refactor(llm_models): 移除官方Gemini客户端并改用aiohttp实现
官方的 `google-generativeai` 库存在一些问题且似乎已不再积极维护，导致依赖关系和稳定性方面存在风险。 为提高稳定性和可维护性，现已移除基于该官方库的 `gemini_client.py` 实现。相应地，在配置文件模板中，`client_type` 已从 "gemini" 更新为 "aiohttp_gemini"，以引导用户使用新的、基于 `aiohttp` 的异步客户端。
This commit is contained in:
minecraft1024a
@@ -1,601 +0,0 @@
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import asyncio
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import io
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import base64
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from typing import Callable, AsyncIterator, Optional, Coroutine, Any, List, Dict
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import google.generativeai as genai
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from google.generativeai.types import (
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GenerateContentResponse,
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HarmCategory,
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HarmBlockThreshold,
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)
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try:
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# 尝试从较新的API导入
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from google.generativeai.types import SafetySetting, GenerationConfig
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except ImportError:
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# 回退到基本类型
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SafetySetting = Dict
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GenerationConfig = Dict
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from src.config.api_ada_configs import ModelInfo, APIProvider
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from src.common.logger import get_logger
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from .base_client import APIResponse, UsageRecord, BaseClient, client_registry
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from ..exceptions import (
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RespParseException,
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NetworkConnectionError,
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RespNotOkException,
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ReqAbortException,
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)
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from ..payload_content.message import Message, RoleType
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from ..payload_content.resp_format import RespFormat, RespFormatType
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from ..payload_content.tool_option import ToolOption, ToolParam, ToolCall
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# 定义兼容性类型
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ContentDict = Dict
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PartDict = Dict
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ToolDict = Dict
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FunctionDeclaration = Dict
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Tool = Dict
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ContentListUnion = List[Dict]
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ContentUnion = Dict
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Content = Dict
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Part = Dict
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ThinkingConfig = Dict
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GenerateContentConfig = Dict
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EmbedContentConfig = Dict
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EmbedContentResponse = Dict
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# 定义异常类型
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class ClientError(Exception):
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pass
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class ServerError(Exception):
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pass
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class UnknownFunctionCallArgumentError(Exception):
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pass
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class UnsupportedFunctionError(Exception):
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pass
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class FunctionInvocationError(Exception):
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pass
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logger = get_logger("Gemini客户端")
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SAFETY_SETTINGS = [
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{"category": HarmCategory.HARM_CATEGORY_HATE_SPEECH, "threshold": HarmBlockThreshold.BLOCK_NONE},
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{"category": HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, "threshold": HarmBlockThreshold.BLOCK_NONE},
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{"category": HarmCategory.HARM_CATEGORY_HARASSMENT, "threshold": HarmBlockThreshold.BLOCK_NONE},
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{"category": HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, "threshold": HarmBlockThreshold.BLOCK_NONE},
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]
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def _convert_messages(
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messages: list[Message],
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) -> tuple[List[Dict], list[str] | None]:
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"""
