push placeholder for huggingchat

.dockerignore

@@ -1,11 +0,0 @@
-Dockerfile
-.vscode/
-.idea
-.gitignore
-LICENSE
-README.md
-node_modules/
-.svelte-kit/
-.env*
-!.env
-.env.local

.env

@@ -1,158 +0,0 @@
-# Use .env.local to change these variables
-# DO NOT EDIT THIS FILE WITH SENSITIVE DATA
-
-MONGODB_URL=#your mongodb URL here
-MONGODB_DB_NAME=chat-ui
-MONGODB_DIRECT_CONNECTION=false
-
-COOKIE_NAME=hf-chat
-HF_TOKEN=#hf_<token> from https://huggingface.co/settings/token
-HF_API_ROOT=https://api-inference.huggingface.co/models
-
-OPENAI_API_KEY=#your openai api key here
-ANTHROPIC_API_KEY=#your anthropic api key here
-CLOUDFLARE_ACCOUNT_ID=#your cloudflare account id here
-CLOUDFLARE_API_TOKEN=#your cloudflare api token here
-COHERE_API_TOKEN=#your cohere api token here
-
-HF_ACCESS_TOKEN=#LEGACY! Use HF_TOKEN instead
-
-# used to activate search with web functionality. disabled if none are defined. choose one of the following:
-YDC_API_KEY=#your docs.you.com api key here
-SERPER_API_KEY=#your serper.dev api key here
-SERPAPI_KEY=#your serpapi key here
-SERPSTACK_API_KEY=#your serpstack api key here
-USE_LOCAL_WEBSEARCH=#set to true to parse google results yourself, overrides other API keys
-SEARXNG_QUERY_URL=# where '<query>' will be replaced with query keywords see https://docs.searxng.org/dev/search_api.html eg https://searxng.yourdomain.com/search?q=<query>&engines=duckduckgo,google&format=json
-
-WEBSEARCH_ALLOWLIST=`[]` # if it's defined, allow websites from only this list.
-WEBSEARCH_BLOCKLIST=`[]` # if it's defined, block websites from this list.
-
-# Parameters to enable open id login
-OPENID_CONFIG=`{
-  "PROVIDER_URL": "",
-  "CLIENT_ID": "",
-  "CLIENT_SECRET": "",
-  "SCOPES": "",
-  "NAME_CLAIM": ""
-}`
-
-# /!\ legacy openid settings, prefer the config above
-OPENID_CLIENT_ID=
-OPENID_CLIENT_SECRET=
-OPENID_SCOPES="openid profile" # Add "email" for some providers like Google that do not provide preferred_username
-OPENID_NAME_CLAIM="name" # Change to "username" for some providers that do not provide name
-OPENID_PROVIDER_URL=https://huggingface.co # for Google, use https://accounts.google.com
-OPENID_TOLERANCE=
-OPENID_RESOURCE=
-
-# Parameters to enable a global mTLS context for client fetch requests
-USE_CLIENT_CERTIFICATE=false
-CERT_PATH=#
-KEY_PATH=#
-CA_PATH=#
-CLIENT_KEY_PASSWORD=#
-REJECT_UNAUTHORIZED=true
-
-TEXT_EMBEDDING_MODELS = `[
-  {
-    "name": "Xenova/gte-small",
-    "displayName": "Xenova/gte-small",
-    "description": "Local embedding model running on the server.",
-    "chunkCharLength": 512,
-    "endpoints": [
-      { "type": "transformersjs" }
-    ]
-  }
-]`
-
-# 'name', 'userMessageToken', 'assistantMessageToken' are required
-MODELS=`[
-    {
-      "name": "mistralai/Mistral-7B-Instruct-v0.1",
-      "displayName": "mistralai/Mistral-7B-Instruct-v0.1",
-      "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": 0.1,
-        "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?"
-        }
-      ]
-    }
-]`
-
-OLD_MODELS=`[]`# any removed models, `{ name: string, displayName?: string, id?: string }`
-TASK_MODEL= # name of the model used for tasks such as summarizing title, creating query, etc.
-
-PUBLIC_ORIGIN=#https://huggingface.co
-PUBLIC_SHARE_PREFIX=#https://hf.co/chat
-PUBLIC_GOOGLE_ANALYTICS_ID=#G-XXXXXXXX / Leave empty to disable
-PUBLIC_PLAUSIBLE_SCRIPT_URL=#/js/script.js / Leave empty to disable
-PUBLIC_ANNOUNCEMENT_BANNERS=`[
-    {
-    "title": "Code Llama 70B is available! 🦙",
-    "linkTitle": "try it",
-    "linkHref": "https://huggingface.co/chat?model=codellama/CodeLlama-70b-Instruct-hf"
-  }
-]`
-
-PUBLIC_APPLE_APP_ID=#1234567890 / Leave empty to disable
-
-PARQUET_EXPORT_DATASET=
-PARQUET_EXPORT_HF_TOKEN=
-ADMIN_API_SECRET=# secret to admin API calls, like computing usage stats or exporting parquet data
-
-PARQUET_EXPORT_SECRET=#DEPRECATED, use ADMIN_API_SECRET instead
-
-RATE_LIMIT= # /!\ Legacy definition of messages per minute. Use USAGE_LIMITS.messagesPerMinute instead
-MESSAGES_BEFORE_LOGIN=# how many messages a user can send in a conversation before having to login. set to 0 to force login right away
-
-APP_BASE="" # base path of the app, e.g. /chat, left blank as default
-PUBLIC_APP_NAME=ChatUI # name used as title throughout the app
-PUBLIC_APP_ASSETS=chatui # used to find logos & favicons in static/$PUBLIC_APP_ASSETS
-PUBLIC_APP_COLOR=blue # can be any of tailwind colors: https://tailwindcss.com/docs/customizing-colors#default-color-palette
-PUBLIC_APP_DESCRIPTION=# description used throughout the app (if not set, a default one will be used)
-PUBLIC_APP_DATA_SHARING=#set to 1 to enable options & text regarding data sharing
-PUBLIC_APP_DISCLAIMER=#set to 1 to show a disclaimer on login page
-PUBLIC_APP_DISCLAIMER_MESSAGE="Disclaimer: AI is an area of active research with known problems such as biased generation and misinformation. Do not use this application for high-stakes decisions or advice. Do not insert your personal data, especially sensitive, like health data."
-LLM_SUMMERIZATION=true
-
-EXPOSE_API=true
-# PUBLIC_APP_NAME=HuggingChat
-# PUBLIC_APP_ASSETS=huggingchat
-# PUBLIC_APP_COLOR=yellow
-# PUBLIC_APP_DESCRIPTION="Making the community's best AI chat models available to everyone."
-# PUBLIC_APP_DATA_SHARING=1
-# PUBLIC_APP_DISCLAIMER=1
-
-ENABLE_ASSISTANTS=false #set to true to enable assistants feature
-ENABLE_ASSISTANTS_RAG=false # /!\ This will let users specify arbitrary URLs that the server will then request. Make sure you have the proper firewall rules in place. 
-REQUIRE_FEATURED_ASSISTANTS=false
-ENABLE_LOCAL_FETCH=false #set to true to disable the blocklist for local fetches. Only enable this if you have the proper firewall rules to prevent SSRF attacks and understand the implications.
-ALTERNATIVE_REDIRECT_URLS=`[]` #valide alternative redirect URL for OAuth
-WEBHOOK_URL_REPORT_ASSISTANT=#provide webhook url to get notified when an assistant gets reported
-
-ALLOWED_USER_EMAILS=`[]` # if it's defined, only these emails will be allowed to use the app
-
-USAGE_LIMITS=`{}`
-ALLOW_INSECURE_COOKIES=false # recommended to keep this to false but set to true if you need to run over http without tls
-METRICS_PORT=
-LOG_LEVEL=info

