mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-07-23 04:46:07 +00:00
feat(agent-runner): add plugin runner host integration
This commit is contained in:
huanghuoguoguo
@@ -1,45 +0,0 @@
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from __future__ import annotations
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import abc
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import typing
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from ..core import app
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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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preregistered_runners: list[typing.Type[RequestRunner]] = []
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def runner_class(name: str):
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"""注册一个请求运行器"""
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def decorator(cls: typing.Type[RequestRunner]) -> typing.Type[RequestRunner]:
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cls.name = name
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preregistered_runners.append(cls)
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return cls
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return decorator
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class RequestRunner(abc.ABC):
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"""请求运行器"""
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name: str = None
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ap: app.Application
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pipeline_config: dict
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def __init__(self, ap: app.Application, pipeline_config: dict):
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self.ap = ap
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self.pipeline_config = pipeline_config
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@abc.abstractmethod
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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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pass
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@@ -1,288 +0,0 @@
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from __future__ import annotations
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import typing
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import json
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import base64
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from langbot.pkg.provider import runner
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from langbot.pkg.core import app
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import langbot_plugin.api.entities.builtin.provider.message as provider_message
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from langbot.pkg.utils import image
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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from langbot.libs.coze_server_api.client import AsyncCozeAPIClient
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@runner.runner_class('coze-api')
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class CozeAPIRunner(runner.RequestRunner):
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"""Coze API 对话请求器"""
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def __init__(self, ap: app.Application, pipeline_config: dict):
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self.pipeline_config = pipeline_config
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self.ap = ap
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self.agent_token = pipeline_config['ai']['coze-api']['api-key']
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self.bot_id = pipeline_config['ai']['coze-api'].get('bot-id')
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self.chat_timeout = pipeline_config['ai']['coze-api'].get('timeout')
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self.auto_save_history = pipeline_config['ai']['coze-api'].get('auto_save_history')
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self.api_base = pipeline_config['ai']['coze-api'].get('api-base')
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self.coze = AsyncCozeAPIClient(self.agent_token, self.api_base)
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def _process_thinking_content(
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self,
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content: str,
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) -> tuple[str, str]:
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"""处理思维链内容
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Args:
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content: 原始内容
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Returns:
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(处理后的内容, 提取的思维链内容)
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"""
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remove_think = self.pipeline_config.get('output', {}).get('misc', {}).get('remove-think', False)
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thinking_content = ''
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# 从 content 中提取 <think> 标签内容
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if content and '<think>' in content and '</think>' in content:
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import re
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think_pattern = r'<think>(.*?)</think>'
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think_matches = re.findall(think_pattern, content, re.DOTALL)
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if think_matches:
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thinking_content = '\n'.join(think_matches)
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# 移除 content 中的 <think> 标签
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content = re.sub(think_pattern, '', content, flags=re.DOTALL).strip()
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# 根据 remove_think 参数决定是否保留思维链
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if remove_think:
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return content, ''
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else:
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# 如果有思维链内容，将其以 <think> 格式添加到 content 开头
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if thinking_content:
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content = f'<think>\n{thinking_content}\n</think>\n{content}'.strip()
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return content, thinking_content
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async def _preprocess_user_message(self, query: pipeline_query.Query) -> list[dict]:
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"""预处理用户消息，转换为Coze消息格式
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Returns:
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list[dict]: Coze消息列表
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"""
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messages = []
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if isinstance(query.user_message.content, list):
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# 多模态消息处理
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content_parts = []
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for ce in query.user_message.content:
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if ce.type == 'text':
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content_parts.append({'type': 'text', 'text': ce.text})
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elif ce.type == 'image_base64':
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image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
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file_bytes = base64.b64decode(image_b64)
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file_id = await self._get_file_id(file_bytes)
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content_parts.append({'type': 'image', 'file_id': file_id})
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elif ce.type == 'file':
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# 处理文件，上传到Coze
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file_id = await self._get_file_id(ce.file)
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content_parts.append({'type': 'file', 'file_id': file_id})
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# 创建多模态消息
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if content_parts:
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messages.append(
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{
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'role': 'user',
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'content': json.dumps(content_parts),
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'content_type': 'object_string',
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'meta_data': None,
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}
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)
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elif isinstance(query.user_message.content, str):
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# 纯文本消息
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messages.append(
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{'role': 'user', 'content': query.user_message.content, 'content_type': 'text', 'meta_data': None}
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)
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return messages
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async def _get_file_id(self, file) -> str:
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"""上传文件到Coze服务
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Args:
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file: 文件
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Returns:
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str: 文件ID
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"""
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file_id = await self.coze.upload(file=file)
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return file_id
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async def _chat_messages(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.Message, None]:
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"""调用聊天助手（非流式）
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注意：由于cozepy没有提供非流式API，这里使用流式API并在结束后一次性返回完整内容
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"""
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user_id = f'{query.launcher_type.value}_{query.launcher_id}'
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# 预处理用户消息
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additional_messages = await self._preprocess_user_message(query)
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# 获取会话ID
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conversation_id = None
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# 收集完整内容
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full_content = ''
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full_reasoning = ''
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try:
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# 调用Coze API流式接口
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async for chunk in self.coze.chat_messages(
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bot_id=self.bot_id,
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user_id=user_id,
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additional_messages=additional_messages,
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conversation_id=conversation_id,
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timeout=self.chat_timeout,
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auto_save_history=self.auto_save_history,
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stream=True,
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):
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self.ap.logger.debug(f'coze-chat-stream: {chunk}')
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event_type = chunk.get('event')
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data = chunk.get('data', {})
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# Removed debug print statement to avoid cluttering logs in production
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if event_type == 'conversation.message.delta':
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# 收集内容
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if 'content' in data:
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full_content += data.get('content', '')
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# 收集推理内容（如果有）
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if 'reasoning_content' in data:
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full_reasoning += data.get('reasoning_content', '')
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elif event_type.split('.')[-1] == 'done': # 本地部署coze时，结束event不为done
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# 保存会话ID
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if 'conversation_id' in data:
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conversation_id = data.get('conversation_id')
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elif event_type == 'error':
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# 处理错误
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error_msg = f'Coze API错误: {data.get("message", "未知错误")}'
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yield provider_message.Message(
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role='assistant',
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content=error_msg,
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)
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return
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# 处理思维链内容
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content, thinking_content = self._process_thinking_content(full_content)
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if full_reasoning:
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remove_think = self.pipeline_config.get('output', {}).get('misc', {}).get('remove-think', False)
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if not remove_think:
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content = f'<think>\n{full_reasoning}\n</think>\n{content}'.strip()
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# 一次性返回完整内容
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yield provider_message.Message(
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role='assistant',
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content=content,
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)
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# 保存会话ID
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if conversation_id and query.session.using_conversation:
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query.session.using_conversation.uuid = conversation_id
