bc0784f41d
* Initial plan * Fix ModelScope enable_thinking parameter for non-streaming calls Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> * Tighten enable_thinking condition to only Qwen/Qwen3 models Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> * qwen3 model handle * Update astrbot/core/provider/sources/openai_source.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
556 lines
20 KiB
Python
556 lines
20 KiB
Python
import base64
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import json
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import os
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import inspect
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import random
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import asyncio
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import astrbot.core.message.components as Comp
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from openai import AsyncOpenAI, AsyncAzureOpenAI
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from openai.types.chat.chat_completion import ChatCompletion
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from openai._exceptions import NotFoundError, UnprocessableEntityError
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from openai.lib.streaming.chat._completions import ChatCompletionStreamState
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from astrbot.core.utils.io import download_image_by_url
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from astrbot.core.message.message_event_result import MessageChain
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from astrbot.api.provider import Provider
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from astrbot import logger
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from astrbot.core.provider.func_tool_manager import FuncCall
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from typing import List, AsyncGenerator
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from ..register import register_provider_adapter
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from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
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@register_provider_adapter(
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"openai_chat_completion", "OpenAI API Chat Completion 提供商适配器"
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)
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class ProviderOpenAIOfficial(Provider):
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def __init__(
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self,
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provider_config,
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provider_settings,
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default_persona=None,
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) -> None:
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super().__init__(
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provider_config,
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provider_settings,
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default_persona,
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)
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self.chosen_api_key = None
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self.api_keys: List = provider_config.get("key", [])
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self.chosen_api_key = self.api_keys[0] if len(self.api_keys) > 0 else None
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self.timeout = provider_config.get("timeout", 120)
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if isinstance(self.timeout, str):
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self.timeout = int(self.timeout)
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# 适配 azure openai #332
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if "api_version" in provider_config:
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# 使用 azure api
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self.client = AsyncAzureOpenAI(
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api_key=self.chosen_api_key,
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api_version=provider_config.get("api_version", None),
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base_url=provider_config.get("api_base", None),
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timeout=self.timeout,
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)
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else:
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# 使用 openai api
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self.client = AsyncOpenAI(
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api_key=self.chosen_api_key,
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base_url=provider_config.get("api_base", None),
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timeout=self.timeout,
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)
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self.default_params = inspect.signature(
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self.client.chat.completions.create
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).parameters.keys()
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model_config = provider_config.get("model_config", {})
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model = model_config.get("model", "unknown")
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self.set_model(model)
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async def get_models(self):
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try:
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models_str = []
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models = await self.client.models.list()
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models = sorted(models.data, key=lambda x: x.id)
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for model in models:
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models_str.append(model.id)
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return models_str
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except NotFoundError as e:
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raise Exception(f"获取模型列表失败：{e}")
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async def _query(self, payloads: dict, tools: FuncCall) -> LLMResponse:
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if tools:
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model = payloads.get("model", "").lower()
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omit_empty_param_field = "gemini" in model
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tool_list = tools.get_func_desc_openai_style(
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omit_empty_parameter_field=omit_empty_param_field
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)
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if tool_list:
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payloads["tools"] = tool_list
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# 不在默认参数中的参数放在 extra_body 中
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extra_body = {}
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to_del = []
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for key in payloads.keys():
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if key not in self.default_params:
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extra_body[key] = payloads[key]
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to_del.append(key)
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for key in to_del:
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del payloads[key]
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# 针对 qwen3 模型的特殊处理：非流式调用必须设置 enable_thinking=false
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model = payloads.get("model", "")
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if "qwen3" in model.lower():
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extra_body["enable_thinking"] = False
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completion = await self.client.chat.completions.create(
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**payloads, stream=False, extra_body=extra_body
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)
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if not isinstance(completion, ChatCompletion):
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raise Exception(
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f"API 返回的 completion 类型错误：{type(completion)}: {completion}。"
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)
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logger.debug(f"completion: {completion}")
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llm_response = await self.parse_openai_completion(completion, tools)
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return llm_response
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async def _query_stream(
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self, payloads: dict, tools: FuncCall
