mirror of https://github.com/Mai-with-u/MaiBot.git
exynos
GitHub
parent
c014808e5d
commit
3a6acc5df9
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@ -0,0 +1,687 @@
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import asyncio
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import io
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import json
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import re
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import base64
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from collections.abc import Iterable
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from typing import Callable, Any, Coroutine, Optional
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from json_repair import repair_json
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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 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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EmptyResponseException,
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)
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from ..payload_content.message import Message, RoleType, SUPPORTED_IMAGE_FORMATS
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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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GEMINI_COMPATIBLE_IMAGE_FORMATS = ["png", "jpg", "jpeg", "webp", "heic", "heif"]
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def _normalize_format_list(formats: Iterable[str]) -> list[str]:
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return sorted({fmt.lower() for fmt in formats if isinstance(fmt, str) and fmt})
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def _is_gemini_like_model(model_info: ModelInfo) -> bool:
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identifier = model_info.model_identifier.lower()
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name = model_info.name.lower()
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if "gemini" in identifier or "gemini" in name:
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return True
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backend = model_info.extra_params.get("backend") if isinstance(model_info.extra_params, dict) else None
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if isinstance(backend, str) and backend.lower() == "gemini":
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return True
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underlying = model_info.extra_params.get("underlying_provider") if isinstance(model_info.extra_params, dict) else None
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if isinstance(underlying, str) and underlying.lower() == "gemini":
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return True
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return False
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def _resolve_supported_image_formats(model_info: ModelInfo) -> list[str] | None:
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extra_formats = (
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model_info.extra_params.get("supported_image_formats")
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if isinstance(model_info.extra_params, dict)
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else None
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)
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if isinstance(extra_formats, (list, tuple, set)):
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normalized = _normalize_format_list(extra_formats)
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if normalized:
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return normalized
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if _is_gemini_like_model(model_info):
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return GEMINI_COMPATIBLE_IMAGE_FORMATS
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return None
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def _find_unsupported_image_formats(message_list: list[Message], allowed_formats: Iterable[str]) -> set[str]:
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allowed = {fmt.lower() for fmt in allowed_formats}
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unsupported: set[str] = set()
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for message in message_list:
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if isinstance(message.content, list):
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for item in message.content:
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if isinstance(item, tuple):
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fmt = item[0].lower()
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if fmt not in allowed:
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unsupported.add(fmt)
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return unsupported
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def _convert_messages(
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messages: list[Message],
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supported_image_formats: Iterable[str] | None = None,
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) -> 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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allowed_formats = {fmt.lower() for fmt in (supported_image_formats or SUPPORTED_IMAGE_FORMATS)}
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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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image_format = item[0].lower()
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if image_format not in allowed_formats:
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raise ValueError(
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f"不受支持的图片格式: {image_format}. 允许的格式: {', '.join(sorted(allowed_formats))}"