.env.ci

@@ -1 +0,0 @@
-MONGODB_URL=mongodb://localhost:27017/

.env.template

@@ -1,298 +0,0 @@
-# template used in production for HuggingChat.
-
-MODELS=`[
-  {
-    "name" : "CohereForAI/c4ai-command-r-plus",
-    "tokenizer": "Xenova/c4ai-command-r-v01-tokenizer",
-    "description": "Command R+ is Cohere's latest LLM and is the first open weight model to beat GPT4 in the Chatbot Arena!",
-    "modelUrl": "https://huggingface.co/CohereForAI/c4ai-command-r-plus",
-    "websiteUrl": "https://docs.cohere.com/docs/command-r-plus",
-    "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/cohere-logo.png",
-    "parameters": {
-      "stop": ["<|END_OF_TURN_TOKEN|>"],
-      "truncate" : 28672,
-      "max_new_tokens" : 4096,
-      "temperature" : 0.3
-    },
-    "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?"
-      }
-    ]
-  },
-  {
-    "name" : "meta-llama/Meta-Llama-3-70B-Instruct",
-    "description": "Generation over generation, Meta Llama 3 demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning.",
-    "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/meta-logo.png",
-    "modelUrl": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct",
-    "websiteUrl": "https://llama.meta.com/llama3/",
-    "tokenizer" : "philschmid/meta-llama-3-tokenizer",
-    "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": {
-      "stop": ["<|eot_id|>"],
-      "truncate": 6144,
-      "max_new_tokens": 2047
-    }
-  },
-  {
-    "name" : "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
-    "tokenizer": "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
-    "description": "Zephyr 141B-A35B is a fine-tuned version of Mistral 8x22B, trained using ORPO, a novel alignment algorithm.",
-    "modelUrl": "https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
-    "websiteUrl": "https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
-    "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/zephyr-logo.png",
-    "parameters": {
-      "truncate" : 24576,
-      "max_new_tokens" : 8192,
-    },
-    "preprompt" : "You are Zephyr, an assistant developed by KAIST AI, Argilla, and Hugging Face. You should give concise responses to very simple questions, but provide thorough responses to more complex and open-ended questions. You are happy to help with writing, analysis, question answering, math, coding, and all sorts of other tasks.",
-    "promptExamples" : [
-      {
-        "title": "Write a poem",
-        "prompt": "Write a poem to help me remember the first 10 elements on the periodic table, giving each element its own line."
-      }, {
-        "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?"
-      }
-    ]
-  },
-  {
-    "name" : "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "description" : "The latest MoE model from Mistral AI! 8x7B and outperforms Llama 2 70B in most benchmarks.",
-    "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/mistral-logo.png",
-    "websiteUrl" : "https://mistral.ai/news/mixtral-of-experts/",
-    "modelUrl": "https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "tokenizer": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "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" : 0.6,
-      "top_p" : 0.95,
-      "repetition_penalty" : 1.2,
-      "top_k" : 50,
-      "truncate" : 24576,
-      "max_new_tokens" : 8192,
-      "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?"
-      }
-    ]
-  },
-  {
-      "name" : "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
-      "description" : "Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the Mixtral 8x7B MoE LLM.",
-      "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/nous-logo.png",
-      "websiteUrl" : "https://nousresearch.com/",
-      "modelUrl": "https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
-      "tokenizer": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
-      "chatPromptTemplate" : "{{#if @root.preprompt}}<|im_start|>system\n{{@root.preprompt}}<|im_end|>\n{{/if}}{{#each messages}}{{#ifUser}}<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n{{/ifUser}}{{#ifAssistant}}{{content}}<|im_end|>\n{{/ifAssistant}}{{/each}}",
-      "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": 0.7,
-        "top_p": 0.95,
-        "repetition_penalty": 1,
-        "top_k": 50,
-        "truncate": 24576,
-        "max_new_tokens": 2048,
-        "stop": ["<|im_end|>"]
-      }
-    },
-      {
-    "name" : "google/gemma-1.1-7b-it",
-    "description": "Gemma 7B 1.1 is the latest release in the Gemma family of lightweight models built by Google, trained using a novel RLHF method.",
-    "websiteUrl" : "https://blog.google/technology/developers/gemma-open-models/",
-    "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/google-logo.png",
-    "modelUrl": "https://huggingface.co/google/gemma-1.1-7b-it",
-    "preprompt": "",
-    "chatPromptTemplate" : "{{#each messages}}{{#ifUser}}<start_of_turn>user\n{{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}}<end_of_turn>\n<start_of_turn>model\n{{/ifUser}}{{#ifAssistant}}{{content}}<end_of_turn>\n{{/ifAssistant}}{{/each}}",
-    "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": {
-        "do_sample": true,
-        "truncate": 7168,
-        "max_new_tokens": 1024,
-        "stop" : ["<end_of_turn>"]
-      }
-  },
-
-        {
-      "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.",
-      "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/mistral-logo.png",
-      "websiteUrl": "https://mistral.ai/news/announcing-mistral-7b/",
-      "modelUrl": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
-      "tokenizer": "mistralai/Mistral-7B-Instruct-v0.2",
-      "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?"
-        }
-      ]
-    },
-    {
-      "name": "microsoft/Phi-3-mini-4k-instruct",
-      "tokenizer": "microsoft/Phi-3-mini-4k-instruct",
-      "description" : "Phi-3 Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model built upon datasets used for Phi-2.",
-      "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/microsoft-logo.png",
-      "modelUrl": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
-      "websiteUrl": "https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/",
-      "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|>"],
-        "max_new_tokens": 1024,
-        "truncate": 3071
-      },
-      "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?"
-        }
-      ]
-    },
-    {
-      "name": "meta-llama/Meta-Llama-3-8B-Instruct",
-      "tokenizer" : "philschmid/meta-llama-3-tokenizer",
-      "parameters": {
-        "temperature": 0.1,
-        "stop": ["<|eot_id|>"],
-        "truncate": 1024,
-      },
-      "unlisted": true
-    }
-]`
-
-OLD_MODELS=`[
-  {"name":"bigcode/starcoder"},
-  {"name":"OpenAssistant/oasst-sft-6-llama-30b-xor"},
-  {"name":"HuggingFaceH4/zephyr-7b-alpha"},
-  {"name":"openchat/openchat_3.5"},
-  {"name":"openchat/openchat-3.5-1210"},
-  {"name": "tiiuae/falcon-180B-chat"},
-  {"name": "codellama/CodeLlama-34b-Instruct-hf"},
-  {"name": "google/gemma-7b-it"},
-  {"name": "meta-llama/Llama-2-70b-chat-hf"},
-  {"name": "codellama/CodeLlama-70b-Instruct-hf"},
-  {"name": "openchat/openchat-3.5-0106"}
-]`
-
-TASK_MODEL='meta-llama/Meta-Llama-3-8B-Instruct'
-
-TEXT_EMBEDDING_MODELS = `[
-  {
-    "name": "bge-base-en-v1-5-sxa",
-    "displayName": "bge-base-en-v1-5-sxa",
-    "chunkCharLength": 512,
-    "endpoints": [
-      { "type": "tei",
-        "url" : "https://huggingchat-tei.hf.space/"
-      }
-    ]
-  }
-]`
-
-
-APP_BASE="/chat"
-PUBLIC_ORIGIN=https://huggingface.co
-PUBLIC_SHARE_PREFIX=https://hf.co/chat
-PUBLIC_ANNOUNCEMENT_BANNERS=`[]`
-
-PUBLIC_APP_NAME=HuggingChat
-PUBLIC_APP_ASSETS=huggingchat
-PUBLIC_APP_COLOR=yellow
-PUBLIC_APP_DESCRIPTION="Making the community's best AI chat models available to everyone."
-PUBLIC_APP_DISCLAIMER_MESSAGE="Disclaimer: AI is an area of active research with known problems such as biased generation and misinformation. Do not use this application for high-stakes decisions or advice."
-PUBLIC_APP_DATA_SHARING=0
-PUBLIC_APP_DISCLAIMER=1
-
-PUBLIC_PLAUSIBLE_SCRIPT_URL="/js/script.js"
-PUBLIC_APPLE_APP_ID=6476778843
-# Not part of the .env but set as other variables in the space
-# ADDRESS_HEADER=X-Forwarded-For
-# XFF_DEPTH=2
-
-ENABLE_ASSISTANTS=true
-ENABLE_ASSISTANTS_RAG=true
-REQUIRE_FEATURED_ASSISTANTS=true
-EXPOSE_API=true
-
-ALTERNATIVE_REDIRECT_URLS=`[
-  huggingchat://login/callback
-]`
-
-WEBSEARCH_BLOCKLIST=`["youtube.com", "twitter.com"]`

