Top Related Projects
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
AI chat for every model.
A cross-platform ChatGPT/Gemini UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 ChatGPT/Gemini 应用。
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
A Gradio web UI for Large Language Models.
Quick Overview
The huggingface/chat-ui
repository provides a user interface (UI) for interacting with language models, specifically the Hugging Face Transformers library. It offers a web-based chat interface that allows users to engage in conversational interactions with AI models, making it easier to explore and experiment with these powerful language technologies.
Pros
- Intuitive Chat Interface: The UI provides a familiar and user-friendly chat interface, making it accessible for both technical and non-technical users to interact with language models.
- Supports Multiple Models: The project supports a wide range of language models from the Hugging Face Transformers library, allowing users to experiment with different model capabilities.
- Easy Deployment: The project is designed to be easily deployed, either locally or on a web server, enabling users to quickly set up and start using the chat interface.
- Open-Source and Customizable: As an open-source project, the code is available for users to inspect, modify, and extend to fit their specific needs or preferences.
Cons
- Limited Functionality: The current version of the project focuses primarily on the chat interface, and may lack advanced features or customization options that some users might desire.
- Dependency on Hugging Face Transformers: The project is tightly coupled with the Hugging Face Transformers library, which means users need to be familiar with that ecosystem to fully utilize the chat UI.
- Potential Performance Limitations: Depending on the language model and the user's hardware, the chat interface may experience performance issues, especially when handling large or complex inputs.
- Ongoing Maintenance: As an open-source project, the long-term maintenance and development of the chat UI may depend on the continued involvement of the Hugging Face community.
Code Examples
The huggingface/chat-ui
project is a web-based application, and the majority of the code is written in JavaScript and React. Here are a few examples of the code:
- Rendering the Chat Interface:
import React from 'react';
import { ChatContainer, ChatHeader, ChatInput, ChatMessages } from '@chatui/core';
const ChatUI = () => {
return (
<ChatContainer>
<ChatHeader title="Chat with AI" />
<ChatMessages />
<ChatInput />
</ChatContainer>
);
};
export default ChatUI;
This code sets up the basic structure of the chat interface, including the header, message display, and input field.
- Handling User Input:
import { useCallback, useState } from 'react';
import { useSendMessage } from '@chatui/core';
const ChatInput = () => {
const [inputValue, setInputValue] = useState('');
const { sendMessage } = useSendMessage();
const handleSendMessage = useCallback(() => {
if (inputValue.trim()) {
sendMessage(inputValue);
setInputValue('');
}
}, [inputValue, sendMessage]);
return (
<ChatInput
value={inputValue}
onChange={setInputValue}
onSend={handleSendMessage}
/>
);
};
export default ChatInput;
This code handles the user's input, allowing them to type a message and send it to the chat interface.
- Integrating with a Language Model:
import { useEffect, useState } from 'react';
import { useSendMessage, useReceiveMessage } from '@chatui/core';
import { useModel } from './useModel';
const ChatUI = () => {
const { sendMessage, receiveMessage } = useSendMessage();
const { model, loadModel } = useModel();
const [messages, setMessages] = useState([]);
useEffect(() => {
loadModel('gpt2');
}, [loadModel]);
useEffect(() => {
if (model) {
receiveMessage(async (message) => {
const response = await model.generateText(message.content);
sendMessage(response);
});
}
}, [model, receiveMessage, sendMessage]);
return (
<ChatContainer>
<ChatHeader title="Chat with AI" />
<ChatMessages
Competitor Comparisons
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Pros of FastChat
- More comprehensive, offering a full stack for training, serving, and evaluating LLMs
- Supports a wider range of models, including Vicuna, Alpaca, and LLaMA
- Provides advanced features like model quantization and multi-GPU inference
Cons of FastChat
- Steeper learning curve due to its more complex architecture
- Requires more setup and configuration compared to chat-ui's simpler approach
- May be overkill for users who only need a basic chat interface
Code Comparison
FastChat (server setup):
from fastchat.serve.controller import Controller
from fastchat.serve.model_worker import ModelWorker
from fastchat.serve.openai_api_server import OpenAIAPIServer
controller = Controller()
worker = ModelWorker(controller.controller_addr, worker_addr, model_path)
api_server = OpenAIAPIServer(controller)
chat-ui (basic usage):
import { ChatUI } from "@huggingface/chat-ui";
const chatUI = new ChatUI({
apiKey: "your-api-key",
model: "gpt-3.5-turbo",
});
chatUI.render("#chat-container");
Both repositories offer chat interfaces for language models, but FastChat provides a more comprehensive solution for deploying and managing LLMs, while chat-ui focuses on a simpler, front-end oriented approach. The choice between them depends on the specific requirements and complexity of the project.
AI chat for every model.
Pros of chatbot-ui
- More customizable UI with themes and layout options
- Supports multiple AI models and providers (OpenAI, Anthropic, etc.)
