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hf_public_repos/chat-ui/Dockerfile
# syntax=docker/dockerfile:1 ARG INCLUDE_DB=false FROM node:20-slim AS base ENV PLAYWRIGHT_SKIP_BROWSER_GC=1 # 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:$PATH WORKDIR /app # add a .env.local if the user doesn't bind a volume to it RUN touch /app/.env.local RUN npm i --no-package-lock --no-save playwright@1.47.0 USER root RUN apt-get update RUN apt-get install gnupg curl -y RUN npx playwright install --with-deps chromium RUN chown -R 1000:1000 /home/user/.npm USER user COPY --chown=1000 .env /app/.env COPY --chown=1000 entrypoint.sh /app/entrypoint.sh COPY --chown=1000 gcp-*.json /app/ COPY --chown=1000 package.json /app/package.json COPY --chown=1000 package-lock.json /app/package-lock.json RUN chmod +x /app/entrypoint.sh FROM node:20 AS builder WORKDIR /app COPY --link --chown=1000 package-lock.json package.json ./ ARG APP_BASE= ARG PUBLIC_APP_COLOR=blue ENV BODY_SIZE_LIMIT=15728640 RUN --mount=type=cache,target=/app/.npm \ npm set cache /app/.npm && \ npm ci COPY --link --chown=1000 . . RUN git config --global --add safe.directory /app && \ npm run build # mongo image FROM mongo:7 AS mongo # image to be used if INCLUDE_DB is false FROM base AS local_db_false # image to be used if INCLUDE_DB is true FROM base AS local_db_true # copy mongo from the other stage COPY --from=mongo /usr/bin/mongo* /usr/bin/ ENV MONGODB_URL=mongodb://localhost:27017 USER root RUN mkdir -p /data/db RUN chown -R 1000:1000 /data/db USER user # final image FROM local_db_${INCLUDE_DB} AS final # build arg to determine if the database should be included ARG INCLUDE_DB=false ENV INCLUDE_DB=${INCLUDE_DB} # svelte requires APP_BASE at build time so it must be passed as a build arg ARG APP_BASE= # tailwind requires the primary theme to be known at build time so it must be passed as a build arg ARG PUBLIC_APP_COLOR=blue ARG PUBLIC_COMMIT_SHA= ENV PUBLIC_COMMIT_SHA=${PUBLIC_COMMIT_SHA} ENV BODY_SIZE_LIMIT=15728640 #import the build & dependencies COPY --from=builder --chown=1000 /app/build /app/build COPY --from=builder --chown=1000 /app/node_modules /app/node_modules CMD ["/bin/bash", "-c", "/app/entrypoint.sh"]
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hf_public_repos
hf_public_repos/chat-ui/.dockerignore
Dockerfile .vscode/ .idea .gitignore LICENSE README.md node_modules/ .svelte-kit/ .env* !.env .env.local
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hf_public_repos
hf_public_repos/chat-ui/tailwind.config.cjs
const defaultTheme = require("tailwindcss/defaultTheme"); const colors = require("tailwindcss/colors"); /** @type {import('tailwindcss').Config} */ export default { darkMode: "class", mode: "jit", content: ["./src/**/*.{html,js,svelte,ts}"], theme: { extend: { colors: { primary: colors[process.env.PUBLIC_APP_COLOR], }, fontSize: { xxs: "0.625rem", smd: "0.94rem", }, }, }, plugins: [ require("tailwind-scrollbar")({ nocompatible: true }), require("@tailwindcss/typography"), ], };
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hf_public_repos
hf_public_repos/chat-ui/svelte.config.js
import adapter from "@sveltejs/adapter-node"; import { vitePreprocess } from "@sveltejs/vite-plugin-svelte"; import dotenv from "dotenv"; import { execSync } from "child_process"; dotenv.config({ path: "./.env.local" }); dotenv.config({ path: "./.env" }); function getCurrentCommitSHA() { try { return execSync("git rev-parse HEAD").toString(); } catch (error) { console.error("Error getting current commit SHA:", error); return "unknown"; } } process.env.PUBLIC_VERSION ??= process.env.npm_package_version; process.env.PUBLIC_COMMIT_SHA ??= getCurrentCommitSHA(); /** @type {import('@sveltejs/kit').Config} */ const config = { // Consult https://kit.svelte.dev/docs/integrations#preprocessors // for more information about preprocessors preprocess: vitePreprocess(), kit: { adapter: adapter(), paths: { base: process.env.APP_BASE || "", relative: false, }, csrf: { // handled in hooks.server.ts, because we can have multiple valid origins checkOrigin: false, }, csp: { directives: { ...(process.env.ALLOW_IFRAME === "true" ? {} : { "frame-ancestors": ["'none'"] }), }, }, }, }; export default config;
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hf_public_repos/chat-ui/package-lock.json
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hf_public_repos
hf_public_repos/chat-ui/tsconfig.json
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hf_public_repos
hf_public_repos/chat-ui/LICENSE
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 1. Definitions. "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. 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0
hf_public_repos
hf_public_repos/chat-ui/PRIVACY.md
## 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 <privacy@huggingface.co>. ## 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>
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hf_public_repos
hf_public_repos/chat-ui/.npmrc
engine-strict=true
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hf_public_repos
hf_public_repos/chat-ui/.eslintignore
.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
0
hf_public_repos
hf_public_repos/chat-ui/README.md
# Chat UI **Find the docs at [hf.co/docs/chat-ui](https://huggingface.co/docs/chat-ui/index).** ![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. [Quickstart](#quickstart) 1. [No Setup Deploy](#no-setup-deploy) 2. [Setup](#setup) 3. [Launch](#launch) 4. [Web Search](#web-search) 5. [Text Embedding Models](#text-embedding-models) 6. [Extra parameters](#extra-parameters) 7. [Common issues](#common-issues) 8. [Deploying to a HF Space](#deploying-to-a-hf-space) 9. [Building](#building) ## Quickstart ### Docker image You can deploy a chat-ui instance in a single command using the docker image. Get your huggingface token from [here](https://huggingface.co/settings/tokens). ```bash docker run -p 3000 -e HF_TOKEN=hf_*** -v db:/data ghcr.io/huggingface/chat-ui-db:latest ``` Take a look at the [`.env` file](https://github.com/huggingface/chat-ui/blob/main/.env) and the readme to see all the environment variables that you can set. We have endpoint support for all OpenAI API compatible local services as well as many other providers like Anthropic, Cloudflare, Google Vertex AI, etc. ### Local setup You can quickly start a locally running chat-ui & LLM text-generation server thanks to chat-ui's [llama.cpp server support](https://huggingface.co/docs/chat-ui/configuration/models/providers/llamacpp). **Step 1 (Start llama.cpp server):** Install llama.cpp w/ brew (for Mac): ```bash # install llama.cpp brew install llama.cpp ``` or [build directly from the source](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) for your target device: ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make ``` Next, start the server with the [LLM of your choice](https://huggingface.co/models?library=gguf): ```bash # start llama.cpp server (using hf.co/microsoft/Phi-3-mini-4k-instruct-gguf as an example) llama-server --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf --hf-file Phi-3-mini-4k-instruct-q4.gguf -c 4096 ``` A local LLaMA.cpp HTTP Server will start on `http://localhost:8080`. Read more [here](https://huggingface.co/docs/chat-ui/configuration/models/providers/llamacpp). **Step 3 (make sure you have MongoDb running locally):** ```bash docker run -d -p 27017:27017 --name mongo-chatui mongo:latest ``` Read more [here](#database). **Step 4 (clone chat-ui):** ```bash git clone https://github.com/huggingface/chat-ui cd chat-ui ``` **Step 5 (tell chat-ui to use local llama.cpp server):** Add the following to your `.env.local`: ```ini MODELS=`[ { "name": "microsoft/Phi-3-mini-4k-instruct", "endpoints": [{ "type" : "llamacpp", "baseURL": "http://localhost:8080" }], }, ]` ``` Read more [here](https://huggingface.co/docs/chat-ui/configuration/models/providers/llamacpp). **Step 6 (start chat-ui):** ```bash npm install npm run dev -- --open ``` Read more [here](#launch). <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/llamacpp-dark.png" height="auto"/> ## 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. ### Trusted header authentication You can set the env variable `TRUSTED_EMAIL_HEADER` to point to the header that contains the user's email address. This will allow you to authenticate users from the header. This setup is usually combined with a proxy that will be in front of chat-ui and will handle the auth and set the header. > [!WARNING] > Make sure to only allow requests to chat-ui through your proxy which handles authentication, otherwise users could authenticate as anyone by setting the header manually! Only set this up if you understand the implications and know how to do it correctly. Here is a list of header names for common auth providers: - Tailscale Serve: `Tailscale-User-Login` - Cloudflare Access: `Cf-Access-Authenticated-User-Email` - oauth2-proxy: `X-Forwarded-Email` ### 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/)) or `SEARCHAPI_KEY` ([searchapi.io](https://www.searchapi.io/)) to your `.env.local`. You can also simply enable the local google websearch by setting `USE_LOCAL_WEBSEARCH=true` in your `.env.local` or specify a SearXNG instance by adding the query URL to `SEARXNG_QUERY_URL`. You can enable javascript when parsing webpages to improve compatibility with `WEBSEARCH_JAVASCRIPT=true` at the cost of increased CPU usage. You'll want at least 4 cores when enabling. ### 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 support [IDEFICS](https://huggingface.co/blog/idefics) (hosted on TGI), OpenAI and Claude 3 as multimodal models. You can enable it by setting `multimodal: true` in your `MODELS` configuration. For IDEFICS, you must have a [PRO HF Api token](https://huggingface.co/settings/tokens). For OpenAI, see the [OpenAI section](#OpenAI). For Anthropic, see the [Anthropic section](#anthropic). ```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) and [vllm](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). 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. Parameters not supported by OpenAI (e.g., top_k, repetition_penalty, etc.) must be set in the extraBody of endpoints. Be aware that setting them in parameters will cause them to be omitted. ``` MODELS=`[ { "name": "text-generation-webui", "id": "text-generation-webui", "parameters": { "temperature": 0.9, "top_p": 0.95, "max_new_tokens": 1024, "stop": [] }, "endpoints": [{ "type" : "openai", "baseURL": "http://localhost:8000/v1", "extraBody": { "repetition_penalty": 1.2, "top_k": 50, "truncate": 1000 } }] } ]` ``` The `openai` type includes official OpenAI models. You can add, for example, GPT4/GPT3.5 as a "openai" model: ``` OPENAI_API_KEY=#your openai api key here MODELS=`[{ "name": "gpt-4", "displayName": "GPT 4", "endpoints" : [{ "type": "openai" }] }, { "name": "gpt-3.5-turbo", "displayName": "GPT 3.5 Turbo", "endpoints" : [{ "type": "openai" }] }]` ``` You may also consume any model provider that provides compatible OpenAI API endpoint. For example, you may self-host [Portkey](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 [microsoft/Phi-3-mini-4k-instruct-gguf](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) as an example model: ```bash # install llama.cpp brew install llama.cpp # start llama.cpp server llama-server --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf --hf-file Phi-3-mini-4k-instruct-q4.gguf -c 4096 ``` ```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 (including multimodal ones via `multmodal: true`) through the official SDK. You may provide your API key via the `ANTHROPIC_API_KEY` env variable, or alternatively, through the `endpoints.apiKey` as per the following example. ``` MODELS=`[ { "name": "claude-3-haiku-20240307", "displayName": "Claude 3 Haiku", "description": "Fastest and most compact model for near-instant responsiveness", "multimodal": true, "parameters": { "max_new_tokens": 4096, }, "endpoints": [ { "type": "anthropic", // optionals "apiKey": "sk-ant-...", "baseURL": "https://api.anthropic.com", "defaultHeaders": {}, "defaultQuery": {} } ] }, { "name": "claude-3-sonnet-20240229", "displayName": "Claude 