.eslintignore

@@ -1,13 +0,0 @@
-.DS_Store
-node_modules
-/build
-/.svelte-kit
-/package
-.env
-.env.*
-!.env.example
-
-# Ignore files for PNPM, NPM and YARN
-pnpm-lock.yaml
-package-lock.json
-yarn.lock

.eslintrc.cjs

@@ -1,44 +0,0 @@
-module.exports = {
-	root: true,
-	parser: "@typescript-eslint/parser",
-	extends: [
-		"eslint:recommended",
-		"plugin:@typescript-eslint/recommended",
-		"plugin:svelte/recommended",
-		"prettier",
-	],
-	plugins: ["@typescript-eslint"],
-	ignorePatterns: ["*.cjs"],
-	overrides: [
-		{
-			files: ["*.svelte"],
-			parser: "svelte-eslint-parser",
-			parserOptions: {
-				parser: "@typescript-eslint/parser",
-			},
-		},
-	],
-	parserOptions: {
-		sourceType: "module",
-		ecmaVersion: 2020,
-		extraFileExtensions: [".svelte"],
-	},
-	rules: {
-		"no-shadow": ["error"],
-		"@typescript-eslint/no-explicit-any": "error",
-		"@typescript-eslint/no-non-null-assertion": "error",
-		"@typescript-eslint/no-unused-vars": [
-			// prevent variables with a _ prefix from being marked as unused
-			"error",
-			{
-				argsIgnorePattern: "^_",
-			},
-		],
-		"object-shorthand": ["error", "always"],
-	},
-	env: {
-		browser: true,
-		es2017: true,
-		node: true,
-	},
-};

.github/release.yml

@@ -1,16 +0,0 @@
-changelog:
-  exclude:
-    labels:
-      - huggingchat
-      - CI/CD
-      - documentation
-  categories:
-    - title: Features
-      labels:
-        - enhancement
-    - title: Bugfixes
-      labels:
-        - bug
-    - title: Other changes
-      labels:
-        - "*"

.github/workflows/build-image.yml

@@ -1,125 +0,0 @@
-name: Build and Publish Image
-
-on:
-  push:
-    branches:
-      - "main"
-  pull_request:
-    branches:
-      - "*"
-    paths:
-      - "Dockerfile"
-      - "entrypoint.sh"
-  workflow_dispatch:
-  release:
-    types: [published, edited]
-
-jobs:
-  build-and-publish-image-with-db:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Extract package version
-        id: package-version
-        run: |
-          VERSION=$(jq -r .version package.json)
-          echo "VERSION=$VERSION" >> $GITHUB_OUTPUT
-          MAJOR=$(echo $VERSION | cut -d '.' -f1)
-          echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
-          MINOR=$(echo $VERSION | cut -d '.' -f1).$(echo $VERSION | cut -d '.' -f2)
-          echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
-
-      - name: Docker metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: |
-            ghcr.io/huggingface/chat-ui-db
-          tags: |
-            type=raw,value=${{ steps.package-version.outputs.VERSION }},enable=${{github.event_name == 'release'}}
-            type=raw,value=${{ steps.package-version.outputs.MAJOR }},enable=${{github.event_name == 'release'}}
-            type=raw,value=${{ steps.package-version.outputs.MINOR }},enable=${{github.event_name == 'release'}}
-            type=raw,value=latest,enable={{is_default_branch}}
-            type=sha,enable={{is_default_branch}}
-
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to GitHub Container Registry
-        if: github.event_name != 'pull_request'
-        uses: docker/login-action@v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.repository_owner }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build and Publish Docker Image with DB
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: Dockerfile
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          platforms: linux/amd64,linux/arm64
-          build-args: |
-            INCLUDE_DB=true
-  build-and-publish-image-nodb:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Extract package version
-        id: package-version
-        run: |
-          VERSION=$(jq -r .version package.json)
-          echo "VERSION=$VERSION" >> $GITHUB_OUTPUT
-          MAJOR=$(echo $VERSION | cut -d '.' -f1)
-          echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
-          MINOR=$(echo $VERSION | cut -d '.' -f1).$(echo $VERSION | cut -d '.' -f2)
-          echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
-
-      - name: Docker metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: |
-            ghcr.io/huggingface/chat-ui
-          tags: |
-            type=raw,value=${{ steps.package-version.outputs.VERSION }},enable=${{github.event_name == 'release'}}
-            type=raw,value=${{ steps.package-version.outputs.MAJOR }},enable=${{github.event_name == 'release'}}
-            type=raw,value=${{ steps.package-version.outputs.MINOR }},enable=${{github.event_name == 'release'}}
-            type=raw,value=latest,enable={{is_default_branch}}
-            type=sha,enable={{is_default_branch}}
-
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to GitHub Container Registry
-        if: github.event_name != 'pull_request'
-        uses: docker/login-action@v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.repository_owner }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build and Publish Docker Image without DB
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: Dockerfile
-          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          platforms: linux/amd64,linux/arm64
-          build-args: |
-            INCLUDE_DB=false

.github/workflows/deploy-release.yml

@@ -1,44 +0,0 @@
-name: Deploy to production
-on:
-  # run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-jobs:
-  update-env:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "npm"
-      - run: npm install ci
-      - name: "Update DOTENV_LOCAL in prod"
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-          SERPER_API_KEY: ${{ secrets.SERPER_API_KEY }}
-          OPENID_CONFIG: ${{ secrets.OPENID_CONFIG }}
-          MONGODB_URL: ${{ secrets.MONGODB_URL }}
-          HF_DEPLOYMENT_TOKEN: ${{ secrets.HF_DEPLOYMENT_TOKEN }}
-          WEBHOOK_URL_REPORT_ASSISTANT: ${{ secrets.WEBHOOK_URL_REPORT_ASSISTANT }}
-          ADMIN_API_SECRET: ${{ secrets.ADMIN_API_SECRET }}
-          USAGE_LIMITS: ${{ secrets.USAGE_LIMITS }}
-          MESSAGES_BEFORE_LOGIN: ${{ secrets.MESSAGES_BEFORE_LOGIN }}
-        run: npm run updateProdEnv
-  sync-to-hub:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check large files
-        uses: ActionsDesk/[email protected]
-        with:
-          filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces
-      - uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Push to hub
-        env:
-          HF_DEPLOYMENT_TOKEN: ${{ secrets.HF_DEPLOYMENT_TOKEN }}
-        run: git push https://nsarrazin:[email protected]/spaces/huggingchat/chat-ui main

.github/workflows/deploy-staging.yml

@@ -1,24 +0,0 @@
-name: Deploy to staging environment
-on:
-  push:
-    branches: [main]
-
-  # to run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-jobs:
-  sync-to-hub:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Check large files
-        uses: ActionsDesk/[email protected]
-        with:
-          filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces
-      - uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Push to hub
-        env:
-          HF_DEPLOYMENT_TOKEN: ${{ secrets.HF_DEPLOYMENT_TOKEN }}
-        run: git push https://nsarrazin:[email protected]/spaces/huggingchat/chat-ui-staging main

.github/workflows/lint-and-test.yml

@@ -1,56 +0,0 @@
-name: Lint and test
-on:
-  pull_request:
-  push:
-    branches:
-      - main
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-
-    steps:
-      - uses: actions/checkout@v3
-
-      - uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "npm"
-      - run: |
-          npm install ci
-      - name: "Checking lint/format errors"
-        run: |
-          npm run lint
-      - name: "Checking type errors"
-        run: |
-          npm run check
-  test:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-
-    services:
-      mongodb:
-        image: mongo:6.0.5
-        ports:
-          - 27017:27017
-
-    steps:
-      - uses: actions/checkout@v3
-
-      - uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "npm"
-      - run: |
-          npm ci
-      - name: "Tests"
-        run: |
-          npm run test
-  build-check:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    steps:
-      - uses: actions/checkout@v3
-      - name: Build Docker image
-        run: docker build --secret id=DOTENV_LOCAL,src=.env.ci -t chat-ui:latest .