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except Exception as e:
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self.ap.logger.error(f'Coze API错误: {str(e)}')
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yield provider_message.Message(
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role='assistant',
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content=f'Coze API调用失败: {str(e)}',
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)
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async def _chat_messages_chunk(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
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"""调用聊天助手（流式）"""
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user_id = f'{query.launcher_type.value}_{query.launcher_id}'
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# 预处理用户消息
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additional_messages = await self._preprocess_user_message(query)
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# 获取会话ID
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conversation_id = None
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start_reasoning = False
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stop_reasoning = False
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message_idx = 1
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is_final = False
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full_content = ''
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remove_think = self.pipeline_config.get('output', {}).get('misc', {}).get('remove-think', False)
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try:
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# 调用Coze API流式接口
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async for chunk in self.coze.chat_messages(
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bot_id=self.bot_id,
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user_id=user_id,
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additional_messages=additional_messages,
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conversation_id=conversation_id,
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timeout=self.chat_timeout,
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auto_save_history=self.auto_save_history,
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stream=True,
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):
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self.ap.logger.debug(f'coze-chat-stream-chunk: {chunk}')
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event_type = chunk.get('event')
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data = chunk.get('data', {})
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content = ''
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if event_type == 'conversation.message.delta':
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message_idx += 1
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# 处理内容增量
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if 'reasoning_content' in data and not remove_think:
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reasoning_content = data.get('reasoning_content', '')
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if reasoning_content and not start_reasoning:
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content = '<think/>\n'
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start_reasoning = True
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content += reasoning_content
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if 'content' in data:
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if data.get('content', ''):
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content += data.get('content', '')
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if not stop_reasoning and start_reasoning:
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content = f'</think>\n{content}'
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stop_reasoning = True
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elif event_type.split('.')[-1] == 'done': # 本地部署coze时，结束event不为done
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# 保存会话ID
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if 'conversation_id' in data:
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conversation_id = data.get('conversation_id')
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if query.session.using_conversation:
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query.session.using_conversation.uuid = conversation_id
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is_final = True
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elif event_type == 'error':
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# 处理错误
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error_msg = f'Coze API错误: {data.get("message", "未知错误")}'
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yield provider_message.MessageChunk(role='assistant', content=error_msg, finish_reason='error')
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return
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full_content += content
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if message_idx % 8 == 0 or is_final:
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if full_content:
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yield provider_message.MessageChunk(role='assistant', content=full_content, is_final=is_final)
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except Exception as e:
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self.ap.logger.error(f'Coze API流式调用错误: {str(e)}')
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yield provider_message.MessageChunk(
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role='assistant', content=f'Coze API流式调用失败: {str(e)}', finish_reason='error'
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)
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async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
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"""运行"""
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msg_seq = 0
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if await query.adapter.is_stream_output_supported():
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async for msg in self._chat_messages_chunk(query):
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if isinstance(msg, provider_message.MessageChunk):
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msg_seq += 1
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msg.msg_sequence = msg_seq
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yield msg
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else:
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async for msg in self._chat_messages(query):
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yield msg
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@@ -1,355 +0,0 @@
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from __future__ import annotations
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import typing
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import re
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import dashscope
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from .. import runner
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from ...core import app
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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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class DashscopeAPIError(Exception):
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"""Dashscope API 请求失败"""
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def __init__(self, message: str):
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self.message = message
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super().__init__(self.message)
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@runner.runner_class('dashscope-app-api')
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class DashScopeAPIRunner(runner.RequestRunner):
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"阿里云百炼DashsscopeAPI对话请求器"
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# 运行器内部使用的配置
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app_type: str # 应用类型
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app_id: str # 应用ID
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api_key: str # API Key
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references_quote: (
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str # 引用资料提示（当展示回答来源功能开启时，这个变量会作为引用资料名前的提示，可在provider.json中配置）
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)
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def __init__(self, ap: app.Application, pipeline_config: dict):
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"""初始化"""
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self.ap = ap
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self.pipeline_config = pipeline_config
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valid_app_types = ['agent', 'workflow']
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self.app_type = self.pipeline_config['ai']['dashscope-app-api']['app-type']
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# 检查配置文件中使用的应用类型是否支持
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if self.app_type not in valid_app_types:
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raise DashscopeAPIError(f'不支持的 Dashscope 应用类型: {self.app_type}')
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# 初始化Dashscope 参数配置
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self.app_id = self.pipeline_config['ai']['dashscope-app-api']['app-id']
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self.api_key = self.pipeline_config['ai']['dashscope-app-api']['api-key']
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self.references_quote = self.pipeline_config['ai']['dashscope-app-api']['references_quote']
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def _replace_references(self, text, references_dict):
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"""阿里云百炼平台的自定义应用支持资料引用，此函数可以将引用标签替换为参考资料"""
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# 匹配 <ref>[index_id]</ref> 形式的字符串
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pattern = re.compile(r'<ref>\[(.*?)\]</ref>')
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def replacement(match):
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# 获取引用编号
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ref_key = match.group(1)
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if ref_key in references_dict:
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# 如果有对应的参考资料按照provider.json中的reference_quote返回提示，来自哪个参考资料文件
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return f'({self.references_quote} {references_dict[ref_key]})'
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else:
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# 如果没有对应的参考资料，保留原样
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return match.group(0)
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# 使用 re.sub() 进行替换
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return pattern.sub(replacement, text)
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async def _preprocess_user_message(self, query: pipeline_query.Query) -> tuple[str, list[str]]:
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"""预处理用户消息，提取纯文本，阿里云提供的上传文件方法过于复杂，暂不支持上传文件（包括图片）"""
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plain_text = ''
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image_ids = []
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if isinstance(query.user_message.content, list):
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for ce in query.user_message.content:
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if ce.type == 'text':
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plain_text += ce.text
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# 暂时不支持上传图片，保留代码以便后续扩展
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# elif ce.type == "image_base64":
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# image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
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# file_bytes = base64.b64decode(image_b64)
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# file = ("img.png", file_bytes, f"image/{image_format}")
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# file_upload_resp = await self.dify_client.upload_file(
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# file,
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# f"{query.session.launcher_type.value}_{query.session.launcher_id}",
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# )
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# image_id = file_upload_resp["id"]
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# image_ids.append(image_id)
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elif isinstance(query.user_message.content, str):
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plain_text = query.user_message.content
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return plain_text, image_ids
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||||
async def _agent_messages(
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self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""Dashscope 智能体对话请求"""
|
||||
|
||||
# 局部变量
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||||
chunk = None # 流式传输的块
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||||
pending_content = '' # 待处理的Agent输出内容
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||||
references_dict = {} # 用于存储引用编号和对应的参考资料
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||||
plain_text = '' # 用户输入的纯文本信息
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||||
image_ids = [] # 用户输入的图片ID列表 （暂不支持）
|
||||
|
||||
think_start = False
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||||
think_end = False
|
||||
|
||||
plain_text, image_ids = await self._preprocess_user_message(query)
|
||||
has_thoughts = True # 获取思考过程
|
||||
remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
|
||||
if remove_think:
|
||||
has_thoughts = False
|
||||
# 发送对话请求
|
||||
response = dashscope.Application.call(
|
||||
api_key=self.api_key, # 智能体应用的API Key
|
||||
app_id=self.app_id, # 智能体应用的ID
|
||||
prompt=plain_text, # 用户输入的文本信息
|
||||
stream=True, # 流式输出
|
||||
incremental_output=True, # 增量输出，使用流式输出需要开启增量输出
|
||||
session_id=query.session.using_conversation.uuid, # 会话ID用于，多轮对话
|
||||
enable_thinking=has_thoughts,
|
||||
has_thoughts=has_thoughts,
|
||||
# rag_options={ # 主要用于文件交互，暂不支持
|
||||
# "session_file_ids": ["FILE_ID1"], # FILE_ID1 替换为实际的临时文件ID,逗号隔开多个
|
||||
# }
|
||||
)
|
||||
idx_chunk = 0
|
||||
try:
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
if is_stream:
|
||||
for chunk in response:
|
||||
if chunk.get('status_code') != 200:
|
||||
raise DashscopeAPIError(
|
||||
f'Dashscope API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
|
||||
)
|
||||
if not chunk:
|
||||
continue
|
||||