- Advanced features like conversation branching and custom instructions
Cons of chatbot-ui
- Requires more setup and configuration
- Less integrated with Hugging Face ecosystem
- May have a steeper learning curve for beginners
Code Comparison
chatbot-ui:
const handleNewConversation = () => {
const newConversation: Conversation = {
id: uuidv4(),
name: t('New Conversation'),
messages: [],
model: OpenAIModels[defaultModelId],
prompt: DEFAULT_SYSTEM_PROMPT,
temperature: DEFAULT_TEMPERATURE,
folderId: null,
};
dispatch({ field: 'selectedConversation', value: newConversation });
dispatch({ field: 'conversations', value: [...conversations, newConversation] });
};
chat-ui:
def create_conversation(user_id: str) -> Conversation:
conversation = Conversation(
id=str(uuid.uuid4()),
created_at=datetime.utcnow(),
user_id=user_id,
messages=[],
)
db.add(conversation)
db.commit()
return conversation
The code snippets show different approaches to creating new conversations. chatbot-ui uses TypeScript and manages state with a dispatch function, while chat-ui uses Python and interacts directly with a database.
A cross-platform ChatGPT/Gemini UI (Web / PWA / Linux / Win / MacOS). 一键拥有你自己的跨平台 ChatGPT/Gemini 应用。
Pros of ChatGPT-Next-Web
- More user-friendly interface with a modern, sleek design
- Supports multiple languages and themes out of the box
- Offers easy one-click deployment options (e.g., Vercel, Railway)
Cons of ChatGPT-Next-Web
- Less customizable for developers compared to chat-ui
- Primarily focused on ChatGPT, while chat-ui supports various AI models
- May have a steeper learning curve for non-technical users
Code Comparison
ChatGPT-Next-Web (Next.js):
import { useState } from 'react';
import { Chat } from '../components/Chat';
export default function Home() {
const [messages, setMessages] = useState([]);
return <Chat messages={messages} setMessages={setMessages} />;
}
chat-ui (Vue.js):
<template>
<div class="chat-container">
<ChatMessages :messages="messages" />
<ChatInput @send="sendMessage" />
</div>
</template>
<script>
export default {
data() {
return { messages: [] };
},
methods: {
sendMessage(text) {
// Handle message sending
},
},
};
</script>
Both projects use modern JavaScript frameworks, but ChatGPT-Next-Web uses React with Next.js, while chat-ui uses Vue.js. ChatGPT-Next-Web's code structure is more compact, while chat-ui separates template and logic more clearly.
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
Pros of ChuanhuChatGPT
- Supports multiple language models, including GPT-3.5, GPT-4, and Claude
- Offers a user-friendly interface with customizable themes
- Includes features like conversation history and API key management
Cons of ChuanhuChatGPT
- Less focus on enterprise-level deployment and scalability
- May have a steeper learning curve for developers unfamiliar with Gradio
Code Comparison
ChuanhuChatGPT:
def predict(self, inputs, max_length, top_p, temperature, history, past_key_values):
response, history = self.model.chat(
self.tokenizer, inputs, history, max_length=max_length,
top_p=top_p, temperature=temperature, past_key_values=past_key_values
)
return response, history
chat-ui:
const handleSubmit = async (message: string) => {
const response = await fetch('/api/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message })
});
const data = await response.json();
return data.response;
};
The code snippets show different approaches to handling chat interactions. ChuanhuChatGPT uses a Python-based prediction function, while chat-ui employs a JavaScript API call for message handling.
A Gradio web UI for Large Language Models.
Pros of text-generation-webui
- More extensive model support, including local models and various architectures
- Advanced features like fine-tuning, training, and model merging
- Highly customizable interface with multiple chat modes and extensions
Cons of text-generation-webui
- Steeper learning curve due to more complex setup and configuration
- Potentially higher resource requirements for running local models
- Less focus on cloud-based deployment and scalability
Code Comparison
text-generation-webui:
def generate_reply(
prompt, state, stopping_strings=None, is_chat=False, escape_html=False
):
# Complex generation logic with multiple parameters and options
# ...
chat-ui:
async function generateReply(message, conversation) {
// Simpler generation logic focused on API calls
// ...
}
The code comparison highlights the difference in complexity and focus between the two projects. text-generation-webui offers more advanced generation options, while chat-ui emphasizes simplicity and cloud integration.
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Chat UI
Find the docs at hf.co/docs/chat-ui.
A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit app and it powers the HuggingChat app on hf.co/chat.
- Quickstart
- No Setup Deploy
- Setup
- Launch
- Web Search
- Text Embedding Models
- Extra parameters
- Common issues
- Deploying to a HF Space
- Building
Quickstart
You can quickly start a locally running chat-ui & LLM text-generation server thanks to chat-ui's llama.cpp server support.
