mirror of https://github.com/Mai-with-u/MaiBot.git
exynos
GitHub
parent
c014808e5d
commit
3a6acc5df9
|
|
@ -0,0 +1,687 @@
|
|||
import asyncio
|
||||
import io
|
||||
import json
|
||||
import re
|
||||
import base64
|
||||
from collections.abc import Iterable
|
||||
from typing import Callable, Any, Coroutine, Optional
|
||||
from json_repair import repair_json
|
||||
|
||||
from openai import (
|
||||
AsyncOpenAI,
|
||||
APIConnectionError,
|
||||
APIStatusError,
|
||||
NOT_GIVEN,
|
||||
AsyncStream,
|
||||
)
|
||||
from openai.types.chat import (
|
||||
ChatCompletion,
|
||||
ChatCompletionChunk,
|
||||
ChatCompletionMessageParam,
|
||||
ChatCompletionToolParam,
|
||||
)
|
||||
from openai.types.chat.chat_completion_chunk import ChoiceDelta
|
||||
|
||||
from src.config.api_ada_configs import ModelInfo, APIProvider
|
||||
from src.common.logger import get_logger
|
||||
from .base_client import APIResponse, UsageRecord, BaseClient, client_registry
|
||||
from ..exceptions import (
|
||||
RespParseException,
|
||||
NetworkConnectionError,
|
||||
RespNotOkException,
|
||||
ReqAbortException,
|
||||
EmptyResponseException,
|
||||
)
|
||||
from ..payload_content.message import Message, RoleType, SUPPORTED_IMAGE_FORMATS
|
||||
from ..payload_content.resp_format import RespFormat
|
||||
from ..payload_content.tool_option import ToolOption, ToolParam, ToolCall
|
||||
|
||||
logger = get_logger("OpenAI客户端")
|
||||
|
||||
|
||||
GEMINI_COMPATIBLE_IMAGE_FORMATS = ["png", "jpg", "jpeg", "webp", "heic", "heif"]
|
||||
|
||||
|
||||
def _normalize_format_list(formats: Iterable[str]) -> list[str]:
|
||||
return sorted({fmt.lower() for fmt in formats if isinstance(fmt, str) and fmt})
|
||||
|
||||
|
||||
def _is_gemini_like_model(model_info: ModelInfo) -> bool:
|
||||
identifier = model_info.model_identifier.lower()
|
||||
name = model_info.name.lower()
|
||||
|
||||
if "gemini" in identifier or "gemini" in name:
|
||||
return True
|
||||
|
||||
backend = model_info.extra_params.get("backend") if isinstance(model_info.extra_params, dict) else None
|
||||
if isinstance(backend, str) and backend.lower() == "gemini":
|
||||
return True
|
||||
|
||||
underlying = model_info.extra_params.get("underlying_provider") if isinstance(model_info.extra_params, dict) else None
|
||||
if isinstance(underlying, str) and underlying.lower() == "gemini":
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def _resolve_supported_image_formats(model_info: ModelInfo) -> list[str] | None:
|
||||
extra_formats = (
|
||||
model_info.extra_params.get("supported_image_formats")
|
||||
if isinstance(model_info.extra_params, dict)
|
||||
else None
|
||||
)
|
||||
if isinstance(extra_formats, (list, tuple, set)):
|
||||
normalized = _normalize_format_list(extra_formats)
|
||||
if normalized:
|
||||
return normalized
|
||||
|
||||
if _is_gemini_like_model(model_info):
|
||||
return GEMINI_COMPATIBLE_IMAGE_FORMATS
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _find_unsupported_image_formats(message_list: list[Message], allowed_formats: Iterable[str]) -> set[str]:
|
||||
allowed = {fmt.lower() for fmt in allowed_formats}
|
||||
unsupported: set[str] = set()
|
||||
|
||||
for message in message_list:
|
||||
if isinstance(message.content, list):
|
||||
for item in message.content:
|
||||
if isinstance(item, tuple):
|
||||
fmt = item[0].lower()
|
||||
if fmt not in allowed:
|
||||
unsupported.add(fmt)
|
||||
|
||||
return unsupported
|
||||
|
||||
|
||||
def _convert_messages(
|
||||
messages: list[Message],
|
||||
supported_image_formats: Iterable[str] | None = None,
|
||||
) -> list[ChatCompletionMessageParam]:
|
||||
"""
|
||||
转换消息格式 - 将消息转换为OpenAI API所需的格式
|
||||
:param messages: 消息列表
|
||||
:return: 转换后的消息列表
|
||||
"""
|
||||
|
||||
allowed_formats = {fmt.lower() for fmt in (supported_image_formats or SUPPORTED_IMAGE_FORMATS)}
|
||||
|
||||
def _convert_message_item(message: Message) -> ChatCompletionMessageParam:
|
||||
"""
|
||||
转换单个消息格式
|
||||
:param message: 消息对象
|
||||
:return: 转换后的消息字典
|
||||
"""
|
||||
|
||||
# 添加Content
|
||||
content: str | list[dict[str, Any]]
|
||||
if isinstance(message.content, str):
|
||||
content = message.content
|
||||
elif isinstance(message.content, list):
|
||||
content = []
|
||||
for item in message.content:
|
||||
if isinstance(item, tuple):
|
||||
image_format = item[0].lower()
|
||||
if image_format not in allowed_formats:
|
||||
raise ValueError(
|
||||
f"不受支持的图片格式: {image_format}. 允许的格式: {', '.join(sorted(allowed_formats))}"
|
||||
)
|
||||
content.append(
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"url": f"data:image/{image_format};base64,{item[1]}"},
|
||||
}
|
||||
)
|
||||
elif isinstance(item, str):
|
||||
content.append({"type": "text", "text": item})
|
||||
else:
|
||||
raise RuntimeError("无法触及的代码：请使用MessageBuilder类构建消息对象")
|
||||
|
||||
ret = {
|
||||
"role": message.role.value,
|
||||
"content": content,
|
||||
}
|
||||
|
||||
# 添加工具调用ID
|
||||
if message.role == RoleType.Tool:
|
||||
if not message.tool_call_id:
|
||||
raise ValueError("无法触及的代码：请使用MessageBuilder类构建消息对象")
|
||||
ret["tool_call_id"] = message.tool_call_id
|
||||
|
||||
return ret # type: ignore
|
||||
|
||||
return [_convert_message_item(message) for message in messages]
|
||||
|
||||
|
||||
def _convert_tool_options(tool_options: list[ToolOption]) -> list[dict[str, Any]]:
|
||||
"""
|
||||
转换工具选项格式 - 将工具选项转换为OpenAI API所需的格式
|
||||
:param tool_options: 工具选项列表
|
||||
:return: 转换后的工具选项列表
|
||||
"""
|
||||
|
||||
def _convert_tool_param(tool_option_param: ToolParam) -> dict[str, Any]:
|
||||
"""
|
||||
转换单个工具参数格式
|
||||
:param tool_option_param: 工具参数对象
|
||||
:return: 转换后的工具参数字典
|
||||
"""
|
||||
return_dict: dict[str, Any] = {
|
||||
"type": tool_option_param.param_type.value,
|
||||
"description": tool_option_param.description,
|
||||
}
|
||||
if tool_option_param.enum_values:
|
||||
return_dict["enum"] = tool_option_param.enum_values
|
||||
return return_dict
|
||||
|
||||
def _convert_tool_option_item(tool_option: ToolOption) -> dict[str, Any]:
|
||||
"""
|
||||
转换单个工具项格式
|
||||
:param tool_option: 工具选项对象
|
||||
:return: 转换后的工具选项字典
|
||||
"""
|
||||
ret: dict[str, Any] = {
|
||||
"name": tool_option.name,
|
||||
"description": tool_option.description,
|
||||
}
|
||||
if tool_option.params:
|
||||
ret["parameters"] = {
|
||||
"type": "object",
|
||||
"properties": {param.name: _convert_tool_param(param) for param in tool_option.params},
|
||||
"required": [param.name for param in tool_option.params if param.required],
|
||||
}
|
||||
return ret
|
||||
|
||||
return [
|
||||
{
|
||||
"type": "function",
|
||||
"function": _convert_tool_option_item(tool_option),
|
||||
}
|
||||
for tool_option in tool_options
|
||||
]
|
||||
|
||||
|
||||
def _process_delta(
|
||||
delta: ChoiceDelta,
|
||||
has_rc_attr_flag: bool,
|
||||
in_rc_flag: bool,
|
||||
rc_delta_buffer: io.StringIO,
|
||||
fc_delta_buffer: io.StringIO,
|
||||
tool_calls_buffer: list[tuple[str, str, io.StringIO]],
|
||||
) -> bool:
|
||||
# 接收content
|
||||
if has_rc_attr_flag:
|
||||
# 有独立的推理内容块，则无需考虑content内容的判读
|
||||
if hasattr(delta, "reasoning_content") and delta.reasoning_content: # type: ignore
|
||||
# 如果有推理内容，则将其写入推理内容缓冲区
|
||||
assert isinstance(delta.reasoning_content, str) # type: ignore
|
||||
rc_delta_buffer.write(delta.reasoning_content) # type: ignore
|
||||
elif delta.content:
|
||||
# 如果有正式内容，则将其写入正式内容缓冲区
|
||||
fc_delta_buffer.write(delta.content)
|
||||
elif hasattr(delta, "content") and delta.content is not None:
|
||||
# 没有独立的推理内容块，但有正式内容
|
||||
if in_rc_flag:
|
||||
# 当前在推理内容块中
|
||||
if delta.content == "</think>":
|
||||
# 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
|
||||
in_rc_flag = False
|
||||
else:
|
||||
# 其他情况视为推理内容，加入推理内容缓冲区
|
||||
rc_delta_buffer.write(delta.content)
|
||||
elif delta.content == "<think>" and not fc_delta_buffer.getvalue():
|
||||
# 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
|
||||
# 则将其视为推理内容的开始标记，进入推理内容块
|
||||
in_rc_flag = True
|
||||
else:
|
||||
# 其他情况视为正式内容，加入正式内容缓冲区
|
||||
fc_delta_buffer.write(delta.content)
|
||||
# 接收tool_calls
|
||||
if hasattr(delta, "tool_calls") and delta.tool_calls:
|
||||
tool_call_delta = delta.tool_calls[0]
|
||||
|
||||
if tool_call_delta.index >= len(tool_calls_buffer):
|
||||
# 调用索引号大于等于缓冲区长度，说明是新的工具调用
|
||||
if tool_call_delta.id and tool_call_delta.function and tool_call_delta.function.name:
|
||||
tool_calls_buffer.append(
|
||||
(
|
||||
tool_call_delta.id,
|
||||
tool_call_delta.function.name,
|
||||
io.StringIO(),
|
||||
)
|
||||
)
|
||||
else:
|
||||
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。")
|
||||
|
||||
if tool_call_delta.function and tool_call_delta.function.arguments:
|
||||
# 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
|
||||
tool_calls_buffer[tool_call_delta.index][2].write(tool_call_delta.function.arguments)
|
||||
|
||||
return in_rc_flag
|
||||
|
||||
|
||||
def _build_stream_api_resp(
|
||||
_fc_delta_buffer: io.StringIO,
|
||||
_rc_delta_buffer: io.StringIO,
|
||||
_tool_calls_buffer: list[tuple[str, str, io.StringIO]],