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转换消息格式 - 将消息转换为Gemini API所需的格式
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:param messages: 消息列表
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:return: 转换后的消息列表(和可能存在的system消息)
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"""
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def _get_correct_mime_type(image_format: str) -> str:
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"""
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获取正确的MIME类型，修复jpg到jpeg的映射问题
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:param image_format: 图片格式
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:return: 正确的MIME类型
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"""
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# 标准化格式名称，解决jpg/jpeg兼容性问题
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format_mapping = {
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"jpg": "jpeg",
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"jpeg": "jpeg",
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"png": "png",
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"webp": "webp",
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"heic": "heic",
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"heif": "heif",
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"gif": "gif"
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}
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normalized_format = format_mapping.get(image_format.lower(), image_format.lower())
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return f"image/{normalized_format}"
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def _convert_message_item(message: Message) -> Dict:
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"""
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转换单个消息格式，除了system和tool类型的消息
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:param message: 消息对象
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:return: 转换后的消息字典
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"""
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# 将openai格式的角色重命名为gemini格式的角色
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if message.role == RoleType.Assistant:
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role = "model"
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elif message.role == RoleType.User:
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role = "user"
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# 添加Content
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if isinstance(message.content, str):
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content = [{"text": message.content}]
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elif isinstance(message.content, list):
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content = []
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for item in message.content:
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if isinstance(item, tuple):
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content.append({
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"inline_data": {
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"mime_type": _get_correct_mime_type(item[0]),
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"data": item[1]
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}
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})
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elif isinstance(item, str):
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content.append({"text": item})
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else:
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raise RuntimeError("无法触及的代码：请使用MessageBuilder类构建消息对象")
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return {"role": role, "parts": content}
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temp_list: List[Dict] = []
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system_instructions: list[str] = []
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for message in messages:
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if message.role == RoleType.System:
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if isinstance(message.content, str):
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system_instructions.append(message.content)
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else:
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raise ValueError("你tm怎么往system里面塞图片base64？")
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elif message.role == RoleType.Tool:
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if not message.tool_call_id:
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raise ValueError("无法触及的代码：请使用MessageBuilder类构建消息对象")
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else:
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temp_list.append(_convert_message_item(message))
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if system_instructions:
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# 如果有system消息，就把它加上去
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ret: tuple = (temp_list, system_instructions)
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else:
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# 如果没有system消息，就直接返回
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ret: tuple = (temp_list, None)
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return ret
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def _convert_tool_options(tool_options: list[ToolOption]) -> list[FunctionDeclaration]:
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"""
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转换工具选项格式 - 将工具选项转换为Gemini API所需的格式
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:param tool_options: 工具选项列表
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:return: 转换后的工具对象列表
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"""
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def _convert_tool_param(tool_option_param: ToolParam) -> dict:
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"""
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转换单个工具参数格式
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:param tool_option_param: 工具参数对象
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:return: 转换后的工具参数字典
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"""
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return_dict: dict[str, Any] = {
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"type": tool_option_param.param_type.value,
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"description": tool_option_param.description,
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}
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if tool_option_param.enum_values:
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return_dict["enum"] = tool_option_param.enum_values
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return return_dict
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def _convert_tool_option_item(tool_option: ToolOption) -> FunctionDeclaration:
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"""
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转换单个工具项格式
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:param tool_option: 工具选项对象
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:return: 转换后的Gemini工具选项对象
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"""
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ret: dict[str, Any] = {
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"name": tool_option.name,
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"description": tool_option.description,
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}
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if tool_option.params:
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ret["parameters"] = {
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"type": "object",
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"properties": {param.name: _convert_tool_param(param) for param in tool_option.params},
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"required": [param.name for param in tool_option.params if param.required],
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}
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ret1 = FunctionDeclaration(**ret)
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return ret1
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return [_convert_tool_option_item(tool_option) for tool_option in tool_options]
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def _process_delta(
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delta: GenerateContentResponse,
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fc_delta_buffer: io.StringIO,
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tool_calls_buffer: list[tuple[str, str, dict[str, Any]]],
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):
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if not hasattr(delta, "candidates") or not delta.candidates:
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raise RespParseException(delta, "响应解析失败，缺失candidates字段")
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if delta.text:
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fc_delta_buffer.write(delta.text)
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if delta.function_calls: # 为什么不用hasattr呢，是因为这个属性一定有，即使是个空的
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for call in delta.function_calls:
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try:
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if not isinstance(call.args, dict): # gemini返回的function call参数就是dict格式的了
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raise RespParseException(delta, "响应解析失败，工具调用参数无法解析为字典类型")
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if not call.id or not call.name:
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raise RespParseException(delta, "响应解析失败，工具调用缺失id或name字段")
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tool_calls_buffer.append(
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(
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call.id,
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call.name,
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call.args or {}, # 如果args是None，则转换为一个空字典
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)
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)
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except Exception as e:
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raise RespParseException(delta, "响应解析失败，无法解析工具调用参数") from e
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def _build_stream_api_resp(
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_fc_delta_buffer: io.StringIO,
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_tool_calls_buffer: list[tuple[str, str, dict]],
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) -> APIResponse:
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# sourcery skip: simplify-len-comparison, use-assigned-variable
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resp = APIResponse()
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if _fc_delta_buffer.tell() > 0:
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# 如果正式内容缓冲区不为空，则将其写入APIResponse对象
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resp.content = _fc_delta_buffer.getvalue()
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_fc_delta_buffer.close()
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if len(_tool_calls_buffer) > 0:
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# 如果工具调用缓冲区不为空，则将其解析为ToolCall对象列表
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resp.tool_calls = []
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for call_id, function_name, arguments_buffer in _tool_calls_buffer:
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if arguments_buffer is not None:
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arguments = arguments_buffer
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if not isinstance(arguments, dict):
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raise RespParseException(
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None,
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f"响应解析失败，工具调用参数无法解析为字典类型。工具调用参数原始响应：\n{arguments_buffer}",
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)
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else:
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arguments = None
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resp.tool_calls.append(ToolCall(call_id, function_name, arguments))
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return resp
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async def _default_stream_response_handler(
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resp_stream: AsyncIterator[GenerateContentResponse],
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interrupt_flag: asyncio.Event | None,
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) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
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"""
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流式响应处理函数 - 处理Gemini API的流式响应
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:param resp_stream: 流式响应对象,是一个神秘的iterator，我完全不知道这个玩意能不能跑，不过遍历一遍之后它就空了，如果跑不了一点的话可以考虑改成别的东西
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:return: APIResponse对象
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"""
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_fc_delta_buffer = io.StringIO() # 正式内容缓冲区，用于存储接收到的正式内容
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_tool_calls_buffer: list[tuple[str, str, dict]] = [] # 工具调用缓冲区，用于存储接收到的工具调用
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_usage_record = None # 使用情况记录
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def _insure_buffer_closed():
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if _fc_delta_buffer and not _fc_delta_buffer.closed:
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_fc_delta_buffer.close()
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async for chunk in resp_stream:
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# 检查是否有中断量
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if interrupt_flag and interrupt_flag.is_set():
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# 如果中断量被设置，则抛出ReqAbortException
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raise ReqAbortException("请求被外部信号中断")
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_process_delta(
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chunk,
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_fc_delta_buffer,
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_tool_calls_buffer,
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)
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if chunk.usage_metadata:
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# 如果有使用情况，则将其存储在APIResponse对象中
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_usage_record = (
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chunk.usage_metadata.prompt_token_count or 0,
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(chunk.usage_metadata.candidates_token_count or 0) + (chunk.usage_metadata.thoughts_token_count or 0),
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chunk.usage_metadata.total_token_count or 0,
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)
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try:
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return _build_stream_api_resp(
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_fc_delta_buffer,
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_tool_calls_buffer,
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), _usage_record
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except Exception:
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# 确保缓冲区被关闭
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_insure_buffer_closed()
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raise
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def _default_normal_response_parser(
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resp: GenerateContentResponse,
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) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
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"""
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解析对话补全响应 - 将Gemini API响应解析为APIResponse对象
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:param resp: 响应对象
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:return: APIResponse对象
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"""
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api_response = APIResponse()