Step 1 (Start llama.cpp server):
Install llama.cpp w/ brew (for Mac):
# install llama.cpp
brew install llama.cpp
or build directly from the source for your target device:
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make
Next, start the server with the LLM of your choice:
# start llama.cpp server (using hf.co/microsoft/Phi-3-mini-4k-instruct-gguf as an example)
llama-server --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf --hf-file Phi-3-mini-4k-instruct-q4.gguf -c 4096
A local LLaMA.cpp HTTP Server will start on http://localhost:8080
. Read more here.
Step 2 (tell chat-ui to use local llama.cpp server):
Add the following to your .env.local
:
MODELS=`[
{
"name": "Local microsoft/Phi-3-mini-4k-instruct-gguf",
"tokenizer": "microsoft/Phi-3-mini-4k-instruct-gguf",
"preprompt": "",
"chatPromptTemplate": "<s>{{preprompt}}{{#each messages}}{{#ifUser}}<|user|>\n{{content}}<|end|>\n<|assistant|>\n{{/ifUser}}{{#ifAssistant}}{{content}}<|end|>\n{{/ifAssistant}}{{/each}}",
"parameters": {
"stop": ["<|end|>", "<|endoftext|>", "<|assistant|>"],
"temperature": 0.7,
"max_new_tokens": 1024,
"truncate": 3071
},
"endpoints": [{
"type" : "llamacpp",
"baseURL": "http://localhost:8080"
}],
},
]`
Read more here.
Step 3 (make sure you have MongoDb running locally):
docker run -d -p 27017:27017 --name mongo-chatui mongo:latest
Read more here.
Step 4 (start chat-ui):
git clone https://github.com/huggingface/chat-ui
cd chat-ui
npm install
npm run dev -- --open
Read more here.
No Setup Deploy
If you don't want to configure, setup, and launch your own Chat UI yourself, you can use this option as a fast deploy alternative.
You can deploy your own customized Chat UI instance with any supported LLM of your choice on Hugging Face Spaces. To do so, use the chat-ui template available here.
Set HF_TOKEN
in Space secrets to deploy a model with gated access or a model in a private repository. It's also compatible with Inference for PROs curated list of powerful models with higher rate limits. Make sure to create your personal token first in your User Access Tokens settings.
Read the full tutorial here.
Setup
The default config for Chat UI is stored in the .env
file. You will need to override some values to get Chat UI to run locally. This is done in .env.local
.
Start by creating a .env.local
file in the root of the repository. The bare minimum config you need to get Chat UI to run locally is the following:
MONGODB_URL=<the URL to your MongoDB instance>
HF_TOKEN=<your access token>
Database
The chat history is stored in a MongoDB instance, and having a DB instance available is needed for Chat UI to work.
You can use a local MongoDB instance. The easiest way is to spin one up using docker:
docker run -d -p 27017:27017 --name mongo-chatui mongo:latest
In which case the url of your DB will be MONGODB_URL=mongodb://localhost:27017
.
Alternatively, you can use a free MongoDB Atlas instance for this, Chat UI should fit comfortably within their free tier. After which you can set the MONGODB_URL
variable in .env.local
to match your instance.
Hugging Face Access Token
If you use a remote inference endpoint, you will need a Hugging Face access token to run Chat UI locally. You can get one from your Hugging Face profile.
Launch
After you're done with the .env.local
file you can run Chat UI locally with:
npm install
npm run dev
Web Search
Chat UI features a powerful Web Search feature. It works by:
- Generating an appropriate search query from the user prompt.
- Performing web search and extracting content from webpages.
- Creating embeddings from texts using a text embedding model.
- From these embeddings, find the ones that are closest to the user query using a vector similarity search. Specifically, we use
inner product
distance. - Get the corresponding texts to those closest embeddings and perform Retrieval-Augmented Generation (i.e. expand user prompt by adding those texts so that an LLM can use this information).
Text Embedding Models
By default (for backward compatibility), when TEXT_EMBEDDING_MODELS
environment variable is not defined, transformers.js embedding models will be used for embedding tasks, specifically, Xenova/gte-small model.
You can customize the embedding model by setting TEXT_EMBEDDING_MODELS
in your .env.local
file. For example:
TEXT_EMBEDDING_MODELS = `[
{
"name": "Xenova/gte-small",
"displayName": "Xenova/gte-small",
"description": "locally running embedding",
"chunkCharLength": 512,
"endpoints": [
{"type": "transformersjs"}
]
},
{
"name": "intfloat/e5-base-v2",
"displayName": "intfloat/e5-base-v2",
"description": "hosted embedding model",
"chunkCharLength": 768,
"preQuery": "query: ", # See https://huggingface.co/intfloat/e5-base-v2#faq
"prePassage": "passage: ", # See https://huggingface.co/intfloat/e5-base-v2#faq
"endpoints": [
{
"type": "tei",
"url": "http://127.0.0.1:8080/",
"authorization": "TOKEN_TYPE TOKEN" // optional authorization field. Example: "Basic VVNFUjpQQVNT"
}
]
}
]`
The required fields are name
, chunkCharLength
and endpoints
.
