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85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
from __future__ import annotations
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import json
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import typing
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from .. import runner
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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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@runner.runner_class('local-agent')
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class LocalAgentRunner(runner.RequestRunner):
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"""本地Agent请求运行器"""
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async def run(
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self, query: core_entities.Query
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) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""运行请求"""
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pending_tool_calls = []
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req_messages = (
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query.prompt.messages.copy() + query.messages.copy() + [query.user_message]
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)
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# 首次请求
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msg = await query.use_llm_model.requester.invoke_llm(
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query,
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query.use_llm_model,
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req_messages,
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query.use_funcs,
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extra_args=query.use_llm_model.model_entity.extra_args,
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)
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yield msg
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pending_tool_calls = msg.tool_calls
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req_messages.append(msg)
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# 持续请求，只要还有待处理的工具调用就继续处理调用
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while pending_tool_calls:
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for tool_call in pending_tool_calls:
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try:
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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',
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content=json.dumps(func_ret, ensure_ascii=False),
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tool_call_id=tool_call.id,
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)
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yield msg
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req_messages.append(msg)
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except Exception as e:
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# 工具调用出错，添加一个报错信息到 req_messages
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err_msg = llm_entities.Message(
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role='tool', content=f'err: {e}', tool_call_id=tool_call.id
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)
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yield err_msg
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req_messages.append(err_msg)
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# 处理完所有调用，再次请求
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msg = await query.use_llm_model.requester.invoke_llm(
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query,
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query.use_llm_model,
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req_messages,
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query.use_funcs,
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extra_args=query.use_llm_model.model_entity.extra_args,
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)
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yield msg
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pending_tool_calls = msg.tool_calls
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req_messages.append(msg)
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