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)
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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/{image_format};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 # type: ignore
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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, Any]:
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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) -> 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: # type: ignore
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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) # type: ignore
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rc_delta_buffer.write(delta.reasoning_content) # type: ignore
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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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if tool_call_delta.id and tool_call_delta.function and tool_call_delta.function.name:
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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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else:
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logger.warning("工具调用索引号大于等于缓冲区长度，但缺少ID或函数信息。")
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if tool_call_delta.function and 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(repair_json(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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if not resp.content and not resp.tool_calls:
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raise EmptyResponseException()
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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, Optional[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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# 空 choices / usage-only 帧的防御
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if not hasattr(event, "choices") or not event.choices:
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if hasattr(event, "usage") and event.usage:
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_usage_record = (
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event.usage.prompt_tokens or 0,
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event.usage.completion_tokens or 0,
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event.usage.total_tokens or 0,
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)
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||||||
|
continue # 跳过本帧，避免访问 choices[0]
|
||||||
|
delta = event.choices[0].delta # 获取当前块的delta内容
|
||||||
|
|
||||||
|
if hasattr(delta, "reasoning_content") and delta.reasoning_content: # type: ignore
|
||||||
|
# 标记：有独立的推理内容块
|
||||||
|
_has_rc_attr_flag = True
|
||||||
|
|
||||||
|
_in_rc_flag = _process_delta(
|
||||||
|
delta,
|
||||||
|
_has_rc_attr_flag,
|
||||||
|
_in_rc_flag,
|
||||||
|
_rc_delta_buffer,
|
||||||
|
_fc_delta_buffer,
|
||||||
|
_tool_calls_buffer,
|
||||||
|
)
|
||||||
|
|
||||||
|
if event.usage:
|
||||||
|
# 如果有使用情况，则将其存储在APIResponse对象中
|
||||||
|
_usage_record = (
|
||||||
|
event.usage.prompt_tokens or 0,
|
||||||
|
event.usage.completion_tokens or 0,
|
||||||
|
event.usage.total_tokens or 0,
|
||||||
|
)
|
||||||
|
|
||||||
|
try:
|
||||||
|
return _build_stream_api_resp(
|
||||||
|
_fc_delta_buffer,
|
||||||
|
_rc_delta_buffer,
|
||||||
|
_tool_calls_buffer,
|
||||||
|
), _usage_record
|
||||||
|
except Exception:
|
||||||
|
# 确保缓冲区被关闭
|
||||||
|
_insure_buffer_closed()
|
||||||
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
pattern = re.compile(
|
||||||
|
r"<think>(?P<think>.*?)</think>(?P<content>.*)|<think>(?P<think_unclosed>.*)|(?P<content_only>.+)",
|
||||||
|
re.DOTALL,
|
||||||
|
)
|
||||||
|
"""用于解析推理内容的正则表达式"""
|
||||||
|
|
||||||
|
|
||||||
|
def _default_normal_response_parser(
|
||||||
|
resp: ChatCompletion,
|
||||||
|
) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
|
||||||
|
"""
|
||||||
|
解析对话补全响应 - 将OpenAI API响应解析为APIResponse对象
|
||||||
|
:param resp: 响应对象
|
||||||
|
:return: APIResponse对象
|
||||||
|
"""
|
||||||
|
api_response = APIResponse()
|
||||||
|
|
||||||
|
if not hasattr(resp, "choices") or len(resp.choices) == 0:
|
||||||
|
raise EmptyResponseException("响应解析失败，缺失choices字段或choices列表为空")
|
||||||
|
message_part = resp.choices[0].message
|
||||||
|
|
||||||
|
if hasattr(message_part, "reasoning_content") and message_part.reasoning_content: # type: ignore
|
||||||
|
# 有有效的推理字段
|
||||||
|
api_response.content = message_part.content
|
||||||
|
api_response.reasoning_content = message_part.reasoning_content # type: ignore
|
||||||
|
elif message_part.content:
|
||||||
|
# 提取推理和内容
|
||||||
|
match = pattern.match(message_part.content)
|
||||||
|
if not match:
|
||||||
|
raise RespParseException(resp, "响应解析失败，无法捕获推理内容和输出内容")
|
||||||
|
if match.group("think") is not None:
|
||||||
|
result = match.group("think").strip(), match.group("content").strip()
|
||||||
|
elif match.group("think_unclosed") is not None:
|
||||||
|
result = match.group("think_unclosed").strip(), None
|
||||||
|
else:
|
||||||
|
result = None, match.group("content_only").strip()
|
||||||
|
api_response.reasoning_content, api_response.content = result
|
||||||
|
|
||||||
|
# 提取工具调用
|
||||||
|
if message_part.tool_calls:
|
||||||
|
api_response.tool_calls = []
|
||||||
|
for call in message_part.tool_calls:
|
||||||
|
try:
|
||||||
|
arguments = json.loads(repair_json(call.function.arguments))
|
||||||
|
if not isinstance(arguments, dict):
|
||||||
|
raise RespParseException(resp, "响应解析失败，工具调用参数无法解析为字典类型")
|
||||||
|
api_response.tool_calls.append(ToolCall(call.id, call.function.name, arguments))
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
raise RespParseException(resp, "响应解析失败，无法解析工具调用参数") from e
|
||||||
|
|
||||||
|
# 提取Usage信息
|