Supported text embedding backends are: transformers.js
, TEI
and OpenAI
. transformers.js
models run locally as part of chat-ui
, whereas TEI
models run in a different environment & accessed through an API endpoint. openai
models are accessed through the OpenAI API.
When more than one embedding models are supplied in .env.local
file, the first will be used by default, and the others will only be used on LLM's which configured embeddingModel
to the name of the model.
Extra parameters
OpenID connect
The login feature is disabled by default and users are attributed a unique ID based on their browser. But if you want to use OpenID to authenticate your users, you can add the following to your .env.local
file:
OPENID_CONFIG=`{
PROVIDER_URL: "<your OIDC issuer>",
CLIENT_ID: "<your OIDC client ID>",
CLIENT_SECRET: "<your OIDC client secret>",
SCOPES: "openid profile",
TOLERANCE: // optional
RESOURCE: // optional
}`
These variables will enable the openID sign-in modal for users.
Trusted header authentication
You can set the env variable TRUSTED_EMAIL_HEADER
to point to the header that contains the user's email address. This will allow you to authenticate users from the header. This setup is usually combined with a proxy that will be in front of chat-ui and will handle the auth and set the header.
[!WARNING] Make sure to only allow requests to chat-ui through your proxy which handles authentication, otherwise users could authenticate as anyone by setting the header manually! Only set this up if you understand the implications and know how to do it correctly.
Here is a list of header names for common auth providers:
- Tailscale Serve:
Tailscale-User-Login
- Cloudflare Access:
Cf-Access-Authenticated-User-Email
- oauth2-proxy:
X-Forwarded-Email
Theming
You can use a few environment variables to customize the look and feel of chat-ui. These are by default:
PUBLIC_APP_NAME=ChatUI
PUBLIC_APP_ASSETS=chatui
PUBLIC_APP_COLOR=blue
PUBLIC_APP_DESCRIPTION="Making the community's best AI chat models available to everyone."
PUBLIC_APP_DATA_SHARING=
PUBLIC_APP_DISCLAIMER=
PUBLIC_APP_NAME
The name used as a title throughout the app.PUBLIC_APP_ASSETS
Is used to find logos & favicons instatic/$PUBLIC_APP_ASSETS
, current options arechatui
andhuggingchat
.PUBLIC_APP_COLOR
Can be any of the tailwind colors.PUBLIC_APP_DATA_SHARING
Can be set to 1 to add a toggle in the user settings that lets your users opt-in to data sharing with models creator.PUBLIC_APP_DISCLAIMER
If set to 1, we show a disclaimer about generated outputs on login.
Web Search config
You can enable the web search through an API by adding YDC_API_KEY
(docs.you.com) or SERPER_API_KEY
(serper.dev) or SERPAPI_KEY
(serpapi.com) or SERPSTACK_API_KEY
(serpstack.com) or SEARCHAPI_KEY
(searchapi.io) to your .env.local
.
You can also simply enable the local google websearch by setting USE_LOCAL_WEBSEARCH=true
in your .env.local
or specify a SearXNG instance by adding the query URL to SEARXNG_QUERY_URL
.
You can enable javascript when parsing webpages to improve compatibility with WEBSEARCH_JAVASCRIPT=true
at the cost of increased CPU usage. You'll want at least 4 cores when enabling.
Custom models
You can customize the parameters passed to the model or even use a new model by updating the MODELS
variable in your .env.local
. The default one can be found in .env
and looks like this :
MODELS=`[
{
"name": "mistralai/Mistral-7B-Instruct-v0.2",
"displayName": "mistralai/Mistral-7B-Instruct-v0.2",
"description": "Mistral 7B is a new Apache 2.0 model, released by Mistral AI that outperforms Llama2 13B in benchmarks.",
"websiteUrl": "https://mistral.ai/news/announcing-mistral-7b/",
"preprompt": "",
"chatPromptTemplate" : "<s>{{#each messages}}{{#ifUser}}[INST] {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}} [/INST]{{/ifUser}}{{#ifAssistant}}{{content}}</s>{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.3,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 3072,
"max_new_tokens": 1024,
"stop": ["</s>"]
},
"promptExamples": [
{
"title": "Write an email from bullet list",
"prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
}, {
"title": "Code a snake game",
"prompt": "Code a basic snake game in python, give explanations for each step."
}, {
"title": "Assist in a task",
"prompt": "How do I make a delicious lemon cheesecake?"