idx_chunk += 1
|
||||
# 获取流式传输的output
|
||||
stream_output = chunk.get('output', {})
|
||||
stream_think = stream_output.get('thoughts') or []
|
||||
if stream_think and stream_think[0].get('thought'):
|
||||
if not think_start:
|
||||
think_start = True
|
||||
pending_content += f'<think>\n{stream_think[0].get("thought")}'
|
||||
else:
|
||||
# 继续输出 reasoning_content
|
||||
pending_content += stream_think[0].get('thought')
|
||||
elif think_start and (not stream_think or stream_think[0].get('thought') == '') and not think_end:
|
||||
think_end = True
|
||||
pending_content += '\n</think>\n'
|
||||
if stream_output.get('text') is not None:
|
||||
pending_content += stream_output.get('text')
|
||||
# 是否是流式最后一个chunk
|
||||
is_final = False if stream_output.get('finish_reason', False) == 'null' else True
|
||||
|
||||
# 获取模型传出的参考资料列表
|
||||
references_dict_list = stream_output.get('doc_references', [])
|
||||
|
||||
# 从模型传出的参考资料信息中提取用于替换的字典
|
||||
if references_dict_list is not None:
|
||||
for doc in references_dict_list:
|
||||
if doc.get('index_id') is not None:
|
||||
references_dict[doc.get('index_id')] = doc.get('doc_name')
|
||||
|
||||
# 将参考资料替换到文本中
|
||||
pending_content = self._replace_references(pending_content, references_dict)
|
||||
|
||||
if idx_chunk % 8 == 0 or is_final:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
is_final=is_final,
|
||||
)
|
||||
# 保存当前会话的session_id用于下次对话的语境
|
||||
query.session.using_conversation.uuid = stream_output.get('session_id')
|
||||
else:
|
||||
for chunk in response:
|
||||
if chunk.get('status_code') != 200:
|
||||
raise DashscopeAPIError(
|
||||
f'Dashscope API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
|
||||
)
|
||||
if not chunk:
|
||||
continue
|
||||
idx_chunk += 1
|
||||
# 获取流式传输的output
|
||||
stream_output = chunk.get('output', {})
|
||||
stream_think = stream_output.get('thoughts') or []
|
||||
if stream_think and stream_think[0].get('thought'):
|
||||
if not think_start:
|
||||
think_start = True
|
||||
pending_content += f'<think>\n{stream_think[0].get("thought")}'
|
||||
else:
|
||||
# 继续输出 reasoning_content
|
||||
pending_content += stream_think[0].get('thought')
|
||||
elif think_start and (not stream_think or stream_think[0].get('thought') == '') and not think_end:
|
||||
think_end = True
|
||||
pending_content += '\n</think>\n'
|
||||
if stream_output.get('text') is not None:
|
||||
pending_content += stream_output.get('text')
|
||||
|
||||
# 保存当前会话的session_id用于下次对话的语境
|
||||
query.session.using_conversation.uuid = stream_output.get('session_id')
|
||||
|
||||
# 获取模型传出的参考资料列表
|
||||
references_dict_list = stream_output.get('doc_references', [])
|
||||
|
||||
# 从模型传出的参考资料信息中提取用于替换的字典
|
||||
if references_dict_list is not None:
|
||||
for doc in references_dict_list:
|
||||
if doc.get('index_id') is not None:
|
||||
references_dict[doc.get('index_id')] = doc.get('doc_name')
|
||||
|
||||
# 将参考资料替换到文本中
|
||||
pending_content = self._replace_references(pending_content, references_dict)
|
||||
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
)
|
||||
|
||||
async def _workflow_messages(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""Dashscope 工作流对话请求"""
|
||||
|
||||
# 局部变量
|
||||
chunk = None # 流式传输的块
|
||||
pending_content = '' # 待处理的Agent输出内容
|
||||
references_dict = {} # 用于存储引用编号和对应的参考资料
|
||||
plain_text = '' # 用户输入的纯文本信息
|
||||
image_ids = [] # 用户输入的图片ID列表 （暂不支持）
|
||||
|
||||
plain_text, image_ids = await self._preprocess_user_message(query)
|
||||
|
||||
biz_params = {}
|
||||
biz_params.update(query.variables)
|
||||
|
||||
# 发送对话请求
|
||||
response = dashscope.Application.call(
|
||||
api_key=self.api_key, # 智能体应用的API Key
|
||||
app_id=self.app_id, # 智能体应用的ID
|
||||
prompt=plain_text, # 用户输入的文本信息
|
||||
stream=True, # 流式输出
|
||||
incremental_output=True, # 增量输出，使用流式输出需要开启增量输出
|
||||
session_id=query.session.using_conversation.uuid, # 会话ID用于，多轮对话
|
||||
biz_params=biz_params, # 工作流应用的自定义输入参数传递
|
||||
flow_stream_mode='message_format', # 消息模式，输出/结束节点的流式结果
|
||||
# rag_options={ # 主要用于文件交互，暂不支持
|
||||
# "session_file_ids": ["FILE_ID1"], # FILE_ID1 替换为实际的临时文件ID,逗号隔开多个
|
||||
# }
|
||||
)
|
||||
|
||||
# 处理API返回的流式输出
|
||||
try:
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
idx_chunk = 0
|
||||
if is_stream:
|
||||
for chunk in response:
|
||||
if chunk.get('status_code') != 200:
|
||||
raise DashscopeAPIError(
|
||||
f'Dashscope API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
|
||||
)
|
||||
if not chunk:
|
||||
continue
|
||||
idx_chunk += 1
|
||||
# 获取流式传输的output
|
||||
stream_output = chunk.get('output', {})
|
||||
if stream_output.get('workflow_message') is not None:
|
||||
pending_content += stream_output.get('workflow_message').get('message').get('content')
|
||||
# if stream_output.get('text') is not None:
|
||||
# pending_content += stream_output.get('text')
|
||||
|
||||
is_final = False if stream_output.get('finish_reason', False) == 'null' else True
|
||||
|
||||
# 获取模型传出的参考资料列表
|
||||
references_dict_list = stream_output.get('doc_references', [])
|
||||
|
||||
# 从模型传出的参考资料信息中提取用于替换的字典
|
||||
if references_dict_list is not None:
|
||||
for doc in references_dict_list:
|
||||
if doc.get('index_id') is not None:
|
||||
references_dict[doc.get('index_id')] = doc.get('doc_name')
|
||||
|
||||
# 将参考资料替换到文本中
|
||||
pending_content = self._replace_references(pending_content, references_dict)
|
||||
if idx_chunk % 8 == 0 or is_final:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
is_final=is_final,
|
||||
)
|
||||
|
||||
# 保存当前会话的session_id用于下次对话的语境
|
||||
query.session.using_conversation.uuid = stream_output.get('session_id')
|
||||
|
||||
else:
|
||||
for chunk in response:
|
||||
if chunk.get('status_code') != 200:
|
||||
raise DashscopeAPIError(
|
||||
f'Dashscope API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
|
||||
)
|
||||
if not chunk:
|
||||
continue
|
||||
|
||||
# 获取流式传输的output
|
||||
stream_output = chunk.get('output', {})
|
||||
if stream_output.get('text') is not None:
|
||||
pending_content += stream_output.get('text')
|
||||
|
||||
is_final = False if stream_output.get('finish_reason', False) == 'null' else True
|
||||
|
||||
# 保存当前会话的session_id用于下次对话的语境
|
||||
query.session.using_conversation.uuid = stream_output.get('session_id')
|
||||
|
||||
# 获取模型传出的参考资料列表
|
||||
references_dict_list = stream_output.get('doc_references', [])
|
||||
|
||||
# 从模型传出的参考资料信息中提取用于替换的字典
|
||||
if references_dict_list is not None:
|
||||
for doc in references_dict_list:
|
||||
if doc.get('index_id') is not None:
|
||||
references_dict[doc.get('index_id')] = doc.get('doc_name')
|
||||
|
||||
# 将参考资料替换到文本中
|
||||
pending_content = self._replace_references(pending_content, references_dict)
|
||||
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
)
|
||||
|
||||
async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""运行"""
|
||||
msg_seq = 0
|
||||
if self.app_type == 'agent':
|
||||
async for msg in self._agent_messages(query):
|
||||
if isinstance(msg, provider_message.MessageChunk):
|
||||
msg_seq += 1
|
||||
msg.msg_sequence = msg_seq
|
||||
yield msg
|
||||
elif self.app_type == 'workflow':
|
||||
async for msg in self._workflow_messages(query):
|
||||
if isinstance(msg, provider_message.MessageChunk):
|
||||
msg_seq += 1
|
||||
msg.msg_sequence = msg_seq
|
||||
yield msg
|
||||
else:
|
||||
raise DashscopeAPIError(f'不支持的 Dashscope 应用类型: {self.app_type}')
|
||||
@@ -1,511 +0,0 @@
|
||||
"""DeerFlow LangGraph API Runner
|
||||
|
||||
参考 astrbot 的 deerflow_agent_runner 实现，适配 LangBot 的 Runner 接口。
|
||||
|
||||
特点：
|
||||
- 使用 LangGraph HTTP API 接入 deer-flow 后端
|
||||
- 自动管理 thread_id（按 session 隔离）
|
||||
- 支持 SSE 流式响应解析
|
||||
- 支持 streaming/非流式两种输出
|
||||
- 处理 values / messages-tuple / custom 三种事件
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import hashlib
|
||||
import json
|
||||
import typing
|
||||
from collections import deque
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
|
||||
from langbot.pkg.provider import runner
|
||||
from langbot.pkg.core import app
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
from langbot.libs.deerflow_api import client, errors, stream_utils
|
||||
|
||||
|
||||
_MAX_VALUES_HISTORY = 200
|
||||
|
||||
|
||||
@dataclass
|
||||
class _StreamState:
|
||||
"""流式状态跟踪"""
|
||||
|
||||
latest_text: str = ''
|
||||
prev_text_for_streaming: str = ''
|
||||
clarification_text: str = ''
|
||||
task_failures: list[str] = field(default_factory=list)
|
||||
seen_message_ids: set[str] = field(default_factory=set)
|
||||
seen_message_order: deque[str] = field(default_factory=deque)
|
||||
no_id_message_fingerprints: dict[int, str] = field(default_factory=dict)
|
||||
baseline_initialized: bool = False
|
||||
has_values_text: bool = False
|
||||
run_values_messages: list[dict[str, typing.Any]] = field(default_factory=list)
|
||||
timed_out: bool = False
|
||||
|
||||
|
||||
@runner.runner_class('deerflow-api')
|
||||
class DeerFlowAPIRunner(runner.RequestRunner):
|
||||
"""DeerFlow LangGraph API 对话请求器"""
|
||||
|
||||
deerflow_client: client.AsyncDeerFlowClient
|
||||
|
||||
def __init__(self, ap: app.Application, pipeline_config: dict):
|
||||
super().__init__(ap, pipeline_config)
|
||||
|
||||
cfg = self.pipeline_config['ai']['deerflow-api']
|
||||
|
||||
api_base = cfg.get('api-base', '').strip()
|
||||
if not api_base or not api_base.startswith(('http://', 'https://')):
|
||||
raise errors.DeerFlowAPIError(
|
||||
message='DeerFlow API Base URL 格式错误，必须以 http:// 或 https:// 开头',
|
||||
)
|
||||
|
||||
self.api_base = api_base
|
||||
self.api_key = cfg.get('api-key', '')
|
||||
self.auth_header = cfg.get('auth-header', '')
|
||||
self.assistant_id = cfg.get('assistant-id', 'lead_agent')
|
||||
self.model_name = cfg.get('model-name', '')
|
||||
self.thinking_enabled = bool(cfg.get('thinking-enabled', False))
|
||||
self.plan_mode = bool(cfg.get('plan-mode', False))
|
||||
self.subagent_enabled = bool(cfg.get('subagent-enabled', False))
|
||||
self.max_concurrent_subagents = int(cfg.get('max-concurrent-subagents', 3))
|
||||
self.timeout = int(cfg.get('timeout', 300))
|
||||
self.recursion_limit = int(cfg.get('recursion-limit', 1000))
|
||||
|
||||
self.deerflow_client = client.AsyncDeerFlowClient(
|
||||
api_base=self.api_base,
|
||||
api_key=self.api_key,
|
||||
auth_header=self.auth_header,
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 辅助方法
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _fingerprint_message(self, message: dict[str, typing.Any]) -> str:
|
||||
try:
|
||||
raw = json.dumps(message, sort_keys=True, ensure_ascii=False, default=str)
|
||||
except (TypeError, ValueError):
|
||||
raw = repr(message)
|
||||
return hashlib.sha1(raw.encode('utf-8', errors='ignore')).hexdigest()
|
||||
|
||||
def _remember_seen_message_id(self, state: _StreamState, msg_id: str) -> None:
|
||||
if not msg_id or msg_id in state.seen_message_ids:
|
||||
return
|
||||
state.seen_message_ids.add(msg_id)
|
||||
state.seen_message_order.append(msg_id)
|
||||
while len(state.seen_message_order) > _MAX_VALUES_HISTORY:
|
||||
dropped = state.seen_message_order.popleft()
|
||||
state.seen_message_ids.discard(dropped)
|
||||
|
||||
def _extract_new_messages_from_values(
|
||||
self,
|
||||
values_messages: list[typing.Any],
|
||||
state: _StreamState,
|
||||
) -> list[dict[str, typing.Any]]:
|
||||
new_messages: list[dict[str, typing.Any]] = []
|
||||
no_id_indexes_seen: set[int] = set()
|
||||
for idx, msg in enumerate(values_messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
msg_id = stream_utils.get_message_id(msg)
|
||||
if msg_id:
|
||||
if msg_id in state.seen_message_ids:
|
||||
continue
|
||||
self._remember_seen_message_id(state, msg_id)
|
||||
new_messages.append(msg)
|
||||
continue
|
||||
|
||||
no_id_indexes_seen.add(idx)
|
||||
fp = self._fingerprint_message(msg)
|
||||
if state.no_id_message_fingerprints.get(idx) == fp:
|
||||
continue
|
||||
state.no_id_message_fingerprints[idx] = fp
|
||||
new_messages.append(msg)
|
||||
|
||||
for idx in list(state.no_id_message_fingerprints.keys()):
|
||||
if idx not in no_id_indexes_seen:
|
||||
state.no_id_message_fingerprints.pop(idx, None)
|
||||
return new_messages
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 用户输入处理
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_user_content(
|
||||
self,
|
||||
prompt: str,
|
||||
image_urls: list[str],
|
||||
) -> typing.Any:
|
||||
"""构建 LangGraph 兼容的 user content（支持多模态）"""
|
||||
if not image_urls:
|
||||
return prompt
|
||||
|
||||
content: list[dict[str, typing.Any]] = []
|
||||
if prompt:
|
||||
content.append({'type': 'text', 'text': prompt})
|
||||
for url in image_urls:
|
||||
if not isinstance(url, str):
|
||||
continue
|
||||
url = url.strip()
|
||||
if not url:
|
||||
continue
|
||||
if url.startswith(('http://', 'https://', 'data:')):
|
||||
content.append({'type': 'image_url', 'image_url': {'url': url}})
|
||||
return content if content else prompt
|
||||
|
||||
def _preprocess_user_message(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> tuple[str, list[str]]:
|
||||
"""提取用户消息的纯文本与图片 URL 列表"""
|
||||
plain_text = ''
|
||||
image_urls: list[str] = []
|
||||
|
||||
if isinstance(query.user_message.content, str):
|
||||
plain_text = query.user_message.content
|
||||
elif isinstance(query.user_message.content, list):
|
||||
for ce in query.user_message.content:
|
||||
if ce.type == 'text':
|
||||
plain_text += ce.text
|
||||
elif ce.type == 'image_base64':
|
||||
# 转换为 data URI 形式
|
||||
b64 = getattr(ce, 'image_base64', '')
|
||||
if b64:
|
||||
if not b64.startswith('data:'):
|
||||
b64 = f'data:image/png;base64,{b64}'
|
||||
image_urls.append(b64)
|
||||
elif ce.type == 'image_url':
|
||||
url = getattr(ce, 'image_url', '')
|
||||
if url:
|
||||
image_urls.append(url)
|
||||
|
||||
return plain_text, image_urls
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 请求构造
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_messages(
|
||||
self,
|
||||
prompt: str,
|
||||
image_urls: list[str],
|
||||
system_prompt: str = '',
|
||||
) -> list[dict[str, typing.Any]]:
|
||||
messages: list[dict[str, typing.Any]] = []
|
||||
if system_prompt:
|
||||
messages.append({'role': 'system', 'content': system_prompt})
|
||||
messages.append(
|
||||
{
|
||||
'role': 'user',
|
||||
'content': self._build_user_content(prompt, image_urls),
|
||||
}
|
||||
)
|
||||
return messages
|
||||
|
||||
def _build_runtime_configurable(self, thread_id: str) -> dict[str, typing.Any]:
|
||||
cfg: dict[str, typing.Any] = {
|
||||
'thread_id': thread_id,
|
||||
'thinking_enabled': self.thinking_enabled,
|
||||
'is_plan_mode': self.plan_mode,
|
||||
'subagent_enabled': self.subagent_enabled,
|
||||
}
|
||||
if self.subagent_enabled:
|
||||
cfg['max_concurrent_subagents'] = self.max_concurrent_subagents
|
||||
if self.model_name:
|
||||
cfg['model_name'] = self.model_name
|
||||
return cfg
|
||||
|
||||
def _build_payload(
|
||||
self,
|
||||
thread_id: str,
|
||||
prompt: str,
|
||||
image_urls: list[str],
|
||||
system_prompt: str = '',
|
||||
) -> dict[str, typing.Any]:
|
||||
runtime_configurable = self._build_runtime_configurable(thread_id)
|
||||
return {
|
||||
'assistant_id': self.assistant_id,
|
||||
'input': {
|
||||
'messages': self._build_messages(prompt, image_urls, system_prompt),
|
||||
},
|
||||
'stream_mode': ['values', 'messages-tuple', 'custom'],
