title | emoji | colorFrom | colorTo | sdk | pinned | license | base_path | app_port | failure_strategy | load_balancing_strategy |
---|---|---|---|---|---|---|---|---|---|---|
chat-ui |
🔥 |
purple |
purple |
docker |
false |
apache-2.0 |
/chat |
3000 |
rollback |
random |
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
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.
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:
- 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).
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.
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 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
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.
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.
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.
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 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:"]
}
}
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 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"
}
]
}]`
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.
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"
}
]
}
]`
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": {}
}
]
}
]`
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.
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"
-->
}
]
}
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"
-->
}
]
}
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",
"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,
// Optional
"tools": [{
"googleSearchRetrieval": {
"disableAttribution": true
}
}]
}]
},
]`
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 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.
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": [
...
]
}
]`
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.
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.
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
.
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.
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.