}
]
}
]`
You can change things like the parameters, or customize the preprompt to better suit your needs. You can also add more models by adding more objects to the array, with different preprompts for example.
chatPromptTemplate
When querying the model for a chat response, the chatPromptTemplate
template is used. messages
is an array of chat messages, it has the format [{ content: string }, ...]
. To identify if a message is a user message or an assistant message the ifUser
and ifAssistant
block helpers can be used.
The following is the default chatPromptTemplate
, although newlines and indentiation have been added for readability. You can find the prompts used in production for HuggingChat here.
{{preprompt}}
{{#each messages}}
{{#ifUser}}{{@root.userMessageToken}}{{content}}{{@root.userMessageEndToken}}{{/ifUser}}
{{#ifAssistant}}{{@root.assistantMessageToken}}{{content}}{{@root.assistantMessageEndToken}}{{/ifAssistant}}
{{/each}}
{{assistantMessageToken}}
Multi modal model
We currently support IDEFICS (hosted on TGI), OpenAI and Claude 3 as multimodal models. You can enable it by setting multimodal: true
in your MODELS
configuration. For IDEFICS, you must have a PRO HF Api token. For OpenAI, see the OpenAI section. For Anthropic, see the Anthropic section.
{
"name": "HuggingFaceM4/idefics-80b-instruct",
"multimodal" : true,
"description": "IDEFICS is the new multimodal model by Hugging Face.",
"preprompt": "",
"chatPromptTemplate" : "{{#each messages}}{{#ifUser}}User: {{content}}{{/ifUser}}<end_of_utterance>\nAssistant: {{#ifAssistant}}{{content}}\n{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.1,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 12,
"truncate": 1000,
"max_new_tokens": 1024,
"stop": ["<end_of_utterance>", "User:", "\nUser:"]
}
}
Running your own models using a custom endpoint
If you want to, instead of hitting models on the Hugging Face Inference API, you can run your own models locally.
A good option is to hit a text-generation-inference endpoint. This is what is done in the official Chat UI Spaces Docker template for instance: both this app and a text-generation-inference server run inside the same container.
To do this, you can add your own endpoints to the MODELS
variable in .env.local
, by adding an "endpoints"
key for each model in MODELS
.
{
// rest of the model config here
"endpoints": [{
"type" : "tgi",
"url": "https://HOST:PORT",
}]
}
If endpoints
are left unspecified, ChatUI will look for the model on the hosted Hugging Face inference API using the model name.
OpenAI API compatible models
Chat UI can be used with any API server that supports OpenAI API compatibility, for example text-generation-webui, LocalAI, FastChat, llama-cpp-python, and ialacol and vllm.
The following example config makes Chat UI works with text-generation-webui, the endpoint.baseUrl
is the url of the OpenAI API compatible server, this overrides the baseUrl to be used by OpenAI instance. The endpoint.completion
determine which endpoint to be used, default is chat_completions
which uses v1/chat/completions
, change to endpoint.completion
to completions
to use the v1/completions
endpoint.
Parameters not supported by OpenAI (e.g., top_k, repetition_penalty, etc.) must be set in the extraBody of endpoints. Be aware that setting them in parameters will cause them to be omitted.
MODELS=`[
{
"name": "text-generation-webui",
"id": "text-generation-webui",
"parameters": {
"temperature": 0.9,
"top_p": 0.95,
"max_new_tokens": 1024,
"stop": []
},
"endpoints": [{
"type" : "openai",
"baseURL": "http://localhost:8000/v1",
"extraBody": {
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000
}
}]
}
]`
The openai
type includes official OpenAI models. You can add, for example, GPT4/GPT3.5 as a "openai" model:
OPENAI_API_KEY=#your openai api key here
MODELS=`[{
"name": "gpt-4",
"displayName": "GPT 4",
"endpoints" : [{
"type": "openai"
}]
},
{
"name": "gpt-3.5-turbo",
"displayName": "GPT 3.5 Turbo",
"endpoints" : [{
"type": "openai"
}]
}]`
You may also consume any model provider that provides compatible OpenAI API endpoint. For example, you may self-host Portkey gateway and experiment with Claude or GPTs offered by Azure OpenAI. Example for Claude from Anthropic:
MODELS=`[{
"name": "claude-2.1",
"displayName": "Claude 2.1",
"description": "Anthropic has been founded by former OpenAI researchers...",
"parameters": {
"temperature": 0.5,
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "openai",
"baseURL": "https://gateway.example.com/v1",
"defaultHeaders": {
"x-portkey-config": '{"provider":"anthropic","api_key":"sk-ant-abc...xyz"}'
}
}
]
}]`
Example for GPT 4 deployed on Azure OpenAI:
MODELS=`[{
"id": "gpt-4-1106-preview",
"name": "gpt-4-1106-preview",
"displayName": "gpt-4-1106-preview",
"parameters": {
"temperature": 0.5,
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "openai",
"baseURL": "https://{resource-name}.openai.azure.com/openai/deployments/{deployment-id}",
"defaultHeaders": {
"api-key": "{api-key}"
},
"defaultQuery": {
"api-version": "2023-05-15"
}
}
]
}]`
Or try Mistral from Deepinfra:
Note, apiKey can either be set custom per endpoint, or globally using
OPENAI_API_KEY
variable.
