[Feature]: 希望后续增加兼容这个API方法

[sanxianxiaohuntun]

这是一个？

现有功能优化

详细描述

QChatGPT\pkg\provider\modelmgr\apis\chatcmpl.py

兼容后代码

from __future__ import annotations

import asyncio
import typing
import json
import base64
from typing import AsyncGenerator

import openai
import openai.types.chat.chat_completion as chat_completion
import httpx
import aiohttp
import async_lru

from .. import api, entities, errors
from ....core import entities as core_entities, app
from ... import entities as llm_entities
from ...tools import entities as tools_entities
from ....utils import image


@api.requester_class("openai-chat-completions")
class OpenAIChatCompletions(api.LLMAPIRequester):
    """OpenAI ChatCompletion API 请求器"""

    client: openai.AsyncClient

    requester_cfg: dict

    def __init__(self, ap: app.Application):
        self.ap = ap

        self.requester_cfg = self.ap.provider_cfg.data['requester']['openai-chat-completions']

    async def initialize(self):

        self.client = openai.AsyncClient(
            api_key="",
            base_url=self.requester_cfg['base-url'],
            timeout=self.requester_cfg['timeout'],
            http_client=httpx.AsyncClient(
                proxies=self.ap.proxy_mgr.get_forward_proxies()
            )
        )

    async def _req(
        self,
        args: dict,
    ) -> chat_completion.ChatCompletion:
        return await self.client.chat.completions.create(**args)

    async def _make_msg(
        self,
        chat_completion: typing.Union[chat_completion.ChatCompletion, str],  # 支持字符串类型
    ) -> llm_entities.Message:
        if isinstance(chat_completion, str):
            # 如果是字符串类型，去掉开头的 'data: ' 部分并处理每个数据块
            if chat_completion.startswith("data:"):
                # 分割多个 data 部分
                parts = chat_completion.split("data:")[1:]  # 分割并去掉空数据
                combined_message = ""

                for part in parts:
                    part = part.strip()  # 去除多余空格
                    try:
                        # 尝试解析每个 data 部分
                        part_data = json.loads(part)
                        if isinstance(part_data, dict) and 'choices' in part_data:
                            # 提取有效数据并拼接
                            choices = part_data['choices']
                            for choice in choices:
                                if 'delta' in choice and 'content' in choice['delta']:
                                    combined_message += choice['delta']['content']
                        else:
                            raise ValueError("Invalid response structure in part")
                    except Exception as e:
                        self.ap.logger.error(f"Failed to parse chat completion part: {e}")
                        continue  # 如果当前部分解析失败，则跳过并继续处理下一个部分

                # 将所有内容合并为一个完整的响应
                chatcmpl_message = {"content": combined_message, "role": "assistant"}

            else:
                # 如果响应不是以 'data:' 开头，直接处理
                try:
                    chat_completion = json.loads(chat_completion)
                    if isinstance(chat_completion, dict) and 'choices' in chat_completion:
                        chatcmpl_message = chat_completion['choices'][0]['message']
                    else:
                        raise ValueError("Invalid response structure: missing 'choices' key")
                except Exception as e:
                    self.ap.logger.error(f"Failed to parse chat completion string: {e}")
                    raise ValueError(f"Invalid response format: {chat_completion}")
        else:
            # 如果是 ChatCompletion 对象，直接处理
            chatcmpl_message = chat_completion.choices[0].message.dict()

        # 确保 'role' 字段存在且不为 None
        if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
            chatcmpl_message['role'] = 'assistant'

        message = llm_entities.Message(**chatcmpl_message)

        return message

    async def _closure(
        self,
        req_messages: list[dict],
        use_model: entities.LLMModelInfo,
        use_funcs: list[tools_entities.LLMFunction] = None,
    ) -> llm_entities.Message:
        self.client.api_key = use_model.token_mgr.get_token()

        args = self.requester_cfg['args'].copy()
        args["model"] = use_model.name if use_model.model_name is None else use_model.model_name

        if use_funcs:
            tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)

            if tools:
                args["tools"] = tools

        # 设置此次请求中的messages
        messages = req_messages.copy()

        # 检查vision
        for msg in messages:
            if 'content' in msg and isinstance(msg["content"], list):
                for me in msg["content"]:
                    if me["type"] == "image_url":
                        me["image_url"]['url'] = await self.get_base64_str(me["image_url"]['url'])

