OpenWebUIAI

This notebook provides a quick overview for getting started with Open WebUI

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution. You can find how to use it in OpenWebUI

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
[OpenWebUIAI]	langchain_community	❌	✅	✅

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	❌	❌	❌	✅	✅	✅	✅

Setup

To access OpenWebUIChatCompletionsModel models you'll need to create an Open WebUI account, get an API key.

Credentials

Head to https://docs.openwebui.com/ to sign up for Open WebUI and generate an API key. Once you've done this set the OPENWEBUI_API_KEY environment variable:

Installation

The LangChain ModuleName integration lives in the langchain_community package:

%pip install -qU langchain_community openwebui-api

Note: you may need to restart the kernel to use updated packages.

Instantiation

import getpass
import os


openwebui_api_key = os.environ["OPENWEBUI_API_KEY"]
openwebui_api_base = os.environ["OPENWEBUI_API_BASE"]

from langchain_community.chat_models import OpenWebUIAI


llm = OpenWebUIAI(
    temperature=0.1,
    api_key=openwebui_api_key,
    api_base=openwebui_api_base,
    model="gemini-2.0-pro-exp-02-05",
)

API Reference:OpenWebUIAI

Invocation

from langchain_core.messages import HumanMessage


response = llm.invoke(input=[HumanMessage(content="Hello")])
response

API Reference:HumanMessage

Chaining

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough


prompt = ChatPromptTemplate.from_template("""please answer the question below: {question}""")
retriever_chain = (
    {"question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

API Reference:ChatPromptTemplate | StrOutputParser | RunnablePassthrough

AIMessage(content=' Ich liebe Programmieren.\n\n', additional_kwargs={}, response_metadata={}, id='run-ffc4ace1-b73a-4fb3-ad0f-57e60a0f9b8d-0')

API reference

https://docs.openwebui.com/getting-started/api-endpoints

Related

• Chat model conceptual guide
• Chat model how-to guides