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851a7a549e
- 【新功能】统一图片生成与编辑API `create_image`，支持文生图、图生图及多图输入 - 【新功能】引入LLM响应验证机制，通过 `validation_policy` 和 `response_validator` 确保响应内容符合预期，例如强制返回图片 - 【新功能】适配OpenRouter API，扩展LLM服务提供商支持，并添加OpenRouter特定请求头 - 【重构】将日志净化逻辑重构至 `log_sanitizer` 模块，提供统一的净化入口，并应用于NoneBot消息、LLM请求/响应日志 - 【修复】优化Gemini适配器，正确解析图片生成响应中的Base64图片数据，并更新模型能力注册表
285 lines
11 KiB
Python
285 lines
11 KiB
Python
"""
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LLM 生成配置相关类和函数
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"""
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from collections.abc import Callable
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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field
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from zhenxun.services.log import logger
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from zhenxun.utils.pydantic_compat import model_dump
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from ..types import LLMResponse
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from ..types.enums import ResponseFormat
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from ..types.exceptions import LLMErrorCode, LLMException
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class ModelConfigOverride(BaseModel):
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"""模型配置覆盖参数"""
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temperature: float | None = Field(
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default=None, ge=0.0, le=2.0, description="生成温度"
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)
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max_tokens: int | None = Field(default=None, gt=0, description="最大输出token数")
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top_p: float | None = Field(default=None, ge=0.0, le=1.0, description="核采样参数")
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top_k: int | None = Field(default=None, gt=0, description="Top-K采样参数")
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frequency_penalty: float | None = Field(
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default=None, ge=-2.0, le=2.0, description="频率惩罚"
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)
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presence_penalty: float | None = Field(
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default=None, ge=-2.0, le=2.0, description="存在惩罚"
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)
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repetition_penalty: float | None = Field(
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default=None, ge=0.0, le=2.0, description="重复惩罚"
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)
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stop: list[str] | str | None = Field(default=None, description="停止序列")
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response_format: ResponseFormat | dict[str, Any] | None = Field(
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default=None, description="期望的响应格式"
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)
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response_mime_type: str | None = Field(
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default=None, description="响应MIME类型（Gemini专用）"
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)
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response_schema: dict[str, Any] | None = Field(
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default=None, description="JSON响应模式"
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)
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thinking_budget: float | None = Field(
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default=None, ge=0.0, le=1.0, description="思考预算"
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)
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include_thoughts: bool | None = Field(
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default=None, description="是否在响应中包含思维过程（Gemini专用）"
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)
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safety_settings: dict[str, str] | None = Field(default=None, description="安全设置")
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response_modalities: list[str] | None = Field(
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default=None, description="响应模态类型"
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)
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enable_code_execution: bool | None = Field(
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default=None, description="是否启用代码执行"
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)
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enable_grounding: bool | None = Field(
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default=None, description="是否启用信息来源关联"
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)
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enable_caching: bool | None = Field(default=None, description="是否启用响应缓存")
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custom_params: dict[str, Any] | None = Field(default=None, description="自定义参数")
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validation_policy: dict[str, Any] | None = Field(
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default=None, description="声明式的响应验证策略 (例如: {'require_image': True})"
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)
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response_validator: Callable[[LLMResponse], None] | None = Field(
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default=None, description="一个高级回调函数，用于验证响应，验证失败时应抛出异常"
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)
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model_config = ConfigDict(arbitrary_types_allowed=True)
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def to_dict(self) -> dict[str, Any]:
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"""转换为字典，排除None值"""
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model_data = model_dump(self, exclude_none=True)
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result = {}
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for key, value in model_data.items():
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if key == "custom_params" and isinstance(value, dict):
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result.update(value)
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else:
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result[key] = value
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return result
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def merge_with_base_config(
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self,
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base_temperature: float | None = None,
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base_max_tokens: int | None = None,
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) -> dict[str, Any]:
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"""与基础配置合并，覆盖参数优先"""
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merged = {}
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if base_temperature is not None:
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merged["temperature"] = base_temperature
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if base_max_tokens is not None:
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merged["max_tokens"] = base_max_tokens
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override_dict = self.to_dict()
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merged.update(override_dict)
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return merged
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class LLMGenerationConfig(ModelConfigOverride):
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"""LLM 生成配置，继承模型配置覆盖参数"""
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def to_api_params(self, api_type: str, model_name: str) -> dict[str, Any]:
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"""转换为API参数，支持不同API类型的参数名映射"""
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_ = model_name
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params = {}
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if self.temperature is not None:
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params["temperature"] = self.temperature
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if self.max_tokens is not None:
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if api_type == "gemini":
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params["maxOutputTokens"] = self.max_tokens
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else:
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params["max_tokens"] = self.max_tokens
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if api_type == "gemini":
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if self.top_k is not None:
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params["topK"] = self.top_k
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if self.top_p is not None:
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params["topP"] = self.top_p
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else:
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if self.top_k is not None:
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params["top_k"] = self.top_k
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if self.top_p is not None:
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params["top_p"] = self.top_p
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if api_type in ["openai", "deepseek", "zhipu", "general_openai_compat"]:
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if self.frequency_penalty is not None:
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params["frequency_penalty"] = self.frequency_penalty
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if self.presence_penalty is not None:
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params["presence_penalty"] = self.presence_penalty
