mirror of https://github.com/Mai-with-u/MaiBot.git
523 lines
20 KiB
Python
523 lines
20 KiB
Python
import asyncio
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import io
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import json
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import re
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from collections.abc import Iterable
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from typing import Callable, Any
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from openai import (
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AsyncOpenAI,
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APIConnectionError,
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APIStatusError,
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NOT_GIVEN,
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AsyncStream,
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)
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from openai.types.chat import (
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ChatCompletion,
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ChatCompletionChunk,
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ChatCompletionMessageParam,
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ChatCompletionToolParam,
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)
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from openai.types.chat.chat_completion_chunk import ChoiceDelta
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from .base_client import APIResponse, UsageRecord
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from src.config.api_ada_configs import ModelInfo, APIProvider
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from .base_client import BaseClient, client_registry
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from src.common.logger import get_logger
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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
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from ..payload_content.tool_option import ToolOption, ToolParam, ToolCall
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logger = get_logger("OpenAI客户端")
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def _convert_messages(messages: list[Message]) -> list[ChatCompletionMessageParam]:
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"""
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转换消息格式 - 将消息转换为OpenAI 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
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"""
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def _convert_message_item(message: Message) -> ChatCompletionMessageParam:
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"""
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转换单个消息格式
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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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# 添加Content
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content: str | list[dict[str, Any]]
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if isinstance(message.content, str):
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content = 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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{
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"type": "image_url",
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"image_url": {"url": f"data:image/{item[0].lower()};base64,{item[1]}"},
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}
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)
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elif isinstance(item, str):
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content.append({"type": "text", "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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ret = {
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"role": message.role.value,
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"content": content,
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}
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# 添加工具调用ID
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if 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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ret["tool_call_id"] = message.tool_call_id
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return ret
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return [_convert_message_item(message) for message in messages]
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def _convert_tool_options(tool_options: list[ToolOption]) -> list[dict[str, Any]]:
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"""
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转换工具选项格式 - 将工具选项转换为OpenAI API所需的格式
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:param tool_options: 工具选项列表
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:return: 转换后的工具选项列表
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"""
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def _convert_tool_param(tool_option_param: ToolParam) -> dict[str, str]:
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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 {
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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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def _convert_tool_option_item(tool_option: ToolOption) -> dict[str, Any]:
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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: 转换后的工具选项字典
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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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return ret
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return [
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{
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"type": "function",
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"function": _convert_tool_option_item(tool_option),
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}
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for tool_option in tool_options
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]
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def _process_delta(
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delta: ChoiceDelta,
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has_rc_attr_flag: bool,
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in_rc_flag: bool,
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rc_delta_buffer: io.StringIO,
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fc_delta_buffer: io.StringIO,
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tool_calls_buffer: list[tuple[str, str, io.StringIO]],
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) -> bool:
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# 接收content
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if has_rc_attr_flag:
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# 有独立的推理内容块，则无需考虑content内容的判读
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if hasattr(delta, "reasoning_content") and delta.reasoning_content:
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# 如果有推理内容，则将其写入推理内容缓冲区
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assert isinstance(delta.reasoning_content, str)
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rc_delta_buffer.write(delta.reasoning_content)
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elif delta.content:
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# 如果有正式内容，则将其写入正式内容缓冲区
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fc_delta_buffer.write(delta.content)
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elif hasattr(delta, "content") and delta.content is not None:
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# 没有独立的推理内容块，但有正式内容
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if in_rc_flag:
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# 当前在推理内容块中
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if delta.content == "</think>":
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# 如果当前内容是</think>，则将其视为推理内容的结束标记，退出推理内容块
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in_rc_flag = False
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else:
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# 其他情况视为推理内容，加入推理内容缓冲区
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rc_delta_buffer.write(delta.content)
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elif delta.content == "<think>" and not fc_delta_buffer.getvalue():
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# 如果当前内容是<think>，且正式内容缓冲区为空，说明<think>为输出的首个token
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# 则将其视为推理内容的开始标记，进入推理内容块
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in_rc_flag = True
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else:
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# 其他情况视为正式内容，加入正式内容缓冲区
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fc_delta_buffer.write(delta.content)
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# 接收tool_calls
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if hasattr(delta, "tool_calls") and delta.tool_calls:
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tool_call_delta = delta.tool_calls[0]
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if tool_call_delta.index >= len(tool_calls_buffer):