.gitignore

@@ -1,15 +0,0 @@
-.DS_Store
-node_modules
-/build
-/.svelte-kit
-/package
-.env
-.env.*
-vite.config.js.timestamp-*
-vite.config.ts.timestamp-*
-SECRET_CONFIG
-.idea
-!.env.ci
-!.env
-!.env.template
-gcp-*.json

.npmrc

@@ -1 +0,0 @@
-engine-strict=true

.prettierignore

@@ -1,13 +0,0 @@
-.DS_Store
-node_modules
-/build
-/.svelte-kit
-/package
-.env
-.env.*
-!.env.example
-
-# Ignore files for PNPM, NPM and YARN
-pnpm-lock.yaml
-package-lock.json
-yarn.lock

.prettierrc

@@ -1,8 +0,0 @@
-{
-	"useTabs": true,
-	"trailingComma": "es5",
-	"printWidth": 100,
-	"plugins": ["prettier-plugin-svelte", "prettier-plugin-tailwindcss"],
-	"pluginSearchDirs": ["."],
-	"overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }]
-}

.vscode/settings.json

@@ -1,8 +0,0 @@
-{
-	"editor.formatOnSave": true,
-	"editor.defaultFormatter": "esbenp.prettier-vscode",
-	"editor.codeActionsOnSave": {
-		"source.fixAll": "explicit"
-	},
-	"eslint.validate": ["javascript", "svelte"]
-}

Dockerfile

@@ -1,88 +0,0 @@
-# syntax=docker/dockerfile:1
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-ARG INCLUDE_DB=false
-
-# stage that install the dependencies
-FROM node:20 as builder-production
-
-WORKDIR /app
-
-COPY --link --chown=1000 package-lock.json package.json ./
-RUN --mount=type=cache,target=/app/.npm \
-        npm set cache /app/.npm && \
-        npm ci --omit=dev
-
-FROM builder-production as builder
-
-ARG APP_BASE=
-ARG PUBLIC_APP_COLOR=blue
-
-RUN --mount=type=cache,target=/app/.npm \
-        npm set cache /app/.npm && \
-        npm ci
-
-COPY --link --chown=1000 . .
-
-RUN npm run build
-
-# mongo image
-FROM mongo:latest as mongo
-
-# image to be used if INCLUDE_DB is false
-FROM node:20-slim as local_db_false
-
-# image to be used if INCLUDE_DB is true
-FROM node:20-slim as local_db_true
-
-RUN apt-get update
-RUN apt-get install gnupg curl -y
-# copy mongo from the other stage
-COPY --from=mongo /usr/bin/mongo* /usr/bin/
-
-ENV MONGODB_URL=mongodb://localhost:27017
-RUN mkdir -p /data/db
-RUN chown -R 1000:1000 /data/db
-
-# final image
-FROM local_db_${INCLUDE_DB} as final
-
-# build arg to determine if the database should be included
-ARG INCLUDE_DB=false
-ENV INCLUDE_DB=${INCLUDE_DB}
-
-# svelte requires APP_BASE at build time so it must be passed as a build arg
-ARG APP_BASE=
-# tailwind requires the primary theme to be known at build time so it must be passed as a build arg
-ARG PUBLIC_APP_COLOR=blue
-
-
-# install dotenv-cli
-RUN npm install -g dotenv-cli
-
-# switch to a user that works for spaces
-RUN userdel -r node
-RUN useradd -m -u 1000 user
-USER user
-
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-WORKDIR /app
-
-# add a .env.local if the user doesn't bind a volume to it
-RUN touch /app/.env.local
-
-# get the default config, the entrypoint script and the server script
-COPY --chown=1000 package.json /app/package.json
-COPY --chown=1000 .env /app/.env
-COPY --chown=1000 entrypoint.sh /app/entrypoint.sh
-COPY --chown=1000 gcp-*.json /app/
-
-#import the build & dependencies
-COPY --from=builder --chown=1000 /app/build /app/build
-COPY --from=builder --chown=1000 /app/node_modules /app/node_modules
-
-RUN chmod +x /app/entrypoint.sh
-
-CMD ["/bin/bash", "-c", "/app/entrypoint.sh"]

LICENSE

@@ -1,203 +0,0 @@
-Copyright 2018- The Hugging Face team. All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
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-      "Work" shall mean the work of authorship, whether in Source or
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-      whether in tort (including negligence), contract, or otherwise,
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PRIVACY.md

@@ -1,42 +0,0 @@
-## Privacy
-
-> Last updated: April 15, 2024
-
-Users of HuggingChat are authenticated through their HF user account.
-
-We endorse Privacy by Design. As such, your conversations are private to you and will not be shared with anyone, including model authors, for any purpose, including for research or model training purposes.
-
-You conversation data will only be stored to let you access past conversations. You can click on the Delete icon to delete any past conversation at any moment.
-
-🗓 Please also consult huggingface.co's main privacy policy at <https://huggingface.co/privacy>. To exercise any of your legal privacy rights, please send an email to <[email protected]>.
-
-## About available LLMs
-
-The goal of this app is to showcase that it is now possible to build an open source alternative to ChatGPT. 💪
-
-We aim to always provide a diverse set of state of the art open LLMs, hence we rotate the available models over time.
-
-Historically, HuggingChat has been running models such as:
-
-- [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
-- [CodeLlama 35B](https://about.fb.com/news/2023/08/code-llama-ai-for-coding/)
-- [Falcon 180B](https://www.tii.ae/news/technology-innovation-institute-introduces-worlds-most-powerful-open-llm-falcon-180b)
-- [Mistral 7B](https://mistral.ai/news/announcing-mistral-7b/)
-- [Cohere Command R+](https://huggingface.co/chat/models/CohereForAI/c4ai-command-r-plus)
-- [Google Gemma 7B](https://huggingface.co/chat/models/google/gemma-1.1-7b-it)
-
-This is only a partial list. Check the [models](https://huggingface.co/chat/models/) page for up-to-date list of the best available LLMs.
-
-## Technical details
-
-This app is running in a [Space](https://huggingface.co/docs/hub/spaces-overview), which entails that the code for this UI is publicly visible [inside the Space repo](https://huggingface.co/spaces/huggingchat/chat-ui/tree/main).
-
-**Further development takes place on the [huggingface/chat-ui GitHub repo](https://github.com/huggingface/chat-ui).**
-
-The inference backend is running the optimized [text-generation-inference](https://github.com/huggingface/text-generation-inference) on HuggingFace's Inference API infrastructure.
-
-It is therefore possible to deploy a copy of this app to a Space and customize it (swap model, add some UI elements, or store user messages according to your own Terms and conditions). You can also 1-click deploy your own instance using the [Chat UI Spaces Docker template](https://huggingface.co/new-space?template=huggingchat/chat-ui-template).
-
-We welcome any feedback on this app: please participate to the public discussion at <https://huggingface.co/spaces/huggingchat/chat-ui/discussions>
-
-<a target="_blank" href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-a-discussion-xl.svg" title="open a discussion"></a>