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if not hasattr(resp, "candidates") or not resp.candidates:
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raise RespParseException(resp, "响应解析失败，缺失candidates字段")
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try:
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if resp.candidates[0].content and resp.candidates[0].content.parts:
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for part in resp.candidates[0].content.parts:
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if not part.text:
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continue
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if part.thought:
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api_response.reasoning_content = (
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api_response.reasoning_content + part.text if api_response.reasoning_content else part.text
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)
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except Exception as e:
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logger.warning(f"解析思考内容时发生错误: {e}，跳过解析")
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if resp.text:
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api_response.content = resp.text
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if resp.function_calls:
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api_response.tool_calls = []
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for call in resp.function_calls:
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try:
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if not isinstance(call.args, dict):
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raise RespParseException(resp, "响应解析失败，工具调用参数无法解析为字典类型")
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if not call.name:
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raise RespParseException(resp, "响应解析失败，工具调用缺失name字段")
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api_response.tool_calls.append(ToolCall(call.id or "gemini-tool_call", call.name, call.args or {}))
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except Exception as e:
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raise RespParseException(resp, "响应解析失败，无法解析工具调用参数") from e
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if resp.usage_metadata:
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_usage_record = (
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resp.usage_metadata.prompt_token_count or 0,
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(resp.usage_metadata.candidates_token_count or 0) + (resp.usage_metadata.thoughts_token_count or 0),
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||||||
resp.usage_metadata.total_token_count or 0,
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
_usage_record = None
|
|
||||||
|
|
||||||
api_response.raw_data = resp
|
|
||||||
|
|
||||||
return api_response, _usage_record
|
|
||||||
|
|
||||||
|
|
||||||
@client_registry.register_client_class("gemini")
|
|
||||||
class GeminiClient(BaseClient):
|
|
||||||
def __init__(self, api_provider: APIProvider):
|
|
||||||
super().__init__(api_provider)
|
|
||||||
# 配置 Google Generative AI
|
|
||||||
genai.configure(api_key=api_provider.api_key)
|
|
||||||
|
|
||||||
async def get_response(
|
|
||||||
self,
|
|
||||||
model_info: ModelInfo,
|
|
||||||
message_list: list[Message],
|
|
||||||
tool_options: list[ToolOption] | None = None,
|
|
||||||
max_tokens: int = 1024,
|
|
||||||
temperature: float = 0.4,
|
|
||||||
response_format: RespFormat | None = None,
|
|
||||||
stream_response_handler: Optional[
|
|
||||||
Callable[
|
|
||||||
[AsyncIterator[GenerateContentResponse], asyncio.Event | None],
|
|
||||||
Coroutine[Any, Any, tuple[APIResponse, Optional[tuple[int, int, int]]]],
|
|
||||||
]
|
|
||||||
] = None,
|
|
||||||
async_response_parser: Optional[
|
|
||||||
Callable[[GenerateContentResponse], tuple[APIResponse, Optional[tuple[int, int, int]]]]
|
|
||||||
] = None,
|
|
||||||
interrupt_flag: asyncio.Event | None = None,
|
|
||||||
extra_params: dict[str, Any] | None = None,
|
|
||||||
) -> APIResponse:
|
|
||||||
"""
|
|
||||||
获取对话响应
|
|
||||||
Args:
|
|
||||||
model_info: 模型信息
|
|
||||||
message_list: 对话体
|
|
||||||
tool_options: 工具选项（可选，默认为None）
|
|
||||||
max_tokens: 最大token数（可选，默认为1024）
|
|
||||||
temperature: 温度（可选，默认为0.7）
|
|
||||||
response_format: 响应格式（默认为text/plain,如果是输入的JSON Schema则必须遵守OpenAPI3.0格式,理论上和openai是一样的，暂不支持其它相应格式输入）
|
|
||||||
stream_response_handler: 流式响应处理函数（可选，默认为default_stream_response_handler）
|
|
||||||
async_response_parser: 响应解析函数（可选，默认为default_response_parser）
|
|
||||||
interrupt_flag: 中断信号量（可选，默认为None）
|
|
||||||
Returns:
|
|
||||||
APIResponse对象，包含响应内容、推理内容、工具调用等信息
|
|
||||||
"""
|
|
||||||
if stream_response_handler is None:
|
|
||||||
stream_response_handler = _default_stream_response_handler
|
|
||||||
|
|
||||||
if async_response_parser is None:
|
|
||||||
async_response_parser = _default_normal_response_parser
|
|
||||||
|
|
||||||
# 将messages构造为Gemini API所需的格式
|
|
||||||
messages = _convert_messages(message_list)
|
|
||||||
# 将tool_options转换为Gemini API所需的格式
|
|
||||||
tools = _convert_tool_options(tool_options) if tool_options else None
|
|
||||||
# 将response_format转换为Gemini API所需的格式
|
|
||||||
generation_config_dict = {
|
|
||||||
"max_output_tokens": max_tokens,
|
|
||||||
"temperature": temperature,
|
|
||||||
"response_modalities": ["TEXT"],
|
|
||||||
"thinking_config": {
|
|
||||||
"include_thoughts": True,
|
|
||||||
"thinking_budget": (
|
|
||||||
extra_params["thinking_budget"]
|
|
||||||
if extra_params and "thinking_budget" in extra_params
|
|
||||||