|
||||
# DeerFlow 2.0 从 config.configurable 读取运行时覆盖
|
||||
# 同时保留 context 字段做向后兼容
|
||||
'context': dict(runtime_configurable),
|
||||
'config': {
|
||||
'recursion_limit': self.recursion_limit,
|
||||
'configurable': runtime_configurable,
|
||||
},
|
||||
}
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Session/Thread 管理
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
async def _ensure_thread_id(self, query: pipeline_query.Query) -> str:
|
||||
"""从 query.session 取/创建 deerflow thread_id
|
||||
|
||||
LangBot 使用 `query.session.using_conversation.uuid` 持久化 conversation id，
|
||||
我们复用这个字段存储 deerflow thread_id（与 Dify Runner 同样做法）。
|
||||
"""
|
||||
thread_id = query.session.using_conversation.uuid or ''
|
||||
if thread_id:
|
||||
return thread_id
|
||||
|
||||
thread = await self.deerflow_client.create_thread(timeout=min(30, self.timeout))
|
||||
thread_id = thread.get('thread_id', '')
|
||||
if not thread_id:
|
||||
raise errors.DeerFlowAPIError(message=f'DeerFlow create thread 返回数据缺少 thread_id: {thread}')
|
||||
|
||||
query.session.using_conversation.uuid = thread_id
|
||||
return thread_id
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 流式事件处理
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _handle_values_event(
|
||||
self,
|
||||
data: typing.Any,
|
||||
state: _StreamState,
|
||||
) -> str | None:
|
||||
"""处理 values 事件，返回新的完整文本（增量基础上的全量）"""
|
||||
values_messages = stream_utils.extract_messages_from_values_data(data)
|
||||
if not values_messages:
|
||||
return None
|
||||
|
||||
new_messages: list[dict[str, typing.Any]] = []
|
||||
if not state.baseline_initialized:
|
||||
state.baseline_initialized = True
|
||||
for idx, msg in enumerate(values_messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
new_messages.append(msg)
|
||||
msg_id = stream_utils.get_message_id(msg)
|
||||
if msg_id:
|
||||
self._remember_seen_message_id(state, msg_id)
|
||||
continue
|
||||
state.no_id_message_fingerprints[idx] = self._fingerprint_message(msg)
|
||||
else:
|
||||
new_messages = self._extract_new_messages_from_values(values_messages, state)
|
||||
|
||||
latest_text = ''
|
||||
if new_messages:
|
||||
state.run_values_messages.extend(new_messages)
|
||||
if len(state.run_values_messages) > _MAX_VALUES_HISTORY:
|
||||
state.run_values_messages = state.run_values_messages[-_MAX_VALUES_HISTORY:]
|
||||
latest_text = stream_utils.extract_latest_ai_text(state.run_values_messages)
|
||||
if latest_text:
|
||||
state.has_values_text = True
|
||||
latest_clarification = stream_utils.extract_latest_clarification_text(
|
||||
state.run_values_messages,
|
||||
)
|
||||
if latest_clarification:
|
||||
state.clarification_text = latest_clarification
|
||||
|
||||
return latest_text or None
|
||||
|
||||
def _handle_message_event(
|
||||
self,
|
||||
data: typing.Any,
|
||||
state: _StreamState,
|
||||
) -> str | None:
|
||||
"""处理 messages-tuple 事件，返回增量文本
|
||||
|
||||
当 values 事件已经提供完整文本时，跳过 messages-tuple 的增量
|
||||
"""
|
||||
delta = stream_utils.extract_ai_delta_from_event_data(data)
|
||||
if delta and not state.has_values_text:
|
||||
state.latest_text += delta
|
||||
return delta
|
||||
|
||||
maybe_clar = stream_utils.extract_clarification_from_event_data(data)
|
||||
if maybe_clar:
|
||||
state.clarification_text = maybe_clar
|
||||
return None
|
||||
|
||||
def _build_final_text(self, state: _StreamState) -> str:
|
||||
"""构建最终输出文本"""
|
||||
if state.clarification_text:
|
||||
return state.clarification_text
|
||||
|
||||
# 优先使用最后一条 AI message 的文本
|
||||
latest_ai = stream_utils.extract_latest_ai_message(state.run_values_messages)
|
||||
if latest_ai:
|
||||
text = stream_utils.extract_text(latest_ai.get('content'))
|
||||
if text:
|
||||
if state.timed_out:
|
||||
text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
|
||||
return text
|
||||
|
||||
if state.latest_text:
|
||||
text = state.latest_text
|
||||
if state.timed_out:
|
||||
text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
|
||||
return text
|
||||
|
||||
# 提取任务失败信息作兜底
|
||||
failure_text = stream_utils.build_task_failure_summary(state.task_failures)
|
||||
if failure_text:
|
||||
return failure_text
|
||||
|
||||
return 'DeerFlow 返回空响应'
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 主流程
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
async def _stream_messages_chunk(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
"""流式输出生成器"""
|
||||
plain_text, image_urls = self._preprocess_user_message(query)
|
||||
|
||||
system_prompt = ''
|
||||
# LangBot 的 pipeline 通常通过 prompt-preprocess 已注入 system prompt
|
||||
# 这里保持空，让 prompt-preprocess 的内容作为 user message 一并送给 deerflow
|
||||
|
||||
thread_id = await self._ensure_thread_id(query)
|
||||
payload = self._build_payload(
|
||||
thread_id=thread_id,
|
||||
prompt=plain_text or 'continue',
|
||||
image_urls=image_urls,
|
||||
system_prompt=system_prompt,
|
||||
)
|
||||
|
||||
state = _StreamState()
|
||||
prev_text = ''
|
||||
message_idx = 0
|
||||
|
||||
try:
|
||||
async for event in self.deerflow_client.stream_run(
|
||||
thread_id=thread_id,
|
||||
payload=payload,
|
||||
timeout=self.timeout,
|
||||
):
|
||||
event_type = event.get('event')
|
||||
data = event.get('data')
|
||||
|
||||
if event_type == 'values':
|
||||
new_full = self._handle_values_event(data, state)
|
||||
if new_full and new_full != prev_text:
|
||||
delta = new_full[len(prev_text) :] if new_full.startswith(prev_text) else new_full
|
||||
prev_text = new_full
|
||||
if delta:
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=new_full,
|
||||
is_final=False,
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type in {'messages-tuple', 'messages', 'message'}:
|
||||
delta = self._handle_message_event(data, state)
|
||||
if delta:
|
||||
prev_text = state.latest_text
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=prev_text,
|
||||
is_final=False,
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type == 'custom':
|
||||
state.task_failures.extend(
|
||||
stream_utils.extract_task_failures_from_custom_event(data),
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type == 'error':
|
||||
raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
|
||||
|
||||
if event_type == 'end':
|
||||
break
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
|
||||
state.timed_out = True
|
||||
|
||||
# 最终消息
|
||||
final_text = self._build_final_text(state)
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=final_text,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def _messages(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""非流式聚合输出"""
|
||||
plain_text, image_urls = self._preprocess_user_message(query)
|
||||
|
||||
thread_id = await self._ensure_thread_id(query)
|
||||
payload = self._build_payload(
|
||||
thread_id=thread_id,
|
||||
prompt=plain_text or 'continue',
|
||||
image_urls=image_urls,
|
||||
)
|
||||
|
||||
state = _StreamState()
|
||||
|
||||
try:
|
||||
async for event in self.deerflow_client.stream_run(
|
||||
thread_id=thread_id,
|
||||
payload=payload,
|
||||
timeout=self.timeout,
|
||||
):
|
||||
event_type = event.get('event')
|
||||
data = event.get('data')
|
||||
|
||||
if event_type == 'values':
|
||||
self._handle_values_event(data, state)
|
||||
continue
|
||||
|
||||
if event_type in {'messages-tuple', 'messages', 'message'}:
|
||||
self._handle_message_event(data, state)
|
||||
continue
|
||||
|
||||
if event_type == 'custom':
|
||||
state.task_failures.extend(
|
||||
stream_utils.extract_task_failures_from_custom_event(data),
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type == 'error':
|
||||
raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
|
||||
|
||||
if event_type == 'end':
|
||||
break
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
|
||||
state.timed_out = True
|
||||
|
||||
final_text = self._build_final_text(state)
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=final_text,
|
||||
)
|
||||
|
||||
async def run(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""主入口：根据 adapter 是否支持流式输出，选择流式或非流式"""
|
||||
if await query.adapter.is_stream_output_supported():
|
||||
msg_idx = 0
|
||||
async for msg in self._stream_messages_chunk(query):
|
||||
msg_idx += 1
|
||||
msg.msg_sequence = msg_idx
|
||||
yield msg
|
||||
else:
|
||||
async for msg in self._messages(query):
|
||||
yield msg
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,180 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
import httpx
|
||||
import uuid
|
||||
import traceback
|
||||
|
||||
from .. import runner
|
||||
from ...core import app
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
@runner.runner_class('langflow-api')
|
||||
class LangflowAPIRunner(runner.RequestRunner):
|
||||
"""Langflow API 对话请求器"""
|
||||
|
||||
def __init__(self, ap: app.Application, pipeline_config: dict):
|
||||
self.ap = ap
|
||||
self.pipeline_config = pipeline_config
|
||||
|
||||
async def _build_request_payload(self, query: pipeline_query.Query) -> dict:
|
||||
"""构建请求负载
|
||||
|
||||
Args:
|
||||
query: 用户查询对象
|
||||
|
||||
Returns:
|
||||
dict: 请求负载
|
||||
"""
|
||||
# 获取用户消息文本
|
||||
user_message_text = ''
|
||||
if isinstance(query.user_message.content, str):
|
||||
user_message_text = query.user_message.content
|
||||
elif isinstance(query.user_message.content, list):
|
||||
for item in query.user_message.content:
|
||||
if item.type == 'text':
|
||||
user_message_text += item.text
|
||||
|
||||
# 从配置中获取 input_type 和 output_type，如果未配置则使用默认值
|
||||
input_type = self.pipeline_config['ai']['langflow-api'].get('input_type', 'chat')
|
||||
output_type = self.pipeline_config['ai']['langflow-api'].get('output_type', 'chat')
|
||||
|
||||
# 构建基本负载
|
||||
payload = {
|
||||
'output_type': output_type,
|
||||
'input_type': input_type,
|
||||
'input_value': user_message_text,
|
||||
'session_id': str(uuid.uuid4()),
|
||||
}
|
||||
|
||||
# 如果配置中有tweaks，则添加到负载中
|
||||
tweaks = json.loads(self.pipeline_config['ai']['langflow-api'].get('tweaks'))
|
||||
if tweaks:
|
||||
payload['tweaks'] = tweaks
|
||||
|
||||
return payload
|
||||
|
||||
async def run(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
|
||||
"""运行请求
|
||||
|
||||
Args:
|
||||
query: 用户查询对象
|
||||
|
||||
Yields:
|
||||
Message: 回复消息
|
||||
"""
|
||||
# 检查是否支持流式输出
|
||||
is_stream = False
|
||||
try:
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
|
||||
# 从配置中获取API参数
|
||||
base_url = self.pipeline_config['ai']['langflow-api']['base-url']
|
||||
api_key = self.pipeline_config['ai']['langflow-api']['api-key']
|
||||
flow_id = self.pipeline_config['ai']['langflow-api']['flow-id']
|
||||
|
||||
# 构建API URL
|
||||
url = f'{base_url.rstrip("/")}/api/v1/run/{flow_id}'
|
||||
|
||||
# 构建请求负载
|
||||
payload = await self._build_request_payload(query)
|
||||
|
||||
# 设置请求头
|
||||
headers = {'Content-Type': 'application/json', 'x-api-key': api_key}
|
||||
|
||||
# 发送请求
|
||||
async with httpx.AsyncClient() as client:
|
||||
if is_stream:
|
||||
# 流式请求
|
||||
async with client.stream('POST', url, json=payload, headers=headers, timeout=120.0) as response:
|
||||
response.raise_for_status()
|
||||
|
||||
accumulated_content = ''
|
||||
message_count = 0
|
||||
|
||||
async for line in response.aiter_lines():
|
||||
data_str = line
|
||||
|
||||
if data_str.startswith('data: '):
|
||||
data_str = data_str[6:] # 移除 "data: " 前缀
|
||||
|
||||
try:
|
||||
data = json.loads(data_str)
|
||||
|
||||
# 提取消息内容
|
||||
message_text = ''
|
||||
if 'outputs' in data and len(data['outputs']) > 0:
|
||||
output = data['outputs'][0]
|
||||
if 'outputs' in output and len(output['outputs']) > 0:
|
||||
inner_output = output['outputs'][0]
|
||||
if 'outputs' in inner_output and 'message' in inner_output['outputs']:
|
||||
message_data = inner_output['outputs']['message']
|
||||
if 'message' in message_data:
|
||||
message_text = message_data['message']
|
||||
|
||||
# 如果没有找到消息，尝试其他可能的路径
|
||||
if not message_text and 'messages' in data:
|
||||
messages = data['messages']
|
||||
if messages and len(messages) > 0:
|
||||
message_text = messages[0].get('message', '')
|
||||
|
||||
if message_text:
|
||||
# 更新累积内容
|
||||
accumulated_content = message_text
|
||||
message_count += 1
|
||||
|
||||
# 每8条消息或有新内容时生成一个chunk
|
||||
if message_count % 8 == 0 or len(message_text) > 0:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant', content=accumulated_content, is_final=False
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
# 如果不是JSON，跳过这一行
|
||||
traceback.print_exc()
|
||||
continue
|
||||
|
||||
# 发送最终消息
|
||||
yield provider_message.MessageChunk(role='assistant', content=accumulated_content, is_final=True)
|
||||
else:
|
||||
# 非流式请求
|
||||
response = await client.post(url, json=payload, headers=headers, timeout=120.0)
|
||||
response.raise_for_status()
|
||||
|
||||
# 解析响应
|
||||
response_data = response.json()
|
||||
|
||||
# 提取消息内容
|
||||
# 根据Langflow API文档，响应结构可能在outputs[0].outputs[0].outputs.message.message中
|
||||
message_text = ''
|
||||
if 'outputs' in response_data and len(response_data['outputs']) > 0:
|
||||
output = response_data['outputs'][0]
|
||||
if 'outputs' in output and len(output['outputs']) > 0:
|
||||
inner_output = output['outputs'][0]
|
||||
if 'outputs' in inner_output and 'message' in inner_output['outputs']:
|
||||
message_data = inner_output['outputs']['message']
|
||||
if 'message' in message_data:
|
||||
message_text = message_data['message']
|
||||
|
||||
# 如果没有找到消息，尝试其他可能的路径
|
||||
if not message_text and 'messages' in response_data:
|
||||
messages = response_data['messages']
|
||||
if messages and len(messages) > 0:
|
||||
message_text = messages[0].get('message', '')
|
||||
|
||||
# 如果仍然没有找到消息，返回完整响应的字符串表示
|
||||
if not message_text:
|
||||
message_text = json.dumps(response_data, ensure_ascii=False, indent=2)
|
||||
|
||||
# 生成回复消息
|
||||
if is_stream:
|
||||
yield provider_message.MessageChunk(role='assistant', content=message_text, is_final=True)
|
||||
else:
|
||||
reply_message = provider_message.Message(role='assistant', content=message_text)
|
||||
yield reply_message
|
||||
@@ -1,645 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import copy
|
||||
import typing
|
||||
from .. import runner
|
||||
from ...telemetry import features as telemetry_features
|
||||
from ..modelmgr import requester as modelmgr_requester
|
||||
from ..tools.loaders.native import EXEC_TOOL_NAME
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
import langbot_plugin.api.entities.builtin.rag.context as rag_context
|
||||
|
||||
|
||||
rag_combined_prompt_template = """
|
||||
The following are relevant context entries retrieved from the knowledge base.
|
||||
Please use them to answer the user's message.
|
||||
Respond in the same language as the user's input.
|
||||
|
||||
<context>
|
||||
{rag_context}
|
||||
</context>
|
||||
|
||||
<user_message>
|
||||
{user_message}
|
||||
</user_message>
|
||||
"""
|
||||
|
||||
SANDBOX_EXEC_TOOL_NAME = 'sandbox_exec'
|
||||
SANDBOX_EXEC_SYSTEM_GUIDANCE = (
|
||||
'When sandbox_exec is available, use it for exact calculations, statistics, structured data parsing, '
|
||||
'and code execution instead of estimating mentally. If the user provides numbers, tables, CSV-like text, '
|
||||
'JSON, or other data and asks for a computed answer, prefer running a short Python script in sandbox_exec '
|
||||
'and then answer from the tool result.'