3 Sonnet", "description": "Ideal balance of intelligence and speed", "multimodal": true, "parameters": { "max_new_tokens": 4096, }, "endpoints": [ { "type": "anthropic", // optionals "apiKey": "sk-ant-...", "baseURL": "https://api.anthropic.com", "defaultHeaders": {}, "defaultQuery": {} } ] }, { "name": "claude-3-opus-20240229", "displayName": "Claude 3 Opus", "description": "Most powerful model for highly complex tasks", "multimodal": true, "parameters": { "max_new_tokens": 4096 }, "endpoints": [ { "type": "anthropic", // optionals "apiKey": "sk-ant-...", "baseURL": "https://api.anthropic.com", "defaultHeaders": {}, "defaultQuery": {} } ] } ]` ``` We also support using Anthropic models running on Vertex AI. Authentication is done using Google Application Default Credentials. Project ID can be provided through the `endpoints.projectId` as per the following example: ``` MODELS=`[ { "name": "claude-3-sonnet@20240229", "displayName": "Claude 3 Sonnet", "description": "Ideal balance of intelligence and speed", "multimodal": true, "parameters": { "max_new_tokens": 4096, }, "endpoints": [ { "type": "anthropic-vertex", "region": "us-central1", "projectId": "gcp-project-id", // optionals "defaultHeaders": {}, "defaultQuery": {} } ] }, { "name": "claude-3-haiku@20240307", "displayName": "Claude 3 Haiku", "description": "Fastest, most compact model for near-instant responsiveness", "multimodal": true, "parameters": { "max_new_tokens": 4096 }, "endpoints": [ { "type": "anthropic-vertex", "region": "us-central1", "projectId": "gcp-project-id", // optionals "defaultHeaders": {}, "defaultQuery": {} } ] } ]` ``` #### 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" --> } ] } ``` #### 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", "multimodal": true, "endpoints" : [{ "type": "vertex", "project": "abc-xyz", "location": "europe-west3", "extraBody": { "model_version": "gemini-1.5-pro-preview-0409", }, // Optional "safetyThreshold": "BLOCK_MEDIUM_AND_ABOVE", "apiEndpoint": "", // alternative api endpoint url, "tools": [{ "googleSearchRetrieval": { "disableAttribution": true } }], "multimodal": { "image": { "supportedMimeTypes": ["image/png", "image/jpeg", "image/webp"], "preferredMimeType": "image/png", "maxSizeInMB": 5, "maxWidth": 2000, "maxHeight": 1000, } } }] }, ]` ``` ##### 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 `COOKIE_SECURE=false` and `COOKIE_SAMESITE=lax` 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 `chart/env/prod.yaml` file. It is the source of truth for the environment variables used for our CI/CD pipeline. For HuggingChat, as we need to customize the app color, as well as the base path, we build a custom docker image. You can find the workflow here. > [!TIP] > If you want to make changes to the model config used in production for HuggingChat, you should do so against `chart/env/prod.yaml`. ### 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 `chart/env/prod.yaml` 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. ### Building the docker images locally You can build the docker images locally using the following commands: ```bash docker build -t chat-ui-db:latest --build-arg INCLUDE_DB=true . docker build -t chat-ui:latest --build-arg INCLUDE_DB=false . docker build -t huggingchat:latest --build-arg INCLUDE_DB=false --build-arg APP_BASE=/chat --build-arg PUBLIC_APP_COLOR=yellow . ```
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hf_public_repos
hf_public_repos/chat-ui/.env
# Use .env.local to change these variables # DO NOT EDIT THIS FILE WITH SENSITIVE DATA ### MongoDB ### MONGODB_URL=#your mongodb URL here, use chat-ui-db image if you don't want to set this MONGODB_DB_NAME=chat-ui MONGODB_DIRECT_CONNECTION=false ### Endpoints config ### HF_API_ROOT=https://api-inference.huggingface.co/models # HF_TOKEN is used for a lot of things, not only for inference but also fetching tokenizers, etc. # We recommend using an HF_TOKEN even if you use a local endpoint. HF_TOKEN= #get it from https://huggingface.co/settings/token # API Keys for providers, you will need to specify models in the MODELS section but these keys can be kept secret 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 GOOGLE_GENAI_API_KEY=#your google genai api token here ### Models ### ## Models can support many different endpoints, check the documentation for more details MODELS=`[ { "name": "NousResearch/Hermes-3-Llama-3.1-8B", "description": "Nous Research's latest Hermes 3 release in 8B size.", "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?" } ] } ]` ## Text Embedding Models used for websearch # Default is a model that runs locally on CPU. TEXT_EMBEDDING_MODELS = `[ { "name": "Xenova/gte-small", "displayName": "Xenova/gte-small", "description": "Local embedding model running on the server.", "chunkCharLength": 512, "endpoints": [ { "type": "transformersjs" } ] } ]` ## Removed models, useful for migrating conversations # { name: string, displayName?: string, id?: string, transferTo?: string }` OLD_MODELS=`[]` ## Task model # name of the model used for tasks such as summarizing title, creating query, etc. # if not set, the first model in MODELS will be used TASK_MODEL= ### Authentication ### # Parameters to enable open id login OPENID_CONFIG= 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 # if it's defined, only these emails will be allowed to use login ALLOWED_USER_EMAILS=`[]` # valid alternative redirect URLs for OAuth, used for HuggingChat apps ALTERNATIVE_REDIRECT_URLS=`[]` ### Cookies # name of the cookie used to store the session COOKIE_NAME=hf-chat # specify secure behaviour for cookies COOKIE_SAMESITE=# can be "lax", "strict", "none" or left empty COOKIE_SECURE=# set to true to only allow cookies over https ### Websearch ### ## API Keys used to activate search with web functionality. websearch is 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 SEARCHAPI_KEY=#your searchapi 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 BING_SUBSCRIPTION_KEY=#your key ## Websearch configuration PLAYWRIGHT_ADBLOCKER=true WEBSEARCH_ALLOWLIST=`[]` # if it's defined, allow websites from only this list. WEBSEARCH_BLOCKLIST=`[]` # if it's defined, block websites from this list. WEBSEARCH_JAVASCRIPT=true # CPU usage reduces by 60% on average by disabling javascript. Enable to improve website compatibility WEBSEARCH_TIMEOUT = 3500 # in milliseconds, determines how long to wait to load a page before timing out ENABLE_LOCAL_FETCH=false #set to true to allow fetches on the local network. /!\ Only enable this if you have the proper firewall rules to prevent SSRF attacks and understand the implications. ## Public app configuration ## PUBLIC_APP_GUEST_MESSAGE=# a message to the guest user. If not set, no message will be shown. Only used if you have authentication enabled. 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_DESCRIPTION=# description used throughout the app PUBLIC_APP_DATA_SHARING=# Set to 1 to enable an option in the user settings to share conversations with model authors PUBLIC_APP_DISCLAIMER=# Set to 1 to show a disclaimer on login page PUBLIC_APP_DISCLAIMER_MESSAGE=# Message to show on the login page PUBLIC_ANNOUNCEMENT_BANNERS=`[ { "title": "chat-ui is now open source!", "linkTitle": "check it out", "linkHref": "https://github.com/huggingface/chat-ui" } ]` PUBLIC_SMOOTH_UPDATES=false # set to true to enable smoothing of messages client-side, can be CPU intensive PUBLIC_ORIGIN=#https://huggingface.co PUBLIC_SHARE_PREFIX=#https://hf.co/chat # mostly huggingchat specific PUBLIC_GOOGLE_ANALYTICS_ID=#G-XXXXXXXX / Leave empty to disable PUBLIC_PLAUSIBLE_SCRIPT_URL=#/js/script.js / Leave empty to disable PUBLIC_APPLE_APP_ID=#1234567890 / Leave empty to disable ### Feature Flags ### LLM_SUMMARIZATION=true # generate conversation titles with LLMs 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 # require featured assistants to show in the list COMMUNITY_TOOLS=false # set to true to enable community tools EXPOSE_API=true # make the /api routes available ALLOW_IFRAME=true # Allow the app to be embedded in an iframe ### Tools ### # Check out public config in `chart/env/prod.yaml` for more details TOOLS=`[]` ### Rate limits ### # See `src/lib/server/usageLimits.ts` # { # conversations: number, # how many conversations # messages: number, # how many messages in a conversation # assistants: number, # how many assistants # messageLength: number, # how long can a message be before we cut it off # messagesPerMinute: number, # how many messages per minute # tools: number # how many tools # } USAGE_LIMITS=`{}` ### HuggingFace specific ### # Let user authenticate with their HF token in the /api routes. This is only useful if you have OAuth configured with huggingface. USE_HF_TOKEN_IN_API=false ## Feature flag & admin settings # Used for setting early access & admin flags to users HF_ORG_ADMIN= HF_ORG_EARLY_ACCESS= WEBHOOK_URL_REPORT_ASSISTANT=#provide slack webhook url to get notified for reports/feature requests ### Metrics ### METRICS_ENABLED=false METRICS_PORT=5565 LOG_LEVEL=info ### Parquet export ### # Not in use anymore but useful to export conversations to a parquet file as a HuggingFace dataset PARQUET_EXPORT_DATASET= PARQUET_EXPORT_HF_TOKEN= ADMIN_API_SECRET=# secret to admin API calls, like computing usage stats or exporting parquet data ### Docker build variables ### # These values cannot be updated at runtime # They need to be passed when building the docker image # See https://github.com/huggingface/chat-ui/main/.github/workflows/deploy-prod.yml#L44-L47 APP_BASE="" # base path of the app, e.g. /chat, left blank as default PUBLIC_APP_COLOR=blue # can be any of tailwind colors: https://tailwindcss.com/docs/customizing-colors#default-color-palette ### Body size limit for SvelteKit https://svelte.dev/docs/kit/adapter-node#Environment-variables-BODY_SIZE_LIMIT BODY_SIZE_LIMIT=15728640 PUBLIC_COMMIT_SHA= ### LEGACY parameters HF_ACCESS_TOKEN=#LEGACY! Use HF_TOKEN instead ALLOW_INSECURE_COOKIES=false # LEGACY! Use COOKIE_SECURE and COOKIE_SAMESITE instead PARQUET_EXPORT_SECRET=#DEPRECATED, use ADMIN_API_SECRET instead RATE_LIMIT= # /!\ DEPRECATED definition of messages per minute. Use USAGE_LIMITS.messagesPerMinute instead 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=
0
hf_public_repos
hf_public_repos/chat-ui/.eslintrc.cjs
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: { "require-yield": "off", "@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, }, };
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hf_public_repos
hf_public_repos/chat-ui/.prettierignore
.DS_Store node_modules /build /.svelte-kit /package /chart .env .env.* !.env.example # Ignore files for PNPM, NPM and YARN pnpm-lock.yaml package-lock.json yarn.lock
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hf_public_repos
hf_public_repos/chat-ui/package.json
