open webui与langgraph集成

最新推荐文章于 2025-11-24 16:59:03 发布

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open webui作为大模型的一个大前端，而langgraph又是开发工作流及Agent的利器，open webui提供了与langgraph集成的机制。其具体实现依赖于Pipelines。本文基于前面的一个简单的具有搜索功能的chatbot与open webui集成。

第一步：实现graph服务

1）创建服务目录，比如/home/langgraph/example

2）从https://github.com/open-webui/pipelines/tree/main/examples/pipelines/integrations/langgraph_pipeline 下载代码

3）修改源码

用如下代码替换langgraph_example.py文件中内容：

import os
import json
import getpass
from typing import Annotated, Literal
from typing_extensions import TypedDict

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from langgraph.config import get_stream_writer
from langchain_tavily import TavilySearch
from langgraph.prebuilt import ToolNode, tools_condition

os.environ["TAVILY_API_KEY"] = "tvly-……"
tool = TavilySearch(max_results=2)
tools = [tool]

'''
Define Langgraph
'''
def generate_custom_stream(type: Literal["think","normal"], content: str):
content = "\n"+content+"\n"
custom_stream_writer = get_stream_writer()
return custom_stream_writer({type:content})

class State(TypedDict):
messages: Annotated[list, add_messages]

llm = ChatOpenAI(
model = 'qwen-plus',
api_key = "sk-3……",
base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1")
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
think_response = llm_with_tools.invoke(["Please reasoning:"] + state["messages"])

normal_response = llm_with_tools.invoke(state["messages"])
generate_custom_stream("think", think_response.content)
generate_custom_stream("normal", normal_response.content)
return {"messages": [normal_response]}

graph_builder = StateGraph(State)
graph_builder.add_node("chatbot", chatbot)#增加chatbot节点到工作流图

tool_node = ToolNode(tools=tools) #生成工具节点
graph_builder.add_node("tools", tool_node) #把工具节点增加到工作流图中

graph_builder.add_conditional_edges( "chatbot", tools_condition,)#增加条件边
graph_builder.add_edge("tools", "chatbot")#增加从tools—>chatbot的边
graph_builder.add_edge(START, "chatbot")#增加从START—>chatbot的边

graph = graph_builder.compile()

'''
Define api processing
'''
app = FastAPI(
title="Langgraph API",
description="Langgraph API",
)

@app.get("/test")
async def test():
return {"message": "Hello World"}

@app.post("/stream")
async def stream(inputs: State):
async def event_stream():
try:
stream_start_msg = {
'choices':
[
{
'delta': {},
'finish_reason': None
}
]
}

# Stream start
yield f"data: {json.dumps(stream_start_msg)}\n\n"

# Processing langgraph stream response with <think> block support
async for event in graph.astream(input=inputs, stream_mode="custom"):
print(event)
think_content = event.get("think", None)
normal_content = event.get("normal", None)

think_msg = {
'choices':
[
{
'delta':
{
'reasoning_content': think_content,
},
'finish_reason': None
}
]
}

normal_msg = {
'choices':
[
{
'delta':
{
'content': normal_content,
},
'finish_reason': None
}
]
}

yield f"data: {json.dumps(think_msg)}\n\n"
yield f"data: {json.dumps(normal_msg)}\n\n"

# End of the stream
stream_end_msg = {
'choices': [
{
'delta': {},
'finish_reason': 'stop'
}
]
}
yield f"data: {json.dumps(stream_end_msg)}\n\n"

except Exception as e:
# Simply print the error information
print(f"An error occurred: {e}")

return StreamingResponse(
event_stream(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
}
)

4）安装依赖

在应用目录下运行pip命令：

#pip install -r requirements.txt

5）启动服务

#unicorn langgraph_example:app --reload #服务启动后运行端口缺省为8000

第二步：安装流水线

1）在open webui，从【管理员面板】—>【设置】—>【Pipeline】进入流水线安装页面：

2）选择【点击此处选择 py文件】，从本地找到并选择langgraph_stream_pipeline.py文件，并点击【这里】

处理成功后，下面会显示Pipeline阀门和Api Url（可以根据实际情况调整Url）。最后点击【保存】。

anggraph_stream_pipeline.py内容如下：

import os
import requests
from pydantic import BaseModel, Field
from typing import List, Union, Generator, Iterator

class Pipeline:
class Valves(BaseModel):
API_URL: str = Field(default="http://127.0.0.1:9000/stream", description="Langgraph API URL")

def __init__(self):
self.id = "LangGraph stream" #模型列表中显示的名字，可自行修改
self.name = "LangGraph stream"
# Initialize valve paramaters
self.valves = self.Valves(
**{k: os.getenv(k, v.default) for k, v in self.Valves.model_fields.items()}
)

async def on_startup(self):
# This function is called when the server is started.
print(f"on_startup: {__name__}")
pass

async def on_shutdown(self):
# This function is called when the server is shutdown.
print(f"on_shutdown: {__name__}")
pass

def pipe(
self,
user_message: str,
model_id: str,
messages: List[dict],
body: dict
) -> Union[str, Generator, Iterator]:

data = {
"messages": [[msg['role'], msg['content']] for msg in messages],
}

headers = {
'accept': 'text/event-stream',
'Content-Type': 'application/json',
}

response = requests.post(self.valves.API_URL, json=data, headers=headers, stream=True)

response.raise_for_status()

return response.iter_lines()