else int(max_tokens / 2) # 默认思考预算为最大token数的一半，防止空回复
|
|
||||||
),
|
|
||||||
},
|
|
||||||
"safety_settings": SAFETY_SETTINGS, # 防止空回复问题
|
|
||||||
}
|
|
||||||
if tools:
|
|
||||||
generation_config_dict["tools"] = {"function_declarations": tools}
|
|
||||||
if messages[1]:
|
|
||||||
# 如果有system消息，则将其添加到配置中
|
|
||||||
generation_config_dict["system_instructions"] = messages[1]
|
|
||||||
if response_format and response_format.format_type == RespFormatType.TEXT:
|
|
||||||
generation_config_dict["response_mime_type"] = "text/plain"
|
|
||||||
elif response_format and response_format.format_type in (RespFormatType.JSON_OBJ, RespFormatType.JSON_SCHEMA):
|
|
||||||
generation_config_dict["response_mime_type"] = "application/json"
|
|
||||||
generation_config_dict["response_schema"] = response_format.to_dict()
|
|
||||||
|
|
||||||
generation_config = generation_config_dict
|
|
||||||
|
|
||||||
try:
|
|
||||||
# 创建模型实例
|
|
||||||
model = genai.GenerativeModel(model_info.model_identifier)
|
|
||||||
|
|
||||||
if model_info.force_stream_mode:
|
|
||||||
req_task = asyncio.create_task(
|
|
||||||
model.generate_content_async(
|
|
||||||
contents=messages[0],
|
|
||||||
generation_config=generation_config,
|
|
||||||
stream=True
|
|
||||||
)
|
|
||||||
)
|
|
||||||
while not req_task.done():
|
|
||||||
if interrupt_flag and interrupt_flag.is_set():
|
|
||||||
# 如果中断量存在且被设置，则取消任务并抛出异常
|
|
||||||
req_task.cancel()
|
|
||||||
raise ReqAbortException("请求被外部信号中断")
|
|
||||||
await asyncio.sleep(0.1) # 等待0.1秒后再次检查任务&中断信号量状态
|
|
||||||
resp, usage_record = await stream_response_handler(req_task.result(), interrupt_flag)
|
|
||||||
else:
|
|
||||||
req_task = asyncio.create_task(
|
|
||||||
model.generate_content_async(
|
|
||||||
contents=messages[0],
|
|
||||||
generation_config=generation_config
|
|
||||||
)
|
|
||||||
)
|
|
||||||
while not req_task.done():
|
|
||||||
if interrupt_flag and interrupt_flag.is_set():
|
|
||||||
# 如果中断量存在且被设置，则取消任务并抛出异常
|
|
||||||
req_task.cancel()
|
|
||||||
raise ReqAbortException("请求被外部信号中断")
|
|
||||||
await asyncio.sleep(0.5) # 等待0.5秒后再次检查任务&中断信号量状态
|
|
||||||
|
|
||||||
resp, usage_record = async_response_parser(req_task.result())
|
|
||||||
except Exception as e:
|
|
||||||
# 处理Google Generative AI异常
|
|
||||||
if "rate limit" in str(e).lower():
|
|
||||||
raise RespNotOkException(429, "请求频率过高，请稍后再试") from None
|
|
||||||
elif "quota" in str(e).lower():
|
|
||||||
raise RespNotOkException(429, "配额已用完") from None
|
|
||||||
elif "invalid" in str(e).lower() or "bad request" in str(e).lower():
|
|
||||||
raise RespNotOkException(400, f"请求无效：{str(e)}") from None
|
|
||||||
elif "permission" in str(e).lower() or "forbidden" in str(e).lower():
|
|
||||||
raise RespNotOkException(403, "权限不足") from None
|
|
||||||
else:
|
|
||||||
raise NetworkConnectionError() from e
|
|
||||||
|
|
||||||
if usage_record:
|
|
||||||
resp.usage = UsageRecord(
|
|
||||||
model_name=model_info.name,
|
|
||||||
provider_name=model_info.api_provider,
|
|
||||||
prompt_tokens=usage_record[0],
|
|
||||||
completion_tokens=usage_record[1],
|
|
||||||
total_tokens=usage_record[2],
|
|
||||||
)
|
|
||||||
|
|
||||||
return resp
|
|
||||||
|
|
||||||
async def get_embedding(
|
|
||||||
self,
|
|
||||||
model_info: ModelInfo,
|
|
||||||
embedding_input: str,
|
|
||||||
extra_params: dict[str, Any] | None = None,
|
|
||||||
) -> APIResponse:
|
|
||||||
"""
|
|
||||||
获取文本嵌入
|
|
||||||
:param model_info: 模型信息
|
|
||||||
:param embedding_input: 嵌入输入文本
|
|
||||||
:return: 嵌入响应
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
raw_response: EmbedContentResponse = await self.client.aio.models.embed_content(
|
|
||||||
model=model_info.model_identifier,
|
|
||||||
contents=embedding_input,
|
|
||||||
config=EmbedContentConfig(task_type="SEMANTIC_SIMILARITY"),
|
|
||||||
)
|
|
||||||
except (ClientError, ServerError) as e:
|
|
||||||
# 重封装ClientError和ServerError为RespNotOkException
|
|
||||||
raise RespNotOkException(e.code) from None
|
|
||||||
except Exception as e:
|
|
||||||
raise NetworkConnectionError() from e
|
|
||||||
|
|
||||||
response = APIResponse()
|
|
||||||
|
|
||||||
# 解析嵌入响应和使用情况
|
|
||||||
if hasattr(raw_response, "embeddings") and raw_response.embeddings:
|
|
||||||
response.embedding = raw_response.embeddings[0].values
|
|
||||||
else:
|
|
||||||
raise RespParseException(raw_response, "响应解析失败，缺失embeddings字段")
|
|
||||||
|
|
||||||
response.usage = UsageRecord(
|
|
||||||
model_name=model_info.name,
|
|
||||||
provider_name=model_info.api_provider,
|
|
||||||
prompt_tokens=len(embedding_input),
|
|
||||||
completion_tokens=0,
|
|
||||||
total_tokens=len(embedding_input),
|
|
||||||
)
|
|
||||||
|
|
||||||
return response
|
|
||||||
|
|
||||||
def get_audio_transcriptions(
|
|
||||||
self, model_info: ModelInfo, audio_base64: str, extra_params: dict[str, Any] | None = None
|
|
||||||
) -> APIResponse:
|
|
||||||
"""
|
|
||||||
获取音频转录
|
|
||||||
:param model_info: 模型信息
|
|
||||||
:param audio_base64: 音频文件的Base64编码字符串
|
|
||||||
:param extra_params: 额外参数（可选）
|
|
||||||
:return: 转录响应
|
|
||||||
"""
|
|
||||||
generation_config_dict = {
|
|
||||||
"max_output_tokens": 2048,
|
|
||||||
"response_modalities": ["TEXT"],
|
|
||||||
"thinking_config": ThinkingConfig(
|
|
||||||
include_thoughts=True,
|
|
||||||
thinking_budget=(
|
|
||||||
extra_params["thinking_budget"] if extra_params and "thinking_budget" in extra_params else 1024
|
|
||||||
),
|
|
||||||
),
|
|
||||||
"safety_settings": SAFETY_SETTINGS,
|
|
||||||
}
|
|
||||||
generate_content_config = GenerateContentConfig(**generation_config_dict)
|
|
||||||
prompt = "Generate a transcript of the speech. The language of the transcript should **match the language of the speech**."