||||||
|
if resp.usage:
|
||||||
|
_usage_record = (
|
||||||
|
resp.usage.prompt_tokens or 0,
|
||||||
|
resp.usage.completion_tokens or 0,
|
||||||
|
resp.usage.total_tokens or 0,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
_usage_record = None
|
||||||
|
|
||||||
|
# 将原始响应存储在原始数据中
|
||||||
|
api_response.raw_data = resp
|
||||||
|
|
||||||
|
if not api_response.content and not api_response.tool_calls:
|
||||||
|
raise EmptyResponseException()
|
||||||
|
|
||||||
|
return api_response, _usage_record
|
||||||
|
|
||||||
|
|
||||||
|
@client_registry.register_client_class("openai")
|
||||||
|
class OpenaiClient(BaseClient):
|
||||||
|
def __init__(self, api_provider: APIProvider):
|
||||||
|
super().__init__(api_provider)
|
||||||
|
self.client: AsyncOpenAI = AsyncOpenAI(
|
||||||
|
base_url=api_provider.base_url,
|
||||||
|
api_key=api_provider.api_key,
|
||||||
|
max_retries=0,
|
||||||
|
timeout=api_provider.timeout,
|
||||||
|
)
|
||||||
|
|
||||||
|
async def get_response(
|
||||||
|
self,
|
||||||
|
model_info: ModelInfo,
|
||||||
|
message_list: list[Message],
|
||||||
|
tool_options: list[ToolOption] | None = None,
|
||||||
|
max_tokens: int = 1024,
|
||||||
|
temperature: float = 0.7,
|
||||||
|
response_format: RespFormat | None = None,
|
||||||
|
stream_response_handler: Optional[
|
||||||
|
Callable[
|
||||||
|
[AsyncStream[ChatCompletionChunk], asyncio.Event | None],
|
||||||
|
Coroutine[Any, Any, tuple[APIResponse, Optional[tuple[int, int, int]]]],
|
||||||
|
]
|
||||||
|
] = None,
|
||||||
|
async_response_parser: Optional[
|
||||||
|
Callable[[ChatCompletion], 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（可选，默认为 NotGiven ）
|
||||||
|
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:
|
||||||
|
(响应文本, 推理文本, 工具调用, 其他数据)
|
||||||
|
"""
|
||||||
|
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
|
||||||
|
|
||||||
|
supported_formats_override = _resolve_supported_image_formats(model_info)
|
||||||
|
allowed_formats = supported_formats_override or self.get_support_image_formats()
|
||||||
|
|
||||||
|
if supported_formats_override:
|
||||||
|
unsupported_formats = _find_unsupported_image_formats(message_list, allowed_formats)
|
||||||
|
if unsupported_formats:
|
||||||
|
allowed_display = ", ".join(allowed_formats)
|
||||||
|
unsupported_display = ", ".join(sorted(unsupported_formats))
|
||||||
|
raise RespNotOkException(
|
||||||
|
400,
|
||||||
|
f"当前模型不支持以下图片格式: {unsupported_display}，请转换为 {allowed_display} 格式后重试。",
|
||||||
|
)
|
||||||
|
|
||||||
|
# 将messages构造为OpenAI API所需的格式
|
||||||
|
try:
|
||||||
|
messages: Iterable[ChatCompletionMessageParam] = _convert_messages(message_list, allowed_formats)
|
||||||
|
except ValueError as e:
|
||||||
|
raise RespNotOkException(400, str(e)) from e
|
||||||
|
# 将tool_options转换为OpenAI API所需的格式
|
||||||
|
tools: Iterable[ChatCompletionToolParam] = _convert_tool_options(tool_options) if tool_options else NOT_GIVEN # type: ignore
|
||||||
|
|
||||||
|
try:
|
||||||
|
if model_info.force_stream_mode:
|
||||||
|
req_task = asyncio.create_task(
|
||||||
|
self.client.chat.completions.create(
|
||||||
|
model=model_info.model_identifier,
|
||||||
|
messages=messages,
|
||||||
|
tools=tools,
|
||||||
|
temperature=temperature,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
stream=True,
|
||||||
|
response_format=NOT_GIVEN,
|
||||||
|
extra_body=extra_params,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
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:
|
||||||
|
# 发送请求并获取响应
|
||||||
|
# start_time = time.time()
|
||||||
|
req_task = asyncio.create_task(
|
||||||
|
self.client.chat.completions.create(
|
||||||
|
model=model_info.model_identifier,
|
||||||
|
messages=messages,
|
||||||
|
tools=tools,
|
||||||
|
temperature=temperature,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
stream=False,
|
||||||
|
response_format=NOT_GIVEN,
|
||||||
|
extra_body=extra_params,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
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0.5秒后再次检查任务&中断信号量状态
|
||||||
|
|
||||||
|
# logger.info(f"OpenAI请求时间: {model_info.model_identifier} {time.time() - start_time} \n{messages}")
|
||||||
|
|
||||||
|
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,
|
||||||
|
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 = await self.client.embeddings.create(
|
||||||
|
model=model_info.model_identifier,
|
||||||
|
input=embedding_input,
|
||||||
|
extra_body=extra_params,
|
||||||
|
)
|
||||||
|
except APIConnectionError as e:
|
||||||
|
# 添加详细的错误信息以便调试
|
||||||
|
logger.error(f"OpenAI API连接错误（嵌入模型）: {str(e)}")
|
||||||
|
logger.error(f"错误类型: {type(e)}")
|
||||||
|
if hasattr(e, "__cause__") and e.__cause__:
|
||||||
|
logger.error(f"底层错误: {str(e.__cause__)}")
|
||||||
|
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 or 0,
|
||||||
|
completion_tokens=getattr(raw_response.usage, "completion_tokens", 0),
|
||||||
|
total_tokens=raw_response.usage.total_tokens or 0,
|
||||||
|
)
|
||||||
|
|
||||||
|
return response
|
||||||
|
|
||||||
|
async 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: base64编码的音频数据
|
||||||
|
:extra_params: 附加的请求参数
|
||||||
|
:return: 音频转录响应
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
raw_response = await self.client.audio.transcriptions.create(
|
||||||
|
model=model_info.model_identifier,
|
||||||
|
file=("audio.wav", io.BytesIO(base64.b64decode(audio_base64))),
|
||||||
|
extra_body=extra_params,
|
||||||
|
)
|
||||||
|
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()
|
||||||
|
# 解析转录响应
|
||||||
|
if hasattr(raw_response, "text"):
|
||||||
|
response.content = raw_response.text
|
||||||
|
else:
|
||||||
|
raise RespParseException(
|
||||||
|
raw_response,
|
||||||
|
"响应解析失败，缺失转录文本。",
|
||||||
|
)
|
||||||
|
return response
|
||||||
|
|
||||||
|
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 ["jpg", "jpeg", "png", "webp", "gif"]
|
||||||
Loading…
Reference in New Issue