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) -> AsyncGenerator[LLMResponse, None]:
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"""流式查询API，逐步返回结果"""
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if tools:
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model = payloads.get("model", "").lower()
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omit_empty_param_field = "gemini" in model
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tool_list = tools.get_func_desc_openai_style(
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omit_empty_parameter_field=omit_empty_param_field
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)
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if tool_list:
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payloads["tools"] = tool_list
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# 不在默认参数中的参数放在 extra_body 中
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extra_body = {}
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to_del = []
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for key in payloads.keys():
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if key not in self.default_params:
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extra_body[key] = payloads[key]
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to_del.append(key)
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for key in to_del:
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del payloads[key]
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stream = await self.client.chat.completions.create(
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**payloads, stream=True, extra_body=extra_body
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)
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llm_response = LLMResponse("assistant", is_chunk=True)
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state = ChatCompletionStreamState()
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async for chunk in stream:
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try:
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state.handle_chunk(chunk)
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except Exception as e:
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logger.warning("Saving chunk state error: " + str(e))
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if len(chunk.choices) == 0:
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continue
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delta = chunk.choices[0].delta
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# 处理文本内容
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if delta.content:
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completion_text = delta.content
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llm_response.result_chain = MessageChain(
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chain=[Comp.Plain(completion_text)]
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)
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yield llm_response
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final_completion = state.get_final_completion()
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llm_response = await self.parse_openai_completion(final_completion, tools)
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yield llm_response
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async def parse_openai_completion(
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self, completion: ChatCompletion, tools: FuncCall
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):
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"""解析 OpenAI 的 ChatCompletion 响应"""
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llm_response = LLMResponse("assistant")
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if len(completion.choices) == 0:
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raise Exception("API 返回的 completion 为空。")
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choice = completion.choices[0]
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if choice.message.content is not None:
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# text completion
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completion_text = str(choice.message.content).strip()
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llm_response.result_chain = MessageChain().message(completion_text)
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if choice.message.tool_calls:
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# tools call (function calling)
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args_ls = []
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func_name_ls = []
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tool_call_ids = []
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for tool_call in choice.message.tool_calls:
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if isinstance(tool_call, str):
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# workaround for #1359
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tool_call = json.loads(tool_call)
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for tool in tools.func_list:
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if tool.name == tool_call.function.name:
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# workaround for #1454
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if isinstance(tool_call.function.arguments, str):
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args = json.loads(tool_call.function.arguments)
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else:
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args = tool_call.function.arguments
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args_ls.append(args)
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func_name_ls.append(tool_call.function.name)
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tool_call_ids.append(tool_call.id)
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llm_response.role = "tool"
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llm_response.tools_call_args = args_ls
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llm_response.tools_call_name = func_name_ls
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llm_response.tools_call_ids = tool_call_ids
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if choice.finish_reason == "content_filter":
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raise Exception(
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"API 返回的 completion 由于内容安全过滤被拒绝(非 AstrBot)。"
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)
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if llm_response.completion_text is None and not llm_response.tools_call_args:
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logger.error(f"API 返回的 completion 无法解析：{completion}。")
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raise Exception(f"API 返回的 completion 无法解析：{completion}。")
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llm_response.raw_completion = completion
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return llm_response
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async def _prepare_chat_payload(
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self,
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prompt: str,
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image_urls: list[str] | None = None,
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contexts: list | None = None,
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system_prompt: str | None = None,
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tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
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model: str | None = None,
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**kwargs,
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) -> tuple:
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"""准备聊天所需的有效载荷和上下文"""
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if contexts is None:
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contexts = []
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new_record = await self.assemble_context(prompt, image_urls)
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context_query = [*contexts, new_record]
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if system_prompt:
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context_query.insert(0, {"role": "system", "content": system_prompt})
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for part in context_query:
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if "_no_save" in part:
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del part["_no_save"]
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# tool calls result
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if tool_calls_result:
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if isinstance(tool_calls_result, ToolCallsResult):
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context_query.extend(tool_calls_result.to_openai_messages())
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else:
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for tcr in tool_calls_result:
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context_query.extend(tcr.to_openai_messages())
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model_config = self.provider_config.get("model_config", {})
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model_config["model"] = model or self.get_model()
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payloads = {"messages": context_query, **model_config}
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return payloads, context_query
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async def _handle_api_error(
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self,
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e: Exception,
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payloads: dict,
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context_query: list,
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func_tool: FuncCall,
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chosen_key: str,
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available_api_keys: List[str],
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retry_cnt: int,
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max_retries: int,
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) -> tuple:
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"""处理API错误并尝试恢复"""
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if "429" in str(e):
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logger.warning(
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f"API 调用过于频繁，尝试使用其他 Key 重试。当前 Key: {chosen_key[:12]}"
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)
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# 最后一次不等待
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if retry_cnt < max_retries - 1:
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await asyncio.sleep(1)
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available_api_keys.remove(chosen_key)
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if len(available_api_keys) > 0:
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chosen_key = random.choice(available_api_keys)
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return (
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False,
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chosen_key,
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available_api_keys,
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payloads,
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context_query,
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func_tool,
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)
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else:
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raise e
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elif "maximum context length" in str(e):
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logger.warning(
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f"上下文长度超过限制。尝试弹出最早的记录然后重试。当前记录条数: {len(context_query)}"
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)
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await self.pop_record(context_query)
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payloads["messages"] = context_query
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return (
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False,
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chosen_key,
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available_api_keys,
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payloads,
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context_query,
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func_tool,
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)
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elif "The model is not a VLM" in str(e): # siliconcloud
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# 尝试删除所有 image
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new_contexts = await self._remove_image_from_context(context_query)
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payloads["messages"] = new_contexts
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context_query = new_contexts
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return (
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False,
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chosen_key,
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available_api_keys,
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payloads,
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context_query,
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func_tool,
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)
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elif (
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"Function calling is not enabled" in str(e)
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or ("tool" in str(e).lower() and "support" in str(e).lower())
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or ("function" in str(e).lower() and "support" in str(e).lower())
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):
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# openai, ollama, gemini openai, siliconcloud 的错误提示与 code 不统一，只能通过字符串匹配
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logger.info(
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f"{self.get_model()} 不支持函数工具调用，已自动去除，不影响使用。"
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)
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if "tools" in payloads:
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del payloads["tools"]
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return False, chosen_key, available_api_keys, payloads, context_query, None
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else:
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logger.error(f"发生了错误。Provider 配置如下: {self.provider_config}")
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if "tool" in str(e).lower() and "support" in str(e).lower():
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logger.error("疑似该模型不支持函数调用工具调用。请输入 /tool off_all")
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if "Connection error." in str(e):
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proxy = os.environ.get("http_proxy", None)
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if proxy:
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logger.error(
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f"可能为代理原因，请检查代理是否正常。当前代理: {proxy}"
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)
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raise e
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async def text_chat(
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self,
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prompt,
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session_id=None,
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image_urls=None,
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func_tool=None,
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contexts=None,
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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) -> LLMResponse:
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payloads, context_query = await self._prepare_chat_payload(
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prompt,
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image_urls,
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contexts,
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system_prompt,
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tool_calls_result,
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model=model,
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**kwargs,
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)
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llm_response = None
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max_retries = 10
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available_api_keys = self.api_keys.copy()
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chosen_key = random.choice(available_api_keys)
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last_exception = None
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retry_cnt = 0
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for retry_cnt in range(max_retries):
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try:
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self.client.api_key = chosen_key
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llm_response = await self._query(payloads, func_tool)
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break
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except UnprocessableEntityError as e:
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logger.warning(f"不可处理的实体错误：{e}，尝试删除图片。")
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# 尝试删除所有 image
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new_contexts = await self._remove_image_from_context(context_query)