|
|
||||||
try:
|
|
||||||
raw_response: GenerateContentResponse = self.client.models.generate_content(
|
|
||||||
model=model_info.model_identifier,
|
|
||||||
contents=[
|
|
||||||
Content(
|
|
||||||
role="user",
|
|
||||||
parts=[
|
|
||||||
Part.from_text(text=prompt),
|
|
||||||
Part.from_bytes(data=base64.b64decode(audio_base64), mime_type="audio/wav"),
|
|
||||||
],
|
|
||||||
)
|
|
||||||
],
|
|
||||||
config=generate_content_config,
|
|
||||||
)
|
|
||||||
resp, usage_record = _default_normal_response_parser(raw_response)
|
|
||||||
except (ClientError, ServerError) as e:
|
|
||||||
# 重封装ClientError和ServerError为RespNotOkException
|
|
||||||
raise RespNotOkException(e.code) from None
|
|
||||||
except Exception as e:
|
|
||||||
raise NetworkConnectionError() from e
|
|
||||||
|
|
||||||
if usage_record:
|
|
||||||
resp.usage = UsageRecord(
|
|
||||||
model_name=model_info.name,
|
|
||||||
provider_name=model_info.api_provider,
|
|
||||||
prompt_tokens=usage_record[0],
|
|
||||||
completion_tokens=usage_record[1],
|
|
||||||
total_tokens=usage_record[2],
|
|
||||||
)
|
|
||||||
|
|
||||||
return resp
|
|
||||||
|
|
||||||
def get_support_image_formats(self) -> list[str]:
|
|
||||||
"""
|
|
||||||
获取支持的图片格式
|
|
||||||
:return: 支持的图片格式列表
|
|
||||||
"""
|
|
||||||
return ["png", "jpg", "jpeg", "webp", "heic", "heif"]
|
|
||||||
@@ -1,5 +1,5 @@
|
|||||||
[inner]
|
[inner]
|
||||||
version = "1.2.7"
|
version = "1.2.8"
|
||||||
|
|
||||||
# 配置文件版本号迭代规则同bot_config.toml
|
# 配置文件版本号迭代规则同bot_config.toml
|
||||||
|
|
||||||
@@ -25,7 +25,7 @@ retry_interval = 10
|
|||||||
name = "Google"
|
name = "Google"
|
||||||
base_url = "https://api.google.com/v1"
|
base_url = "https://api.google.com/v1"
|
||||||
api_key = "your-google-api-key-1"
|
api_key = "your-google-api-key-1"
|
||||||
client_type = "gemini"
|
client_type = "aiohttp_gemini" # 官方的gemini客户端现在已经死了
|
||||||
max_retry = 2
|
max_retry = 2
|
||||||
timeout = 30
|
timeout = 30
|
||||||
retry_interval = 10
|
retry_interval = 10
|
||||||
|
|||||||
Reference in New Issue
Block a user