mirror of
https://github.com/RockChinQ/QChatGPT.git
synced 2024-11-16 11:42:44 +08:00
117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
from __future__ import annotations
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import asyncio
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import typing
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import json
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import openai
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import openai.types.chat.chat_completion as chat_completion
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from .. import api
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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class OpenAIChatCompletion(api.LLMAPIRequester):
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client: openai.AsyncClient
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async def initialize(self):
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self.client = openai.AsyncClient(
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api_key="",
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base_url=self.ap.cfg_mgr.data["openai_config"]["reverse_proxy"],
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timeout=self.ap.cfg_mgr.data["process_message_timeout"],
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)
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async def _req(
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self,
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args: dict,
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) -> chat_completion.ChatCompletion:
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self.ap.logger.debug(f"req chat_completion with args {args}")
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return await self.client.chat.completions.create(**args)
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async def _make_msg(
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self,
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chat_completion: chat_completion.ChatCompletion,
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) -> llm_entities.Message:
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chatcmpl_message = chat_completion.choices[0].message.dict()
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message = llm_entities.Message(**chatcmpl_message)
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return message
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async def _closure(
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self,
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req_messages: list[dict],
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conversation: core_entities.Conversation,
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) -> llm_entities.Message:
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self.client.api_key = conversation.use_model.token_mgr.get_token()
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args = self.ap.cfg_mgr.data["completion_api_params"].copy()
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args["model"] = conversation.use_model.name
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if conversation.use_model.tool_call_supported:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(conversation)
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if tools:
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args["tools"] = tools
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# 设置此次请求中的messages
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messages = req_messages
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args["messages"] = messages
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# 发送请求
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resp = await self._req(args)
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# 处理请求结果
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message = await self._make_msg(resp)
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return message
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async def request(
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self, query: core_entities.Query, conversation: core_entities.Conversation
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) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""请求"""
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pending_tool_calls = []
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req_messages = [
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m.dict(exclude_none=True) for m in conversation.prompt.messages
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] + [m.dict(exclude_none=True) for m in conversation.messages]
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# req_messages.append({"role": "user", "content": str(query.message_chain)})
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msg = await self._closure(req_messages, conversation)
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yield msg
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pending_tool_calls = msg.tool_calls
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req_messages.append(msg.dict(exclude_none=True))
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while pending_tool_calls:
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for tool_call in pending_tool_calls:
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func = tool_call.function
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parameters = json.loads(func.arguments)
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func_ret = await self.ap.tool_mgr.execute_func_call(
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query, func.name, parameters
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)
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msg = llm_entities.Message(
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role="tool", content=json.dumps(func_ret, ensure_ascii=False), tool_call_id=tool_call.id
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)
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yield msg
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req_messages.append(msg.dict(exclude_none=True))
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# 处理完所有调用，继续请求
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msg = await self._closure(req_messages, conversation)
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yield msg
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pending_tool_calls = msg.tool_calls
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req_messages.append(msg.dict(exclude_none=True))
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