|
|
openai_messages = self.convert_messages_to_openai_format(messages)
|
|||
|
|
|
|||
|
|
body = {
|
|||
|
|
"model": model.model_name,
|
|||
|
|
"messages": openai_messages,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if tools:
|
|||
|
|
body["tools"] = tools
|
|||
|
|
if tool_choice:
|
|||
|
|
body["tool_choice"] = tool_choice
|
|||
|
|
|
|||
|
|
body = self.apply_config_override(model, body, config)
|
|||
|
|
return RequestData(url=url, headers=headers, body=body)
|
|||
|
|
|
|||
|
|
def parse_response(
|
|||
|
|
self,
|
|||
|
|
model: "LLMModel",
|
|||
|
|
response_json: dict[str, Any],
|
|||
|
|
is_advanced: bool = False,
|
|||
|
|
) -> ResponseData:
|
|||
|
|
"""解析响应 - 直接使用基类的 OpenAI 格式解析"""
|
|||
|
|
return self.parse_openai_response(response_json)
|
|||
|
|
|
|||
|
|
def prepare_embedding_request(
|
|||
|
|
self,
|
|||
|
|
model: "LLMModel",
|
|||
|
|
api_key: str,
|
|||
|
|
texts: list[str],
|
|||
|
|
task_type: "EmbeddingTaskType | str",
|
|||
|
|
**kwargs: Any,
|
|||
|
|
) -> RequestData:
|
|||
|
|
"""准备嵌入请求 - OpenAI兼容格式"""
|
|||
|
|
url = self.get_api_url(model, self.get_embedding_endpoint())
|
|||
|
|
headers = self.get_base_headers(api_key)
|
|||
|
|
|
|||
|
|
body = {
|
|||
|
|
"model": model.model_name,
|
|||
|
|
"input": texts,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 应用额外的配置参数
|
|||
|
|
if kwargs:
|
|||
|
|
body.update(kwargs)
|
|||
|
|
|
|||
|
|
return RequestData(url=url, headers=headers, body=body)
|
|||
|
|
|
|||
|
|
def parse_embedding_response(self, response_json: dict[str, Any]) -> list[list[float]]:
|
|||
|
|
"""解析嵌入响应 - OpenAI兼容格式"""
|
|||
|
|
self.validate_embedding_response(response_json)
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
data = response_json.get("data", [])
|
|||
|
|
if not data:
|
|||
|
|
raise LLMException(
|
|||
|
|
"嵌入响应中没有数据",
|
|||
|
|
code=LLMErrorCode.EMBEDDING_FAILED,
|
|||
|
|
details=response_json,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
embeddings = []
|
|||
|
|
for item in data:
|
|||
|
|
if "embedding" in item:
|
|||
|
|
embeddings.append(item["embedding"])
|
|||
|
|
else:
|
|||
|
|
raise LLMException(
|
|||
|
|
"嵌入响应格式错误：缺少embedding字段",
|
|||
|
|
code=LLMErrorCode.EMBEDDING_FAILED,
|
|||
|
|
details=item,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return embeddings
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"解析嵌入响应失败: {e}", e=e)
|
|||
|
|
raise LLMException(
|
|||
|
|
f"解析嵌入响应失败: {e}",
|
|||
|
|
code=LLMErrorCode.EMBEDDING_FAILED,
|
|||
|
|
cause=e,
|
|||
|
|
)
|