{ "name": "chat-ui", "version": "0.9.4", "private": true, "packageManager": "npm@9.5.0", "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": "vitest", "updateLocalEnv": "node --loader ts-node/esm scripts/updateLocalEnv.ts", "populate": "vite-node --options.transformMode.ssr='/.*/' scripts/populate.ts", "prepare": "husky" }, "devDependencies": { "@faker-js/faker": "^8.4.1", "@iconify-json/carbon": "^1.1.16", "@iconify-json/eos-icons": "^1.1.6", "@sveltejs/adapter-node": "^5.2.0", "@sveltejs/kit": "^2.8.3", "@tailwindcss/typography": "^0.5.9", "@types/dompurify": "^3.0.5", "@types/express": "^4.17.21", "@types/js-yaml": "^4.0.9", "@types/jsdom": "^21.1.1", "@types/jsonpath": "^0.2.4", "@types/katex": "^0.16.7", "@types/mime-types": "^2.1.4", "@types/minimist": "^1.2.5", "@types/node": "^22.1.0", "@types/parquetjs": "^0.10.3", "@types/sbd": "^1.0.5", "@types/uuid": "^9.0.8", "@typescript-eslint/eslint-plugin": "^6.x", "@typescript-eslint/parser": "^6.x", "dompurify": "^3.1.6", "eslint": "^8.28.0", "eslint-config-prettier": "^8.5.0", "eslint-plugin-svelte": "^2.30.0", "isomorphic-dompurify": "^2.13.0", "js-yaml": "^4.1.0", "minimist": "^1.2.8", "mongodb-memory-server": "^10.1.2", "prettier": "^2.8.0", "prettier-plugin-svelte": "^2.10.1", "prettier-plugin-tailwindcss": "^0.2.7", "prom-client": "^15.1.2", "svelte": "^4.2.19", "svelte-check": "^3.8.5", "ts-node": "^10.9.1", "tslib": "^2.4.1", "typescript": "^5.0.0", "unplugin-icons": "^0.16.1", "vite": "^5.4.11", "vite-node": "^1.3.1", "vitest": "^2.1.4" }, "type": "module", "dependencies": { "@aws-sdk/credential-providers": "^3.592.0", "@cliqz/adblocker-playwright": "^1.27.2", "@gradio/client": "^1.1.1", "@huggingface/hub": "^0.5.1", "@huggingface/inference": "^2.7.0", "@huggingface/transformers": "^3.0.0-alpha.6", "@iconify-json/bi": "^1.1.21", "@playwright/browser-chromium": "^1.43.1", "@resvg/resvg-js": "^2.6.2", "autoprefixer": "^10.4.14", "aws-sigv4-fetch": "^4.0.1", "aws4": "^1.13.0", "browser-image-resizer": "^2.4.1", "date-fns": "^2.29.3", "dotenv": "^16.0.3", "express": "^4.21.0", "file-type": "^19.4.1", "google-auth-library": "^9.13.0", "handlebars": "^4.7.8", "highlight.js": "^11.7.0", "husky": "^9.0.11", "image-size": "^1.0.2", "ip-address": "^9.0.5", "jose": "^5.3.0", "jsdom": "^22.0.0", "json5": "^2.2.3", "jsonpath": "^1.1.1", "katex": "^0.16.11", "lint-staged": "^15.2.7", "marked": "^12.0.1", "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", "playwright": "^1.44.1", "postcss": "^8.4.31", "saslprep": "^1.0.3", "satori": "^0.10.11", "satori-html": "^0.3.2", "sbd": "^1.0.19", "serpapi": "^1.1.1", "sharp": "^0.33.4", "tailwind-scrollbar": "^3.0.0", "tailwindcss": "^3.4.0", "uuid": "^10.0.0", "zod": "^3.22.3" }, "optionalDependencies": { "@anthropic-ai/sdk": "^0.32.1", "@anthropic-ai/vertex-sdk": "^0.4.1", "@aws-sdk/client-bedrock-runtime": "^3.631.0", "@google-cloud/vertexai": "^1.1.0", "@google/generative-ai": "^0.14.1", "aws4fetch": "^1.0.17", "cohere-ai": "^7.9.0", "openai": "^4.44.0" } }
0
hf_public_repos
hf_public_repos/chat-ui/postcss.config.js
export default { plugins: { tailwindcss: {}, autoprefixer: {}, }, };
0
hf_public_repos
hf_public_repos/chat-ui/entrypoint.sh
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
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hf_public_repos
hf_public_repos/chat-ui/PROMPTS.md
# 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}} ```
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hf_public_repos
hf_public_repos/chat-ui/.prettierrc
{ "useTabs": true, "trailingComma": "es5", "printWidth": 100, "plugins": ["prettier-plugin-svelte", "prettier-plugin-tailwindcss"], "pluginSearchDirs": ["."], "overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }] }
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hf_public_repos
hf_public_repos/chat-ui/.env.ci
MONGODB_URL=mongodb://localhost:27017/
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hf_public_repos
hf_public_repos/chat-ui/vite.config.ts
import { sveltekit } from "@sveltejs/kit/vite"; import Icons from "unplugin-icons/vite"; import { promises } from "fs"; import { defineConfig } from "vitest/config"; // used to load fonts server side for thumbnail generation function loadTTFAsArrayBuffer() { return { name: "load-ttf-as-array-buffer", async transform(_src, id) { if (id.endsWith(".ttf")) { return `export default new Uint8Array([ ${new Uint8Array(await promises.readFile(id))} ]).buffer`; } }, }; } export default defineConfig({ plugins: [ sveltekit(), Icons({ compiler: "svelte", }), loadTTFAsArrayBuffer(), ], optimizeDeps: { include: [ "browser-image-resizer", "uuid", "@huggingface/transformers", "sharp", "@gradio/client", ], }, server: { open: "/", }, test: { setupFiles: ["./scripts/setupTest.ts"], deps: { inline: ["@sveltejs/kit"] }, globals: true, testTimeout: 10000, }, });
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/chart/values.yaml
image: repository: ghcr.io/huggingface name: chat-ui tag: 0.0.0-latest pullPolicy: IfNotPresent replicas: 3 domain: huggingface.co networkPolicy: enabled: false allowedBlocks: [] service: type: NodePort annotations: { } serviceAccount: enabled: false create: false name: "" automountServiceAccountToken: true annotations: { } ingress: enabled: true path: "/" annotations: { } # className: "nginx" tls: { } # secretName: XXX resources: requests: cpu: 2 memory: 4Gi limits: cpu: 2 memory: 4Gi nodeSelector: {} tolerations: [] envVars: { } infisical: enabled: false env: "" project: "huggingchat-v2-a1" url: "" resyncInterval: 60 operatorSecretName: "huggingchat-operator-secrets" operatorSecretNamespace: "hub-utils" # Allow to environment injections on top or instead of infisical extraEnvFrom: [] extraEnv: [] autoscaling: enabled: false minReplicas: 1 maxReplicas: 2 targetMemoryUtilizationPercentage: "" targetCPUUtilizationPercentage: "" monitoring: enabled: false
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/chart/Chart.yaml
apiVersion: v2 name: chat-ui version: 0.0.0-latest type: application icon: https://huggingface.co/front/assets/huggingface_logo-noborder.svg
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/network-policy.yaml
{{- if $.Values.networkPolicy.enabled }} apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: {{ include "name" . }} namespace: {{ .Release.Namespace }} spec: egress: - ports: - port: 53 protocol: UDP to: - namespaceSelector: matchLabels: kubernetes.io/metadata.name: kube-system podSelector: matchLabels: k8s-app: kube-dns - to: {{- range $ip := .Values.networkPolicy.allowedBlocks }} - ipBlock: cidr: {{ $ip | quote }} {{- end }} - to: - ipBlock: cidr: 0.0.0.0/0 except: - 10.0.0.0/8 - 172.16.0.0/12 - 192.168.0.0/16 - 169.254.169.254/32 podSelector: matchLabels: {{ include "labels.standard" . | nindent 6 }} policyTypes: - Egress {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/config.yaml
apiVersion: v1 kind: ConfigMap metadata: labels: {{ include "labels.standard" . | nindent 4 }} name: {{ include "name" . }} namespace: {{ .Release.Namespace }} data: {{- range $key, $value := $.Values.envVars }} {{ $key }}: {{ $value | quote }} {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/service-account.yaml
{{- if and .Values.serviceAccount.enabled .Values.serviceAccount.create }} apiVersion: v1 kind: ServiceAccount automountServiceAccountToken: {{ .Values.serviceAccount.automountServiceAccountToken }} metadata: name: "{{ .Values.serviceAccount.name | default (include "name" .) }}" namespace: {{ .Release.Namespace }} labels: {{ include "labels.standard" . | nindent 4 }} {{- with .Values.serviceAccount.annotations }} annotations: {{- toYaml . | nindent 4 }} {{- end }} {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/infisical.yaml
{{- if .Values.infisical.enabled }} apiVersion: secrets.infisical.com/v1alpha1 kind: InfisicalSecret metadata: name: {{ include "name" $ }}-infisical-secret namespace: {{ $.Release.Namespace }} spec: authentication: universalAuth: credentialsRef: secretName: {{ .Values.infisical.operatorSecretName | quote }} secretNamespace: {{ .Values.infisical.operatorSecretNamespace | quote }} secretsScope: envSlug: {{ .Values.infisical.env | quote }} projectSlug: {{ .Values.infisical.project | quote }} secretsPath: / hostAPI: {{ .Values.infisical.url | quote }} managedSecretReference: creationPolicy: Owner secretName: {{ include "name" $ }}-secs secretNamespace: {{ .Release.Namespace | quote }} secretType: Opaque resyncInterval: {{ .Values.infisical.resyncInterval }} {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/deployment.yaml
apiVersion: apps/v1 kind: Deployment metadata: labels: {{ include "labels.standard" . | nindent 4 }} name: {{ include "name" . }} namespace: {{ .Release.Namespace }} {{- if .Values.infisical.enabled }} annotations: secrets.infisical.com/auto-reload: "true" {{- end }} spec: progressDeadlineSeconds: 600 {{- if not $.Values.autoscaling.enabled }} replicas: {{ .Values.replicas }} {{- end }} revisionHistoryLimit: 10 selector: matchLabels: {{ include "labels.standard" . | nindent 6 }} strategy: rollingUpdate: maxSurge: 25% maxUnavailable: 25% type: RollingUpdate template: metadata: labels: {{ include "labels.standard" . | nindent 8 }} {{- if $.Values.envVars.NODE_LOG_STRUCTURED_DATA }} annotations: co.elastic.logs/json.expand_keys: "true" {{- end }} spec: {{- if .Values.serviceAccount.enabled }} serviceAccountName: "{{ .Values.serviceAccount.name | default (include "name" .) }}" {{- end }} containers: - name: chat-ui image: "{{ .Values.image.repository }}/{{ .Values.image.name }}:{{ .Values.image.tag }}" imagePullPolicy: {{ .Values.image.pullPolicy }} readinessProbe: failureThreshold: 30 periodSeconds: 10 httpGet: path: {{ $.Values.envVars.APP_BASE | default "" }}/healthcheck port: {{ $.Values.envVars.APP_PORT | default 3000 | int }} livenessProbe: failureThreshold: 30 periodSeconds: 10 httpGet: path: {{ $.Values.envVars.APP_BASE | default "" }}/healthcheck port: {{ $.Values.envVars.APP_PORT | default 3000 | int }} ports: - containerPort: {{ $.Values.envVars.APP_PORT | default 3000 | int }} name: http protocol: TCP {{- if $.Values.monitoring.enabled }} - containerPort: {{ $.Values.envVars.METRICS_PORT | default 5565 | int }} name: metrics protocol: TCP {{- end }} resources: {{ toYaml .Values.resources | nindent 12 }} {{- with $.Values.extraEnv }} env: {{- toYaml . | nindent 14 }} {{- end }} envFrom: - configMapRef: name: {{ include "name" . }} {{- if $.Values.infisical.enabled }} - secretRef: name: {{ include "name" $ }}-secs {{- end }} {{- with $.Values.extraEnvFrom }} {{- toYaml . | nindent 14 }} {{- end }} nodeSelector: {{ toYaml .Values.nodeSelector | nindent 8 }} tolerations: {{ toYaml .Values.tolerations | nindent 8 }} volumes: - name: config configMap: name: {{ include "name" . }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/service.yaml
apiVersion: v1 kind: Service metadata: name: "{{ include "name" . }}" annotations: {{ toYaml .Values.service.annotations | nindent 4 }} namespace: {{ .Release.Namespace }} labels: {{ include "labels.standard" . | nindent 4 }} spec: ports: - name: http port: 80 protocol: TCP targetPort: http {{- if $.Values.monitoring.enabled }} - name: metrics port: 5565 protocol: TCP targetPort: metrics {{- end }} selector: {{ include "labels.standard" . | nindent 4 }} type: {{.Values.service.type}}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/_helpers.tpl
{{- define "name" -}} {{- default $.Release.Name | trunc 63 | trimSuffix "-" -}} {{- end -}} {{- define "app.name" -}} chat-ui {{- end -}} {{- define "labels.standard" -}} release: {{ $.Release.Name | quote }} heritage: {{ $.Release.Service | quote }} chart: "{{ include "name" . }}" app: "{{ include "app.name" . }}" {{- end -}} {{- define "labels.resolver" -}} release: {{ $.Release.Name | quote }} heritage: {{ $.Release.Service | quote }} chart: "{{ include "name" . }}" app: "{{ include "app.name" . }}-resolver" {{- end -}}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/service-monitor.yaml
{{- if $.Values.monitoring.enabled }} apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: labels: {{ include "labels.standard" . | nindent 4 }} name: {{ include "name" . }} namespace: {{ .Release.Namespace }} spec: selector: matchLabels: {{ include "labels.standard" . | nindent 6 }} endpoints: - port: metrics path: /metrics interval: 15s {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/hpa.yaml
{{- if $.Values.autoscaling.enabled }} apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: labels: {{ include "labels.standard" . | nindent 4 }} name: {{ include "name" . }} namespace: {{ .Release.Namespace }} spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: {{ include "name" . }} minReplicas: {{ $.Values.autoscaling.minReplicas }} maxReplicas: {{ $.Values.autoscaling.maxReplicas }} metrics: {{- if ne "" $.Values.autoscaling.targetMemoryUtilizationPercentage }} - type: Resource resource: name: memory target: type: Utilization averageUtilization: {{ $.Values.autoscaling.targetMemoryUtilizationPercentage | int }} {{- end }} {{- if ne "" $.Values.autoscaling.targetCPUUtilizationPercentage }} - type: Resource resource: name: cpu target: type: Utilization averageUtilization: {{ $.Values.autoscaling.targetCPUUtilizationPercentage | int }} {{- end }} behavior: scaleDown: stabilizationWindowSeconds: 600 policies: - type: Percent value: 10 periodSeconds: 60 scaleUp: stabilizationWindowSeconds: 0 policies: - type: Pods value: 1 periodSeconds: 30 {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/templates/ingress.yaml
