File size: 23,099 Bytes
24d11d4
a388980
24d11d4
 
 
 
 
4c4fd67
48ea851
f177e4b
1f2d94c
24d11d4
 
a388980
11dbfc8
8e300b6
a265324
7dd8724
11dbfc8
569bde3
fae93d9
 
8e300b6
3a01622
 
 
 
569bde3
73b316c
569bde3
 
 
8d89bc1
 
3cbea34
569bde3
 
fae93d9
 
 
 
 
 
11dbfc8
8100ea5
2272dad
3cbea34
11dbfc8
 
fae93d9
d16d319
fae93d9
3ebd805
abe7804
a3810f8
fae93d9
 
5741be4
 
fae93d9
5741be4
932ee7e
5741be4
fae93d9
5741be4
2272dad
a3810f8
fae93d9
7764421
fae93d9
7764421
fae93d9
 
 
 
7764421
8e300b6
 
 
 
4606755
 
3a01622
2272dad
 
8e300b6
3a01622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fae93d9
7764421
fae93d9
 
 
 
8100ea5
e69cb4a
 
 
 
 
 
 
 
7764421
fae93d9
 
 
7457e8c
 
 
 
8100ea5
7457e8c
 
 
830b0b6
7457e8c
 
 
 
 
 
 
 
 
 
8100ea5
b46dc11
f02ffb2
cd5cd0c
 
b46dc11
fae93d9
 
 
 
8100ea5
fae93d9
 
22e7bfa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fae93d9
 
 
 
 
 
22e7bfa
fae93d9
 
 
 
22e7bfa
31ef570
fae93d9
7457e8c
7764421
 
fae93d9
 
0e5c445
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8833b5b
0e5c445
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9db8ced
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
73a5c0d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e5c445
9db8ced
 
 
 
 
 
 
 
 
 
e5cb7bb
9db8ced
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e5cb7bb
9db8ced
 
 
 
14f0244
 
 
 
 
 
 
 
 
 
 
e5cb7bb
14f0244
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e5cb7bb
14f0244
 
0e5c445
447c0ca
0e5c445
2272dad
0e5c445
 
 
 
 
 
 
 
 
 
2272dad
0e5c445
 
 
447c0ca
 
0e5c445
0dace21
0e5c445
e34af36
66adc5d
 
 
0dace21
e34af36
 
 
0dace21
 
 
 
 
e34af36
 
0dace21
 
8100ea5
e34af36
f730778
 
 
 
0dace21
 
 
3cbea34
a1afcb6
b46dc11
0dace21
e34af36
0dace21
8100ea5
e34af36
f730778
 
 
 
 
 
 
 
 
0dace21
 
fae93d9
0e5c445
 
 
 
 
 
 
 
3a01622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fae93d9
 
 
 
11dbfc8
 
 
 
 
 
 
 
 
 
 
69c0464
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
---
title: chat-ui
emoji: 🔥
colorFrom: purple
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0
base_path: /chat
app_port: 3000
failure_strategy: rollback
---

# Chat UI

![Chat UI repository thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chatui-websearch.png)

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](https://huggingface.co/chat).

0. [No Setup Deploy](#no-setup-deploy)
1. [Setup](#setup)
2. [Launch](#launch)
3. [Web Search](#web-search)
4. [Text Embedding Models](#text-embedding-models)
5. [Extra parameters](#extra-parameters)
6. [Deploying to a HF Space](#deploying-to-a-hf-space)
7. [Building](#building)

## 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](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending) of your choice on [Hugging Face Spaces](https://huggingface.co/spaces). To do so, use the chat-ui template [available here](https://huggingface.co/new-space?template=huggingchat/chat-ui-template).

Set `HF_TOKEN` in [Space secrets](https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables) to deploy a model with gated access or a model in a private repository. It's also compatible with [Inference for PROs](https://huggingface.co/blog/inference-pro) curated list of powerful models with higher rate limits. Make sure to create your personal token first in your [User Access Tokens settings](https://huggingface.co/settings/tokens).

Read the full tutorial [here](https://huggingface.co/docs/hub/spaces-sdks-docker-chatui#chatui-on-spaces).

## 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:

```env
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:

```bash
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](https://www.mongodb.com/pricing) 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](https://huggingface.co/settings/tokens).

## Launch

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

```bash
npm install
npm run dev
```

## Web Search

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 a text embedding 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](https://huggingface.co/papers/2005.11401) (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](https://huggingface.co/docs/transformers.js) embedding models will be used for embedding tasks, specifically, [Xenova/gte-small](https://huggingface.co/Xenova/gte-small) model.