|
||||
)
|
||||
|
||||
|
||||
# Hard cap on tool-call rounds within a single agent turn. A looping or
|
||||
# adversarial model can otherwise emit tool calls indefinitely (each potentially
|
||||
# a sandbox exec), yielding a non-terminating request and runaway cost. Set
|
||||
# generously so it never interrupts legitimate multi-step agentic workflows.
|
||||
MAX_TOOL_CALL_ROUNDS = 128
|
||||
|
||||
|
||||
def _model_has_ability(model: modelmgr_requester.RuntimeLLMModel, ability: str) -> bool:
|
||||
return ability in (model.model_entity.abilities or [])
|
||||
|
||||
|
||||
class _StreamAccumulator:
|
||||
"""Accumulate streamed content and fragmented OpenAI-style tool calls."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
msg_sequence: int = 0,
|
||||
initial_content: str | None = None,
|
||||
remove_think: bool = False,
|
||||
):
|
||||
self.tool_calls_map: dict[str, provider_message.ToolCall] = {}
|
||||
self.msg_idx = 0
|
||||
self.accumulated_content = initial_content or ''
|
||||
self.last_role = 'assistant'
|
||||
self.msg_sequence = msg_sequence
|
||||
self.remove_think = remove_think
|
||||
self._think_state = None
|
||||
if remove_think:
|
||||
from ..modelmgr.requesters.litellmchat import _ThinkStripState
|
||||
|
||||
self._think_state = _ThinkStripState()
|
||||
|
||||
def add(self, msg: provider_message.MessageChunk) -> provider_message.MessageChunk | None:
|
||||
self.msg_idx += 1
|
||||
|
||||
if msg.role:
|
||||
self.last_role = msg.role
|
||||
|
||||
if msg.content:
|
||||
content = msg.content
|
||||
if self._think_state is not None:
|
||||
content = self._think_state.feed(content)
|
||||
self.accumulated_content += content
|
||||
|
||||
if msg.tool_calls:
|
||||
for tool_call in msg.tool_calls:
|
||||
if tool_call.id not in self.tool_calls_map:
|
||||
self.tool_calls_map[tool_call.id] = provider_message.ToolCall(
|
||||
id=tool_call.id,
|
||||
type=tool_call.type,
|
||||
function=provider_message.FunctionCall(
|
||||
name=tool_call.function.name if tool_call.function else '',
|
||||
arguments='',
|
||||
),
|
||||
)
|
||||
if tool_call.function and tool_call.function.arguments:
|
||||
self.tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
|
||||
|
||||
if msg.is_final:
|
||||
self._flush_think_state()
|
||||
|
||||
if self.msg_idx % 8 == 0 or msg.is_final:
|
||||
self.msg_sequence += 1
|
||||
return provider_message.MessageChunk(
|
||||
role=self.last_role,
|
||||
content=self._maybe_strip_think(self.accumulated_content),
|
||||
tool_calls=list(self.tool_calls_map.values()) if (self.tool_calls_map and msg.is_final) else None,
|
||||
is_final=msg.is_final,
|
||||
msg_sequence=self.msg_sequence,
|
||||
)
|
||||
|
||||
return None
|
||||
|
||||
def final_message(self) -> provider_message.MessageChunk:
|
||||
self._flush_think_state()
|
||||
return provider_message.MessageChunk(
|
||||
role=self.last_role,
|
||||
content=self._maybe_strip_think(self.accumulated_content),
|
||||
tool_calls=list(self.tool_calls_map.values()) if self.tool_calls_map else None,
|
||||
msg_sequence=self.msg_sequence,
|
||||
)
|
||||
|
||||
def _maybe_strip_think(self, content: str) -> str:
|
||||
if not self.remove_think or not content:
|
||||
return content
|
||||
|
||||
from ..modelmgr.requesters.litellmchat import LiteLLMRequester
|
||||
|
||||
return LiteLLMRequester._strip_think(content)
|
||||
|
||||
def _flush_think_state(self) -> None:
|
||||
if self._think_state is None:
|
||||
return
|
||||
pending = self._think_state.flush()
|
||||
if pending:
|
||||
self.accumulated_content += pending
|
||||
|
||||
|
||||
@runner.runner_class('local-agent')
|
||||
class LocalAgentRunner(runner.RequestRunner):
|
||||
"""Local agent request runner"""
|
||||
|
||||
async def _inject_inbound_attachments(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
user_message: provider_message.Message,
|
||||
) -> None:
|
||||
"""Persist inbound attachments into the sandbox and tell the model.
|
||||
|
||||
No-op when the box service is unavailable or there are no attachments.
|
||||
On success, appends an extra text ContentElement to the user message
|
||||
listing the in-sandbox paths and the outbox convention, and stashes the
|
||||
descriptors in ``query.variables['_sandbox_inbound_attachments']``.
|
||||
"""
|
||||
box_service = getattr(self.ap, 'box_service', None)
|
||||
if box_service is None or not getattr(box_service, 'available', False):
|
||||
return
|
||||
try:
|
||||
attachments = await box_service.materialize_inbound_attachments(query)
|
||||
except Exception as e: # never break the chat turn over attachment IO
|
||||
self.ap.logger.warning(f'Inbound attachment materialization failed: {e}')
|
||||
return
|
||||
if not attachments:
|
||||
return
|
||||
|
||||
query.variables['_sandbox_inbound_attachments'] = attachments
|
||||
|
||||
lines = [
|
||||
'The user sent attachments. They have been saved into the sandbox and are '
|
||||
'available to the exec/read/write tools at these paths:'
|
||||
]
|
||||
for att in attachments:
|
||||
lines.append(f'- {att["type"]}: {att["path"]} ({att["size"]} bytes)')
|
||||
outbox_dir = f'{box_service.OUTBOX_MOUNT_DIR}/{query.query_id}'
|
||||
lines.append(
|
||||
'If you produce any file (image, audio, document, etc.) that should be sent '
|
||||
f'back to the user, write it into {outbox_dir}/ (create the directory if '
|
||||
'needed). Every file placed there will be delivered to the user automatically.'
|
||||
)
|
||||
note = '\n'.join(lines)
|
||||
|
||||
# Voice/File attachments are now available to the agent via the sandbox
|
||||
# (exec/read/write tools). Their raw bytes must NOT be forwarded to the
|
||||
# chat model as multimodal content: providers reject non-image file
|
||||
# parts ("Invalid user message ... ensure all user messages are valid
|
||||
# OpenAI chat completion messages"). Strip those content elements and
|
||||
# rely on the sandbox-path note instead. Images are kept so vision
|
||||
# models can still see them.
|
||||
_model_unsafe_types = {'file_base64', 'file_url'}
|
||||
if isinstance(user_message.content, list):
|
||||
user_message.content = [
|
||||
ce for ce in user_message.content if getattr(ce, 'type', None) not in _model_unsafe_types
|
||||
]
|
||||
|
||||
if isinstance(user_message.content, str):
|
||||
user_message.content = [
|
||||
provider_message.ContentElement.from_text(user_message.content),
|
||||
provider_message.ContentElement.from_text(note),
|
||||
]
|
||||
elif isinstance(user_message.content, list):
|
||||
user_message.content.append(provider_message.ContentElement.from_text(note))
|
||||
else:
|
||||
user_message.content = [provider_message.ContentElement.from_text(note)]
|
||||
|
||||
def _build_request_messages(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
user_message: provider_message.Message,
|
||||
) -> list[provider_message.Message]:
|
||||
req_messages = query.prompt.messages.copy() + query.messages.copy()
|
||||
|
||||
if any(getattr(tool, 'name', None) == EXEC_TOOL_NAME for tool in query.use_funcs or []):
|
||||
req_messages.append(
|
||||
provider_message.Message(
|
||||
role='system',
|
||||
content=self.ap.box_service.get_system_guidance(query.query_id),
|
||||
)
|
||||
)
|
||||
|
||||
req_messages.append(user_message)
|
||||
return req_messages
|
||||
|
||||
async def _get_model_candidates(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> list[modelmgr_requester.RuntimeLLMModel]:
|
||||
"""Build ordered list of models to try: primary model + fallback models."""
|
||||
candidates = []
|
||||
|
||||
# Primary model
|
||||
if query.use_llm_model_uuid:
|
||||
try:
|
||||
primary = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
|
||||
candidates.append(primary)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'Primary model {query.use_llm_model_uuid} not found')
|
||||
|
||||
# Fallback models
|
||||
fallback_uuids = (query.variables or {}).get('_fallback_model_uuids', [])
|
||||
for fb_uuid in fallback_uuids:
|
||||
try:
|
||||
fb_model = await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
|
||||
candidates.append(fb_model)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
|
||||
|
||||
return candidates
|
||||
|
||||
async def _invoke_with_fallback(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
candidates: list[modelmgr_requester.RuntimeLLMModel],
|
||||
messages: list,
|
||||
funcs: list,
|
||||
remove_think: bool,
|
||||
) -> tuple[provider_message.Message, modelmgr_requester.RuntimeLLMModel]:
|
||||
"""Try non-streaming invocation with sequential fallback. Returns (message, model_used)."""
|
||||
last_error = None
|
||||
for model in candidates:
|
||||
try:
|
||||
msg = await model.provider.invoke_llm(
|
||||
query,
|
||||
model,
|
||||
messages,
|
||||
funcs if _model_has_ability(model, 'func_call') else [],
|
||||
extra_args=model.model_entity.extra_args,
|
||||
remove_think=remove_think,
|
||||
)
|
||||
return msg, model
|
||||
except Exception as e:
|
||||
last_error = e
|
||||
self.ap.logger.warning(f'Model {model.model_entity.name} failed: {e}, trying next fallback...')
|
||||
raise last_error or RuntimeError('No model candidates available')
|
||||
|
||||
async def _invoke_stream_with_fallback(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
candidates: list[modelmgr_requester.RuntimeLLMModel],
|
||||
messages: list,
|
||||
funcs: list,
|
||||
remove_think: bool,
|
||||
) -> tuple[typing.AsyncGenerator, modelmgr_requester.RuntimeLLMModel]:
|
||||
"""Try streaming invocation with sequential fallback. Returns (stream_generator, model_used).
|
||||
|
||||
Fallback is only possible before any chunks have been yielded to the client.
|
||||
Once streaming starts, the model is committed.
|
||||
"""
|
||||
last_error = None
|
||||
for model in candidates:
|
||||
try:
|
||||
stream = model.provider.invoke_llm_stream(
|
||||
query,
|
||||
model,
|
||||
messages,
|
||||
funcs if _model_has_ability(model, 'func_call') else [],
|
||||
extra_args=model.model_entity.extra_args,
|
||||
remove_think=remove_think,
|
||||
)
|
||||
# Attempt to get the first chunk to verify the stream works
|
||||
first_chunk = await stream.__anext__()
|
||||
|
||||
async def _chain_stream(first, rest):
|
||||
yield first
|
||||
async for chunk in rest:
|
||||
yield chunk
|
||||
|
||||
return _chain_stream(first_chunk, stream), model
|
||||
except StopAsyncIteration:
|
||||
# Empty stream — treat as success (model returned nothing)
|
||||
async def _empty_stream():
|
||||
return
|
||||
yield # make it a generator
|
||||
|
||||
return _empty_stream(), model
|
||||
except Exception as e:
|
||||
last_error = e
|
||||
self.ap.logger.warning(f'Model {model.model_entity.name} stream failed: {e}, trying next fallback...')
|
||||
raise last_error or RuntimeError('No model candidates available')
|
||||
|
||||
async def run(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
|
||||
"""Run request"""
|
||||
pending_tool_calls = []
|
||||
initial_response_emitted = False
|
||||
|
||||
# Get knowledge bases list from query variables (set by PreProcessor,
|
||||
# may have been modified by plugins during PromptPreProcessing)
|
||||
kb_uuids = query.variables.get('_knowledge_base_uuids', [])
|
||||
|
||||
user_message = copy.deepcopy(query.user_message)
