title: chat-ui
emoji: 🔥
colorFrom: purple
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0
base_path: /chat
app_port: 3000
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.
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 with only a few clicks to Hugging Face Spaces thanks to the Chat UI Spaces Docker template. Get started here.
If you'd like to deploy a model with gated access or a model in a private repository, you can simply provide HUGGING_FACE_HUB_TOKEN in Space secrets. You need to set its value to an access token you can get from here.
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_ACCESS_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
You will need a Hugging Face access token to run Chat UI locally, if you use a remote inference endpoint. 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 Google query from the user prompt.
- Performing Google search and extracting content from webpages.
- Creating embeddings from texts using transformers.js. Specifically, using Xenova/gte-small model.
- From these embeddings, find the ones that are closest to the user query using vector similarity search. Specifically, we use
inner productdistance. - Get the corresponding texts to those closest embeddings and perform Retrieval-Augmented Generation (i.e. expand user prompt by adding those texts so that a LLM can use this information).
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_PROVIDER_URL=<your OIDC issuer>
OPENID_CLIENT_ID=<your OIDC client ID>
OPENID_CLIENT_SECRET=<your OIDC client secret>
These variables will enable the openID sign-in modal for users.
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_DATA_SHARING=
PUBLIC_APP_DISCLAIMER=
PUBLIC_APP_NAMEThe name used as a title throughout the app.PUBLIC_APP_ASSETSIs used to find logos & favicons instatic/$PUBLIC_APP_ASSETS, current options arechatuiandhuggingchat.PUBLIC_APP_COLORCan be any of the tailwind colors.PUBLIC_APP_DATA_SHARINGCan 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_DISCLAIMERIf set to 1, we show a disclaimer about generated outputs on login.
Web Search
You can enable the web search by adding either SERPER_API_KEY (serper.dev) or SERPAPI_KEY (serpapi.com) to your .env.local.
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": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"datasetName": "OpenAssistant/oasst1",
"description": "A good alternative to ChatGPT",
"websiteUrl": "https://open-assistant.io",
"userMessageToken": "<|prompter|>", # This does not need to be a token, can be any string
"assistantMessageToken": "<|assistant|>", # This does not need to be a token, can be any string
"userMessageEndToken": "<|endoftext|>", # Applies only to user messages. Can be any string.
"assistantMessageEndToken": "<|endoftext|>", # Applies only to assistant messages. Can be any string.
"preprompt": "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n-----\n",
"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?"
}
],
"parameters": {
"temperature": 0.9,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 1024,
"stop": ["<|endoftext|>"] # This does not need to be tokens, can be any list of strings
}
}
]`
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.
Custom prompt templates:
By default the prompt is constructed using userMessageToken, assistantMessageToken, userMessageEndToken, assistantMessageEndToken, preprompt parameters and a series of default templates.
However, these templates can be modified by setting the chatPromptTemplate and webSearchQueryPromptTemplate parameters. Note that if WebSearch is not enabled, only chatPromptTemplate needs to be set. The template language is https://handlebarsjs.com. The templates have access to the model's prompt parameters (preprompt, etc.). However, if the templates are specified it is recommended to inline the prompt parameters, as using the references ({{preprompt}}) is deprecated.
For example:
<System>You are an AI, called ChatAI.</System>
{{#each messages}}
{{#ifUser}}<User>{{content}}</User>{{/ifUser}}
{{#ifAssistant}}<Assistant>{{content}}</Assistant>{{/ifAssistant}}
{{/each}}
<Assistant>
chatPromptTemplate
When quering 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 idenify 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.
{{preprompt}}
{{#each messages}}
{{#ifUser}}{{@root.userMessageToken}}{{content}}{{@root.userMessageEndToken}}{{/ifUser}}
{{#ifAssistant}}{{@root.assistantMessageToken}}{{content}}{{@root.assistantMessageEndToken}}{{/ifAssistant}}
{{/each}}
{{assistantMessageToken}}
webSearchQueryPromptTemplate
When performing a websearch, the search query is constructed using the webSearchQueryPromptTemplate template. It is recommended that that the prompt instructs the chat model to only return a few keywords.
The following is the default webSearchQueryPromptTemplate.
{{userMessageToken}}
My question is: {{message.content}}.
Based on the conversation history (my previous questions are: {{previousMessages}}), give me an appropriate query to answer my question for google search. You should not say more than query. You should not say any words except the query. For the context, today is {{currentDate}}
{{userMessageEndToken}}
{{assistantMessageToken}}
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": [{"url": "https://HOST:PORT"}]
}
If endpoints is left unspecified, ChatUI will look for the model on the hosted Hugging Face inference API using the model name.
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",
}
]
Amazon SageMaker
You can also specify your Amazon SageMaker instance as an endpoint for chat-ui. The config goes like this:
"endpoints": [
{
"host" : "sagemaker",
"url": "", // your aws sagemaker url here
"accessKey": "",
"secretKey" : "",
"sessionToken": "", // optional
"weight": 1
}
You can get the accessKey and secretKey from your AWS user, under programmatic access.
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.
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
}
...
]
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.