You can customize the embedding model by setting `TEXT_EMBEDDING_MODELS` in your `.env.local` file. For example:

```env
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`](https://huggingface.co/docs/transformers.js) and [`TEI`](https://github.com/huggingface/text-embeddings-inference). `transformers.js` models run locally as part of `chat-ui`, whereas `TEI` models run in a different environment & accessed through an API endpoint.

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:

```env
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.

### Theming

You can use a few environment variables to customize the look and feel of chat-ui. These are by default:

```env
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](https://tailwindcss.com/docs/customizing-colors#default-color-palette).
- `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](https://docs.you.com)) or `SERPER_API_KEY` ([serper.dev](https://serper.dev/)) or `SERPAPI_KEY` ([serpapi.com](https://serpapi.com/)) or `SERPSTACK_API_KEY` ([serpstack.com](https://serpstack.com/)) to your `.env.local`.

You can also simply enable the local websearch by setting `USE_LOCAL_WEBSEARCH=true` in 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 :

```env
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](https://github.com/huggingface/chat-ui/blob/main/PROMPTS.md).

```prompt
{{preprompt}}
{{#each messages}}
  {{#ifUser}}{{@root.userMessageToken}}{{content}}{{@root.userMessageEndToken}}{{/ifUser}}
  {{#ifAssistant}}{{@root.assistantMessageToken}}{{content}}{{@root.assistantMessageEndToken}}{{/ifAssistant}}
{{/each}}
{{assistantMessageToken}}
```

#### Multi modal model

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

```env
    {
      "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](https://github.com/huggingface/text-generation-inference) endpoint. This is what is done in the official [Chat UI Spaces Docker template](https://huggingface.co/new-space?template=huggingchat/chat-ui-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`.

```env
{
// 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](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai), [LocalAI](https://github.com/go-skynet/LocalAI), [FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/openai_api.md), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), and [ialacol](https://github.com/chenhunghan/ialacol).

The following example config makes Chat UI works with [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai), 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"
      }]
}]`
```

You may also consume any model provider that provides compatible OpenAI API endpoint. For example, you may self-host [Portkey](https://github.com/Portkey-AI/gateway) 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://gateway.example.com/v1",
          "defaultHeaders": {
              "x-portkey-config": '{"provider":"azure-openai","resource_name":"abc-fr","deployment_id":"gpt-4-1106-preview","api_version":"2023-03-15-preview","api_key":"abc...xyz"}'
          }
      }
  ]
}]`
```

Or try Mistral from [Deepinfra](https://deepinfra.com/mistralai/Mistral-7B-Instruct-v0.1/api?example=openai-http):

> 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 Zephyr as an example model:

1. Get [the weights](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/tree/main) 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`:

```env
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

```cli
ollama run mistral
```

Then specify the endpoints like so:

```env
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"
        }
      ]
  }
]`
```

#### Amazon

You can also specify your Amazon SageMaker instance as an endpoint for chat-ui. The config goes like this:

```env
"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 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](https://huggingface.co/settings/tokens).

You can then add the generated information and the `authorization` parameter to your `.env.local`.

```env
"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.

```env
"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`:

```env
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": [
        ...
      ]
  }
]`
```

## 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:

```bash
npm run build
```

You can preview the production build with `npm run preview`.

> To deploy your app, you may need to install an [adapter](https://kit.svelte.dev/docs/adapters) for your target environment.

## Config changes for HuggingChat

The config file for HuggingChat is stored in the `.env.template` file at the root of the repository. It is the single source of truth that is used to generate the actual `.env.local` file using our CI/CD pipeline. See [updateProdEnv](https://github.com/huggingface/chat-ui/blob/cdb33a9583f5339ade724db615347393ef48f5cd/scripts/updateProdEnv.ts) for more details.

> [!TIP]
> If you want to make changes to model config for HuggingChat, you should do so against `.env.template`.

We currently use the following secrets for deploying HuggingChat in addition to the `.env.template` above:

- `MONGODB_URL`
- `HF_TOKEN`
- `OPENID_CONFIG`
- `SERPER_API_KEY`

They are defined as secrets in the repository.

### Testing config changes locally

You can test the config changes locally by first creating an `.env.SECRET_CONFIG` file with the secrets defined above. Then you can run the following command to generate the `.env.local` file:

```bash
npm run updateLocalEnv
```

This will replace your `.env.local` file with the one that will be used in prod (simply taking `.env.template + .env.SECRET_CONFIG`).