{{- if $.Values.ingress.enabled }} apiVersion: networking.k8s.io/v1 kind: Ingress metadata: annotations: {{ toYaml .Values.ingress.annotations | nindent 4 }} labels: {{ include "labels.standard" . | nindent 4 }} name: {{ include "name" . }} namespace: {{ .Release.Namespace }} spec: {{ if $.Values.ingress.className }} ingressClassName: {{ .Values.ingress.className }} {{ end }} {{- with .Values.ingress.tls }} tls: - hosts: - {{ $.Values.domain | quote }} {{- with .secretName }} secretName: {{ . }} {{- end }} {{- end }} rules: - host: {{ .Values.domain }} http: paths: - backend: service: name: {{ include "name" . }} port: name: http path: {{ $.Values.ingress.path | default "/" }} pathType: Prefix {{- end }}
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hf_public_repos/chat-ui/chart
hf_public_repos/chat-ui/chart/env/prod.yaml
image: repository: huggingface name: chat-ui nodeSelector: role-hub-utils: "true" tolerations: - key: CriticalAddonsOnly operator: Equal serviceAccount: enabled: true create: true name: huggingchat-prod networkPolicy: enabled: true allowedBlocks: - 10.0.252.0/25 - 10.240.0.0/24 ingress: path: "/chat" annotations: alb.ingress.kubernetes.io/healthcheck-path: "/healthcheck" alb.ingress.kubernetes.io/listen-ports: "[{\"HTTP\": 80}, {\"HTTPS\": 443}]" alb.ingress.kubernetes.io/group.name: "hub-prod" alb.ingress.kubernetes.io/scheme: "internet-facing" alb.ingress.kubernetes.io/ssl-redirect: "443" alb.ingress.kubernetes.io/tags: "Env=prod,Project=hub,Terraform=true" alb.ingress.kubernetes.io/target-node-labels: "role-hub-utils=true" kubernetes.io/ingress.class: "alb" envVars: ADDRESS_HEADER: 'X-Forwarded-For' ALTERNATIVE_REDIRECT_URLS: '["huggingchat://login/callback"]' APP_BASE: "/chat" ALLOW_IFRAME: "false" COMMUNITY_TOOLS: "true" COOKIE_SAMESITE: "lax" COOKIE_SECURE: "true" ENABLE_ASSISTANTS: "true" ENABLE_ASSISTANTS_RAG: "true" EXPOSE_API: "true" METRICS_PORT: 5565 LOG_LEVEL: "debug" METRICS_ENABLED: "true" MODELS: > [ { "name": "Qwen/Qwen2.5-72B-Instruct", "tokenizer": "Qwen/Qwen2.5-72B-Instruct", "description": "The latest Qwen open model with improved role-playing, long text generation and structured data understanding.", "modelUrl": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct", "websiteUrl": "https://qwenlm.github.io/blog/qwen2.5/", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png", "preprompt": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.", "parameters": { "stop": ["<|endoftext|>", "<|im_end|>"], "temperature": 0.6, "truncate": 28672, "max_new_tokens": 3072 }, "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-3.1-70B-Instruct", "id": "meta-llama/Meta-Llama-3.1-70B-Instruct", "tokenizer": "nsarrazin/llama3.1-tokenizer", "description": "Ideal for everyday use. A fast and extremely capable model matching closed source models' capabilities.", "modelUrl": "https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct", "websiteUrl": "https://llama.meta.com/", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/meta-logo.png", "tools": true, "preprompt": "", "parameters": { "stop": ["<|endoftext|>", "<|eot_id|>"], "temperature": 0.6, "max_new_tokens": 1024, "truncate": 7167 }, "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": "CohereForAI/c4ai-command-r-plus-08-2024", "tokenizer": "nsarrazin/c4ai-command-r-v01-tokenizer", "description": "Cohere's largest language model, optimized for conversational interaction and tool use. Now with the 2024 update!", "modelUrl": "https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024", "websiteUrl": "https://docs.cohere.com/docs/command-r-plus", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/cohere-logo.png", "tools": true, "parameters": { "stop": ["<|END_OF_TURN_TOKEN|>"], "truncate": 28672, "max_new_tokens": 2048, "temperature": 0.3 }, "promptExamples": [ { "title": "Generate a mouse portrait", "prompt": "Generate the portrait of a scientific mouse in its laboratory." }, { "title": "Review a pull request", "prompt": "Review this pull request: https://github.com/huggingface/chat-ui/pull/1131/files" }, { "title": "Code a snake game", "prompt": "Code a basic snake game in python, give explanations for each step." } ] }, { "name": "Qwen/QwQ-32B-Preview", "preprompt": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step.", "modelUrl": "https://huggingface.co/Qwen/QwQ-32B-Preview", "websiteUrl": "https://qwenlm.github.io/blog/qwq-32b-preview/", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png", "description": "QwQ is an experiment model from the Qwen Team with advanced reasoning capabilities.", "reasoning": { "type": "summarize" }, "parameters": { "stop": ["<|im_end|>"], "truncate": 12288, "max_new_tokens": 4096, "temperature": 0.7, "top_k": 20, "top_p": 0.8, "repetition_penalty": 1.05 }, "promptExamples": [ { "title": "Rs in strawberry", "prompt": "how many R in strawberry?" }, { "title": "Larger number", "prompt": "9.11 or 9.9 which number is larger?" }, { "title": "Measuring 6 liters", "prompt": "I have a 6- and a 12-liter jug. I want to measure exactly 6 liters." } ] }, { "name": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", "tokenizer": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", "modelUrl": "https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", "websiteUrl": "https://www.nvidia.com/", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/nvidia-logo.png", "description": "Nvidia's latest Llama fine-tune, topping alignment benchmarks and optimized for instruction following.", "parameters": { "stop": ["<|eot_id|>", "<|im_end|>"], "temperature": 0.5, "truncate": 28672, "max_new_tokens": 2048 }, "promptExamples": [ { "title": "Rs in strawberry", "prompt": "how many R in strawberry?" }, { "title": "Larger number", "prompt": "9.11 or 9.9 which number is larger?" }, { "title": "Measuring 6 liters", "prompt": "I have a 6- and a 12-liter jug. I want to measure exactly 6 liters." } ], "endpoints": [{ "type": "openai", "baseURL": "https://api-inference.huggingface.co/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF/v1" }] }, { "name": "Qwen/Qwen2.5-Coder-32B-Instruct", "description": "Qwen's latest coding model, in its biggest size yet. SOTA on many coding benchmarks.", "modelUrl": "https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct", "websiteUrl": "https://qwenlm.github.io/blog/qwen2.5-coder-family/", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png", "preprompt": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.", "parameters": { "stop": ["<|im_end|>", "<|endoftext|>"], "temperature": 0.6, "truncate": 28672, "max_new_tokens": 3072 }, "promptExamples": [ { "title": "To-do list web app", "prompt": "Create a simple to-do list application where users can:\n- Add new tasks.\n- Mark tasks as complete.\n- Delete completed tasks.\nThe tasks should persist in the browser's local storage so that they remain available even after a page reload.\n" }, { "title": "Create a REST API", "prompt": "Build a simple REST API using Node.js, TypeScript and Express:\n- POST /items: Accepts a JSON body with name and quantity and adds a new item.\n- GET /items: Returns a list of all items.\n- PUT /items/:id: Updates the name or quantity of an item by its id.\n- DELETE /items/:id: Removes an item by its id.\nUse an in-memory array as the data store (no need for a database). Include basic error handling (e.g., item not found)." }, { "title": "Simple website", "prompt": "Generate a snazzy static landing page for a local coffee shop using HTML and CSS. You can use tailwind using <script src='https://cdn.tailwindcss.com'></script>." } ], "endpoints": [{ "type": "openai", "baseURL": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1" }] }, { "name": "meta-llama/Llama-3.2-11B-Vision-Instruct", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/meta-logo.png", "description": "The latest multimodal model from Meta! Supports image inputs natively.", "websiteUrl": "https://llama.com/", "multimodal": true, "parameters": { "stop": ["<|eot_id|>", "<|im_end|>"], "temperature": 0.6, "truncate": 14336, "max_new_tokens": 1536 }, "endpoints": [{ "type": "openai", "baseURL": "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct/v1", "multimodal": { "image": { "maxSizeInMB": 10, "maxWidth": 560, "maxHeight": 560, "supportedMimeTypes": ["image/png", "image/jpeg", "image/webp"], "preferredMimeType": "image/webp" } } }] }, { "name": "NousResearch/Hermes-3-Llama-3.1-8B", "description": "Nous Research's latest Hermes 3 release in 8B size. Follows instruction closely.", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/nous-logo.png", "websiteUrl": "https://nousresearch.com/", "modelUrl": "https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B", "tokenizer": "NousResearch/Hermes-3-Llama-3.1-8B", "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": ["<|im_end|>"], "temperature": 0.6, "truncate": 14336, "max_new_tokens": 1536 } }, { "name": "mistralai/Mistral-Nemo-Instruct-2407", "tokenizer": "mistralai/Mistral-Nemo-Instruct-2407", "displayName": "mistralai/Mistral-Nemo-Instruct-2407", "description": "A small model with good capabilities in language understanding and commonsense reasoning.", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/mistral-logo.png", "websiteUrl": "https://mistral.ai/news/mistral-nemo/", "modelUrl": "https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407", "preprompt": "", "parameters": { "stop": ["</s>"], "temperature": 0.6, "truncate": 14336, "max_new_tokens": 1536 }, "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-3.5-mini-instruct", "tokenizer": "microsoft/Phi-3.5-mini-instruct", "description": "One of the best small models (3.8B parameters), super fast for simple tasks.", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/microsoft-logo.png", "modelUrl": "https://huggingface.co/microsoft/Phi-3.5-mini-instruct", "websiteUrl": "https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/discover-the-new-multi-lingual-high-quality-phi-3-5-slms/ba-p/4225280/", "preprompt": "", "parameters": { "stop": ["<|end|>", "<|endoftext|>", "<|assistant|>"], "temperature": 0.6, "truncate": 28672, "max_new_tokens": 3072 }, "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": "llhf/Meta-Llama-3.1-8B-Instruct", "tokenizer": { "tokenizerUrl": "https://huggingface.co/nsarrazin/llama3.1-tokenizer/resolve/main/tokenizer.json", "tokenizerConfigUrl": "https://huggingface.co/nsarrazin/llama3.1-tokenizer/raw/main/tokenizer_config.json" }, "parameters": { "temperature": 0.6, "stop": ["<|endoftext|>", "<|eot_id|>"] }, "unlisted": true } ] NODE_ENV: "prod" NODE_LOG_STRUCTURED_DATA: 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" }, { "name": "meta-llama/Meta-Llama-3-70B-Instruct" }, { "name": "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8" }, { "name": "CohereForAI/c4ai-command-r-plus", "transferTo": "CohereForAI/c4ai-command-r-plus-08-2024" }, { "name": "01-ai/Yi-1.5-34B-Chat", "transferTo": "CohereForAI/c4ai-command-r-plus-08-2024" }, { "name": "mistralai/Mixtral-8x7B-Instruct-v0.1", "transferTo": "mistralai/Mistral-Nemo-Instruct-2407" }, { "name": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", "transferTo": "NousResearch/Hermes-3-Llama-3.1-8B" }, { "name": "mistralai/Mistral-7B-Instruct-v0.3", "transferTo": "mistralai/Mistral-Nemo-Instruct-2407" }, { "name": "microsoft/Phi-3-mini-4k-instruct", "transferTo": "microsoft/Phi-3.5-mini-instruct" } ] PUBLIC_ORIGIN: "https://huggingface.co" PUBLIC_SHARE_PREFIX: "https://hf.co/chat" PUBLIC_ANNOUNCEMENT_BANNERS: > [ { "title": "QwQ-32B-Preview from Qwen is now available!", "linkTitle": "Try it out!", "linkHref": "https://huggingface.co/chat/models/Qwen/QwQ-32B-Preview" } ] 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_GUEST_MESSAGE: "You have reached the guest message limit, Sign In with a free Hugging Face account to continue using HuggingChat." PUBLIC_APP_DATA_SHARING: 0 PUBLIC_APP_DISCLAIMER: 1 PUBLIC_PLAUSIBLE_SCRIPT_URL: "/js/script.js" REQUIRE_FEATURED_ASSISTANTS: "true" TASK_MODEL: "llhf/Meta-Llama-3.1-8B-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/" }] }] WEBSEARCH_BLOCKLIST: '["youtube.com", "twitter.com"]' XFF_DEPTH: '2' TOOLS: > [ { "_id": "000000000000000000000001", "displayName": "Image Generation", "description": "Use this tool to generate images based on a prompt.", "color": "yellow", "icon": "camera", "baseUrl": "black-forest-labs/FLUX.1-schnell", "name": "image_generation", "endpoint": "/infer", "inputs": [ { "name": "prompt", "description": "A prompt to generate an image from", "paramType": "required", "type": "str" }, { "name": "seed", "paramType": "fixed", "value": "0", "type": "float" }, { "name": "randomize_seed", "paramType": "fixed", "value": "true", "type": "bool" }, { "name": "width", "description": "numeric value between 256 and 2048", "paramType": "optional", "default": 1024, "type": "float" }, { "name": "height", "description": "numeric value between 256 and 2048", "paramType": "optional", "default": 1024, "type": "float" }, { "name": "num_inference_steps", "paramType": "fixed", "value": "4", "type": "float" } ], "outputComponent": "image", "outputComponentIdx": 0, "showOutput": true }, { "_id": "000000000000000000000002", "displayName": "Document Parser", "description": "Use this tool to parse any document and get its content in markdown format.", "color": "yellow", "icon": "cloud", "baseUrl": "huggingchat/document-parser", "name": "document_parser", "endpoint": "/predict", "inputs": [ { "name": "document", "description": "Filename of the document to parse", "paramType": "required", "type": "file", "mimeTypes": 'application/*' }, { "name": "filename", "paramType": "fixed", "value": "document.pdf", "type": "str" } ], "outputComponent": "textbox", "outputComponentIdx": 0, "showOutput": false }, { "_id": "000000000000000000000003", "name": "edit_image", "baseUrl": "multimodalart/cosxl", "endpoint": "/run_edit", "inputs": [ { "name": "image", "description": "The image path to be edited", "paramType": "required", "type": "file", "mimeTypes": 'image/*' }, { "name": "prompt", "description": "The prompt with which to edit the image", "paramType": "required", "type": "str" }, { "name": "negative_prompt", "paramType": "fixed", "value": "", "type": "str" }, { "name": "guidance_scale", "paramType": "fixed", "value": 6.5, "type": "float" }, { "name": "steps", "paramType": "fixed", "value": 30, "type": "float" } ], "outputComponent": "image", "showOutput": true, "displayName": "Image Editor", "color": "green", "icon": "camera", "description": "This tool lets you edit images", "outputComponentIdx": 0 } ] HF_ORG_ADMIN: '644171cfbd0c97265298aa99' HF_ORG_EARLY_ACCESS: '5e67bd5b1009063689407478' infisical: enabled: true env: "prod-us-east-1" autoscaling: enabled: true minReplicas: 12 maxReplicas: 30 targetMemoryUtilizationPercentage: "50" targetCPUUtilizationPercentage: "50" resources: requests: cpu: 2 memory: 4Gi limits: cpu: 4 memory: 8Gi monitoring: enabled: true
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hf_public_repos/chat-ui/docs
hf_public_repos/chat-ui/docs/source/_toctree.yml
- local: index title: 🤗 Chat UI - title: Installation sections: - local: installation/local title: Local - local: installation/spaces title: Spaces - local: installation/docker title: Docker - local: installation/helm title: Helm - title: Configuration sections: - local: configuration/overview title: Overview - local: configuration/theming title: Theming - local: configuration/open-id title: OpenID - local: configuration/web-search title: Web Search - local: configuration/metrics title: Metrics - local: configuration/embeddings title: Text Embedding Models - title: Models sections: - local: configuration/models/overview title: Overview - local: configuration/models/multimodal title: Multimodal - local: configuration/models/tools title: Tools - title: Providers sections: - local: configuration/models/providers/anthropic title: Anthropic - local: configuration/models/providers/aws title: AWS - local: configuration/models/providers/cloudflare title: Cloudflare - local: configuration/models/providers/cohere title: Cohere - local: configuration/models/providers/google title: Google - local: configuration/models/providers/langserve title: Langserve - local: configuration/models/providers/llamacpp title: Llama.cpp - local: configuration/models/providers/ollama title: Ollama - local: configuration/models/providers/openai title: OpenAI - local: configuration/models/providers/tgi title: TGI - local: configuration/common-issues title: Common Issues - title: Developing sections: - local: developing/architecture title: Architecture - local: developing/copy-huggingchat title: Copy HuggingChat
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hf_public_repos/chat-ui/docs
hf_public_repos/chat-ui/docs/source/index.md
# 🤗 Chat UI Open source chat interface with support for tools, web search, multimodal and many API providers. The app uses MongoDB and SvelteKit behind the scenes. Try the live version of the app called [HuggingChat on hf.co/chat](https://huggingface.co/chat) or [setup your own instance](./installation/spaces). 🔧 **[Tools](./configuration/models/tools)**: Function calling with custom tools and support for [Zero GPU spaces](https://huggingface.co/spaces/enzostvs/zero-gpu-spaces) 🔍 **[Web Search](./configuration/web-search)**: Automated web search, scraping and RAG for all models 🐙 **[Multimodal](./configuration/models/multimodal)**: Accepts image file uploads on supported providers 👤 **[OpenID](./configuration/open-id)**: Optionally setup OpenID for user authentication <div class="flex gap-x-4"> <div> Tools <div class="flex justify-center"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/tools-light.png" height="auto"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/tools-dark.png" height="auto"/> </div> </div> <div> Web Search <div class="flex justify-center"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/websearch-light.png" height="auto"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/websearch-dark.png" height="auto"/> </div> </div> </div> ## Quickstart You can quickly have a locally running chat-ui & LLM text-generation server thanks to chat-ui's [llama.cpp server support](https://huggingface.co/docs/chat-ui/configuration/models/providers/llamacpp). **Step 1 (Start llama.cpp server):** ```bash # install llama.cpp brew install llama.cpp # start llama.cpp server (using hf.co/microsoft/Phi-3-mini-4k-instruct-gguf as an example) llama-server --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf --hf-file Phi-3-mini-4k-instruct-q4.gguf -c 4096 ``` A local LLaMA.cpp HTTP Server will start on `http://localhost:8080`. Read more [here](https://huggingface.co/docs/chat-ui/configuration/models/providers/llamacpp). **Step 2 (tell chat-ui to use local llama.cpp server):** Add the following to your `.env.local`: ```ini MODELS=`[ { "name": "Local microsoft/Phi-3-mini-4k-instruct-gguf", "tokenizer": "microsoft/Phi-3-mini-4k-instruct-gguf", "preprompt": "", "chatPromptTemplate": "<s>{{preprompt}}{{#each messages}}{{#ifUser}}<|user|>\n{{content}}<|end|>\n<|assistant|>\n{{/ifUser}}{{#ifAssistant}}{{content}}<|end|>\n{{/ifAssistant}}{{/each}}", "parameters": { "stop": ["<|end|>", "<|endoftext|>", "<|assistant|>"], "temperature": 0.7, "max_new_tokens": 1024, "truncate": 3071 }, "endpoints": [{ "type" : "llamacpp", "baseURL": "http://localhost:8080" }], }, ]` ``` Read more [here](https://huggingface.co/docs/chat-ui/configuration/models/providers/llamacpp). **Step 3 (make sure you have MongoDb running locally):** ```bash docker run -d -p 27017:27017 --name mongo-chatui mongo:latest ``` Read more [here](https://github.com/huggingface/chat-ui?tab=Readme-ov-file#database). **Step 4 (start chat-ui):** ```bash git clone https://github.com/huggingface/chat-ui cd chat-ui npm install npm run dev -- --open ``` Read more [here](https://github.com/huggingface/chat-ui?tab=readme-ov-file#launch). <div class="flex justify-center"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/llamacpp-light.png" height="auto"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/llamacpp-dark.png" height="auto"/> </div>
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/configuration/open-id.md
# OpenID 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: ```ini 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 }` ``` Redirect URI: `/login/callback`
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hf_public_repos/chat-ui/docs/source/configuration/common-issues.md
# 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.
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hf_public_repos/chat-ui/docs/source/configuration/overview.md
# Configuration Overview Chat UI handles configuration with environment variables. The default config for Chat UI is stored in the `.env` file, which you may use as a reference. You will need to override some values to get Chat UI to run locally. This can be done in `.env.local` or via your environment. The bare minimum configuration to get Chat UI running is: ```ini MONGODB_URL=mongodb://localhost:27017 HF_TOKEN=your_token ``` The following sections detail various sections of the app you may want to configure.
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/configuration/web-search.md
# Web Search Chat UI features a powerful Web Search feature. A high level overview of how it works: 1. Generate an appropriate search query from the user prompt using the `TASK_MODEL` 2. Perform web search via an external provider (i.e. Serper) or via locally scrape Google results 3. Load each search result into playwright and scrape 4. Convert scraped HTML to Markdown tree with headings as parents 5. Create embeddings for each Markdown element 6. Find the embedings clossest to the user query using a vector similarity search (inner product) 7. Get the corresponding Markdown elements and their parent, up to 8000 characters 8. Supply the information as context to the model <div class="flex justify-center"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/websearch-light.png" height="auto"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/websearch-dark.png" height="auto"/> </div> ## Providers Many providers are supported for the web search, or you can use locally scraped Google results. ### Local For locally scraped Google results, put `USE_LOCAL_WEBSEARCH=true` in your `.env.local`. Please note that you may hit rate limits as we make no attempt to make the traffic look legitimate. To avoid this, you may choose a provider, such as Serper, used on the official instance. ### SearXNG > SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled. You may enable support via the `SEARXNG_QUERY_URL` where `<query>` will be replaceed with the query keywords. Please see [the official documentation](https://docs.searxng.org/dev/search_api.html) for more information Example: `https://searxng.yourdomain.com/search?q=<query>&engines=duckduckgo,google&format=json` ### Third Party Many third party providers are supported as well. The official instance uses Serper. ```ini YDC_API_KEY=docs.you.com api key here SERPER_API_KEY=serper.dev api key here SERPAPI_KEY=serpapi key here SERPSTACK_API_KEY=serpstack api key here SEARCHAPI_KEY=searchapi api key here ``` ## Block/Allow List You may block or allow specific websites from the web search results. When using an allow list, only the links in the allowlist will be used. For supported search engines, the links will be blocked from the results directly. Any URL in the results that **partially or fully matches** the entry will be filtered out. ```ini WEBSEARCH_BLOCKLIST=`["youtube.com", "https://example.com/foo/bar"]` WEBSEARCH_ALLOWLIST=`["stackoverflow.com"]` ``` ## Disabling Javascript By default, Playwright will execute all Javascript on the page. This can be intensive, requiring up to 6 cores for full performance, on some webpages. You may block scripts from running by settings `WEBSEARCH_JAVASCRIPT=false`. However, this will not block Javascript inlined in the HTML.
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/configuration/theming.md
# Theming You can use a few environment variables to customize the look and feel of Chat UI. These are by default: ```ini 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.