|
||||
|
||||
# Materialize inbound attachments (images / voices / files) into the
|
||||
# sandbox so the agent's exec/read/write tools can operate on the real
|
||||
# bytes — not just the multimodal copy the model sees. The exact
|
||||
# in-sandbox paths are announced to the model as a system note.
|
||||
await self._inject_inbound_attachments(query, user_message)
|
||||
|
||||
user_message_text = ''
|
||||
|
||||
if isinstance(user_message.content, str):
|
||||
user_message_text = user_message.content
|
||||
elif isinstance(user_message.content, list):
|
||||
for ce in user_message.content:
|
||||
if ce.type == 'text':
|
||||
user_message_text += ce.text
|
||||
break
|
||||
|
||||
if kb_uuids and user_message_text:
|
||||
# only support text for now
|
||||
all_results: list[rag_context.RetrievalResultEntry] = []
|
||||
|
||||
kb_engine_plugins: set[str] = set()
|
||||
|
||||
# Retrieve from each knowledge base
|
||||
for kb_uuid in kb_uuids:
|
||||
kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
|
||||
|
||||
if not kb:
|
||||
self.ap.logger.warning(f'Knowledge base {kb_uuid} not found, skipping')
|
||||
continue
|
||||
|
||||
try:
|
||||
engine_plugin_id = kb.get_knowledge_engine_plugin_id() or 'builtin'
|
||||
except Exception:
|
||||
engine_plugin_id = 'builtin'
|
||||
kb_engine_plugins.add(engine_plugin_id)
|
||||
|
||||
result = await kb.retrieve(
|
||||
user_message_text,
|
||||
settings={
|
||||
'bot_uuid': query.bot_uuid or '',
|
||||
'sender_id': str(query.sender_id),
|
||||
'session_name': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||||
},
|
||||
)
|
||||
|
||||
if result:
|
||||
all_results.extend(result)
|
||||
|
||||
# Telemetry: knowledge base usage (counts and engine categories only)
|
||||
telemetry_features.set_value(
|
||||
query,
|
||||
'kb',
|
||||
{
|
||||
'kb_count': len(kb_uuids),
|
||||
'engine_plugins': sorted(kb_engine_plugins),
|
||||
'retrieved_entries': len(all_results),
|
||||
},
|
||||
)
|
||||
|
||||
# Rerank step: re-score results using a rerank model if configured
|
||||
local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
|
||||
rerank_model_uuid = local_agent_config.get('rerank-model', '')
|
||||
if rerank_model_uuid == '__none__':
|
||||
rerank_model_uuid = ''
|
||||
self.ap.logger.info(
|
||||
f'Rerank config: model_uuid={rerank_model_uuid!r}, '
|
||||
f'results={len(all_results)}, '
|
||||
f'local_agent_keys={list(local_agent_config.keys())}'
|
||||
)
|
||||
if all_results and rerank_model_uuid:
|
||||
try:
|
||||
rerank_model = await self.ap.model_mgr.get_rerank_model_by_uuid(rerank_model_uuid)
|
||||
rerank_top_k = int(local_agent_config.get('rerank-top-k', 5))
|
||||
|
||||
doc_texts = []
|
||||
for entry in all_results:
|
||||
text = ' '.join(c.text for c in entry.content if c.type == 'text' and c.text)
|
||||
doc_texts.append(text)
|
||||
|
||||
doc_texts_capped = doc_texts[:64]
|
||||
scores = await rerank_model.provider.invoke_rerank(
|
||||
model=rerank_model,
|
||||
query=user_message_text,
|
||||
documents=doc_texts_capped,
|
||||
)
|
||||
|
||||
scored = sorted(scores, key=lambda x: x.get('relevance_score', 0), reverse=True)
|
||||
top_indices = [s['index'] for s in scored[:rerank_top_k] if s['index'] < len(all_results)]
|
||||
all_results = [all_results[i] for i in top_indices]
|
||||
|
||||
self.ap.logger.info(
|
||||
f'Rerank complete: {len(doc_texts)} docs reranked -> top {len(all_results)} kept (top_k={rerank_top_k})'
|
||||
)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'Rerank model {rerank_model_uuid} not found, skipping rerank')
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Rerank failed, using original order: {e}')
|
||||
|
||||
final_user_message_text = ''
|
||||
|
||||
if all_results:
|
||||
texts = []
|
||||
idx = 1
|
||||
for entry in all_results:
|
||||
for content in entry.content:
|
||||
if content.type == 'text' and content.text is not None:
|
||||
texts.append(f'[{idx}] {content.text}')
|
||||
idx += 1
|
||||
rag_context_text = '\n\n'.join(texts)
|
||||
final_user_message_text = rag_combined_prompt_template.format(
|
||||
rag_context=rag_context_text, user_message=user_message_text
|
||||
)
|
||||
|
||||
else:
|
||||
final_user_message_text = user_message_text
|
||||
|
||||
self.ap.logger.debug(f'Final user message text: {final_user_message_text}')
|
||||
|
||||
for ce in user_message.content:
|
||||
if ce.type == 'text':
|
||||
ce.text = final_user_message_text
|
||||
break
|
||||
|
||||
mcp_loader = getattr(getattr(self.ap, 'tool_mgr', None), 'mcp_tool_loader', None)
|
||||
if mcp_loader is not None:
|
||||
resource_context = await mcp_loader.build_resource_context_for_query(query)
|
||||
if resource_context:
|
||||
resource_addition = (
|
||||
'\n\nMCP resource context selected by LangBot host:\n'
|
||||
f'{resource_context}\n\n'
|
||||
'Use this context as read-only reference material. If it conflicts with the user message, '
|
||||
'ask for clarification before taking external actions.'
|
||||
)
|
||||
if isinstance(user_message.content, str):
|
||||
user_message.content += resource_addition
|
||||
elif isinstance(user_message.content, list):
|
||||
appended = False
|
||||
for ce in user_message.content:
|
||||
if ce.type == 'text':
|
||||
ce.text = (ce.text or '') + resource_addition
|
||||
appended = True
|
||||
break
|
||||
if not appended:
|
||||
user_message.content.append(
|
||||
provider_message.ContentElement.from_text(resource_addition.strip())
|
||||
)
|
||||
|
||||
req_messages = self._build_request_messages(query, user_message)
|
||||
|
||||
try:
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
|
||||
remove_think = ((query.pipeline_config.get('output') or {}).get('misc') or {}).get('remove-think', False)
|
||||
|
||||
# Build ordered candidate list (primary + fallbacks)
|
||||
candidates = await self._get_model_candidates(query)
|
||||
if not candidates:
|
||||
raise RuntimeError('No LLM model configured for local-agent runner')
|
||||
|
||||
self.ap.logger.debug(
|
||||
f'localagent req: query={query.query_id} req_messages={req_messages} '
|
||||
f'candidates={[m.model_entity.name for m in candidates]}'
|
||||
)
|
||||
|
||||
if not is_stream:
|
||||
# Non-streaming: invoke with fallback
|
||||
msg, use_llm_model = await self._invoke_with_fallback(
|
||||
query,
|
||||
candidates,
|
||||
req_messages,
|
||||
query.use_funcs,
|
||||
remove_think,
|
||||
)
|
||||
final_msg = msg
|
||||
else:
|
||||
# Streaming: invoke with fallback
|
||||
stream_accumulator = _StreamAccumulator(msg_sequence=1, remove_think=remove_think)
|
||||
|
||||
stream_src, use_llm_model = await self._invoke_stream_with_fallback(
|
||||
query,
|
||||
candidates,
|
||||
req_messages,
|
||||
query.use_funcs,
|
||||
remove_think,
|
||||
)
|
||||
async for msg in stream_src:
|
||||
chunk = stream_accumulator.add(msg)
|
||||
if chunk:
|
||||
yield chunk
|
||||
initial_response_emitted = True
|
||||
|
||||
final_msg = stream_accumulator.final_message()
|
||||
|
||||
pending_tool_calls = final_msg.tool_calls
|
||||
first_content = final_msg.content
|
||||
if isinstance(final_msg, provider_message.MessageChunk):
|
||||
first_end_sequence = final_msg.msg_sequence
|
||||
|
||||
if not is_stream:
|
||||
yield final_msg
|
||||
elif not initial_response_emitted:
|
||||
yield final_msg
|
||||
initial_response_emitted = True
|
||||
|
||||
req_messages.append(final_msg)
|
||||
|
||||
# Once a model succeeds, commit to it for the tool call loop
|
||||
# (no fallback mid-conversation — different models may interpret tool results differently)
|
||||
tool_call_round = 0
|
||||
while pending_tool_calls:
|
||||
tool_call_round += 1
|
||||
telemetry_features.set_value(query, 'tool_call_rounds', tool_call_round)
|
||||
if tool_call_round > MAX_TOOL_CALL_ROUNDS:
|
||||
self.ap.logger.warning(
|
||||
f'Tool-call loop reached the {MAX_TOOL_CALL_ROUNDS}-round cap '
|
||||
f'(query_id={query.query_id}); stopping to avoid a non-terminating request.'
|
||||
)
|
||||
break
|
||||
for tool_call in pending_tool_calls:
|
||||
try:
|
||||
func = tool_call.function
|
||||
|
||||
if func.arguments:
|
||||
parameters = json.loads(func.arguments)
|
||||
else:
|
||||
parameters = {}
|
||||
|
||||
func_ret = await self.ap.tool_mgr.execute_func_call(func.name, parameters, query=query)
|
||||
|
||||
# Handle return value content
|
||||
tool_content = None
|
||||
if (
|
||||
isinstance(func_ret, list)
|
||||
and len(func_ret) > 0
|
||||
and isinstance(func_ret[0], provider_message.ContentElement)
|
||||
):
|
||||
tool_content = func_ret
|
||||
else:
|
||||
tool_content = json.dumps(func_ret, ensure_ascii=False)
|
||||
|
||||
if is_stream:
|
||||
msg = provider_message.MessageChunk(
|
||||
role='tool',
|
||||
content=tool_content,
|
||||
tool_call_id=tool_call.id,
|
||||
)
|
||||
else:
|
||||
msg = provider_message.Message(
|
||||
role='tool',
|
||||
content=tool_content,
|
||||
tool_call_id=tool_call.id,
|
||||
)
|
||||
|
||||
yield msg
|
||||
|
||||
req_messages.append(msg)
|
||||
except Exception as e:
|
||||
if is_stream:
|
||||
err_msg = provider_message.MessageChunk(
|
||||
role='tool',
|
||||
content=f'err: {e}',
|
||||
tool_call_id=tool_call.id,
|
||||
is_final=True,
|
||||
)
|
||||
else:
|
||||
err_msg = provider_message.Message(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
|
||||
|
||||
yield err_msg
|
||||
|
||||
req_messages.append(err_msg)
|
||||
|
||||
self.ap.logger.debug(
|
||||
f'localagent req: query={query.query_id} req_messages={req_messages} '
|
||||
f'use_llm_model={use_llm_model.model_entity.name}'
|
||||
)
|
||||
|
||||
if is_stream:
|
||||
stream_accumulator = _StreamAccumulator(
|
||||
msg_sequence=first_end_sequence,
|
||||
initial_content=first_content,
|
||||
remove_think=remove_think,
|
||||
)
|
||||
|
||||
tool_stream_src = use_llm_model.provider.invoke_llm_stream(
|
||||
query,
|
||||
use_llm_model,
|
||||
req_messages,
|
||||
query.use_funcs if _model_has_ability(use_llm_model, 'func_call') else [],
|
||||
extra_args=use_llm_model.model_entity.extra_args,
|
||||
remove_think=remove_think,
|
||||
)
|
||||
async for msg in tool_stream_src:
|
||||
chunk = stream_accumulator.add(msg)
|
||||
if chunk:
|
||||
yield chunk
|
||||
|
||||
final_msg = stream_accumulator.final_message()
|
||||
else:
|
||||
# Non-streaming: use committed model directly (no fallback in tool loop)
|
||||
msg = await use_llm_model.provider.invoke_llm(
|
||||
query,
|
||||
use_llm_model,
|
||||
req_messages,
|
||||
query.use_funcs if _model_has_ability(use_llm_model, 'func_call') else [],
|
||||
extra_args=use_llm_model.model_entity.extra_args,
|
||||
remove_think=remove_think,
|
||||
)
|
||||
|
||||
yield msg
|
||||
final_msg = msg
|
||||
|
||||
pending_tool_calls = final_msg.tool_calls
|
||||
|
||||
req_messages.append(final_msg)
|
||||
@@ -1,277 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
import uuid
|
||||
import aiohttp
|
||||
|
||||
from langbot.pkg.utils import httpclient
|
||||
|
||||
from .. import runner
|
||||
from ...core import app
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
class N8nAPIError(Exception):
|
||||
"""N8n API 请求失败"""
|
||||
|
||||
def __init__(self, message: str):
|
||||
self.message = message
|
||||
super().__init__(self.message)
|
||||
|
||||
|
||||
@runner.runner_class('n8n-service-api')
|
||||
class N8nServiceAPIRunner(runner.RequestRunner):
|
||||
"""N8n Service API 工作流请求器"""
|
||||
|
||||
def __init__(self, ap: app.Application, pipeline_config: dict):
|
||||
self.ap = ap
|
||||
self.pipeline_config = pipeline_config
|
||||
|
||||
# 获取webhook URL
|
||||
self.webhook_url = self.pipeline_config['ai']['n8n-service-api']['webhook-url']
|
||||
|
||||
# 获取超时设置，默认为120秒
|
||||
self.timeout = self.pipeline_config['ai']['n8n-service-api'].get('timeout', 120)
|
||||
|
||||
# 获取输出键名，默认为response
|
||||
self.output_key = self.pipeline_config['ai']['n8n-service-api'].get('output-key', 'response')
|
||||
|
||||
# 获取认证类型，默认为none
|
||||
self.auth_type = self.pipeline_config['ai']['n8n-service-api'].get('auth-type', 'none')
|
||||
|
||||
# 根据认证类型获取相应的认证信息
|
||||
if self.auth_type == 'basic':
|
||||
self.basic_username = self.pipeline_config['ai']['n8n-service-api'].get('basic-username', '')