MODELS=`[{
"name": "mistral-7b",
"displayName": "Mistral 7B",
"description": "A 7B dense Transformer, fast-deployed and easily customisable. Small, yet powerful for a variety of use cases. Supports English and code, and a 8k context window.",
"parameters": {
"temperature": 0.5,
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "openai",
"baseURL": "https://api.deepinfra.com/v1/openai",
"apiKey": "abc...xyz"
}
]
}]`
Llama.cpp API server
chat-ui also supports the llama.cpp API server directly without the need for an adapter. You can do this using the llamacpp
endpoint type.
If you want to run Chat UI with llama.cpp, you can do the following, using microsoft/Phi-3-mini-4k-instruct-gguf as an example model:
# install llama.cpp
brew install llama.cpp
# start llama.cpp server
llama-server --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf --hf-file Phi-3-mini-4k-instruct-q4.gguf -c 4096
MODELS=`[
{
"name": "Local Zephyr",
"chatPromptTemplate": "<|system|>\n{{preprompt}}</s>\n{{#each messages}}{{#ifUser}}<|user|>\n{{content}}</s>\n<|assistant|>\n{{/ifUser}}{{#ifAssistant}}{{content}}</s>\n{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.1,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 2048,
"stop": ["</s>"]
},
"endpoints": [
{
"url": "http://127.0.0.1:8080",
"type": "llamacpp"
}
]
}
]`
Start chat-ui with npm run dev
and you should be able to chat with Zephyr locally.
Ollama
We also support the Ollama inference server. Spin up a model with
ollama run mistral
Then specify the endpoints like so:
MODELS=`[
{
"name": "Ollama Mistral",
"chatPromptTemplate": "<s>{{#each messages}}{{#ifUser}}[INST] {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}} {{content}} [/INST]{{/ifUser}}{{#ifAssistant}}{{content}}</s> {{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.1,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 3072,
"max_new_tokens": 1024,
"stop": ["</s>"]
},
"endpoints": [
{
"type": "ollama",
"url" : "http://127.0.0.1:11434",
"ollamaName" : "mistral"
}
]
}
]`
Anthropic
We also support Anthropic models (including multimodal ones via multmodal: true
) through the official SDK. You may provide your API key via the ANTHROPIC_API_KEY
env variable, or alternatively, through the endpoints.apiKey
as per the following example.
MODELS=`[
{
"name": "claude-3-haiku-20240307",
"displayName": "Claude 3 Haiku",
"description": "Fastest and most compact model for near-instant responsiveness",
"multimodal": true,
"parameters": {
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "anthropic",
// optionals
"apiKey": "sk-ant-...",
"baseURL": "https://api.anthropic.com",
"defaultHeaders": {},
"defaultQuery": {}
}
]
},
{
"name": "claude-3-sonnet-20240229",
"displayName": "Claude 3 Sonnet",
"description": "Ideal balance of intelligence and speed",
"multimodal": true,
"parameters": {
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "anthropic",
// optionals
"apiKey": "sk-ant-...",
"baseURL": "https://api.anthropic.com",
"defaultHeaders": {},
"defaultQuery": {}
}
]
},
{
"name": "claude-3-opus-20240229",
"displayName": "Claude 3 Opus",
"description": "Most powerful model for highly complex tasks",
"multimodal": true,
"parameters": {
"max_new_tokens": 4096
},
"endpoints": [
{
"type": "anthropic",
// optionals
"apiKey": "sk-ant-...",
"baseURL": "https://api.anthropic.com",
"defaultHeaders": {},
"defaultQuery": {}
}
]
}
]`
We also support using Anthropic models running on Vertex AI. Authentication is done using Google Application Default Credentials. Project ID can be provided through the endpoints.projectId
as per the following example:
MODELS=`[
{
"name": "claude-3-sonnet@20240229",
"displayName": "Claude 3 Sonnet",
"description": "Ideal balance of intelligence and speed",
"multimodal": true,
"parameters": {
"max_new_tokens": 4096,
},
"endpoints": [
{
"type": "anthropic-vertex",
"region": "us-central1",
"projectId": "gcp-project-id",
// optionals
"defaultHeaders": {},
"defaultQuery": {}
}
]
},
{
"name": "claude-3-haiku@20240307",
"displayName": "Claude 3 Haiku",
"description": "Fastest, most compact model for near-instant responsiveness",
"multimodal": true,
"parameters": {
"max_new_tokens": 4096
},
"endpoints": [
{
"type": "anthropic-vertex",
"region": "us-central1",
"projectId": "gcp-project-id",
// optionals
"defaultHeaders": {},
"defaultQuery": {}
}
]
}
]`
Amazon
You can also specify your Amazon SageMaker instance as an endpoint for chat-ui. The config goes like this:
"endpoints": [
{
"type" : "aws",
"service" : "sagemaker"
"url": "",
"accessKey": "",
"secretKey" : "",
"sessionToken": "",
"region": "",
"weight": 1
}
]
You can also set "service" : "lambda"
to use a lambda instance.