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# 调用索引号大于等于缓冲区长度，说明是新的工具调用
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tool_calls_buffer.append(
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(
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tool_call_delta.id,
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tool_call_delta.function.name,
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io.StringIO(),
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)
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)
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if tool_call_delta.function.arguments:
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# 如果有工具调用参数，则添加到对应的工具调用的参数串缓冲区中
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tool_calls_buffer[tool_call_delta.index][2].write(tool_call_delta.function.arguments)
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return in_rc_flag
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def _build_stream_api_resp(
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_fc_delta_buffer: io.StringIO,
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_rc_delta_buffer: io.StringIO,
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_tool_calls_buffer: list[tuple[str, str, io.StringIO]],
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) -> APIResponse:
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resp = APIResponse()
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if _rc_delta_buffer.tell() > 0:
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# 如果推理内容缓冲区不为空，则将其写入APIResponse对象
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resp.reasoning_content = _rc_delta_buffer.getvalue()
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_rc_delta_buffer.close()
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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 _tool_calls_buffer:
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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.tell() > 0:
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# 如果参数串缓冲区不为空，则解析为JSON对象
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raw_arg_data = arguments_buffer.getvalue()
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arguments_buffer.close()
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try:
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arguments = json.loads(raw_arg_data)
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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{raw_arg_data}",
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)
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except json.JSONDecodeError as e:
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raise RespParseException(
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None,
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f"响应解析失败，无法解析工具调用参数。工具调用参数原始响应：{raw_arg_data}",
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) from e
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else:
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arguments_buffer.close()
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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: AsyncStream[ChatCompletionChunk],
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interrupt_flag: asyncio.Event | None,
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) -> tuple[APIResponse, tuple[int, int, int]]:
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"""
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流式响应处理函数 - 处理OpenAI API的流式响应
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:param resp_stream: 流式响应对象
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:return: APIResponse对象
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"""
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_has_rc_attr_flag = False # 标记是否有独立的推理内容块
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_in_rc_flag = False # 标记是否在推理内容块中
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_rc_delta_buffer = io.StringIO() # 推理内容缓冲区，用于存储接收到的推理内容
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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, io.StringIO]] = [] # 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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# 确保缓冲区被关闭
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if _rc_delta_buffer and not _rc_delta_buffer.closed:
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_rc_delta_buffer.close()
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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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for _, _, buffer in _tool_calls_buffer:
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if buffer and not buffer.closed:
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buffer.close()
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async for event in resp_stream:
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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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_insure_buffer_closed()
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raise ReqAbortException("请求被外部信号中断")
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delta = event.choices[0].delta # 获取当前块的delta内容
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if hasattr(delta, "reasoning_content") and delta.reasoning_content: # type: ignore
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# 标记：有独立的推理内容块
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_has_rc_attr_flag = True
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_in_rc_flag = _process_delta(
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delta,
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_has_rc_attr_flag,
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_in_rc_flag,
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_rc_delta_buffer,
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_fc_delta_buffer,
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_tool_calls_buffer,
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)
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if event.usage:
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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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event.usage.prompt_tokens,
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event.usage.completion_tokens,
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event.usage.total_tokens,
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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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_rc_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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pattern = re.compile(
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r"<think>(?P<think>.*?)</think>(?P<content>.*)|<think>(?P<think_unclosed>.*)|(?P<content_only>.+)",
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re.DOTALL,
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)
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"""用于解析推理内容的正则表达式"""
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def _default_normal_response_parser(
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resp: ChatCompletion,
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) -> tuple[APIResponse, tuple[int, int, int]]:
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"""
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解析对话补全响应 - 将OpenAI 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, "choices") or len(resp.choices) == 0:
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raise RespParseException(resp, "响应解析失败，缺失choices字段")
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message_part = resp.choices[0].message
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if hasattr(message_part, "reasoning_content") and message_part.reasoning_content: # type: ignore
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# 有有效的推理字段
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api_response.content = message_part.content
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api_response.reasoning_content = message_part.reasoning_content # type: ignore
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elif message_part.content:
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# 提取推理和内容
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match = pattern.match(message_part.content)
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if not match:
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raise RespParseException(resp, "响应解析失败，无法捕获推理内容和输出内容")
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if match.group("think") is not None:
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result = match.group("think").strip(), match.group("content").strip()
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elif match.group("think_unclosed") is not None:
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result = match.group("think_unclosed").strip(), None
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else:
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result = None, match.group("content_only").strip()
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api_response.reasoning_content, api_response.content = result
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# 提取工具调用
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if message_part.tool_calls:
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api_response.tool_calls = []
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for call in message_part.tool_calls:
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try:
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arguments = json.loads(call.function.arguments)
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if not isinstance(arguments, 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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api_response.tool_calls.append(ToolCall(call.id, call.function.name, arguments))
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except json.JSONDecodeError 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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# 提取Usage信息
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if resp.usage:
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_usage_record = (
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resp.usage.prompt_tokens,
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resp.usage.completion_tokens,
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resp.usage.total_tokens,
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)
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else:
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_usage_record = None
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# 将原始响应存储在原始数据中
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api_response.raw_data = resp
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return api_response, _usage_record
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@client_registry.register_client_class("openai")
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class OpenaiClient(BaseClient):
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def __init__(self, api_provider: APIProvider):
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super().__init__(api_provider)
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self.client: AsyncOpenAI = AsyncOpenAI(
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base_url=api_provider.base_url,
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api_key=api_provider.api_key,
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max_retries=0,
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)
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async def get_response(
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self,
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model_info: ModelInfo,
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message_list: list[Message],
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tool_options: list[ToolOption] | None = None,
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max_tokens: int = 1024,
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temperature: float = 0.7,
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response_format: RespFormat | None = None,
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stream_response_handler: Callable[
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[AsyncStream[ChatCompletionChunk], asyncio.Event | None],
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tuple[APIResponse, tuple[int, int, int]],
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]
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| None = None,
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async_response_parser: Callable[[ChatCompletion], tuple[APIResponse, tuple[int, int, int]]] | None = None,
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interrupt_flag: asyncio.Event | None = None,
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) -> APIResponse:
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"""
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获取对话响应
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:param model_info: 模型信息
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:param message_list: 对话体
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:param tool_options: 工具选项（可选，默认为None）
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:param max_tokens: 最大token数（可选，默认为1024）
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:param temperature: 温度（可选，默认为0.7）
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:param response_format: 响应格式（可选，默认为 NotGiven ）
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:param stream_response_handler: 流式响应处理函数（可选，默认为default_stream_response_handler）
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:param async_response_parser: 响应解析函数（可选，默认为default_response_parser）
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:param interrupt_flag: 中断信号量（可选，默认为None）
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:return: (响应文本, 推理文本, 工具调用, 其他数据)
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"""
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if stream_response_handler is None:
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stream_response_handler = _default_stream_response_handler
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if async_response_parser is None:
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async_response_parser = _default_normal_response_parser
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# 将messages构造为OpenAI API所需的格式
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messages: Iterable[ChatCompletionMessageParam] = _convert_messages(message_list)
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# 将tool_options转换为OpenAI API所需的格式
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tools: Iterable[ChatCompletionToolParam] = _convert_tool_options(tool_options) if tool_options else NOT_GIVEN
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try:
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if model_info.force_stream_mode:
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req_task = asyncio.create_task(
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self.client.chat.completions.create(
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model=model_info.model_identifier,
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messages=messages,
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tools=tools,
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temperature=temperature,
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max_tokens=max_tokens,
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stream=True,
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response_format=response_format.to_dict() if response_format else NOT_GIVEN,
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)
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)
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while not req_task.done():
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if interrupt_flag and interrupt_flag.is_set():
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# 如果中断量存在且被设置，则取消任务并抛出异常
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req_task.cancel()
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raise ReqAbortException("请求被外部信号中断")
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await asyncio.sleep(0.1) # 等待0.1秒后再次检查任务&中断信号量状态
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resp, usage_record = await stream_response_handler(req_task.result(), interrupt_flag)
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else:
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# 发送请求并获取响应
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req_task = asyncio.create_task(
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self.client.chat.completions.create(
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model=model_info.model_identifier,
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messages=messages,
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tools=tools,
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temperature=temperature,
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max_tokens=max_tokens,
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stream=False,
|
||
response_format=response_format.to_dict() if response_format else NOT_GIVEN,
|
||
)
|
||
)
|
||
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 APIConnectionError as e:
|
||
# 重封装APIConnectionError为NetworkConnectionError
|
||
raise NetworkConnectionError() from e
|
||
except APIStatusError as e:
|
||
# 重封装APIError为RespNotOkException
|
||
raise RespNotOkException(e.status_code, e.message) 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,
|
||
) -> 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 = await self.client.embeddings.create(
|
||
model=model_info.model_identifier,
|
||
input=embedding_input,
|
||
)
|
||
except APIConnectionError as e:
|
||
raise NetworkConnectionError() from e
|
||
except APIStatusError as e:
|
||
# 重封装APIError为RespNotOkException
|
||
raise RespNotOkException(e.status_code) from e
|
||
|
||
response = APIResponse()
|
||
|
||
# 解析嵌入响应
|
||
if len(raw_response.data) > 0:
|
||
response.embedding = raw_response.data[0].embedding
|
||
else:
|
||
raise RespParseException(
|
||
raw_response,
|
||
"响应解析失败，缺失嵌入数据。",
|
||
)
|
||
|
||
# 解析使用情况
|
||
if hasattr(raw_response, "usage"):
|
||
response.usage = UsageRecord(
|
||
model_name=model_info.name,
|
||
provider_name=model_info.api_provider,
|
||
prompt_tokens=raw_response.usage.prompt_tokens,
|
||
completion_tokens=raw_response.usage.completion_tokens,
|
||
total_tokens=raw_response.usage.total_tokens,
|
||
)
|
||
|
||
return response
|