PROMPTS.md

@@ -1,69 +0,0 @@
-# Prompt templates
-
-These are the templates used to format the conversation history for different models used in HuggingChat. Set them in your `.env.local` [like so](https://github.com/huggingface/chat-ui#chatprompttemplate).
-
-## Llama 2
-
-```env
-<s>[INST] <<SYS>>\n{{preprompt}}\n<</SYS>>\n\n{{#each messages}}{{#ifUser}}{{content}} [/INST] {{/ifUser}}{{#ifAssistant}}{{content}} </s><s>[INST] {{/ifAssistant}}{{/each}}
-```
-
-## CodeLlama
-
-```env
-<s>[INST] <<SYS>>\n{{preprompt}}\n<</SYS>>\n\n{{#each messages}}{{#ifUser}}{{content}} [/INST] {{/ifUser}}{{#ifAssistant}}{{content}} </s><s>[INST] {{/ifAssistant}}{{/each}}
-```
-
-## Falcon
-
-```env
-System: {{preprompt}}\nUser:{{#each messages}}{{#ifUser}}{{content}}\nFalcon:{{/ifUser}}{{#ifAssistant}}{{content}}\nUser:{{/ifAssistant}}{{/each}}
-```
-
-## Mistral
-
-```env
-<s>{{#each messages}}{{#ifUser}}[INST] {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}} {{content}} [/INST]{{/ifUser}}{{#ifAssistant}}{{content}}</s> {{/ifAssistant}}{{/each}}
-```
-
-## Zephyr
-
-```env
-<|system|>\n{{preprompt}}</s>\n{{#each messages}}{{#ifUser}}<|user|>\n{{content}}</s>\n<|assistant|>\n{{/ifUser}}{{#ifAssistant}}{{content}}</s>\n{{/ifAssistant}}{{/each}}
-```
-
-## IDEFICS
-
-```env
-{{#each messages}}{{#ifUser}}User: {{content}}{{/ifUser}}<end_of_utterance>\nAssistant: {{#ifAssistant}}{{content}}\n{{/ifAssistant}}{{/each}}
-```
-
-## OpenChat
-
-```env
-<s>{{#each messages}}{{#ifUser}}GPT4 User: {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}}<|end_of_turn|>GPT4 Assistant: {{/ifUser}}{{#ifAssistant}}{{content}}<|end_of_turn|>{{/ifAssistant}}{{/each}}
-```
-
-## Mixtral
-
-```env
-<s> {{#each messages}}{{#ifUser}}[INST]{{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}} {{content}} [/INST]{{/ifUser}}{{#ifAssistant}} {{content}}</s> {{/ifAssistant}}{{/each}}
-```
-
-## ChatML
-
-```env
-{{#if @root.preprompt}}<|im_start|>system\n{{@root.preprompt}}<|im_end|>\n{{/if}}{{#each messages}}{{#ifUser}}<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n{{/ifUser}}{{#ifAssistant}}{{content}}<|im_end|>\n{{/ifAssistant}}{{/each}}
-```
-
-## CodeLlama 70B
-
-```env
-<s>{{#if @root.preprompt}}Source: system\n\n {{@root.preprompt}} <step> {{/if}}{{#each messages}}{{#ifUser}}Source: user\n\n {{content}} <step> {{/ifUser}}{{#ifAssistant}}Source: assistant\n\n {{content}} <step> {{/ifAssistant}}{{/each}}Source: assistant\nDestination: user\n\n ``
-```
-
-## Gemma
-
-```env
-{{#each messages}}{{#ifUser}}<start_of_turn>user\n{{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}}<end_of_turn>\n<start_of_turn>model\n{{/ifUser}}{{#ifAssistant}}{{content}}<end_of_turn>\n{{/ifAssistant}}{{/each}}
-```

README.md

@@ -3,832 +3,5 @@ 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
-load_balancing_strategy: random
+sdk: static
 ---
-
-# 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. [Common issues](#common-issues)
-7. [Deploying to a HF Space](#deploying-to-a-hf-space)
-8. [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), [`TEI`](https://github.com/huggingface/text-embeddings-inference) and [`OpenAI`](https://platform.openai.com/docs/guides/embeddings). `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](https://platform.openai.com/docs/guides/embeddings).
-
-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 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`.
-
-### 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://{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](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"
-        }
-      ]
-  }
-]`
-```
-
-#### Anthropic
-
-We also support Anthropic models 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-sonnet-20240229",
-      "displayName": "Claude 3 Sonnet",
-      "description": "Ideal balance of intelligence and speed",
-      "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",
-      "parameters": {
-         "max_new_tokens": 4096
-      },
-      "endpoints": [
-        {
-          "type": "anthropic",
-          // optionals
-          "apiKey": "sk-ant-...",
-          "baseURL": "https://api.anthropic.com",
-          defaultHeaders: {},
-          defaultQuery: {}
-        }
-      ]
-  }
-]`
-```
-
-#### 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.
-
-#### 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](https://developers.cloudflare.com/fundamentals/setup/find-account-and-zone-ids/) as well as your [API token](https://developers.cloudflare.com/workers-ai/get-started/rest-api/#1-get-an-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](https://developers.cloudflare.com/workers-ai/models/#text-generation).
-
-```env
-  {
-  "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"
-      -->
-    }
-  ]
-}
-```
-
-> [!NOTE]  
-> Cloudlare Workers AI currently do not support custom sampling parameters like temperature, top_p, etc.
-
-#### 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](https://dashboard.cohere.com/api-keys). 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.
-
-```env
-  {
-    "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](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models)).
-
-To enable:
-
-1. [Select](https://console.cloud.google.com/project) or [create](https://cloud.google.com/resource-manager/docs/creating-managing-projects#creating_a_project) a Google Cloud project.
-1. [Enable billing for your project](https://cloud.google.com/billing/docs/how-to/modify-project).
-1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).
-1. [Set up authentication with a service account](https://cloud.google.com/docs/authentication/getting-started)
-   so you can access the API from your local workstation.
-
-The service account credentials file can be imported as an environmental variable:
-
-```env
-    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
-       }]
-     },
-]`
-
-```
-
-##### 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](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": [
-        ...
-      ]
-  }
-]`
-```
-
-## Common issues
-
-### 403：You 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:
-
-```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 the model config used in production 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`
-
-### 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:
-
-1. Create an [OAuth App on the hub](https://huggingface.co/settings/applications/new) with `openid profile email` permissions. Make sure to set the callback URL to something like `http://localhost:5173/chat/login/callback` which matches the right path for your local instance.
-2. Create a [HF Token](https://huggingface.co/settings/tokens) with your Hugging Face account. You will need a Pro account to be able to access some of the larger models available through HuggingChat.
-3. Create a free account with [serper.dev](https://serper.dev/) (you will get 2500 free search queries)
-4. 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
-
-```env
-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 `.env.template` 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:
-
-```bash
-npm run populate <flags here>
-```
-
-At least one flag must be specified, the following flags are available:
-
-- `reset` - resets the database
-- `all` - populates all tables
-- `users` - populates the users table
-- `settings` - populates the settings table for existing users
-- `assistants` - populates the assistants table for existing users
-- `conversations` - populates the conversations table for existing users
-
-For example, you could use it like so:
-
-```bash
-npm run populate reset
-```
-
-to clear out the database. Then login in the app to create your user and run the following command:
-
-```bash
-npm run populate users settings assistants conversations
-```
-
-to populate the database with fake data, including fake conversations and assistants for your user.

_app/env.js

@@ -0,0 +1 @@
+export const env={}

_app/immutable/assets/0.DViICDYp.css

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_app/immutable/assets/2.Dl1cvM0g.css

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_app/immutable/assets/_layout.DViICDYp.css

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_app/immutable/assets/_page.Dl1cvM0g.css

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_app/immutable/chunks/entry.CsquK5o6.js

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_app/immutable/chunks/scheduler.CtbWrGNo.js

@@ -0,0 +1 @@
+function k(){}function x(t,n){for(const e in n)t[e]=n[e];return t}function w(t){return t()}function z(){return Object.create(null)}function j(t){t.forEach(w)}function F(t){return typeof t=="function"}function P(t,n){return t!=t?n==n:t!==n||t&&typeof t=="object"||typeof t=="function"}function S(t){return Object.keys(t).length===0}function E(t,...n){if(t==null){for(const o of n)o(void 0);return k}const e=t.subscribe(...n);return e.unsubscribe?()=>e.unsubscribe():e}function U(t,n,e){t.$$.on_destroy.push(E(n,e))}function A(t,n,e,o){if(t){const r=g(t,n,e,o);return t[0](r)}}function g(t,n,e,o){return t[1]&&o?x(e.ctx.slice(),t[1](o(n))):e.ctx}function B(t,n,e,o){if(t[2]&&o){const r=t[2](o(e));if(n.dirty===void 0)return r;if(typeof r=="object"){const a=[],f=Math.max(n.dirty.length,r.length);for(let s=0;s<f;s+=1)a[s]=n.dirty[s]|r[s];return a}return n.dirty|r}return n.dirty}function C(t,n,e,o,r,a){if(r){const f=g(n,e,o,a);t.p(f,r)}}function D(t){if(t.ctx.length>32){const n=[],e=t.ctx.length/32;for(let o=0;o<e;o++)n[o]=-1;return n}return-1}let i;function d(t){i=t}function m(){if(!i)throw new Error("Function called outside component initialization");return i}function G(t){m().$$.on_mount.push(t)}function H(t){m().$$.after_update.push(t)}const l=[],p=[];let u=[];const b=[],y=Promise.resolve();let h=!1;function v(){h||(h=!0,y.then(q))}function I(){return v(),y}function O(t){u.push(t)}const _=new Set;let c=0;function q(){if(c!==0)return;const t=i;do{try{for(;c<l.length;){const n=l[c];c++,d(n),M(n.$$)}}catch(n){throw l.length=0,c=0,n}for(d(null),l.length=0,c=0;p.length;)p.pop()();for(let n=0;n<u.length;n+=1){const e=u[n];_.has(e)||(_.add(e),e())}u.length=0}while(l.length);for(;b.length;)b.pop()();h=!1,_.clear(),d(t)}function M(t){if(t.fragment!==null){t.update(),j(t.before_update);const n=t.dirty;t.dirty=[-1],t.fragment&&t.fragment.p(t.ctx,n),t.after_update.forEach(O)}}function J(t){const n=[],e=[];u.forEach(o=>t.indexOf(o)===-1?n.push(o):e.push(o)),e.forEach(o=>o()),u=n}export{A as a,B as b,U as c,H as d,p as e,z as f,D as g,q as h,F as i,S as j,O as k,J as l,i as m,k as n,G as o,d as p,w as q,j as r,P as s,I as t,C as u,l as v,v as w};