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/configuration/metrics.md
# Metrics The server can expose prometheus metrics on port `5565` but is off by default. You may enable the metrics server with `METRICS_ENABLED=true` and change the port with `METRICS_PORT=1234`. <Tip> In development with `npm run dev`, the metrics server does not shutdown gracefully due to Sveltekit not providing hooks for restart. It's recommended to disable the metrics server in this case. </Tip>
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/configuration/embeddings.md
# 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, the [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 where 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. ## Transformers.js The Transformers.js backend uses local CPU for the embedding which can be quite slow. If possible, consider using TEI or OpenAI embeddings instead if you use web search frequently, as performance will improve significantly. ```ini TEXT_EMBEDDING_MODELS = `[ { "name": "Xenova/gte-small", "displayName": "Xenova/gte-small", "description": "locally running embedding", "chunkCharLength": 512, "endpoints": [ { "type": "transformersjs" } ] } ]` ``` ## Text Embeddings Inference (TEI) > Text Embeddings Inference (TEI) is a comprehensive toolkit designed for efficient deployment and serving of open source text embeddings models. It enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE, and E5. Some recommended models at the time of writing (May 2024) are `Snowflake/snowflake-arctic-embed-m` and `BAAI/bge-large-en-v1.5`. You may run TEI locally with GPU support via Docker: `docker run --gpus all -p 8080:80 -v tei-data:/data --name tei ghcr.io/huggingface/text-embeddings-inference:1.2 --model-id YOUR/HF_MODEL` You can then hook this up to your Chat UI instance with the following configuration. ```ini TEXT_EMBEDDING_MODELS=`[ { "name": "YOUR/HF_MODEL", "displayName": "YOUR/HF_MODEL", "preQuery": "Check the model documentation for the preQuery. Not all models have one", "prePassage": "Check the model documentation for the prePassage. Not all models have one", "chunkCharLength": 512, "endpoints": [{ "type": "tei", "url": "http://127.0.0.1:8080/" }] } ]` ``` Examples for `Snowflake/snowflake-arctic-embed-m` and `BAAI/bge-large-en-v1.5`: ```ini TEXT_EMBEDDING_MODELS=`[ { "name": "Snowflake/snowflake-arctic-embed-m", "displayName": "Snowflake/snowflake-arctic-embed-m", "preQuery": "Represent this sentence for searching relevant passages: ", "chunkCharLength": 512, "endpoints": [{ "type": "tei", "url": "http://127.0.0.1:8080/" }] },{ "name": "BAAI/bge-large-en-v1.5", "displayName": "BAAI/bge-large-en-v1.5", "chunkCharLength": 512, "endpoints": [{ "type": "tei", "url": "http://127.0.0.1:8080/" }] } ]` ``` ## OpenAI It's also possible to host your own OpenAI API compatible embedding models. [`Infinity`](https://github.com/michaelfeil/infinity) is one example. You may run it locally with Docker: `docker run -it --gpus all -v infinity-data:/app/.cache -p 7997:7997 michaelf34/infinity:latest v2 --model-id nomic-ai/nomic-embed-text-v1 --port 7997` You can then hook this up to your Chat UI instance with the following configuration. ```ini TEXT_EMBEDDING_MODELS=`[ { "name": "nomic-ai/nomic-embed-text-v1", "displayName": "nomic-ai/nomic-embed-text-v1", "chunkCharLength": 512, "model": { "name": "nomic-ai/nomic-embed-text-v1" }, "endpoints": [ { "type": "openai", "url": "https://127.0.0.1:7997/embeddings" } ] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration
hf_public_repos/chat-ui/docs/source/configuration/models/multimodal.md
# Multimodal We currently support [IDEFICS](https://huggingface.co/blog/idefics) (hosted on [TGI](./providers/tgi)), OpenAI and Anthropic Claude 3 as multimodal models. You can enable it by setting `multimodal: true` in your `MODELS` configuration. For IDEFICS, you must have a [PRO HF Api token](https://huggingface.co/settings/tokens). For OpenAI, see the [OpenAI section](./providers/openai). For Anthropic, see the [Anthropic section](./providers/anthropic). ```ini MODELS=`[ { "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:"] } } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration
hf_public_repos/chat-ui/docs/source/configuration/models/overview.md
# Models Overview 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 : ```ini 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. ## Chat Prompt Template 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). The templating language used is [Handlebars](https://www.npmjs.com/package/handlebars). ```handlebars {{preprompt}} {{#each messages}} {{#ifUser}}{{@root.userMessageToken}}{{content}}{{@root.userMessageEndToken}}{{/ifUser}} {{#ifAssistant }}{{@root.assistantMessageToken}}{{content}}{{@root.assistantMessageEndToken}}{{/ifAssistant}} {{/each}} {{assistantMessageToken}} ``` ## 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`. ```ini "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. ```ini "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 under `TEXT_EMBEDDING_MODELS`, (currently used when web searching). By default it will use the first embedding model, but it can be changed with the field `embeddingModel`: ```ini 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": [ ... ] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration
hf_public_repos/chat-ui/docs/source/configuration/models/tools.md
# Tools Tool calling instructs the model to generate an output matching a user-defined schema, which may be parsed for invoking external tools. The model simply chooses the tools and their parameters. Currently, only `TGI` and `Cohere` with `Command R+` are supported. <div class="flex justify-center"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/tools-light.png" height="auto"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/tools-dark.png" height="auto"/> </div> ## TGI Configuration A custom tokenizer is required for prompting the model for generating tool calls, as well as prompting with the results. The expected format for these tools and the resulting tool calls are hard coded for TGI, so it's likely that only the following configuration will work: ```ini MODELS=`[ { "name" : "CohereForAI/c4ai-command-r-plus", "displayName": "Command R+", "description": "Command R+ is Cohere's latest LLM and is the first open weight model to beat GPT4 in the Chatbot Arena!", "tools": true, "tokenizer": "Xenova/c4ai-command-r-v01-tokenizer", "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 } } ]` ``` ## Cohere Configuration The Cohere provider supports the endpoint native method of tool calling. Refer to the `endpoints/cohere` for implementation details. ```ini MODELS=`[ { "name": "command-r-plus", "displayName": "Command R+", "description": "Command R+ is Cohere's latest LLM and is the first open weight model to beat GPT4 in the Chatbot Arena!", "tools": true, "websiteUrl": "https://docs.cohere.com/docs/command-r-plus", "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/cohere-logo.png", "endpoints": [{ "type": "cohere", "apiKey": "YOUR_API_KEY" }] } ]` ``` ## Adding Tools Tool implementations are placed in `src/lib/server/tools`, with helpers available for easy integration with HuggingFace Zero GPU spaces. In the future, there may be an OpenAPI interface for adding tools. ## Adding Support for Additional Models The TGI implementation uses a custom tokenizer and hard coded schema for supporting tools. The Cohere implementation, on the other hand, uses the native support in the SDK to emit tool calls. This is the recommended way to add support for more models. Please see the `endpoints/cohere` section of the code for implementation details.
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/cloudflare.md
# Cloudflare | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | You may 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). ```ini MODELS=`[ { "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" --> } ] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/llamacpp.md
# Llama.cpp | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | Chat UI supports the llama.cpp API server directly without the need for an adapter. You can do this using the `llamacpp` endpoint type. If you want to run Chat UI with llama.cpp, you can do the following, using [microsoft/Phi-3-mini-4k-instruct-gguf](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) as an example model: ```bash # install llama.cpp brew install llama.cpp # start llama.cpp server llama-server --hf-repo microsoft/Phi-3-mini-4k-instruct-gguf --hf-file Phi-3-mini-4k-instruct-q4.gguf -c 4096 ``` _note: you can swap the `hf-repo` and `hf-file` with your fav GGUF on the [Hub](https://huggingface.co/models?library=gguf). For example: `--hf-repo TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF` for [this repo](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF) & `--hf-file tinyllama-1.1b-chat-v1.0.Q4_0.gguf` for [this file](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/blob/main/tinyllama-1.1b-chat-v1.0.Q4_0.gguf)._ A local LLaMA.cpp HTTP Server will start on `http://localhost:8080` (to change the port or any other default options, please find [LLaMA.cpp HTTP Server readme](https://github.com/ggerganov/llama.cpp/tree/master/examples/server)). Add the following to your `.env.local`: ```ini MODELS=`[ { "name": "Local microsoft/Phi-3-mini-4k-instruct-gguf", "tokenizer": "microsoft/Phi-3-mini-4k-instruct-gguf", "preprompt": "", "chatPromptTemplate": "<s>{{preprompt}}{{#each messages}}{{#ifUser}}<|user|>\n{{content}}<|end|>\n<|assistant|>\n{{/ifUser}}{{#ifAssistant}}{{content}}<|end|>\n{{/ifAssistant}}{{/each}}", "parameters": { "stop": ["<|end|>", "<|endoftext|>", "<|assistant|>"], "temperature": 0.7, "max_new_tokens": 1024, "truncate": 3071 }, "endpoints": [{ "type" : "llamacpp", "baseURL": "http://localhost:8080" }], }, ]` ``` <div class="flex justify-center"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/llamacpp-light.png" height="auto"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/chat-ui/llamacpp-dark.png" height="auto"/> </div>
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/aws.md
# Amazon Web Services (AWS) | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | You may specify your Amazon SageMaker instance as an endpoint for Chat UI: ```ini MODELS=`[{ "name": "your-model", "displayName": "Your Model", "description": "Your description", "parameters": { "max_new_tokens": 4096 }, "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.
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/anthropic.md
# Anthropic | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | Yes | We also support Anthropic models (including multimodal ones via `multmodal: true`) through the official SDK. You may provide your API key via the `ANTHROPIC_API_KEY` env variable, or alternatively, through the `endpoints.apiKey` as per the following example. ```ini MODELS=`[ { "name": "claude-3-haiku-20240307", "displayName": "Claude 3 Haiku", "description": "Fastest and most compact model for near-instant responsiveness", "multimodal": true, "parameters": { "max_new_tokens": 4096, }, "endpoints": [ { "type": "anthropic", // optionals "apiKey": "sk-ant-...", "baseURL": "https://api.anthropic.com", "defaultHeaders": {}, "defaultQuery": {} } ] }, { "name": "claude-3-sonnet-20240229", "displayName": "Claude 3 Sonnet", "description": "Ideal balance of intelligence and speed", "multimodal": true, "parameters": { "max_new_tokens": 4096, }, "endpoints": [ { "type": "anthropic", // optionals "apiKey": "sk-ant-...", "baseURL": "https://api.anthropic.com", "defaultHeaders": {}, "defaultQuery": {} } ] }, { "name": "claude-3-opus-20240229", "displayName": "Claude 3 Opus", "description": "Most powerful model for highly complex tasks", "multimodal": true, "parameters": { "max_new_tokens": 4096 }, "endpoints": [ { "type": "anthropic", // optionals "apiKey": "sk-ant-...", "baseURL": "https://api.anthropic.com", "defaultHeaders": {}, "defaultQuery": {} } ] } ]` ``` ## VertexAI We also support using Anthropic models running on Vertex AI. Authentication is done using Google Application Default Credentials. Project ID can be provided through the `endpoints.projectId` as per the following example: ```ini MODELS=`[ { "name": "claude-3-haiku@20240307", "displayName": "Claude 3 Haiku", "description": "Fastest, most compact model for near-instant responsiveness", "multimodal": true, "parameters": { "max_new_tokens": 4096 }, "endpoints": [ { "type": "anthropic-vertex", "region": "us-central1", "projectId": "gcp-project-id", // optionals "defaultHeaders": {}, "defaultQuery": {} } ] }, { "name": "claude-3-sonnet@20240229", "displayName": "Claude 3 Sonnet", "description": "Ideal balance of intelligence and speed", "multimodal": true, "parameters": { "max_new_tokens": 4096, }, "endpoints": [ { "type": "anthropic-vertex", "region": "us-central1", "projectId": "gcp-project-id", // optionals "defaultHeaders": {}, "defaultQuery": {} } ] }, ]` ```
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/cohere.md
# Cohere | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | Yes | | [Multimodal](../multimodal) | No | You may 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. ```ini MODELS=`[ { "name": "command-r-plus", "displayName": "Command R+", "tools": true, "endpoints": [{ "type": "cohere", <!-- optionally specify these, or use COHERE_API_TOKEN "apiKey": "your-api-token" --> }] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/ollama.md
# Ollama | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | We also support the Ollama inference server. Spin up a model with ```bash ollama run mistral ``` Then specify the endpoints like so: ```ini 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" } ] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/google.md
# Google | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | 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: ```ini 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: ```ini MODELS=`[ { "name": "gemini-1.5-pro", "displayName": "Vertex Gemini Pro 1.5", "endpoints" : [{ "type": "vertex", "project": "abc-xyz", "location": "europe-west3", "extraBody": { "model_version": "gemini-1.5-pro-002", }, // Optional "safetyThreshold": "BLOCK_MEDIUM_AND_ABOVE", "apiEndpoint": "", // alternative api endpoint url, "tools": [{ "googleSearchRetrieval": { "disableAttribution": true } }] }] } ]` ``` ## GenAI Or use the Gemini API API provider [from](https://github.com/google-gemini/generative-ai-js#readme): Make sure that you have an API key from Google Cloud Platform. To get an API key, follow the instructions [here](https://ai.google.dev/gemini-api/docs/api-key). You can either specify them directly in your `.env.local` using the `GOOGLE_GENAI_API_KEY` variables, or you can set them directly in the endpoint config. You can find the list of models available [here](https://ai.google.dev/gemini-api/docs/models/gemini), and experimental models available [here](https://ai.google.dev/gemini-api/docs/models/experimental-models). ```ini MODELS=`[ { "name": "gemini-1.5-flash", "displayName": "Gemini Flash 1.5", "multimodal": true, "endpoints": [ { "type": "genai", // Optional "apiKey": "abc...xyz" "safetyThreshold": "BLOCK_MEDIUM_AND_ABOVE", } ] }, { "name": "gemini-1.5-pro", "displayName": "Gemini Pro 1.5", "multimodal": false, "endpoints": [ { "type": "genai", // Optional "apiKey": "abc...xyz" } ] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/langserve.md
# LangServe | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | LangChain applications that are deployed using LangServe can be called with the following config: ```ini MODELS=`[ { "name": "summarization-chain", "displayName": "Summarization Chain" "endpoints" : [{ "type": "langserve", "url" : "http://127.0.0.1:8100", }] } ]` ```
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/openai.md
# OpenAI | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | No | | [Multimodal](../multimodal) | No | 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) and [vllm](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). 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 `/chat/completions`, change to `endpoint.completion` to `completions` to use the `/completions` endpoint. ```ini 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: ```ini OPENAI_API_KEY=#your openai api key here MODELS=`[{ "name": "gpt-4", "displayName": "GPT 4", "endpoints" : [{ "type": "openai", "apiKey": "or your openai api key here" }] },{ "name": "gpt-3.5-turbo", "displayName": "GPT 3.5 Turbo", "endpoints" : [{ "type": "openai", "apiKey": "or your openai api key here" }] }]` ``` 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: ```ini 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: ```ini 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" } } ] }]` ``` ## DeepInfra 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. ```ini 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" } ] }]` ``` ## Other Some other providers and their `baseURL` for reference. [Groq](https://groq.com/): https://api.groq.com/openai/v1 [Fireworks](https://fireworks.ai/): https://api.fireworks.ai/inference/v1
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hf_public_repos/chat-ui/docs/source/configuration/models
hf_public_repos/chat-ui/docs/source/configuration/models/providers/tgi.md
# Text Generation Inference (TGI) | Feature | Available | | --------------------------- | --------- | | [Tools](../tools) | Yes\* | | [Multimodal](../multimodal) | Yes\* | \* Tools are only supported with the Cohere Command R+ model with the Xenova tokenizers. Please see the [Tools](../tools) section. \* Multimodal is only supported with the IDEFICS model. Please see the [Multimodal](../multimodal) section. By default, if `endpoints` are left unspecified, Chat UI will look for the model on the hosted Hugging Face inference API using the model name, and use your `HF_TOKEN`. Refer to the [overview](/configuration/models/overview) for more information about model configuration. ```ini 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?" } ] } ]` ``` ## 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`. ```ini MODELS=`[{ "name": "your-model-name", "displayName": "Your Model Name", ... other model config "endpoints": [{ "type" : "tgi", "url": "https://HOST:PORT", }] }] ```
0
hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/developing/architecture.md
# Architecture This document discusses the high level overview of the Chat UI codebase. If you're looking to contribute or just want to understand how the codebase works, this is the place for you! ## Overview Chat UI provides a simple interface connecting LLMs to external information and tools. The project uses [MongoDB](https://www.mongodb.com/) and [SvelteKit](https://kit.svelte.dev/) with [Tailwind](https://tailwindcss.com/). ## Code Map This section discusses various modules of the codebase briefly. The headings are not paths since the codebase structure may change. ### `routes` Provides all of the routes rendered with SSR via SvelteKit. The majority of backend and frontend logic can be found here, with some modules being pulled out into `lib` for the client and `lib/server` for the server. ### `textGeneration` Provides a standard interface for most chat features such as model output, web search, assistants and tools. Outputs `MessageUpdate`s which provide fine-grained updates on the request status such as new tokens and web search results. ### `endpoints`/`embeddingEndpoints` Provides a common streaming interface for many third party LLM and embedding providers. ### `websearch` Implements web search querying and RAG. See the [Web Search](../configuration/web-search) section for more information. ### `tools` Provides a common interface for external tools called by LLMs. See the [Tools](../configuration/models/tools) section for more information ### `migrations` Includes all MongoDB migrations for maintaining backwards compatibility across schema changes. Any changes to the schema must include a migration
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/developing/copy-huggingchat.md
# Copy HuggingChat The config file for HuggingChat is stored in the `chart/env/prod.yaml` file. It is the source of truth for the environment variables used for our CI/CD pipeline. For HuggingChat, as we need to customize the app color, as well as the base path, we build a custom docker image. You can find the workflow here. <Tip> If you want to make changes to the model config used in production for HuggingChat, you should do so against `chart/env/prod.yaml`. </Tip> ### 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 ```ini MONGODB_URL=<link to your mongo DB from step 4> HF_TOKEN=<your HF token from step 2> OPENID_CONFIG=`{ PROVIDER_URL: "https://huggingface.co", CLIENT_ID: "<your client ID from step 1>", CLIENT_SECRET: "<your client secret from step 1>", }` SERPER_API_KEY=<your serper API key from step 3> MESSAGES_BEFORE_LOGIN=<can be any numerical value, or set to 0 to require login> ``` You can then run `npm run updateLocalEnv` in the root of chat-ui. This will create a `.env.local` file which combines the `chart/env/prod.yaml` and the `.env.SECRET_CONFIG` file. You can then run `npm run dev` to start your local instance of HuggingChat. ### Populate database <Tip warning={true}> 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. </Tip> 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.
