mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-17 11:14:19 +00:00
Junyan Qin
291 lines
11 KiB
Python
291 lines
11 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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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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import langbot_plugin.api.entities.builtin.provider.message as provider_message
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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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class ToolCallTracker:
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"""工具调用追踪器"""
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def __init__(self):
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self.active_calls: dict[str, dict] = {}
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self.completed_calls: list[provider_message.ToolCall] = []
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async def run(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, 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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try:
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is_stream = await query.adapter.is_stream_output_supported()
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except AttributeError:
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is_stream = False
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remove_think = query.pipeline_config['output'].get('misc', '').get('remove-think')
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use_llm_model = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
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if not is_stream:
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# 非流式输出，直接请求
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msg = await use_llm_model.requester.invoke_llm(
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query,
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use_llm_model,
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req_messages,
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query.use_funcs,
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extra_args=use_llm_model.model_entity.extra_args,
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remove_think=remove_think,
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)
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yield msg
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final_msg = msg
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else:
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# 流式输出，需要处理工具调用
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tool_calls_map: dict[str, provider_message.ToolCall] = {}
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msg_idx = 0
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accumulated_content = '' # 从开始累积的所有内容
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last_role = 'assistant'
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msg_sequence = 1
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async for msg in use_llm_model.requester.invoke_llm_stream(
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query,
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use_llm_model,
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req_messages,
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query.use_funcs,
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extra_args=use_llm_model.model_entity.extra_args,
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remove_think=remove_think,
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):
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msg_idx = msg_idx + 1
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# 记录角色
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if msg.role:
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last_role = msg.role
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# 累积内容
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if msg.content:
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accumulated_content += msg.content
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# 处理工具调用
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if msg.tool_calls:
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for tool_call in msg.tool_calls:
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if tool_call.id not in tool_calls_map:
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tool_calls_map[tool_call.id] = provider_message.ToolCall(
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id=tool_call.id,
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type=tool_call.type,
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function=provider_message.FunctionCall(
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name=tool_call.function.name if tool_call.function else '', arguments=''
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),
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)
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if tool_call.function and tool_call.function.arguments:
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# 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
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tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
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# continue
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# 每8个chunk或最后一个chunk时，输出所有累积的内容
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if msg_idx % 8 == 0 or msg.is_final:
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msg_sequence += 1
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yield provider_message.MessageChunk(
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role=last_role,
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content=accumulated_content, # 输出所有累积内容
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tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
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is_final=msg.is_final,
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msg_sequence=msg_sequence,
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)
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# 创建最终消息用于后续处理
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final_msg = provider_message.MessageChunk(
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role=last_role,
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content=accumulated_content,
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tool_calls=list(tool_calls_map.values()) if tool_calls_map else None,
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msg_sequence=msg_sequence,
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)
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pending_tool_calls = final_msg.tool_calls
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first_content = final_msg.content
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if isinstance(final_msg, provider_message.MessageChunk):
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first_end_sequence = final_msg.msg_sequence
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req_messages.append(final_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(func.name, parameters)
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if is_stream:
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msg = provider_message.MessageChunk(
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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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else:
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msg = provider_message.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 = provider_message.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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if is_stream:
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tool_calls_map = {}
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msg_idx = 0
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accumulated_content = '' # 从开始累积的所有内容
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last_role = 'assistant'
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msg_sequence = first_end_sequence
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async for msg in use_llm_model.requester.invoke_llm_stream(
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query,
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use_llm_model,
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req_messages,
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query.use_funcs,
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extra_args=use_llm_model.model_entity.extra_args,
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remove_think=remove_think,
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):
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msg_idx += 1
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# 记录角色
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if msg.role:
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last_role = msg.role
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# 第一次请求工具调用时的内容
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if msg_idx == 1:
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accumulated_content = first_content if first_content is not None else accumulated_content
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# 累积内容
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if msg.content:
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accumulated_content += msg.content
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# 处理工具调用
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if msg.tool_calls:
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for tool_call in msg.tool_calls:
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if tool_call.id not in tool_calls_map:
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tool_calls_map[tool_call.id] = provider_message.ToolCall(
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id=tool_call.id,
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type=tool_call.type,
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function=provider_message.FunctionCall(
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name=tool_call.function.name if tool_call.function else '', arguments=''
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),
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)
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if tool_call.function and tool_call.function.arguments:
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# 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
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tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
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# 每8个chunk或最后一个chunk时，输出所有累积的内容
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if msg_idx % 8 == 0 or msg.is_final:
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msg_sequence += 1
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yield provider_message.MessageChunk(
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role=last_role,
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content=accumulated_content, # 输出所有累积内容
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tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
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is_final=msg.is_final,
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msg_sequence=msg_sequence,
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)
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final_msg = provider_message.MessageChunk(
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role=last_role,
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content=accumulated_content,
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tool_calls=list(tool_calls_map.values()) if tool_calls_map else None,
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msg_sequence=msg_sequence,
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)
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else:
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# 处理完所有调用，再次请求
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msg = await use_llm_model.requester.invoke_llm(
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query,
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use_llm_model,
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req_messages,
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query.use_funcs,
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extra_args=use_llm_model.model_entity.extra_args,
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remove_think=remove_think,
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)
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yield msg
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final_msg = msg
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pending_tool_calls = final_msg.tool_calls
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req_messages.append(final_msg)
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