mirror of
https://github.com/langbot-app/LangBot.git
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139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
from __future__ import annotations
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import json
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import copy
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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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rag_combined_prompt_template = """
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The following are relevant context entries retrieved from the knowledge base.
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Please use them to answer the user's message.
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Respond in the same language as the user's input.
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<context>
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{rag_context}
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</context>
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<user_message>
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{user_message}
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</user_message>
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"""
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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(self, query: core_entities.Query) -> 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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kb_uuid = query.pipeline_config['ai']['local-agent']['knowledge-base']
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if kb_uuid == '__none__':
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kb_uuid = None
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user_message = copy.deepcopy(query.user_message)
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user_message_text = ''
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if isinstance(user_message.content, str):
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user_message_text = user_message.content
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elif isinstance(user_message.content, list):
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for ce in user_message.content:
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if ce.type == 'text':
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user_message_text += ce.text
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break
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if kb_uuid and user_message_text:
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# only support text for now
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kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
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if not kb:
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self.ap.logger.warning(f'Knowledge base {kb_uuid} not found')
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raise ValueError(f'Knowledge base {kb_uuid} not found')
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result = await kb.retrieve(user_message_text, kb.knowledge_base_entity.top_k)
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final_user_message_text = ''
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if result:
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rag_context = '\n\n'.join(
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f'[{i + 1}] {entry.metadata.get("text", "")}' for i, entry in enumerate(result)
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)
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final_user_message_text = rag_combined_prompt_template.format(
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rag_context=rag_context, user_message=user_message_text
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)
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else:
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final_user_message_text = user_message_text
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self.ap.logger.debug(f'Final user message text: {final_user_message_text}')
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for ce in user_message.content:
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if ce.type == 'text':
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ce.text = final_user_message_text
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break
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req_messages = query.prompt.messages.copy() + query.messages.copy() + [user_message]
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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(query, func.name, parameters)
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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(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
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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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