|
||||
self.basic_password = self.pipeline_config['ai']['n8n-service-api'].get('basic-password', '')
|
||||
elif self.auth_type == 'jwt':
|
||||
self.jwt_secret = self.pipeline_config['ai']['n8n-service-api'].get('jwt-secret', '')
|
||||
self.jwt_algorithm = self.pipeline_config['ai']['n8n-service-api'].get('jwt-algorithm', 'HS256')
|
||||
elif self.auth_type == 'header':
|
||||
self.header_name = self.pipeline_config['ai']['n8n-service-api'].get('header-name', '')
|
||||
self.header_value = self.pipeline_config['ai']['n8n-service-api'].get('header-value', '')
|
||||
|
||||
async def _preprocess_user_message(self, query: pipeline_query.Query) -> str:
|
||||
"""预处理用户消息，提取纯文本
|
||||
|
||||
Returns:
|
||||
str: 纯文本消息
|
||||
"""
|
||||
plain_text = ''
|
||||
|
||||
if isinstance(query.user_message.content, list):
|
||||
for ce in query.user_message.content:
|
||||
if ce.type == 'text':
|
||||
plain_text += ce.text
|
||||
# 注意：n8n webhook目前不支持直接处理图片，如需支持可在此扩展
|
||||
elif isinstance(query.user_message.content, str):
|
||||
plain_text = query.user_message.content
|
||||
|
||||
return plain_text
|
||||
|
||||
async def _process_response(
|
||||
self, response: aiohttp.ClientResponse
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""处理响应——支持流式格式和普通 JSON 格式"""
|
||||
full_content = ''
|
||||
full_text = ''
|
||||
chunk_idx = 0
|
||||
is_final = False
|
||||
message_idx = 0
|
||||
|
||||
buffer = ''
|
||||
decoder = json.JSONDecoder()
|
||||
|
||||
async for raw_chunk in response.content.iter_chunked(1024):
|
||||
if not raw_chunk:
|
||||
continue
|
||||
|
||||
try:
|
||||
# 将 bytes 解码为字符串（容忍错误）
|
||||
if isinstance(raw_chunk, (bytes, bytearray)):
|
||||
chunk_str = raw_chunk.decode('utf-8', errors='replace')
|
||||
else:
|
||||
chunk_str = str(raw_chunk)
|
||||
|
||||
full_text += chunk_str
|
||||
buffer += chunk_str
|
||||
|
||||
# 尝试从 buffer 中循环解析出 JSON 对象（处理多个对象或部分对象）
|
||||
while buffer:
|
||||
buffer = buffer.lstrip()
|
||||
if not buffer:
|
||||
break
|
||||
try:
|
||||
obj, idx = decoder.raw_decode(buffer)
|
||||
buffer = buffer[idx:]
|
||||
|
||||
if not isinstance(obj, dict):
|
||||
# 忽略非字典类型的顶级 JSON
|
||||
continue
|
||||
|
||||
if obj.get('type') == 'item' and 'content' in obj:
|
||||
chunk_idx += 1
|
||||
content = obj['content']
|
||||
full_content += content
|
||||
elif obj.get('type') == 'end':
|
||||
is_final = True
|
||||
|
||||
if is_final or (chunk_idx > 0 and chunk_idx % 8 == 0):
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=full_content,
|
||||
is_final=is_final,
|
||||
msg_sequence=message_idx,
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
# buffer 末尾可能是一个不完整的 JSON，等待更多数据
|
||||
break
|
||||
except Exception as e:
|
||||
# 记录解析失败并继续接收后续 chunk
|
||||
try:
|
||||
preview = chunk_str[:200]
|
||||
except Exception:
|
||||
preview = '<unavailable>'
|
||||
self.ap.logger.warning(f'Failed to process chunk: {e}; chunk preview: {preview}')
|
||||
|
||||
# 流结束后，尝试解析残余 buffer
|
||||
if buffer:
|
||||
try:
|
||||
buffer = buffer.strip()
|
||||
if buffer:
|
||||
obj, _ = decoder.raw_decode(buffer)
|
||||
if isinstance(obj, dict):
|
||||
if obj.get('type') == 'item' and 'content' in obj:
|
||||
chunk_idx += 1
|
||||
full_content += obj['content']
|
||||
elif obj.get('type') == 'end':
|
||||
is_final = True
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=full_content,
|
||||
is_final=is_final,
|
||||
msg_sequence=message_idx,
|
||||
)
|
||||
except Exception as e:
|
||||
preview = buffer[:200]
|
||||
self.ap.logger.warning(f'Failed to parse remaining buffer: {e}; buffer preview: {preview}')
|
||||
|
||||
# n8n 返回普通 JSON 格式（无任何流式 type:item 内容）
|
||||
if chunk_idx == 0:
|
||||
output_content = ''
|
||||
try:
|
||||
response_data = json.loads(full_text.strip())
|
||||
if isinstance(response_data, dict):
|
||||
if self.output_key in response_data:
|
||||
output_content = response_data[self.output_key]
|
||||
else:
|
||||
output_content = json.dumps(response_data, ensure_ascii=False)
|
||||
else:
|
||||
output_content = full_text
|
||||
except json.JSONDecodeError:
|
||||
output_content = full_text
|
||||
self.ap.logger.debug(f'n8n webhook response (non-stream): {full_text[:200]}')
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=output_content,
|
||||
is_final=True,
|
||||
msg_sequence=message_idx + 1,
|
||||
)
|
||||
|
||||
async def _call_webhook(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""调用n8n webhook"""
|
||||
# 生成会话ID（如果不存在）
|
||||
if not query.session.using_conversation.uuid:
|
||||
query.session.using_conversation.uuid = str(uuid.uuid4())
|
||||
|
||||
# Keep query variables in sync with the generated/new conversation id.
|
||||
# query.variables is later merged into payload and would otherwise
|
||||
# overwrite the generated conversation_id with the stale preprocessor
|
||||
# value (usually None for a new conversation).
|
||||
query.variables['conversation_id'] = query.session.using_conversation.uuid
|
||||
|
||||
# 预处理用户消息
|
||||
plain_text = await self._preprocess_user_message(query)
|
||||
|
||||
# 准备请求数据
|
||||
payload = {
|
||||
# 基本消息内容
|
||||
'chatInput': plain_text, # 考虑到之前用户直接用的message model这里添加新键
|
||||
'message': plain_text,
|
||||
'user_message_text': plain_text,
|
||||
'conversation_id': query.session.using_conversation.uuid,
|
||||
'session_id': query.variables.get('session_id', ''),
|
||||
'user_id': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||||
'msg_create_time': query.variables.get('msg_create_time', ''),
|
||||
}
|
||||
|
||||
# 添加所有变量到payload
|
||||
payload.update(query.variables)
|
||||
|
||||
try:
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
|
||||
try:
|
||||
# 准备请求头和认证信息
|
||||
headers = {}
|
||||
auth = None
|
||||
|
||||
# 根据认证类型设置相应的认证信息
|
||||
if self.auth_type == 'basic':
|
||||
# 使用Basic认证
|
||||
auth = aiohttp.BasicAuth(self.basic_username, self.basic_password)
|
||||
self.ap.logger.debug(f'using basic auth: {self.basic_username}')
|
||||
elif self.auth_type == 'jwt':
|
||||
# 使用JWT认证
|
||||
import jwt
|
||||
import time
|
||||
|
||||
# 创建JWT令牌
|
||||
payload_jwt = {
|
||||
'exp': int(time.time()) + 3600, # 1小时过期
|
||||
'iat': int(time.time()),
|
||||
'sub': 'n8n-webhook',
|
||||
}
|
||||
token = jwt.encode(payload_jwt, self.jwt_secret, algorithm=self.jwt_algorithm)
|
||||
|
||||
# 添加到Authorization头
|
||||
headers['Authorization'] = f'Bearer {token}'
|
||||
self.ap.logger.debug('using jwt auth')
|
||||
elif self.auth_type == 'header':
|
||||
# 使用自定义请求头认证
|
||||
headers[self.header_name] = self.header_value
|
||||
self.ap.logger.debug(f'using header auth: {self.header_name}')
|
||||
else:
|
||||
self.ap.logger.debug('no auth')
|
||||
|
||||
# 调用webhook
|
||||
session = httpclient.get_session()
|
||||
async with session.post(
|
||||
self.webhook_url, json=payload, headers=headers, auth=auth, timeout=self.timeout
|
||||
) as response:
|
||||
if response.status != 200:
|
||||
error_text = await response.text()
|
||||
self.ap.logger.error(f'n8n webhook call failed: {response.status}, {error_text}')
|
||||
raise Exception(f'n8n webhook call failed: {response.status}, {error_text}')
|
||||
|
||||
async for chunk in self._process_response(response):
|
||||
if is_stream:
|
||||
yield chunk
|
||||
elif chunk.is_final:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=chunk.content,
|
||||
)
|
||||
except Exception as e:
|
||||
self.ap.logger.error(f'n8n webhook call exception: {str(e)}')
|
||||
raise N8nAPIError(f'n8n webhook call exception: {str(e)}')
|
||||
|
||||
async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""运行请求"""
|
||||
async for msg in self._call_webhook(query):
|
||||
yield msg
|
||||
@@ -1,202 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
import base64
|
||||
import tempfile
|
||||
import os
|
||||
|
||||
from tboxsdk.tbox import TboxClient
|
||||
from tboxsdk.model.file import File, FileType
|
||||
|
||||
from .. import runner
|
||||
from ...core import app
|
||||
from ...utils import image
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
class TboxAPIError(Exception):
|
||||
"""TBox API 请求失败"""
|
||||
|
||||
def __init__(self, message: str):
|
||||
self.message = message
|
||||
super().__init__(self.message)
|
||||
|
||||
|
||||
@runner.runner_class('tbox-app-api')
|
||||
class TboxAPIRunner(runner.RequestRunner):
|
||||
"蚂蚁百宝箱API对话请求器"
|
||||
|
||||
# 运行器内部使用的配置
|
||||
app_id: str # 蚂蚁百宝箱平台中的应用ID
|
||||
api_key: str # 在蚂蚁百宝箱平台中申请的令牌
|
||||
|
||||
def __init__(self, ap: app.Application, pipeline_config: dict):
|
||||
"""初始化"""
|
||||
self.ap = ap
|
||||
self.pipeline_config = pipeline_config
|
||||
|
||||
# 初始化Tbox 参数配置
|
||||
self.app_id = self.pipeline_config['ai']['tbox-app-api']['app-id']
|
||||
self.api_key = self.pipeline_config['ai']['tbox-app-api']['api-key']
|
||||
|
||||
# 初始化Tbox client
|
||||
self.tbox_client = TboxClient(authorization=self.api_key)
|
||||
|
||||
async def _preprocess_user_message(self, query: pipeline_query.Query) -> tuple[str, list[str]]:
|
||||
"""预处理用户消息，提取纯文本，并将图片上传到 Tbox 服务
|
||||
|
||||
Returns:
|
||||
tuple[str, list[str]]: 纯文本和图片的 Tbox 文件ID
|
||||
"""
|
||||
plain_text = ''
|
||||
image_ids = []
|
||||
|
||||
if isinstance(query.user_message.content, list):
|
||||
for ce in query.user_message.content:
|
||||
if ce.type == 'text':
|
||||
plain_text += ce.text
|
||||
elif ce.type == 'image_base64':
|
||||
image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
|
||||
# 创建临时文件
|
||||
file_bytes = base64.b64decode(image_b64)
|
||||
try:
|
||||
with tempfile.NamedTemporaryFile(suffix=f'.{image_format}', delete=False) as tmp_file:
|
||||
tmp_file.write(file_bytes)
|
||||
tmp_file_path = tmp_file.name
|
||||
file_upload_resp = self.tbox_client.upload_file(tmp_file_path)
|
||||
image_id = file_upload_resp.get('data', '')
|
||||
image_ids.append(image_id)
|
||||
finally:
|
||||
# 清理临时文件
|
||||
if os.path.exists(tmp_file_path):
|
||||
os.unlink(tmp_file_path)
|
||||
elif isinstance(query.user_message.content, str):
|
||||
plain_text = query.user_message.content
|
||||
|
||||
return plain_text, image_ids
|
||||
|
||||
async def _agent_messages(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""TBox 智能体对话请求"""
|
||||
|
||||
plain_text, image_ids = await self._preprocess_user_message(query)
|
||||
remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
|
||||
|
||||
try:
|
||||
is_stream = await query.adapter.is_stream_output_supported()
|
||||
except AttributeError:
|
||||
is_stream = False
|
||||
|
||||
# 获取Tbox的conversation_id
|
||||
conversation_id = query.session.using_conversation.uuid or None
|
||||
|
||||
files = None
|
||||
if image_ids:
|
||||
files = [File(file_id=image_id, type=FileType.IMAGE) for image_id in image_ids]
|
||||
|
||||
# 发送对话请求
|
||||
response = self.tbox_client.chat(
|
||||
app_id=self.app_id, # Tbox中智能体应用的ID
|
||||
user_id=query.bot_uuid, # 用户ID
|
||||
query=plain_text, # 用户输入的文本信息
|
||||
stream=is_stream, # 是否流式输出
|
||||
conversation_id=conversation_id, # 会话ID，为None时Tbox会自动创建一个新会话
|
||||
files=files, # 图片内容
|
||||
)
|
||||
|
||||
if is_stream:
|
||||
# 解析Tbox流式输出内容，并发送给上游
|
||||
for chunk in self._process_stream_message(response, query, remove_think):
|
||||
yield chunk
|
||||
else:
|
||||
message = self._process_non_stream_message(response, query, remove_think)
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=message,
|
||||
)
|
||||
|
||||
def _process_non_stream_message(self, response: typing.Dict, query: pipeline_query.Query, remove_think: bool):
|
||||
if response.get('errorCode') != '0':
|
||||
raise TboxAPIError(f'Tbox API 请求失败: {response.get("errorMsg", "")}')
|
||||
payload = response.get('data', {})
|
||||
conversation_id = payload.get('conversationId', '')
|
||||
query.session.using_conversation.uuid = conversation_id
|
||||
thinking_content = payload.get('reasoningContent', [])
|
||||
result = ''
|
||||
if thinking_content and not remove_think:
|
||||
result += f'<think>\n{thinking_content[0].get("text", "")}\n</think>\n'
|
||||
content = payload.get('result', [])
|
||||
if content:
|
||||
result += content[0].get('chunk', '')
|
||||
return result
|
||||
|
||||
def _process_stream_message(
|
||||
self, response: typing.Generator[dict], query: pipeline_query.Query, remove_think: bool