_app/immutable/entry/app.Bje1ZUR5.js

@@ -0,0 +1,2 @@
+const __vite__mapDeps=(i,m=__vite__mapDeps,d=(m.f||(m.f=["../nodes/0.dPy0WIMN.js","../chunks/scheduler.CtbWrGNo.js","../chunks/index.C4D7lu78.js","../assets/0.DViICDYp.css","../nodes/1.BADQ-P6Z.js","../chunks/entry.CsquK5o6.js","../nodes/2.C-zSEB19.js","../assets/2.Dl1cvM0g.css"])))=>i.map(i=>d[i]);
+import{s as V,d as B,o as U,e as A,t as j}from"../chunks/scheduler.CtbWrGNo.js";import{S as W,i as z,s as F,o as h,f as G,g as k,n as p,p as L,m as g,d as w,e as H,c as J,a as K,l as q,q as d,t as Q,b as X,j as Y,r as S,u as E,v as y,w as D,x as R,y as P}from"../chunks/index.C4D7lu78.js";const Z="modulepreload",M=function(a,e){return new URL(a,e).href},I={},C=function(e,n,i){let s=Promise.resolve();if(n&&n.length>0){const u=document.getElementsByTagName("link"),t=document.querySelector("meta[property=csp-nonce]"),r=(t==null?void 0:t.nonce)||(t==null?void 0:t.getAttribute("nonce"));s=Promise.all(n.map(o=>{if(o=M(o,i),o in I)return;I[o]=!0;const f=o.endsWith(".css"),l=f?'[rel="stylesheet"]':"";if(!!i)for(let b=u.length-1;b>=0;b--){const v=u[b];if(v.href===o&&(!f||v.rel==="stylesheet"))return}else if(document.querySelector(`link[href="${o}"]${l}`))return;const _=document.createElement("link");if(_.rel=f?"stylesheet":Z,f||(_.as="script",_.crossOrigin=""),_.href=o,r&&_.setAttribute("nonce",r),document.head.appendChild(_),f)return new Promise((b,v)=>{_.addEventListener("load",b),_.addEventListener("error",()=>v(new Error(`Unable to preload CSS for ${o}`)))})}))}return s.then(()=>e()).catch(u=>{const t=new Event("vite:preloadError",{cancelable:!0});if(t.payload=u,window.dispatchEvent(t),!t.defaultPrevented)throw u})},re={};function $(a){let e,n,i;var s=a[1][0];function u(t,r){return{props:{data:t[3],form:t[2]}}}return s&&(e=E(s,u(a)),a[12](e)),{c(){e&&y(e.$$.fragment),n=h()},l(t){e&&D(e.$$.fragment,t),n=h()},m(t,r){e&&R(e,t,r),k(t,n,r),i=!0},p(t,r){if(r&2&&s!==(s=t[1][0])){if(e){S();const o=e;p(o.$$.fragment,1,0,()=>{P(o,1)}),L()}s?(e=E(s,u(t)),t[12](e),y(e.$$.fragment),g(e.$$.fragment,1),R(e,n.parentNode,n)):e=null}else if(s){const o={};r&8&&(o.data=t[3]),r&4&&(o.form=t[2]),e.$set(o)}},i(t){i||(e&&g(e.$$.fragment,t),i=!0)},o(t){e&&p(e.$$.fragment,t),i=!1},d(t){t&&w(n),a[12](null),e&&P(e,t)}}}function x(a){let e,n,i;var s=a[1][0];function u(t,r){return{props:{data:t[3],$$slots:{default:[ee]},$$scope:{ctx:t}}}}return s&&(e=E(s,u(a)),a[11](e)),{c(){e&&y(e.$$.fragment),n=h()},l(t){e&&D(e.$$.fragment,t),n=h()},m(t,r){e&&R(e,t,r),k(t,n,r),i=!0},p(t,r){if(r&2&&s!==(s=t[1][0])){if(e){S();const o=e;p(o.$$.fragment,1,0,()=>{P(o,1)}),L()}s?(e=E(s,u(t)),t[11](e),y(e.$$.fragment),g(e.$$.fragment,1),R(e,n.parentNode,n)):e=null}else if(s){const o={};r&8&&(o.data=t[3]),r&8215&&(o.$$scope={dirty:r,ctx:t}),e.$set(o)}},i(t){i||(e&&g(e.$$.fragment,t),i=!0)},o(t){e&&p(e.$$.fragment,t),i=!1},d(t){t&&w(n),a[11](null),e&&P(e,t)}}}function ee(a){let e,n,i;var s=a[1][1];function u(t,r){return{props:{data:t[4],form:t[2]}}}return s&&(e=E(s,u(a)),a[10](e)),{c(){e&&y(e.$$.fragment),n=h()},l(t){e&&D(e.$$.fragment,t),n=h()},m(t,r){e&&R(e,t,r),k(t,n,r),i=!0},p(t,r){if(r&2&&s!==(s=t[1][1])){if(e){S();const o=e;p(o.$$.fragment,1,0,()=>{P(o,1)}),L()}s?(e=E(s,u(t)),t[10](e),y(e.$$.fragment),g(e.$$.fragment,1),R(e,n.parentNode,n)):e=null}else if(s){const o={};r&16&&(o.data=t[4]),r&4&&(o.form=t[2]),e.$set(o)}},i(t){i||(e&&g(e.$$.fragment,t),i=!0)},o(t){e&&p(e.$$.fragment,t),i=!1},d(t){t&&w(n),a[10](null),e&&P(e,t)}}}function N(a){let e,n=a[6]&&O(a);return{c(){e=H("div"),n&&n.c(),this.h()},l(i){e=J(i,"DIV",{id:!0,"aria-live":!0,"aria-atomic":!0,style:!0});var s=K(e);n&&n.l(s),s.forEach(w),this.h()},h(){q(e,"id","svelte-announcer"),q(e,"aria-live","assertive"),q(e,"aria-atomic","true"),d(e,"position","absolute"),d(e,"left","0"),d(e,"top","0"),d(e,"clip","rect(0 0 0 0)"),d(e,"clip-path","inset(50%)"),d(e,"overflow","hidden"),d(e,"white-space","nowrap"),d(e,"width","1px"),d(e,"height","1px")},m(i,s){k(i,e,s),n&&n.m(e,null)},p(i,s){i[6]?n?n.p(i,s):(n=O(i),n.c(),n.m(e,null)):n&&(n.d(1),n=null)},d(i){i&&w(e),n&&n.d()}}}function O(a){let e;return{c(){e=Q(a[7])},l(n){e=X(n,a[7])},m(n,i){k(n,e,i)},p(n,i){i&128&&Y(e,n[7])},d(n){n&&w(e)}}}function te(a){let e,n,i,s,u;const t=[x,$],r=[];function o(l,m){return l[1][1]?0:1}e=o(a),n=r[e]=t[e](a);let f=a[5]&&N(a);return{c(){n.c(),i=F(),f&&f.c(),s=h()},l(l){n.l(l),i=G(l),f&&f.l(l),s=h()},m(l,m){r[e].m(l,m),k(l,i,m),f&&f.m(l,m),k(l,s,m),u=!0},p(l,[m]){let _=e;e=o(l),e===_?r[e].p(l,m):(S(),p(r[_],1,1,()=>{r[_]=null}),L(),n=r[e],n?n.p(l,m):(n=r[e]=t[e](l),n.c()),g(n,1),n.m(i.parentNode,i)),l[5]?f?f.p(l,m):(f=N(l),f.c(),f.m(s.parentNode,s)):f&&(f.d(1),f=null)},i(l){u||(g(n),u=!0)},o(l){p(n),u=!1},d(l){l&&(w(i),w(s)),r[e].d(l),f&&f.d(l)}}}function ne(a,e,n){let{stores:i}=e,{page:s}=e,{constructors:u}=e,{components:t=[]}=e,{form:r}=e,{data_0:o=null}=e,{data_1:f=null}=e;B(i.page.notify);let l=!1,m=!1,_=null;U(()=>{const c=i.page.subscribe(()=>{l&&(n(6,m=!0),j().then(()=>{n(7,_=document.title||"untitled page")}))});return n(5,l=!0),c});function b(c){A[c?"unshift":"push"](()=>{t[1]=c,n(0,t)})}function v(c){A[c?"unshift":"push"](()=>{t[0]=c,n(0,t)})}function T(c){A[c?"unshift":"push"](()=>{t[0]=c,n(0,t)})}return a.$$set=c=>{"stores"in c&&n(8,i=c.stores),"page"in c&&n(9,s=c.page),"constructors"in c&&n(1,u=c.constructors),"components"in c&&n(0,t=c.components),"form"in c&&n(2,r=c.form),"data_0"in c&&n(3,o=c.data_0),"data_1"in c&&n(4,f=c.data_1)},a.$$.update=()=>{a.$$.dirty&768&&i.page.set(s)},[t,u,r,o,f,l,m,_,i,s,b,v,T]}class oe extends W{constructor(e){super(),z(this,e,ne,te,V,{stores:8,page:9,constructors:1,components:0,form:2,data_0:3,data_1:4})}}const ae=[()=>C(()=>import("../nodes/0.dPy0WIMN.js"),__vite__mapDeps([0,1,2,3]),import.meta.url),()=>C(()=>import("../nodes/1.BADQ-P6Z.js"),__vite__mapDeps([4,1,2,5]),import.meta.url),()=>C(()=>import("../nodes/2.C-zSEB19.js"),__vite__mapDeps([6,1,2,7]),import.meta.url)],le=[],fe={"/":[2]},ce={handleError:({error:a})=>{console.error(a)},reroute:()=>{}};export{fe as dictionary,ce as hooks,re as matchers,ae as nodes,oe as root,le as server_loads};