You can get the accessKey
and secretKey
from your AWS user, under programmatic access.
Cloudflare Workers AI
You can also use Cloudflare Workers AI to run your own models with serverless inference.
You will need to have a Cloudflare account, then get your account ID as well as your API token for Workers AI.
You can either specify them directly in your .env.local
using the CLOUDFLARE_ACCOUNT_ID
and CLOUDFLARE_API_TOKEN
variables, or you can set them directly in the endpoint config.
You can find the list of models available on Cloudflare here.
{
"name" : "nousresearch/hermes-2-pro-mistral-7b",
"tokenizer": "nousresearch/hermes-2-pro-mistral-7b",
"parameters": {
"stop": ["<|im_end|>"]
},
"endpoints" : [
{
"type" : "cloudflare"
<!-- optionally specify these
"accountId": "your-account-id",
"authToken": "your-api-token"
-->
}
]
}
Cohere
You can also use Cohere to run their models directly from chat-ui. You will need to have a Cohere account, then get your API token. You can either specify it directly in your .env.local
using the COHERE_API_TOKEN
variable, or you can set it in the endpoint config.
Here is an example of a Cohere model config. You can set which model you want to use by setting the id
field to the model name.
{
"name" : "CohereForAI/c4ai-command-r-v01",
"id": "command-r",
"description": "C4AI Command-R is a research release of a 35 billion parameter highly performant generative model",
"endpoints": [
{
"type": "cohere",
<!-- optionally specify these, or use COHERE_API_TOKEN
"apiKey": "your-api-token"
-->
}
]
}
Google Vertex models
Chat UI can connect to the google Vertex API endpoints (List of supported models).
To enable:
- Select or create a Google Cloud project.
- Enable billing for your project.
- Enable the Vertex AI API.
- Set up authentication with a service account so you can access the API from your local workstation.
The service account credentials file can be imported as an environmental variable:
GOOGLE_APPLICATION_CREDENTIALS = clientid.json
Make sure your docker container has access to the file and the variable is correctly set. Afterwards Google Vertex endpoints can be configured as following:
MODELS=`[
//...
{
"name": "gemini-1.5-pro",
"displayName": "Vertex Gemini Pro 1.5",
"multimodal": true,
"endpoints" : [{
"type": "vertex",
"project": "abc-xyz",
"location": "europe-west3",
"model": "gemini-1.5-pro-preview-0409", // model-name
// Optional
"safetyThreshold": "BLOCK_MEDIUM_AND_ABOVE",
"apiEndpoint": "", // alternative api endpoint url,
"tools": [{
"googleSearchRetrieval": {
"disableAttribution": true
}
}],
"multimodal": {
"image": {
"supportedMimeTypes": ["image/png", "image/jpeg", "image/webp"],
"preferredMimeType": "image/png",
"maxSizeInMB": 5,
"maxWidth": 2000,
"maxHeight": 1000;
}
}
}]
},
]`
LangServe
LangChain applications that are deployed using LangServe can be called with the following config:
MODELS=`[
//...
{
"name": "summarization-chain", //model-name
"endpoints" : [{
"type": "langserve",
"url" : "http://127.0.0.1:8100",
}]
},
]`
Custom endpoint authorization
Basic and Bearer
Custom endpoints may require authorization, depending on how you configure them. Authentication will usually be set either with Basic
or Bearer
.
For Basic
we will need to generate a base64 encoding of the username and password.
echo -n "USER:PASS" | base64
VVNFUjpQQVNT
For Bearer
you can use a token, which can be grabbed from here.
You can then add the generated information and the authorization
parameter to your .env.local
.
"endpoints": [
{
"url": "https://HOST:PORT",
"authorization": "Basic VVNFUjpQQVNT",
}
]
Please note that if HF_TOKEN
is also set or not empty, it will take precedence.
Models hosted on multiple custom endpoints
If the model being hosted will be available on multiple servers/instances add the weight
parameter to your .env.local
. The weight
will be used to determine the probability of requesting a particular endpoint.
"endpoints": [
{
"url": "https://HOST:PORT",
"weight": 1
},
{
"url": "https://HOST:PORT",
"weight": 2
}
...