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/installation/local.md
# Running Locally You may start an instance locally for non-production use cases. For production use cases, please see the other installation options. ## Configuration 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. Start by creating a `.env.local` file in the root of the repository as per the [configuration section](../configuration/overview). The bare minimum config you need to get Chat UI to run locally is the following: ```ini MONGODB_URL=<the URL to your MongoDB instance> HF_TOKEN=<your access token> # find your token at hf.co/settings/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 with persistence: ```bash docker run -d -p 27017:27017 -v mongo-chat-ui:/data --name mongo-chat-ui 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. ## Starting the server ```bash npm ci # install dependencies npm run build # build the project npm run preview -- --open # start the server with & open your instance at http://localhost:4173 ```
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/installation/docker.md
# Running on Docker Pre-built docker images are provided with and without MongoDB built in. Refer to the [configuration section](../configuration/overview) for env variables that must be provided. We recommend using the `--env-file` option to avoid leaking secrets into your shell history. ```bash # Without built-in DB docker run -p 3000:3000 --env-file .env.local --name chat-ui ghcr.io/huggingface/chat-ui # With built-in DB docker run -p 3000:3000 --env-file .env.local -v chat-ui:/data --name chat-ui ghcr.io/huggingface/chat-ui-db ```
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/installation/spaces.md
# Running on Huggingface Spaces 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) 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).
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hf_public_repos/chat-ui/docs/source
hf_public_repos/chat-ui/docs/source/installation/helm.md
# Helm <Tip warning={true}> **We highly discourage using the chart**. The Helm chart is a work in progress and should be considered unstable. Breaking changes to the chart may be pushed without migration guides or notice. Contributions welcome! </Tip> For installation on Kubernetes, you may use the helm chart in `/chart`. Please note that no chart repository has been setup, so you'll need to clone the repository and install the chart by path. The production values may be found at `chart/env/prod.yaml`. **Example values.yaml** ```yaml replicas: 1 domain: example.com service: type: ClusterIP resources: requests: cpu: 100m memory: 2Gi limits: # Recommended to use large limits when web search is enabled cpu: "4" memory: 6Gi envVars: MONGODB_URL: mongodb://chat-ui-mongo:27017 # Ensure that your values.yaml will not leak anywhere # PRs welcome for a chart rework with envFrom support! HF_TOKEN: secret_token ```
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/.husky/pre-commit
set -e npx lint-staged --config ./.husky/lint-stage-config.js
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/.husky/lint-stage-config.js
export default { "*.{js,jsx,ts,tsx}": ["prettier --write", "eslint --fix", "eslint"], "*.json": ["prettier --write"], };
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/src/ambient.d.ts
declare module "*.ttf" { const value: ArrayBuffer; export default value; }
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/src/app.d.ts
/// <reference types="@sveltejs/kit" /> /// <reference types="unplugin-icons/types/svelte" /> import type { User } from "$lib/types/User"; // See https://kit.svelte.dev/docs/types#app // for information about these interfaces declare global { namespace App { // interface Error {} interface Locals { sessionId: string; user?: User & { logoutDisabled?: boolean }; } interface Error { message: string; errorId?: ReturnType<typeof crypto.randomUUID>; } // interface PageData {} // interface Platform {} } } export {};
0
hf_public_repos/chat-ui
hf_public_repos/chat-ui/src/app.html
<!DOCTYPE html> <html lang="en" class="h-full"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="theme-color" content="rgb(249, 250, 251)" /> <script> if ( localStorage.theme === "dark" || (!("theme" in localStorage) && window.matchMedia("(prefers-color-scheme: dark)").matches) ) { document.documentElement.classList.add("dark"); document .querySelector('meta[name="theme-color"]') .setAttribute("content", "rgb(26, 36, 50)"); } // For some reason, Sveltekit doesn't let us load env variables from .env here, so we load it from hooks.server.ts window.gaId = "%gaId%"; </script> %sveltekit.head% </head> <body data-sveltekit-preload-data="hover" class="h-full dark:bg-gray-900"> <div id="app" class="contents h-full">%sveltekit.body%</div> <!-- Google Tag Manager --> <script> if (window.gaId) { const script = document.createElement("script"); script.src = "https://www.googletagmanager.com/gtag/js?id=" + window.gaId; script.async = true; document.head.appendChild(script); window.dataLayer = window.dataLayer || []; function gtag() { dataLayer.push(arguments); } gtag("js", new Date()); /// ^ See https://developers.google.com/tag-platform/gtagjs/install gtag("config", window.gaId); gtag("consent", "default", { ad_storage: "denied", analytics_storage: "denied" }); /// ^ See https://developers.google.com/tag-platform/gtagjs/reference#consent /// TODO: ask the user for their consent and update this with gtag('consent', 'update') } </script> </body> </html>
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hf_public_repos/chat-ui
hf_public_repos/chat-ui/src/hooks.server.ts
import { env } from "$env/dynamic/private"; import { env as envPublic } from "$env/dynamic/public"; import type { Handle, HandleServerError } from "@sveltejs/kit"; import { collections } from "$lib/server/database"; import { base } from "$app/paths"; import { findUser, refreshSessionCookie, requiresUser } from "$lib/server/auth"; import { ERROR_MESSAGES } from "$lib/stores/errors"; import { sha256 } from "$lib/utils/sha256"; import { addWeeks } from "date-fns"; import { checkAndRunMigrations } from "$lib/migrations/migrations"; import { building } from "$app/environment"; import { logger } from "$lib/server/logger"; import { AbortedGenerations } from "$lib/server/abortedGenerations"; import { MetricsServer } from "$lib/server/metrics"; import { initExitHandler } from "$lib/server/exitHandler"; import { ObjectId } from "mongodb"; import { refreshAssistantsCounts } from "$lib/jobs/refresh-assistants-counts"; import { refreshConversationStats } from "$lib/jobs/refresh-conversation-stats"; // TODO: move this code on a started server hook, instead of using a "building" flag if (!building) { // Set HF_TOKEN as a process variable for Transformers.JS to see it process.env.HF_TOKEN ??= env.HF_TOKEN; logger.info("Starting server..."); initExitHandler(); checkAndRunMigrations(); if (env.ENABLE_ASSISTANTS) { refreshAssistantsCounts(); } refreshConversationStats(); // Init metrics server MetricsServer.getInstance(); // Init AbortedGenerations refresh process AbortedGenerations.getInstance(); } export const handleError: HandleServerError = async ({ error, event, status, message }) => { // handle 404 if (building) { throw error; } if (event.route.id === null) { return { message: `Page ${event.url.pathname} not found`, }; } const errorId = crypto.randomUUID(); logger.error({ locals: event.locals, url: event.request.url, params: event.params, request: event.request, message, error, errorId, status, stack: error instanceof Error ? error.stack : undefined, }); return { message: "An error occurred", errorId, }; }; export const handle: Handle = async ({ event, resolve }) => { logger.debug({ locals: event.locals, url: event.url.pathname, params: event.params, request: event.request, }); if (event.url.pathname.startsWith(`${base}/api/`) && env.EXPOSE_API !== "true") { return new Response("API is disabled", { status: 403 }); } function errorResponse(status: number, message: string) { const sendJson = event.request.headers.get("accept")?.includes("application/json") || event.request.headers.get("content-type")?.includes("application/json"); return new Response(sendJson ? JSON.stringify({ error: message }) : message, { status, headers: { "content-type": sendJson ? "application/json" : "text/plain", }, }); } if (event.url.pathname.startsWith(`${base}/admin/`) || event.url.pathname === `${base}/admin`) { const ADMIN_SECRET = env.ADMIN_API_SECRET || env.PARQUET_EXPORT_SECRET; if (!ADMIN_SECRET) { return errorResponse(500, "Admin API is not configured"); } if (event.request.headers.get("Authorization") !== `Bearer ${ADMIN_SECRET}`) { return errorResponse(401, "Unauthorized"); } } const token = event.cookies.get(env.COOKIE_NAME); // if the trusted email header is set we use it to get the user email const email = env.TRUSTED_EMAIL_HEADER ? event.request.headers.get(env.TRUSTED_EMAIL_HEADER) : null; let secretSessionId: string | null = null; let sessionId: string | null = null; if (email) { secretSessionId = sessionId = await sha256(email); event.locals.user = { // generate id based on email _id: new ObjectId(sessionId.slice(0, 24)), name: email, email, createdAt: new Date(), updatedAt: new Date(), hfUserId: email, avatarUrl: "", logoutDisabled: true, }; } else if (token) { secretSessionId = token; sessionId = await sha256(token); const user = await findUser(sessionId); if (user) { event.locals.user = user; } } else if (event.url.pathname.startsWith(`${base}/api/`) && env.USE_HF_TOKEN_IN_API === "true") { // if the request goes to the API and no user is available in the header // check if a bearer token is available in the Authorization header const authorization = event.request.headers.get("Authorization"); if (authorization && authorization.startsWith("Bearer ")) { const token = authorization.slice(7); const hash = await sha256(token); sessionId = secretSessionId = hash; // check if the hash is in the DB and get the user // else check against https://huggingface.co/api/whoami-v2 const cacheHit = await collections.tokenCaches.findOne({ tokenHash: hash }); if (cacheHit) { const user = await collections.users.findOne({ hfUserId: cacheHit.userId }); if (!user) { return errorResponse(500, "User not found"); } event.locals.user = user; } else { const response = await fetch("https://huggingface.co/api/whoami-v2", { headers: { Authorization: `Bearer ${token}`, }, }); if (!response.ok) { return errorResponse(401, "Unauthorized"); } const data = await response.json(); const user = await collections.users.findOne({ hfUserId: data.id }); if (!user) { return errorResponse(500, "User not found"); } await collections.tokenCaches.insertOne({ tokenHash: hash, userId: data.id, createdAt: new Date(), updatedAt: new Date(), }); event.locals.user = user; } } } if (!sessionId || !secretSessionId) { secretSessionId = crypto.randomUUID(); sessionId = await sha256(secretSessionId); if (await collections.sessions.findOne({ sessionId })) { return errorResponse(500, "Session ID collision"); } } event.locals.sessionId = sessionId; // CSRF protection const requestContentType = event.request.headers.get("content-type")?.split(";")[0] ?? ""; /** https://developer.mozilla.org/en-US/docs/Web/HTML/Element/form#attr-enctype */ const nativeFormContentTypes = [ "multipart/form-data", "application/x-www-form-urlencoded", "text/plain", ]; if (event.request.method === "POST") { if (nativeFormContentTypes.includes(requestContentType)) { const origin = event.request.headers.get("origin"); if (!origin) { return errorResponse(403, "Non-JSON form requests need to have an origin"); } const validOrigins = [ new URL(event.request.url).host, ...