mirror of
https://github.com/langbot-app/LangBot.git
synced 2025-11-25 11:29:39 +08:00
Merge branch 'master' into rc/new-plugin
This commit is contained in:
Junyan Qin (Chin)
@@ -36,6 +36,6 @@ class RequestRunner(abc.ABC):
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self.pipeline_config = pipeline_config
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@abc.abstractmethod
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async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
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async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
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"""运行请求"""
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pass
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pass
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180
pkg/provider/runners/langflowapi.py
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180
pkg/provider/runners/langflowapi.py
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@@ -0,0 +1,180 @@
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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 httpx
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import uuid
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import traceback
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from .. import runner
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from ...core import app, entities as core_entities
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from .. import entities as llm_entities
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@runner.runner_class('langflow-api')
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class LangflowAPIRunner(runner.RequestRunner):
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"""Langflow API 对话请求器"""
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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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async def _build_request_payload(self, query: core_entities.Query) -> dict:
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"""构建请求负载
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Args:
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query: 用户查询对象
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Returns:
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dict: 请求负载
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"""
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# 获取用户消息文本
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user_message_text = ''
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if isinstance(query.user_message.content, str):
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user_message_text = query.user_message.content
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elif isinstance(query.user_message.content, list):
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for item in query.user_message.content:
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if item.type == 'text':
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user_message_text += item.text
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# 从配置中获取 input_type 和 output_type，如果未配置则使用默认值
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input_type = self.pipeline_config['ai']['langflow-api'].get('input_type', 'chat')
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output_type = self.pipeline_config['ai']['langflow-api'].get('output_type', 'chat')
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# 构建基本负载
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payload = {
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'output_type': output_type,
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'input_type': input_type,
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'input_value': user_message_text,
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'session_id': str(uuid.uuid4()),
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}
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# 如果配置中有tweaks，则添加到负载中
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tweaks = json.loads(self.pipeline_config['ai']['langflow-api'].get('tweaks'))
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if tweaks:
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payload['tweaks'] = tweaks
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return payload
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async def run(
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self, query: core_entities.Query
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) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
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"""运行请求
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Args:
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query: 用户查询对象
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Yields:
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Message: 回复消息
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"""
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# 检查是否支持流式输出
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is_stream = False
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try:
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is_stream = await query.adapter.is_stream_output_supported()
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except AttributeError:
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is_stream = False
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# 从配置中获取API参数
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base_url = self.pipeline_config['ai']['langflow-api']['base-url']
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api_key = self.pipeline_config['ai']['langflow-api']['api-key']
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flow_id = self.pipeline_config['ai']['langflow-api']['flow-id']
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# 构建API URL
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url = f'{base_url.rstrip("/")}/api/v1/run/{flow_id}'
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# 构建请求负载
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payload = await self._build_request_payload(query)
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# 设置请求头
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headers = {'Content-Type': 'application/json', 'x-api-key': api_key}
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# 发送请求
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async with httpx.AsyncClient() as client:
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if is_stream:
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# 流式请求
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async with client.stream('POST', url, json=payload, headers=headers, timeout=120.0) as response:
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print(response)
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response.raise_for_status()
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accumulated_content = ''
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message_count = 0
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async for line in response.aiter_lines():
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data_str = line
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if data_str.startswith('data: '):
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data_str = data_str[6:] # 移除 "data: " 前缀
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try:
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data = json.loads(data_str)
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# 提取消息内容
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message_text = ''
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if 'outputs' in data and len(data['outputs']) > 0:
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output = data['outputs'][0]
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if 'outputs' in output and len(output['outputs']) > 0:
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inner_output = output['outputs'][0]
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if 'outputs' in inner_output and 'message' in inner_output['outputs']:
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message_data = inner_output['outputs']['message']
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if 'message' in message_data:
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message_text = message_data['message']
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# 如果没有找到消息，尝试其他可能的路径
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if not message_text and 'messages' in data:
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messages = data['messages']
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if messages and len(messages) > 0:
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message_text = messages[0].get('message', '')
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if message_text:
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# 更新累积内容
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accumulated_content = message_text
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message_count += 1
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# 每8条消息或有新内容时生成一个chunk
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if message_count % 8 == 0 or len(message_text) > 0:
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yield llm_entities.MessageChunk(
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role='assistant', content=accumulated_content, is_final=False
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)
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except json.JSONDecodeError:
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# 如果不是JSON，跳过这一行
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traceback.print_exc()
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continue
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# 发送最终消息
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yield llm_entities.MessageChunk(role='assistant', content=accumulated_content, is_final=True)
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else:
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# 非流式请求
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response = await client.post(url, json=payload, headers=headers, timeout=120.0)
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response.raise_for_status()
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# 解析响应
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response_data = response.json()
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# 提取消息内容
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# 根据Langflow API文档，响应结构可能在outputs[0].outputs[0].outputs.message.message中
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message_text = ''
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if 'outputs' in response_data and len(response_data['outputs']) > 0:
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output = response_data['outputs'][0]
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if 'outputs' in output and len(output['outputs']) > 0:
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inner_output = output['outputs'][0]
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if 'outputs' in inner_output and 'message' in inner_output['outputs']:
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message_data = inner_output['outputs']['message']
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if 'message' in message_data:
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message_text = message_data['message']
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# 如果没有找到消息，尝试其他可能的路径
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if not message_text and 'messages' in response_data:
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messages = response_data['messages']
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if messages and len(messages) > 0:
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message_text = messages[0].get('message', '')
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# 如果仍然没有找到消息，返回完整响应的字符串表示
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if not message_text:
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message_text = json.dumps(response_data, ensure_ascii=False, indent=2)
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# 生成回复消息
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if is_stream:
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yield llm_entities.MessageChunk(role='assistant', content=message_text, is_final=True)
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else:
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reply_message = llm_entities.Message(role='assistant', content=message_text)
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yield reply_message
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