]
Client Certificate Authentication (mTLS)
Custom endpoints may require client certificate authentication, depending on how you configure them. To enable mTLS between Chat UI and your custom endpoint, you will need to set the USE_CLIENT_CERTIFICATE
to true
, and add the CERT_PATH
and KEY_PATH
parameters to your .env.local
. These parameters should point to the location of the certificate and key files on your local machine. The certificate and key files should be in PEM format. The key file can be encrypted with a passphrase, in which case you will also need to add the CLIENT_KEY_PASSWORD
parameter to your .env.local
.
If you're using a certificate signed by a private CA, you will also need to add the CA_PATH
parameter to your .env.local
. This parameter should point to the location of the CA certificate file on your local machine.
If you're using a self-signed certificate, e.g. for testing or development purposes, you can set the REJECT_UNAUTHORIZED
parameter to false
in your .env.local
. This will disable certificate validation, and allow Chat UI to connect to your custom endpoint.
Specific Embedding Model
A model can use any of the embedding models defined in .env.local
, (currently used when web searching),
by default it will use the first embedding model, but it can be changed with the field embeddingModel
:
TEXT_EMBEDDING_MODELS = `[
{
"name": "Xenova/gte-small",
"chunkCharLength": 512,
"endpoints": [
{"type": "transformersjs"}
]
},
{
"name": "intfloat/e5-base-v2",
"chunkCharLength": 768,
"endpoints": [
{"type": "tei", "url": "http://127.0.0.1:8080/", "authorization": "Basic VVNFUjpQQVNT"},
{"type": "tei", "url": "http://127.0.0.1:8081/"}
]
}
]`
MODELS=`[
{
"name": "Ollama Mistral",
"chatPromptTemplate": "...",
"embeddingModel": "intfloat/e5-base-v2"
"parameters": {
...
},
"endpoints": [
...
]
}
]`
Common issues
403ï¼You don't have access to this conversation
Most likely you are running chat-ui over HTTP. The recommended option is to setup something like NGINX to handle HTTPS and proxy the requests to chat-ui. If you really need to run over HTTP you can add ALLOW_INSECURE_COOKIES=true
to your .env.local
.
Make sure to set your PUBLIC_ORIGIN
in your .env.local
to the correct URL as well.
Deploying to a HF Space
Create a DOTENV_LOCAL
secret to your HF space with the content of your .env.local, and they will be picked up automatically when you run.
Building
To create a production version of your app:
npm run build
You can preview the production build with npm run preview
.
To deploy your app, you may need to install an adapter for your target environment.
Config changes for HuggingChat
The config file for HuggingChat is stored in the chart/env/prod.yaml
file. It is the source of truth for the environment variables used for our CI/CD pipeline. For HuggingChat, as we need to customize the app color, as well as the base path, we build a custom docker image. You can find the workflow here.
[!TIP] If you want to make changes to the model config used in production for HuggingChat, you should do so against
chart/env/prod.yaml
.
Running a copy of HuggingChat locally
If you want to run an exact copy of HuggingChat locally, you will need to do the following first:
- Create an OAuth App on the hub with
openid profile email
permissions. Make sure to set the callback URL to something likehttp://localhost:5173/chat/login/callback
which matches the right path for your local instance. - Create a HF Token with your Hugging Face account. You will need a Pro account to be able to access some of the larger models available through HuggingChat.
- Create a free account with serper.dev (you will get 2500 free search queries)
- Run an instance of mongoDB, however you want. (Local or remote)
You can then create a new .env.SECRET_CONFIG
file with the following content
MONGODB_URL=<link to your mongo DB from step 4>
HF_TOKEN=<your HF token from step 2>
OPENID_CONFIG=`{
PROVIDER_URL: "https://huggingface.co",
CLIENT_ID: "<your client ID from step 1>",
CLIENT_SECRET: "<your client secret from step 1>",
}`
SERPER_API_KEY=<your serper API key from step 3>
MESSAGES_BEFORE_LOGIN=<can be any numerical value, or set to 0 to require login>
You can then run npm run updateLocalEnv
in the root of chat-ui. This will create a .env.local
file which combines the chart/env/prod.yaml
and the .env.SECRET_CONFIG
file. You can then run npm run dev
to start your local instance of HuggingChat.
Populate database
[!WARNING] The
MONGODB_URL
used for this script will be fetched from.env.local
. Make sure it's correct! The command runs directly on the database.
You can populate the database using faker data using the populate
script:
npm run populate <flags here>
At least one flag must be specified, the following flags are available:
reset
- resets the databaseall
- populates all tablesusers
- populates the users tablesettings
- populates the settings table for existing usersassistants
- populates the assistants table for existing usersconversations
- populates the conversations table for existing users
For example, you could use it like so:
npm run populate reset
to clear out the database. Then login in the app to create your user and run the following command:
npm run populate users settings assistants conversations
to populate the database with fake data, including fake conversations and assistants for your user.
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