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if self.repetition_penalty is not None:
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if api_type == "openai":
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logger.warning("OpenAI官方API不支持repetition_penalty参数，已忽略")
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else:
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params["repetition_penalty"] = self.repetition_penalty
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if self.response_format is not None:
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if isinstance(self.response_format, dict):
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if api_type in ["openai", "zhipu", "deepseek", "general_openai_compat"]:
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params["response_format"] = self.response_format
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logger.debug(
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f"为 {api_type} 使用自定义 response_format: "
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f"{self.response_format}"
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)
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elif self.response_format == ResponseFormat.JSON:
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if api_type in ["openai", "zhipu", "deepseek", "general_openai_compat"]:
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params["response_format"] = {"type": "json_object"}
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logger.debug(f"为 {api_type} 启用 JSON 对象输出模式")
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elif api_type == "gemini":
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params["responseMimeType"] = "application/json"
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if self.response_schema:
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params["responseSchema"] = self.response_schema
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logger.debug(f"为 {api_type} 启用 JSON MIME 类型输出模式")
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if self.custom_params:
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custom_mapped = apply_api_specific_mappings(self.custom_params, api_type)
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params.update(custom_mapped)
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if api_type == "gemini":
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if (
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self.response_format != ResponseFormat.JSON
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and self.response_mime_type is not None
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):
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params["responseMimeType"] = self.response_mime_type
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logger.debug(
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f"使用显式设置的 responseMimeType: {self.response_mime_type}"
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)
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if self.response_schema is not None and "responseSchema" not in params:
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params["responseSchema"] = self.response_schema
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if self.thinking_budget is not None or self.include_thoughts is not None:
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thinking_config = params.setdefault("thinkingConfig", {})
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if self.thinking_budget is not None:
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max_budget = 24576
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budget_value = int(self.thinking_budget * max_budget)
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thinking_config["thinkingBudget"] = budget_value
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logger.debug(
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f"已将 thinking_budget (float: {self.thinking_budget}) "
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f"转换为 Gemini API 的整数格式: {budget_value}"
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)
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if self.include_thoughts is not None:
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thinking_config["includeThoughts"] = self.include_thoughts
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logger.debug(f"已设置 includeThoughts: {self.include_thoughts}")
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if self.safety_settings is not None:
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params["safetySettings"] = self.safety_settings
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if self.response_modalities is not None:
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params["responseModalities"] = self.response_modalities
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logger.debug(f"为{api_type}转换配置参数: {len(params)}个参数")
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return params
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def validate_override_params(
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override_config: dict[str, Any] | LLMGenerationConfig | None,
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) -> LLMGenerationConfig:
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"""验证和标准化覆盖参数"""
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if override_config is None:
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return LLMGenerationConfig()
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if isinstance(override_config, dict):
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try:
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filtered_config = {
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k: v for k, v in override_config.items() if v is not None
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}
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return LLMGenerationConfig(**filtered_config)
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except Exception as e:
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logger.warning(f"覆盖配置参数验证失败: {e}")
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raise LLMException(
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f"无效的覆盖配置参数: {e}",
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code=LLMErrorCode.CONFIGURATION_ERROR,
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cause=e,
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)
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return override_config
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def apply_api_specific_mappings(
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params: dict[str, Any], api_type: str
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) -> dict[str, Any]:
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"""应用API特定的参数映射"""
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mapped_params = params.copy()
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if api_type == "gemini":
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if "max_tokens" in mapped_params:
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mapped_params["maxOutputTokens"] = mapped_params.pop("max_tokens")
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if "top_k" in mapped_params:
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mapped_params["topK"] = mapped_params.pop("top_k")
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if "top_p" in mapped_params:
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mapped_params["topP"] = mapped_params.pop("top_p")
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unsupported = ["frequency_penalty", "presence_penalty", "repetition_penalty"]
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for param in unsupported:
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if param in mapped_params:
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logger.warning(f"Gemini 原生API不支持参数 '{param}'，已忽略")
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mapped_params.pop(param)
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elif api_type in ["openai", "deepseek", "zhipu", "general_openai_compat"]:
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if "repetition_penalty" in mapped_params and api_type == "openai":
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logger.warning("OpenAI官方API不支持repetition_penalty参数，已忽略")
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mapped_params.pop("repetition_penalty")
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if "stop" in mapped_params:
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stop_value = mapped_params["stop"]
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if isinstance(stop_value, str):
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mapped_params["stop"] = [stop_value]
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return mapped_params
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def create_generation_config_from_kwargs(**kwargs) -> LLMGenerationConfig:
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"""从关键字参数创建生成配置"""
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model_fields = getattr(LLMGenerationConfig, "model_fields", {})
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known_fields = set(model_fields.keys())
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known_params = {}
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custom_params = {}
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for key, value in kwargs.items():
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if key in known_fields:
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known_params[key] = value
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else:
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custom_params[key] = value
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if custom_params:
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known_params["custom_params"] = custom_params
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return LLMGenerationConfig(**known_params)
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