_app/immutable/entry/start.BZni4wHA.js

@@ -0,0 +1 @@
+import{a as t}from"../chunks/entry.CsquK5o6.js";export{t as start};

_app/immutable/nodes/0.dPy0WIMN.js

@@ -0,0 +1 @@
+import{s as l,a as r,u as i,g as u,b as _}from"../chunks/scheduler.CtbWrGNo.js";import{S as f,i as c,m as p,n as m}from"../chunks/index.C4D7lu78.js";const d=!0,S=Object.freeze(Object.defineProperty({__proto__:null,prerender:d},Symbol.toStringTag,{value:"Module"}));function $(n){let s;const a=n[1].default,t=r(a,n,n[0],null);return{c(){t&&t.c()},l(e){t&&t.l(e)},m(e,o){t&&t.m(e,o),s=!0},p(e,[o]){t&&t.p&&(!s||o&1)&&i(t,a,e,e[0],s?_(a,e[0],o,null):u(e[0]),null)},i(e){s||(p(t,e),s=!0)},o(e){m(t,e),s=!1},d(e){t&&t.d(e)}}}function g(n,s,a){let{$$slots:t={},$$scope:e}=s;return n.$$set=o=>{"$$scope"in o&&a(0,e=o.$$scope)},[e,t]}class v extends f{constructor(s){super(),c(this,s,g,$,l,{})}}export{v as component,S as universal};

_app/immutable/nodes/1.BADQ-P6Z.js

@@ -0,0 +1 @@
+import{s as S,n as _,c as x}from"../chunks/scheduler.CtbWrGNo.js";import{S as j,i as q,e as f,t as d,s as y,c as g,a as h,b as v,d as u,f as C,g as m,h as $,j as E}from"../chunks/index.C4D7lu78.js";import{s as H}from"../chunks/entry.CsquK5o6.js";const P=()=>{const s=H;return{page:{subscribe:s.page.subscribe},navigating:{subscribe:s.navigating.subscribe},updated:s.updated}},k={subscribe(s){return P().page.subscribe(s)}};function w(s){var b;let t,r=s[0].status+"",o,n,i,c=((b=s[0].error)==null?void 0:b.message)+"",l;return{c(){t=f("h1"),o=d(r),n=y(),i=f("p"),l=d(c)},l(e){t=g(e,"H1",{});var a=h(t);o=v(a,r),a.forEach(u),n=C(e),i=g(e,"P",{});var p=h(i);l=v(p,c),p.forEach(u)},m(e,a){m(e,t,a),$(t,o),m(e,n,a),m(e,i,a),$(i,l)},p(e,[a]){var p;a&1&&r!==(r=e[0].status+"")&&E(o,r),a&1&&c!==(c=((p=e[0].error)==null?void 0:p.message)+"")&&E(l,c)},i:_,o:_,d(e){e&&(u(t),u(n),u(i))}}}function z(s,t,r){let o;return x(s,k,n=>r(0,o=n)),[o]}let F=class extends j{constructor(t){super(),q(this,t,z,w,S,{})}};export{F as component};

_app/immutable/nodes/2.C-zSEB19.js

@@ -0,0 +1,3 @@
+import{s as l,n as a}from"../chunks/scheduler.CtbWrGNo.js";import{S as i,i as c,e as o,c as h,k as g,l as m,g as u,d as f}from"../chunks/index.C4D7lu78.js";function d(r){let e,s=`<article class="mx-4 max-w-4xl text-gray-300"><a href="https://huggingface.co/chat" target="_blank" rel="noreferrer" class="mx-auto mb-12 flex w-fit items-center justify-center gap-2 rounded-lg border border-gray-200 p-4 text-xl font-bold !text-white transition-all hover:-translate-y-0.5 hover:border-gray-300 hover:bg-gray-800">Try
+			<h3 class="svelte-1hc9mw2"><img src="/logo.svg" alt="HuggingChat" class="inline w-10"/>
+				HuggingChat</h3></a> <p class="svelte-1hc9mw2">HuggingChat is now available at <a class="link svelte-1hc9mw2" href="https://huggingface.co/chat" target="_blank" rel="noreferrer">hf.co/chat</a> and is no longer hosted as a space.</p> <p class="svelte-1hc9mw2">You can still interact with the community and give feedback in the <a class="link svelte-1hc9mw2" href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions" target="_blank" rel="noreferrer">space&#39;s discussions</a>.</p></article>`;return{c(){e=o("div"),e.innerHTML=s,this.h()},l(t){e=h(t,"DIV",{class:!0,"data-svelte-h":!0}),g(e)!=="svelte-hljhww"&&(e.innerHTML=s),this.h()},h(){m(e,"class","mx-auto w-fit pt-10")},m(t,n){u(t,e,n)},p:a,i:a,o:a,d(t){t&&f(e)}}}class w extends i{constructor(e){super(),c(this,e,null,d,l,{})}}export{w as component};