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payloads["messages"] = new_contexts
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context_query = new_contexts
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except Exception as e:
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last_exception = e
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(
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success,
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chosen_key,
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available_api_keys,
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payloads,
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context_query,
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func_tool,
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) = await self._handle_api_error(
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e,
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payloads,
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context_query,
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func_tool,
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chosen_key,
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available_api_keys,
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retry_cnt,
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max_retries,
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)
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if success:
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break
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if retry_cnt == max_retries - 1:
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logger.error(f"API 调用失败，重试 {max_retries} 次仍然失败。")
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if last_exception is None:
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raise Exception("未知错误")
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raise last_exception
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return llm_response
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async def text_chat_stream(
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self,
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prompt: str,
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session_id: str = None,
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image_urls: List[str] = [],
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func_tool: FuncCall = None,
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contexts=[],
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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) -> AsyncGenerator[LLMResponse, None]:
|
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"""流式对话，与服务商交互并逐步返回结果"""
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payloads, context_query = await self._prepare_chat_payload(
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prompt,
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image_urls,
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contexts,
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system_prompt,
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tool_calls_result,
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model=model,
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**kwargs,
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)
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max_retries = 10
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available_api_keys = self.api_keys.copy()
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chosen_key = random.choice(available_api_keys)
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last_exception = None
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retry_cnt = 0
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for retry_cnt in range(max_retries):
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try:
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self.client.api_key = chosen_key
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async for response in self._query_stream(payloads, func_tool):
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yield response
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break
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except UnprocessableEntityError as e:
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logger.warning(f"不可处理的实体错误：{e}，尝试删除图片。")
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# 尝试删除所有 image
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new_contexts = await self._remove_image_from_context(context_query)
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payloads["messages"] = new_contexts
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context_query = new_contexts
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except Exception as e:
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last_exception = e
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(
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success,
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chosen_key,
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available_api_keys,
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payloads,
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context_query,
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func_tool,
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) = await self._handle_api_error(
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e,
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payloads,
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context_query,
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func_tool,
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chosen_key,
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available_api_keys,
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retry_cnt,
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max_retries,
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)
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if success:
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break
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if retry_cnt == max_retries - 1:
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logger.error(f"API 调用失败，重试 {max_retries} 次仍然失败。")
|
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if last_exception is None:
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raise Exception("未知错误")
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raise last_exception
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async def _remove_image_from_context(self, contexts: List):
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"""
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从上下文中删除所有带有 image 的记录
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"""
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new_contexts = []
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for context in contexts:
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if "content" in context and isinstance(context["content"], list):
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# continue
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new_content = []
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||
for item in context["content"]:
|
||
if isinstance(item, dict) and "image_url" in item:
|
||
continue
|
||
new_content.append(item)
|
||
if not new_content:
|
||
# 用户只发了图片
|
||
new_content = [{"type": "text", "text": "[图片]"}]
|
||
context["content"] = new_content
|
||
new_contexts.append(context)
|
||
return new_contexts
|
||
|
||
def get_current_key(self) -> str:
|
||
return self.client.api_key
|
||
|
||
def get_keys(self) -> List[str]:
|
||
return self.api_keys
|
||
|
||
def set_key(self, key):
|
||
self.client.api_key = key
|
||
|
||
async def assemble_context(self, text: str, image_urls: List[str] = None) -> dict:
|
||
"""组装成符合 OpenAI 格式的 role 为 user 的消息段"""
|
||
if image_urls:
|
||
user_content = {
|
||
"role": "user",
|
||
"content": [{"type": "text", "text": text if text else "[图片]"}],
|
||
}
|
||
for image_url in image_urls:
|
||
if image_url.startswith("http"):
|
||
image_path = await download_image_by_url(image_url)
|
||
image_data = await self.encode_image_bs64(image_path)
|
||
elif image_url.startswith("file:///"):
|
||
image_path = image_url.replace("file:///", "")
|
||
image_data = await self.encode_image_bs64(image_path)
|
||
else:
|
||
image_data = await self.encode_image_bs64(image_url)
|
||
if not image_data:
|
||
logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
|
||
continue
|
||
user_content["content"].append(
|
||
{
|
||
"type": "image_url",
|
||
"image_url": {"url": image_data},
|
||
}
|
||
)
|
||
return user_content
|
||
else:
|
||
return {"role": "user", "content": text}
|
||
|
||
async def encode_image_bs64(self, image_url: str) -> str:
|
||
"""
|
||
将图片转换为 base64
|
||
"""
|
||
if image_url.startswith("base64://"):
|
||
return image_url.replace("base64://", "data:image/jpeg;base64,")
|
||
with open(image_url, "rb") as f:
|
||
image_bs64 = base64.b64encode(f.read()).decode("utf-8")
|
||
return "data:image/jpeg;base64," + image_bs64
|
||
return ""
|