mirror of
https://github.com/langbot-app/LangBot.git
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112 lines
3.7 KiB
Python
112 lines
3.7 KiB
Python
from __future__ import annotations
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import json
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import typing
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from ...platform.types import message as platform_entities
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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(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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req_messages = query.prompt.messages.copy() + query.messages.copy() + [query.user_message]
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pipeline_uuid = query.pipeline_uuid
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pipeline = await self.ap.pipeline_mgr.get_pipeline_by_uuid(pipeline_uuid)
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try:
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if pipeline and pipeline.pipeline_entity.knowledge_base_uuid is not None:
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kb_id = pipeline.pipeline_entity.knowledge_base_uuid
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kb= await self.ap.rag_mgr.load_knowledge_base(kb_id)
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except Exception as e:
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self.ap.logger.error(f'Failed to load knowledge base {kb_id}: {e}')
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kb_id = None
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if kb:
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message = ''
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for msg in query.message_chain:
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if isinstance(msg, platform_entities.Plain):
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message += msg.text
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result = await kb.retrieve(message)
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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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rag_message = llm_entities.Message(
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role="user",
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content="The following are relevant context entries retrieved from the knowledge base. "
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"Please use them to answer the user's question. "
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"Respond in the same language as the user's input.\n\n" + rag_context
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)
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req_messages += [rag_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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