Soulter
320 lines
12 KiB
Python
320 lines
12 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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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 astrbot.core.utils.io import download_image_by_url
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from astrbot.core.db import BaseDatabase
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from astrbot.api.provider import Provider, Personality
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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
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from ..register import register_provider_adapter
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from astrbot.core.provider.entites import LLMResponse
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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: dict,
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provider_settings: dict,
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db_helper: BaseDatabase,
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persistant_history=True,
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default_persona: Personality = 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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persistant_history,
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db_helper,
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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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tool_list = tools.get_func_desc_openai_style()
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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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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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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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llm_response = LLMResponse("assistant")
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if choice.message.content:
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# text completion
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completion_text = str(choice.message.content).strip()
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llm_response.completion_text = 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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for tool in tools.func_list:
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if tool.name == tool_call.function.name:
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args = json.loads(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 not llm_response.completion_text 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 text_chat(
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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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**kwargs,
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) -> LLMResponse:
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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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context_query.extend(tool_calls_result.to_openai_messages())
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model_config = self.provider_config.get("model_config", {})
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model_config["model"] = self.get_model()
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payloads = {"messages": context_query, **model_config}
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llm_response = None
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try:
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llm_response = await self._query(payloads, func_tool)
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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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llm_response = await self._query(payloads, func_tool)
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except Exception as e:
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if "maximum context length" in str(e):
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# 重试 10 次
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retry_cnt = 20
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while retry_cnt > 0:
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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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try:
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await self.pop_record(context_query)
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llm_response = await self._query(payloads, func_tool)
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break
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except Exception as e:
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if "maximum context length" in str(e):
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retry_cnt -= 1
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else:
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raise e
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if retry_cnt == 0:
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llm_response = LLMResponse(
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"err", "err: 请尝试 /reset 清除会话记录。"
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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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llm_response = await self._query(payloads, func_tool)
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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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elif (
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"does not support Function Calling" in str(e)
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or "does not support tools" in str(e)
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or "Function call is not supported" in str(e)
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or "Function calling is not enabled" in str(e)
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or "Tool calling is not supported" in str(e)
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or "No endpoints found that support tool use" in str(e)
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or "model does not support function calling" in str(e)
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or ("tool" in str(e) and "support" in str(e).lower())
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or ("function" in str(e) and "support" in str(e).lower())
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):
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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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llm_response = await self._query(payloads, 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(
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"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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)
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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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return llm_response
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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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flag = False
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for context in contexts:
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if flag:
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flag = False # 删除 image 后，下一条（LLM 响应）也要删除
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continue
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if isinstance(context["content"], list):
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flag = True
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# continue
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new_content = []
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for item in context["content"]:
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if isinstance(item, dict) and "image_url" in item:
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continue
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new_content.append(item)
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if not new_content:
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# 用户只发了图片
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new_content = [{"type": "text", "text": "[图片]"}]
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context["content"] = new_content
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new_contexts.append(context)
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return new_contexts
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def get_current_key(self) -> str:
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return self.client.api_key
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def get_keys(self) -> List[str]:
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return self.api_keys
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def set_key(self, key):
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self.client.api_key = key
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async def assemble_context(self, text: str, image_urls: List[str] = None) -> dict:
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"""组装成符合 OpenAI 格式的 role 为 user 的消息段"""
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if image_urls:
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user_content = {"role": "user", "content": [{"type": "text", "text": text}]}
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for image_url in image_urls:
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if image_url.startswith("http"):
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image_path = await download_image_by_url(image_url)
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image_data = await self.encode_image_bs64(image_path)
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elif image_url.startswith("file:///"):
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image_path = image_url.replace("file:///", "")
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image_data = await self.encode_image_bs64(image_path)
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else:
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image_data = await self.encode_image_bs64(image_url)
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if not image_data:
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logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
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continue
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user_content["content"].append(
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{"type": "image_url", "image_url": {"url": image_data}}
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)
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return user_content
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else:
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return {"role": "user", "content": text}
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async def encode_image_bs64(self, image_url: str) -> str:
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"""
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将图片转换为 base64
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"""
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if image_url.startswith("base64://"):
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return image_url.replace("base64://", "data:image/jpeg;base64,")
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with open(image_url, "rb") as f:
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image_bs64 = base64.b64encode(f.read()).decode("utf-8")
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return "data:image/jpeg;base64," + image_bs64
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return ""
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