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.

0. No Setup Deploy
1. Setup
2. Launch
3. Web Search
4. Extra parameters
5. Deploying to a HF Space
6. Building

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.

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>

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.

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.

After you're done with the .env.local file you can run Chat UI locally with:

npm install
npm run dev

Chat UI features a powerful Web Search feature. It works by:

1. Generating an appropriate search query from the user prompt.
2. Performing web search and extracting content from webpages.
3. Creating embeddings from texts using transformers.js. Specifically, using Xenova/gte-small model.
4. From these embeddings, find the ones that are closest to the user query using a vector similarity search. Specifically, we use inner product distance.
5. 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).

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.

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 in static/$PUBLIC_APP_ASSETS, current options are chatui and huggingchat.
• 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.

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) to your .env.local.

You can also simply enable the local websearch by setting USE_LOCAL_WEBSEARCH=true in your .env.local.

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 and 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.

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}}

We currently only support IDEFICS as a multimodal model, hosted on TGI. You can enable it by using the followin config (if you have a PRO HF Api token):

    {
      "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:"]
      }
    }

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.

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.

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.

MODELS=`[
  {
    "name": "text-generation-webui",
    "id": "text-generation-webui",
    "parameters": {
      "temperature": 0.9,
      "top_p": 0.95,
      "repetition_penalty": 1.2,
      "top_k": 50,
      "truncate": 1000,
      "max_new_tokens": 1024,
      "stop": []
    },
    "endpoints": [{
      "type" : "openai",
      "baseURL": "http://localhost:8000/v1"
    }]
  }
]`

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"
      }]
}]`

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 Zephyr as an example model:

1. Get the weights from the hub
2. Run the server with the following command: ./server -m models/zephyr-7b-beta.Q4_K_M.gguf -c 2048 -np 3
3. Add the following to your .env.local:

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.

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"
        }
      ]
  }
]`

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.

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.

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
}
...
]

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.

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.

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.