_app/version.json

@@ -0,0 +1 @@
+{"version":"1722243467595"}

entrypoint.sh

@@ -1,19 +0,0 @@
-ENV_LOCAL_PATH=/app/.env.local
-
-if test -z "${DOTENV_LOCAL}" ; then
-    if ! test -f "${ENV_LOCAL_PATH}" ; then
-        echo "DOTENV_LOCAL was not found in the ENV variables and .env.local is not set using a bind volume. Make sure to set environment variables properly. "
-    fi;
-else
-    echo "DOTENV_LOCAL was found in the ENV variables. Creating .env.local file."
-    cat <<< "$DOTENV_LOCAL" > ${ENV_LOCAL_PATH}
-fi;
-
-if [ "$INCLUDE_DB" = "true" ] ; then
-    echo "Starting local MongoDB instance"
-    nohup mongod &
-fi;
-
-export PUBLIC_VERSION=$(node -p "require('./package.json').version")
-
-dotenv -e /app/.env -c -- node /app/build/index.js -- --host 0.0.0.0 --port 3000

index.html

@@ -0,0 +1,48 @@
+<!DOCTYPE html>
+<html lang="en" class="bg-gray-900">
+	<head>
+		<meta charset="utf-8" />
+		<link rel="icon" href="./favicon.svg" />
+		<meta name="viewport" content="width=device-width" />
+		
+		<link href="./_app/immutable/assets/0.DViICDYp.css" rel="stylesheet">
+		<link href="./_app/immutable/assets/2.Dl1cvM0g.css" rel="stylesheet">
+		<link rel="modulepreload" href="./_app/immutable/entry/start.BZni4wHA.js">
+		<link rel="modulepreload" href="./_app/immutable/chunks/entry.CsquK5o6.js">
+		<link rel="modulepreload" href="./_app/immutable/chunks/scheduler.CtbWrGNo.js">
+		<link rel="modulepreload" href="./_app/immutable/entry/app.Bje1ZUR5.js">
+		<link rel="modulepreload" href="./_app/immutable/chunks/index.C4D7lu78.js">
+		<link rel="modulepreload" href="./_app/immutable/nodes/0.dPy0WIMN.js">
+		<link rel="modulepreload" href="./_app/immutable/nodes/2.C-zSEB19.js">
+	</head>
+	<body data-sveltekit-preload-data="hover">
+		<div style="display: contents">  <div class="mx-auto w-fit pt-10" data-svelte-h="svelte-hljhww"><article class="mx-4 max-w-4xl text-gray-300"><a href="https://huggingface.co/chat" target="_blank" rel="noreferrer" class="mx-auto mb-12 flex w-fit items-center justify-center gap-2 rounded-lg border border-gray-200 p-4 text-xl font-bold !text-white transition-all hover:-translate-y-0.5 hover:border-gray-300 hover:bg-gray-800">Try
+			<h3 class="svelte-1hc9mw2"><img src="/logo.svg" alt="HuggingChat" class="inline w-10">
+				HuggingChat</h3></a> <p class="svelte-1hc9mw2">HuggingChat is now available at <a class="link svelte-1hc9mw2" href="https://huggingface.co/chat" target="_blank" rel="noreferrer">hf.co/chat</a> and is no longer hosted as a space.</p> <p class="svelte-1hc9mw2">You can still interact with the community and give feedback in the <a class="link svelte-1hc9mw2" href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions" target="_blank" rel="noreferrer">space&#39;s discussions</a>.</p></article> </div> 
+			
+			<script>
+				{
+					__sveltekit_1klgtbu = {
+						base: new URL(".", location).pathname.slice(0, -1)
+					};
+
+					const element = document.currentScript.parentElement;
+
+					const data = [null,null];
+
+					Promise.all([
+						import("./_app/immutable/entry/start.BZni4wHA.js"),
+						import("./_app/immutable/entry/app.Bje1ZUR5.js")
+					]).then(([kit, app]) => {
+						kit.start(app, element, {
+							node_ids: [0, 2],
+							data,
+							form: null,
+							error: null
+						});
+					});
+				}
+			</script>
+		</div>
+	</body>
+</html>

static/huggingchat/manifest.json → manifest.json

@@ -3,7 +3,7 @@
 	"name": "HuggingChat",
 	"short_name": "HuggingChat",
 	"display": "standalone",
-	"start_url": "/chat",
+	"start_url": "/",
 	"icons": [
 		{
 			"src": "/chat/huggingchat/icon-128x128.png",

package.json

@@ -1,95 +0,0 @@
-{
-	"name": "chat-ui",
-	"version": "0.8.3",
-	"private": true,
-	"packageManager": "[email protected]",
-	"scripts": {
-		"dev": "vite dev",
-		"build": "vite build",
-		"preview": "vite preview",
-		"check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json",
-		"check:watch": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json --watch",
-		"lint": "prettier --plugin-search-dir . --check . && eslint .",
-		"format": "prettier --plugin-search-dir . --write .",
-		"test": "MONGODB_URL=mongodb://127.0.0.1:27017/ vitest",
-		"updateLocalEnv": "node --loader ts-node/esm scripts/updateLocalEnv.ts",
-		"updateProdEnv": "node --loader ts-node/esm scripts/updateProdEnv.ts",
-		"populate": "vite-node --options.transformMode.ssr='/.*/' scripts/populate.ts"
-	},
-	"devDependencies": {
-		"@faker-js/faker": "^8.4.1",
-		"@iconify-json/carbon": "^1.1.16",
-		"@iconify-json/eos-icons": "^1.1.6",
-		"@sveltejs/adapter-node": "^1.3.1",
-		"@sveltejs/kit": "^1.30.4",
-		"@tailwindcss/typography": "^0.5.9",
-		"@types/express": "^4.17.21",
-		"@types/jsdom": "^21.1.1",
-		"@types/minimist": "^1.2.5",
-		"@types/parquetjs": "^0.10.3",
-		"@types/uuid": "^9.0.8",
-		"@typescript-eslint/eslint-plugin": "^6.x",
-		"@typescript-eslint/parser": "^6.x",
-		"eslint": "^8.28.0",
-		"eslint-config-prettier": "^8.5.0",
-		"eslint-plugin-svelte": "^2.30.0",
-		"minimist": "^1.2.8",
-		"prettier": "^2.8.0",
-		"prettier-plugin-svelte": "^2.10.1",
-		"prettier-plugin-tailwindcss": "^0.2.7",
-		"prom-client": "^15.1.2",
-		"svelte": "^4.2.8",
-		"svelte-check": "^3.6.2",
-		"ts-node": "^10.9.1",
-		"tslib": "^2.4.1",
-		"typescript": "^5.0.0",
-		"unplugin-icons": "^0.16.1",
-		"vite": "^4.5.3",
-		"vite-node": "^1.3.1",
-		"vitest": "^0.31.0"
-	},
-	"type": "module",
-	"dependencies": {
-		"@huggingface/hub": "^0.5.1",
-		"@huggingface/inference": "^2.6.3",
-		"@iconify-json/bi": "^1.1.21",
-		"@resvg/resvg-js": "^2.6.0",
-		"@xenova/transformers": "^2.16.1",
-		"autoprefixer": "^10.4.14",
-		"browser-image-resizer": "^2.4.1",
-		"date-fns": "^2.29.3",
-		"dotenv": "^16.0.3",
-		"express": "^4.19.2",
-		"handlebars": "^4.7.8",
-		"highlight.js": "^11.7.0",
-		"image-size": "^1.0.2",
-		"ip-address": "^9.0.5",
-		"jsdom": "^22.0.0",
-		"json5": "^2.2.3",
-		"marked": "^12.0.1",
-		"marked-katex-extension": "^5.0.1",
-		"mongodb": "^5.8.0",
-		"nanoid": "^4.0.2",
-		"openid-client": "^5.4.2",
-		"parquetjs": "^0.11.2",
-		"pino": "^9.0.0",
-		"pino-pretty": "^11.0.0",
-		"postcss": "^8.4.31",
-		"saslprep": "^1.0.3",
-		"satori": "^0.10.11",
-		"satori-html": "^0.3.2",
-		"serpapi": "^1.1.1",
-		"sharp": "^0.33.2",
-		"tailwind-scrollbar": "^3.0.0",
-		"tailwindcss": "^3.4.0",
-		"uuid": "^9.0.1",
-		"zod": "^3.22.3"
-	},
-	"optionalDependencies": {
-		"@anthropic-ai/sdk": "^0.17.1",
-		"@google-cloud/vertexai": "^1.1.0",
-		"aws4fetch": "^1.0.17",
-		"cohere-ai": "^7.9.0",
-		"openai": "^4.14.2"
-	}
-}

postcss.config.js

@@ -1,6 +0,0 @@
-export default {
-	plugins: {
-		tailwindcss: {},
-		autoprefixer: {},
-	},
-};