|
||||
):
|
||||
idx_msg = 0
|
||||
pending_content = ''
|
||||
conversation_id = None
|
||||
think_start = False
|
||||
think_end = False
|
||||
for chunk in response:
|
||||
if chunk.get('type', '') == 'chunk':
|
||||
"""
|
||||
Tbox返回的消息内容chunk结构
|
||||
{'lane': 'default', 'payload': {'conversationId': '20250918tBI947065406', 'messageId': '20250918TB1f53230954', 'text': '️'}, 'type': 'chunk'}
|
||||
"""
|
||||
# 如果包含思考过程，拼接</think>
|
||||
if think_start and not think_end:
|
||||
pending_content += '\n</think>\n'
|
||||
think_end = True
|
||||
|
||||
payload = chunk.get('payload', {})
|
||||
if not conversation_id:
|
||||
conversation_id = payload.get('conversationId')
|
||||
query.session.using_conversation.uuid = conversation_id
|
||||
if payload.get('text'):
|
||||
idx_msg += 1
|
||||
pending_content += payload.get('text')
|
||||
elif chunk.get('type', '') == 'thinking' and not remove_think:
|
||||
"""
|
||||
Tbox返回的思考过程chunk结构
|
||||
{'payload': '{"ext_data":{"text":"日期"},"event":"flow.node.llm.thinking","entity":{"node_type":"text-completion","execute_id":"6","group_id":0,"parent_execute_id":"6","node_name":"模型推理","node_id":"TC_5u6gl0"}}', 'type': 'thinking'}
|
||||
"""
|
||||
payload = json.loads(chunk.get('payload', '{}'))
|
||||
if payload.get('ext_data', {}).get('text'):
|
||||
idx_msg += 1
|
||||
content = payload.get('ext_data', {}).get('text')
|
||||
if not think_start:
|
||||
think_start = True
|
||||
pending_content += f'<think>\n{content}'
|
||||
else:
|
||||
pending_content += content
|
||||
elif chunk.get('type', '') == 'error':
|
||||
raise TboxAPIError(
|
||||
f'Tbox API 请求失败: status_code={chunk.get("status_code")} message={chunk.get("message")} request_id={chunk.get("request_id")} '
|
||||
)
|
||||
|
||||
if idx_msg % 8 == 0:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
is_final=False,
|
||||
)
|
||||
|
||||
# Tbox不返回END事件，默认发一个最终消息
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_content,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""运行"""
|
||||
msg_seq = 0
|
||||
async for msg in self._agent_messages(query):
|
||||
if isinstance(msg, provider_message.MessageChunk):
|
||||
msg_seq += 1
|
||||
msg.msg_sequence = msg_seq
|
||||
yield msg
|
||||
@@ -1,351 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
|
||||
|
||||
from langbot.pkg.provider import runner
|
||||
from langbot.pkg.core import app
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
from langbot.libs.weknora_api import client, errors
|
||||
|
||||
|
||||
@runner.runner_class('weknora-api')
|
||||
class WeKnoraAPIRunner(runner.RequestRunner):
|
||||
"""WeKnora API 对话请求器"""
|
||||
|
||||
weknora_client: client.AsyncWeKnoraClient
|
||||
|
||||
def __init__(self, ap: app.Application, pipeline_config: dict):
|
||||
super().__init__(ap, pipeline_config)
|
||||
|
||||
valid_app_types = ['chat', 'agent']
|
||||
if self.pipeline_config['ai']['weknora-api']['app-type'] not in valid_app_types:
|
||||
raise errors.WeKnoraAPIError(
|
||||
f'不支持的 WeKnora 应用类型: {self.pipeline_config["ai"]["weknora-api"]["app-type"]}'
|
||||
)
|
||||
|
||||
api_key = self.pipeline_config['ai']['weknora-api'].get('api-key', '').strip()
|
||||
if not api_key:
|
||||
raise errors.WeKnoraAPIError(
|
||||
'WeKnora API Key 未配置，请在流水线的 WeKnora API 配置中填入 API Key '
|
||||
'(从 WeKnora 前端 设置 → API Keys 生成)'
|
||||
)
|
||||
|
||||
base_url = self.pipeline_config['ai']['weknora-api'].get('base-url', '').strip()
|
||||
if not base_url:
|
||||
raise errors.WeKnoraAPIError('WeKnora Base URL 未配置，请填入服务器地址，例如 http://localhost:8080/api/v1')
|
||||
|
||||
self.weknora_client = client.AsyncWeKnoraClient(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
)
|
||||
|
||||
async def _extract_plain_text(self, query: pipeline_query.Query) -> str:
|
||||
"""从用户消息中提取纯文本内容"""
|
||||
plain_text = ''
|
||||
if isinstance(query.user_message.content, str):
|
||||
plain_text = query.user_message.content
|
||||
elif isinstance(query.user_message.content, list):
|
||||
for ce in query.user_message.content:
|
||||
if ce.type == 'text':
|
||||
plain_text += ce.text
|
||||
|
||||
if not plain_text:
|
||||
plain_text = self.pipeline_config['ai']['weknora-api'].get('base-prompt', '')
|
||||
|
||||
return plain_text
|
||||
|
||||
async def _ensure_session(self, query: pipeline_query.Query) -> str:
|
||||
"""确保会话存在，如果不存在则创建"""
|
||||
session_id = query.session.using_conversation.uuid or ''
|
||||
|
||||
if not session_id:
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
session_id = await self.weknora_client.create_session(title=f'IM Chat - {user_tag}')
|
||||
query.session.using_conversation.uuid = session_id
|
||||
|
||||
return session_id
|
||||
|
||||
async def _agent_chat_messages(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""调用 Agent 智能对话（非流式聚合输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-smart-reasoning')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
web_search_enabled = config.get('web-search-enabled', False)
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
full_answer = ''
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.agent_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
web_search_enabled=web_search_enabled,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
|
||||
if response_type == 'tool_call':
|
||||
# 工具调用
|
||||
tool_data = chunk.get('data', {})
|
||||
tool_name = tool_data.get('tool_name', '')
|
||||
if tool_name:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
tool_calls=[
|
||||
provider_message.ToolCall(
|
||||
id=chunk.get('id', ''),
|
||||
type='function',
|
||||
function=provider_message.FunctionCall(
|
||||
name=tool_name,
|
||||
arguments=json.dumps(tool_data.get('arguments', {})),
|
||||
),
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
elif response_type == 'answer':
|
||||
if content:
|
||||
full_answer += content
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
if full_answer:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=full_answer,
|
||||
)
|
||||
|
||||
async def _chat_messages(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""调用知识库 RAG 问答（非流式聚合输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-quick-answer')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
full_answer = ''
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.knowledge_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
|
||||
if response_type == 'answer':
|
||||
if content:
|
||||
full_answer += content
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
if full_answer:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=full_answer,
|
||||
)
|
||||
|
||||
async def _agent_chat_messages_chunk(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
"""调用 Agent 智能对话（流式输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-smart-reasoning')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
web_search_enabled = config.get('web-search-enabled', False)
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
pending_answer = ''
|
||||
message_idx = 0
|
||||
is_final = False
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.agent_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
web_search_enabled=web_search_enabled,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
done = chunk.get('done', False)
|
||||
|
||||
if response_type == 'tool_call':
|
||||
tool_data = chunk.get('data', {})
|
||||
tool_name = tool_data.get('tool_name', '')
|
||||
if tool_name:
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
tool_calls=[
|
||||
provider_message.ToolCall(
|
||||
id=chunk.get('id', ''),
|
||||
type='function',
|
||||
function=provider_message.FunctionCall(
|
||||
name=tool_name,
|
||||
arguments=json.dumps(tool_data.get('arguments', {})),
|
||||
),
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
elif response_type == 'answer':
|
||||
message_idx += 1
|
||||
if content:
|
||||
pending_answer += content
|
||||
|
||||
if done:
|
||||
is_final = True
|
||||
|
||||
# 每 8 个 chunk 输出一次，或最终输出
|
||||
if message_idx % 8 == 0 or is_final:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=is_final,
|
||||
)
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
# 确保最终消息已发出
|
||||
if not is_final and pending_answer:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def _chat_messages_chunk(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
"""调用知识库 RAG 问答（流式输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-quick-answer')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
pending_answer = ''
|
||||
message_idx = 0
|
||||
is_final = False
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.knowledge_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
done = chunk.get('done', False)
|
||||
|
||||
if response_type == 'answer':
|
||||
message_idx += 1
|
||||
if content:
|
||||
pending_answer += content
|
||||
|
||||
if done:
|
||||
is_final = True
|
||||
|
||||
if message_idx % 8 == 0 or is_final:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=is_final,
|
||||
)
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
if not is_final and pending_answer:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""运行请求"""
|
||||
app_type = self.pipeline_config['ai']['weknora-api']['app-type']
|
||||
|
||||
if await query.adapter.is_stream_output_supported():
|
||||
msg_idx = 0
|
||||
if app_type == 'agent':
|
||||
async for msg in self._agent_chat_messages_chunk(query):
|
||||
msg_idx += 1
|
||||
msg.msg_sequence = msg_idx
|
||||
yield msg
|
||||
elif app_type == 'chat':
|
||||
async for msg in self._chat_messages_chunk(query):
|
||||
msg_idx += 1
|
||||
msg.msg_sequence = msg_idx
|
||||
yield msg
|
||||
else:
|
||||
raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
|
||||
else:
|
||||
if app_type == 'agent':
|
||||
async for msg in self._agent_chat_messages(query):
|
||||
yield msg
|
||||
elif app_type == 'chat':
|
||||
async for msg in self._chat_messages(query):
|
||||
yield msg
|
||||
else:
|
||||
raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
|
||||
@@ -10,6 +10,7 @@ if typing.TYPE_CHECKING:
|
||||
from langbot_plugin.api.entities.events import pipeline_query
|
||||
|
||||
ACTIVATED_SKILLS_KEY = '_activated_skills'
|
||||
ACTIVATED_SKILL_NAMES_STATE_KEY = 'host.activated_skills'
|
||||
PIPELINE_BOUND_SKILLS_KEY = '_pipeline_bound_skills'
|
||||
SKILL_MOUNT_PREFIX = '/workspace/.skills'
|
||||
_SKILL_MOUNT_PATTERN = re.compile(r'/workspace/\.skills/([A-Za-z0-9_-]+)')
|
||||
@@ -111,6 +112,29 @@ def restore_activated_skills(
|
||||
return restored
|
||||
|
||||
|
||||
def restore_activated_skills_from_state(
|
||||
ap: app.Application,
|
||||
query: pipeline_query.Query,
|
||||
state: dict[str, dict[str, typing.Any]],
|
||||
) -> list[str]:
|
||||
"""Restore persisted activated skill names into Query variables.
|
||||
|
||||
The state value stores names only. Full skill metadata is rebuilt from the
|
||||
current pipeline-visible skill cache so removed or unbound skills remain
|
||||
unavailable to native exec/write/edit.
|
||||
"""
|
||||
conversation_state = state.get('conversation', {}) if isinstance(state, dict) else {}
|
||||
skill_names = normalize_skill_names(conversation_state.get(ACTIVATED_SKILL_NAMES_STATE_KEY))
|
||||
restored: list[str] = []
|
||||
for skill_name in skill_names:
|
||||
skill_data = get_visible_skill(ap, query, skill_name)
|
||||
if skill_data is None:
|
||||
continue
|
||||
register_activated_skill(query, skill_data)
|
||||
restored.append(skill_name)
|
||||
return restored
|
||||
|
||||
|
||||
def parse_skill_mount_path(sandbox_path: str) -> tuple[str | None, str]:
|
||||
normalized_path = str(sandbox_path or '/workspace').strip() or '/workspace'
|
||||
if normalized_path == SKILL_MOUNT_PREFIX:
|
||||
|
||||
@@ -92,6 +92,7 @@ class SkillToolLoader(loader.ToolLoader):
|
||||
|
||||
# Register activated skill for sandbox mount path resolution
|
||||
skill_loader.register_activated_skill(query, skill_data)
|
||||
await skill_loader.persist_activated_skill(self.ap, query, skill_name)
|
||||
|
||||
# Return SKILL.md content as Tool Result (injects into context)
|
||||
instructions = skill_data.get('instructions', '')
|
||||
|
||||
Reference in New Issue
Block a user