        args["messages"] = messages

        # 发送请求
        resp = await self._req(args)

        # 处理请求结果
        message = await self._make_msg(resp)

        return message
    
    async def call(
        self,
        model: entities.LLMModelInfo,
        messages: typing.List[llm_entities.Message],
        funcs: typing.List[tools_entities.LLMFunction] = None,
    ) -> llm_entities.Message:
        req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
        for m in messages:
            msg_dict = m.dict(exclude_none=True)
            content = msg_dict.get("content")
            if isinstance(content, list):
                # 检查 content 列表中是否每个部分都是文本
                if all(isinstance(part, dict) and part.get("type") == "text" for part in content):
                    # 将所有文本部分合并为一个字符串
                    msg_dict["content"] = "\n".join(part["text"] for part in content)
            req_messages.append(msg_dict)

        try:
            return await self._closure(req_messages, model, funcs)
        except asyncio.TimeoutError:
            raise errors.RequesterError('请求超时')
        except openai.BadRequestError as e:
            if 'context_length_exceeded' in e.message:
                raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
            else:
                raise errors.RequesterError(f'请求参数错误: {e.message}')
        except openai.AuthenticationError as e:
            raise errors.RequesterError(f'无效的 api-key: {e.message}')
        except openai.NotFoundError as e:
            raise errors.RequesterError(f'请求路径错误: {e.message}')
        except openai.RateLimitError as e:
            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
        except openai.APIError as e:
            raise errors.RequesterError(f'请求错误: {e.message}')

    @async_lru.alru_cache(maxsize=128)
    async def get_base64_str(
        self,
        original_url: str,
    ) -> str:
        base64_image, image_format = await image.qq_image_url_to_base64(original_url)
        return f"data:image/{image_format};base64,{base64_image}"

API返回

[11-20 18:40:45.099] chatcmpl.py (67) - [ERROR] : Failed to parse chat completion string: Extra data: line 3 column 1 (char 45)
[11-20 18:40:45.099] chat.py (94) - [ERROR] : 对话(0)请求失败: Invalid response format: {"choices":[{"delta":{"content":"呜~好舒服"}}]}

data:  {"choices":[{"delta":{"content":"的抚摸呢。"}}]}

data:  {"choices":[{"delta":{"content":"（蹭主人的"}}]}

data:  {"choices":[{"delta":{"content":"手）喵~~"}}]}
[11-20 18:40:47.677] controller.py (98) - [ERROR] : Invalid response format: {"choices":[{"delta":{"content":"呜~好舒服"}}]}

data:  {"choices":[{"delta":{"content":"的抚摸呢。"}}]}

data:  {"choices":[{"delta":{"content":"（蹭主人的"}}]}

data:  {"choices":[{"delta":{"content":"手）喵~~"}}]}

兼容后可正常运行得出结果

呜~好舒服的抚摸呢。（蹭主人的手）

[sanxianxiaohuntun]

主要修改代码

async def _make_msg(
    self,
    chat_completion: typing.Union[chat_completion.ChatCompletion, str],  # 支持字符串类型
) -> llm_entities.Message:
    if isinstance(chat_completion, str):
        # 如果是字符串类型，去掉开头的 'data: ' 部分并处理每个数据块
        if chat_completion.startswith("data:"):
            # 分割多个 data 部分
            parts = chat_completion.split("data:")[1:]  # 分割并去掉空数据
            combined_message = ""

            for part in parts:
                part = part.strip()  # 去除多余空格
                try:
                    # 尝试解析每个 data 部分
                    part_data = json.loads(part)
                    if isinstance(part_data, dict) and 'choices' in part_data:
                        # 提取有效数据并拼接
                        choices = part_data['choices']
                        for choice in choices:
                            if 'delta' in choice and 'content' in choice['delta']:
                                combined_message += choice['delta']['content']
                    else:
                        raise ValueError("Invalid response structure in part")
                except Exception as e:
                    self.ap.logger.error(f"Failed to parse chat completion part: {e}")
                    continue  # 如果当前部分解析失败，则跳过并继续处理下一个部分

            # 将所有内容合并为一个完整的响应
            chatcmpl_message = {"content": combined_message, "role": "assistant"}

        else:
            # 如果响应不是以 'data:' 开头，直接处理
            try:
                chat_completion = json.loads(chat_completion)
                if isinstance(chat_completion, dict) and 'choices' in chat_completion:
                    chatcmpl_message = chat_completion['choices'][0]['message']
                else:
                    raise ValueError("Invalid response structure: missing 'choices' key")
            except Exception as e:
                self.ap.logger.error(f"Failed to parse chat completion string: {e}")
                raise ValueError(f"Invalid response format: {chat_completion}")
    else:
        # 如果是 ChatCompletion 对象，直接处理
        chatcmpl_message = chat_completion.choices[0].message.dict()

    # 确保 'role' 字段存在且不为 None
    if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
        chatcmpl_message['role'] = 'assistant'

    message = llm_entities.Message(**chatcmpl_message)

    return message