|
||||
) -> APIResponse:
|
||||
resp = APIResponse()
|
||||
|
||||
if _rc_delta_buffer.tell() > 0:
|
||||
# 如果推理内容缓冲区不为空，则将其写入APIResponse对象
|
||||
resp.reasoning_content = _rc_delta_buffer.getvalue()
|
||||
_rc_delta_buffer.close()
|
||||
if _fc_delta_buffer.tell() > 0:
|
||||
# 如果正式内容缓冲区不为空，则将其写入APIResponse对象
|
||||
resp.content = _fc_delta_buffer.getvalue()
|
||||
_fc_delta_buffer.close()
|
||||
if _tool_calls_buffer:
|
||||
# 如果工具调用缓冲区不为空，则将其解析为ToolCall对象列表
|
||||
resp.tool_calls = []
|
||||
for call_id, function_name, arguments_buffer in _tool_calls_buffer:
|
||||
if arguments_buffer.tell() > 0:
|
||||
# 如果参数串缓冲区不为空，则解析为JSON对象
|
||||
raw_arg_data = arguments_buffer.getvalue()
|
||||
arguments_buffer.close()
|
||||
try:
|
||||
arguments = json.loads(repair_json(raw_arg_data))
|
||||
if not isinstance(arguments, dict):
|
||||
raise RespParseException(
|
||||
None,
|
||||
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}",
|
||||
)
|
||||
except json.JSONDecodeError as e:
|
||||
raise RespParseException(
|
||||
None,
|
||||
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}",
|
||||
) from e
|
||||
else:
|
||||
arguments_buffer.close()
|
||||
arguments = None
|
||||
|
||||
resp.tool_calls.append(ToolCall(call_id, function_name, arguments))
|
||||
|
||||
if not resp.content and not resp.tool_calls:
|
||||
raise EmptyResponseException()
|
||||
|
||||
return resp
|
||||
|
||||
|
||||
async def _default_stream_response_handler(
|
||||
resp_stream: AsyncStream[ChatCompletionChunk],
|
||||
interrupt_flag: asyncio.Event | None,
|
||||
) -> tuple[APIResponse, Optional[tuple[int, int, int]]]:
|
||||
"""
|
||||
流式响应处理函数 - 处理OpenAI API的流式响应
|
||||
:param resp_stream: 流式响应对象
|
||||
:return: APIResponse对象
|
||||
"""
|
||||
|
||||
_has_rc_attr_flag = False # 标记是否有独立的推理内容块
|
||||
_in_rc_flag = False # 标记是否在推理内容块中
|
||||
_rc_delta_buffer = io.StringIO() # 推理内容缓冲区，用于存储接收到的推理内容
|
||||
_fc_delta_buffer = io.StringIO() # 正式内容缓冲区，用于存储接收到的正式内容
|
||||
_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
|
||||
_usage_record = None # 使用情况记录
|
||||
|
||||
def _insure_buffer_closed():
|
||||
# 确保缓冲区被关闭
|
||||
if _rc_delta_buffer and not _rc_delta_buffer.closed:
|
||||
_rc_delta_buffer.close()
|
||||
if _fc_delta_buffer and not _fc_delta_buffer.closed:
|
||||
_fc_delta_buffer.close()
|
||||
for _, _, buffer in _tool_calls_buffer:
|
||||
if buffer and not buffer.closed:
|
||||
buffer.close()
|
||||
|
||||
async for event in resp_stream:
|
||||
if interrupt_flag and interrupt_flag.is_set():
|
||||
# 如果中断量被设置，则抛出ReqAbortException
|
||||
_insure_buffer_closed()
|
||||
raise ReqAbortException("请求被外部信号中断")
|
||||
# 空 choices / usage-only 帧的防御
|
||||
if not hasattr(event, "choices") or not event.choices:
|
||||
if hasattr(event, "usage") and event.usage:
|
||||
_usage_record = (
|
||||
event.usage.prompt_tokens or 0,
|
||||
event.usage.completion_tokens or 0,
|
||||
event.usage.total_tokens or 0,
|
||||
)
|
||||
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