(envPublic.PUBLIC_ORIGIN ? [new URL(envPublic.PUBLIC_ORIGIN).host] : []), ]; if (!validOrigins.includes(new URL(origin).host)) { return errorResponse(403, "Invalid referer for POST request"); } } } if (event.request.method === "POST") { // if the request is a POST request we refresh the cookie refreshSessionCookie(event.cookies, secretSessionId); await collections.sessions.updateOne( { sessionId }, { $set: { updatedAt: new Date(), expiresAt: addWeeks(new Date(), 2) } } ); } if ( !event.url.pathname.startsWith(`${base}/login`) && !event.url.pathname.startsWith(`${base}/admin`) && !event.url.pathname.startsWith(`${base}/settings`) && !["GET", "OPTIONS", "HEAD"].includes(event.request.method) ) { if ( !event.locals.user && requiresUser && !((env.MESSAGES_BEFORE_LOGIN ? parseInt(env.MESSAGES_BEFORE_LOGIN) : 0) > 0) ) { return errorResponse(401, ERROR_MESSAGES.authOnly); } // if login is not required and the call is not from /settings and we display the ethics modal with PUBLIC_APP_DISCLAIMER // we check if the user has accepted the ethics modal first. // If login is required, `ethicsModalAcceptedAt` is already true at this point, so do not pass this condition. This saves a DB call. if ( !requiresUser && !event.url.pathname.startsWith(`${base}/settings`) && envPublic.PUBLIC_APP_DISCLAIMER === "1" ) { const hasAcceptedEthicsModal = await collections.settings.countDocuments({ sessionId: event.locals.sessionId, ethicsModalAcceptedAt: { $exists: true }, }); if (!hasAcceptedEthicsModal) { return errorResponse(405, "You need to accept the welcome modal first"); } } } let replaced = false; const response = await resolve(event, { transformPageChunk: (chunk) => { // For some reason, Sveltekit doesn't let us load env variables from .env in the app.html template if (replaced || !chunk.html.includes("%gaId%")) { return chunk.html; } replaced = true; return chunk.html.replace("%gaId%", envPublic.PUBLIC_GOOGLE_ANALYTICS_ID); }, }); // Add CSP header to disallow framing if ALLOW_IFRAME is not "true" if (env.ALLOW_IFRAME !== "true") { response.headers.append("Content-Security-Policy", "frame-ancestors 'none';"); } return response; };
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hf_public_repos/chat-ui/src
hf_public_repos/chat-ui/src/styles/highlight-js.css
@import "highlight.js/styles/atom-one-dark";
0
hf_public_repos/chat-ui/src
hf_public_repos/chat-ui/src/styles/main.css
@import "./highlight-js.css"; @tailwind base; @tailwind components; @tailwind utilities; @layer components { .btn { @apply inline-flex flex-shrink-0 cursor-pointer select-none items-center justify-center whitespace-nowrap outline-none transition-all focus:ring disabled:cursor-default; } } @layer utilities { .scrollbar-custom { @apply scrollbar-thin scrollbar-track-transparent scrollbar-thumb-black/10 scrollbar-thumb-rounded-full scrollbar-w-1 hover:scrollbar-thumb-black/20 dark:scrollbar-thumb-white/10 dark:hover:scrollbar-thumb-white/20; } }
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hf_public_repos/chat-ui/src
hf_public_repos/chat-ui/src/routes/+layout.svelte
<script lang="ts"> import "../styles/main.css"; import { onDestroy, onMount } from "svelte"; import { goto } from "$app/navigation"; import { base } from "$app/paths"; import { page } from "$app/stores"; import { env as envPublic } from "$env/dynamic/public"; import { error } from "$lib/stores/errors"; import { createSettingsStore } from "$lib/stores/settings"; import { shareConversation } from "$lib/shareConversation"; import Toast from "$lib/components/Toast.svelte"; import NavMenu from "$lib/components/NavMenu.svelte"; import MobileNav from "$lib/components/MobileNav.svelte"; import titleUpdate from "$lib/stores/titleUpdate"; import DisclaimerModal from "$lib/components/DisclaimerModal.svelte"; import ExpandNavigation from "$lib/components/ExpandNavigation.svelte"; export let data; let isNavOpen = false; let isNavCollapsed = false; let errorToastTimeout: ReturnType<typeof setTimeout>; let currentError: string | null; async function onError() { // If a new different error comes, wait for the current error to hide first if ($error && currentError && $error !== currentError) { clearTimeout(errorToastTimeout); currentError = null; await new Promise((resolve) => setTimeout(resolve, 300)); } currentError = $error; errorToastTimeout = setTimeout(() => { $error = null; currentError = null; }, 3000); } async function deleteConversation(id: string) { try { const res = await fetch(`${base}/conversation/${id}`, { method: "DELETE", headers: { "Content-Type": "application/json", }, }); if (!res.ok) { $error = "Error while deleting conversation, try again."; return; } data.conversations = data.conversations.filter((conv) => conv.id !== id); if ($page.params.id === id) { await goto(`${base}/`, { invalidateAll: true }); } } catch (err) { console.error(err); $error = String(err); } } async function editConversationTitle(id: string, title: string) { try { const res = await fetch(`${base}/conversation/${id}`, { method: "PATCH", headers: { "Content-Type": "application/json", }, body: JSON.stringify({ title }), }); if (!res.ok) { $error = "Error while editing title, try again."; return; } data.conversations = data.conversations.map((conv) => conv.id === id ? { ...conv, title } : conv ); } catch (err) { console.error(err); $error = String(err); } } onDestroy(() => { clearTimeout(errorToastTimeout); }); $: if ($error) onError(); $: if ($titleUpdate) { const convIdx = data.conversations.findIndex(({ id }) => id === $titleUpdate?.convId); if (convIdx != -1) { data.conversations[convIdx].title = $titleUpdate?.title ?? data.conversations[convIdx].title; } $titleUpdate = null; } const settings = createSettingsStore(data.settings); onMount(async () => { if ($page.url.searchParams.has("model")) { await settings .instantSet({ activeModel: $page.url.searchParams.get("model") ?? $settings.activeModel, }) .then(async () => { const query = new URLSearchParams($page.url.searchParams.toString()); query.delete("model"); await goto(`${base}/?${query.toString()}`, { invalidateAll: true, }); }); } if ($page.url.searchParams.has("tools")) { const tools = $page.url.searchParams.get("tools")?.split(","); await settings .instantSet({ tools: [...($settings.tools ?? []), ...(tools ?? [])], }) .then(async () => { const query = new URLSearchParams($page.url.searchParams.toString()); query.delete("tools"); await goto(`${base}/?${query.toString()}`, { invalidateAll: true, }); }); } }); $: mobileNavTitle = ["/models", "/assistants", "/privacy"].includes($page.route.id ?? "") ? "" : data.conversations.find((conv) => conv.id === $page.params.id)?.title; $: showDisclaimer = !$settings.ethicsModalAccepted && $page.url.pathname !== `${base}/privacy` && envPublic.PUBLIC_APP_DISCLAIMER === "1" && !($page.data.shared === true); </script> <svelte:head> <title>{envPublic.PUBLIC_APP_NAME}</title> <meta name="description" content="The first open source alternative to ChatGPT. 💪" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <!-- use those meta tags everywhere except on the share assistant page --> <!-- feel free to refacto if there's a better way --> {#if !$page.url.pathname.includes("/assistant/") && $page.route.id !== "/assistants" && !$page.url.pathname.includes("/models/") && !$page.url.pathname.includes("/tools")} <meta property="og:title" content={envPublic.PUBLIC_APP_NAME} /> <meta property="og:type" content="website" /> <meta property="og:url" content="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}" /> <meta property="og:image" content="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/{envPublic.PUBLIC_APP_ASSETS}/thumbnail.png" /> <meta property="og:description" content={envPublic.PUBLIC_APP_DESCRIPTION} /> {/if} <link rel="icon" href="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/{envPublic.PUBLIC_APP_ASSETS}/favicon.ico" sizes="32x32" /> <link rel="icon" href="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/{envPublic.PUBLIC_APP_ASSETS}/icon.svg" type="image/svg+xml" /> <link rel="apple-touch-icon" href="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/{envPublic.PUBLIC_APP_ASSETS}/apple-touch-icon.png" /> <link rel="manifest" href="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/{envPublic.PUBLIC_APP_ASSETS}/manifest.json" /> {#if envPublic.PUBLIC_PLAUSIBLE_SCRIPT_URL && envPublic.PUBLIC_ORIGIN} <script defer data-domain={new URL(envPublic.PUBLIC_ORIGIN).hostname} src={envPublic.PUBLIC_PLAUSIBLE_SCRIPT_URL} ></script> {/if} {#if envPublic.PUBLIC_APPLE_APP_ID} <meta name="apple-itunes-app" content={`app-id=${envPublic.PUBLIC_APPLE_APP_ID}`} /> {/if} </svelte:head> {#if showDisclaimer} <DisclaimerModal on:close={() => ($settings.ethicsModalAccepted = true)} /> {/if} <ExpandNavigation isCollapsed={isNavCollapsed} on:click={() => (isNavCollapsed = !isNavCollapsed)} classNames="absolute inset-y-0 z-10 my-auto {!isNavCollapsed ? 'left-[290px]' : 'left-0'} *:transition-transform" /> <div class="grid h-full w-screen grid-cols-1 grid-rows-[auto,1fr] overflow-hidden text-smd {!isNavCollapsed ? 'md:grid-cols-[290px,1fr]' : 'md:grid-cols-[0px,1fr]'} transition-[300ms] [transition-property:grid-template-columns] dark:text-gray-300 md:grid-rows-[1fr]" > <MobileNav isOpen={isNavOpen} on:toggle={(ev) => (isNavOpen = ev.detail)} title={mobileNavTitle}> <NavMenu conversations={data.conversations} user={data.user} canLogin={data.user === undefined && data.loginEnabled} on:shareConversation={(ev) => shareConversation(ev.detail.id, ev.detail.title)} on:deleteConversation={(ev) => deleteConversation(ev.detail)} on:editConversationTitle={(ev) => editConversationTitle(ev.detail.id, ev.detail.title)} /> </MobileNav> <nav class=" grid max-h-screen grid-cols-1 grid-rows-[auto,1fr,auto] overflow-hidden *:w-[290px] max-md:hidden" > <NavMenu conversations={data.conversations} user={data.user} canLogin={data.user === undefined && data.loginEnabled} on:shareConversation={(ev) => shareConversation(ev.detail.id, ev.detail.title)} on:deleteConversation={(ev) => deleteConversation(ev.detail)} on:editConversationTitle={(ev) => editConversationTitle(ev.detail.id, ev.detail.title)} /> </nav> {#if currentError} <Toast message={currentError} /> {/if} <slot /> </div>
0
hf_public_repos/chat-ui/src
hf_public_repos/chat-ui/src/routes/+page.svelte
<script lang="ts"> import { goto } from "$app/navigation"; import { base } from "$app/paths"; import { page } from "$app/stores"; import { env as envPublic } from "$env/dynamic/public"; import ChatWindow from "$lib/components/chat/ChatWindow.svelte"; import { ERROR_MESSAGES, error } from "$lib/stores/errors"; import { pendingMessage } from "$lib/stores/pendingMessage"; import { useSettingsStore } from "$lib/stores/settings.js"; import { findCurrentModel } from "$lib/utils/models"; import { onMount } from "svelte"; export let data; let loading = false; let files: File[] = []; const settings = useSettingsStore(); async function createConversation(message: string) { try { loading = true; // check if $settings.activeModel is a valid model // else check if it's an assistant, and use that model // else use the first model const validModels = data.models.map((model) => model.id); let model; if (validModels.includes($settings.activeModel)) { model = $settings.activeModel; } else { if (validModels.includes(data.assistant?.modelId)) { model = data.assistant?.modelId; } else { model = data.models[0].id; } } const res = await fetch(`${base}/conversation`, { method: "POST", headers: { "Content-Type": "application/json", }, body: JSON.stringify({ model, preprompt: $settings.customPrompts[$settings.activeModel], assistantId: data.assistant?._id, }), }); if (!res.ok) { const errorMessage = (await res.json()).message || ERROR_MESSAGES.default; error.set(errorMessage); console.error("Error while creating conversation: ", errorMessage); return; } const { conversationId } = await res.json(); // Ugly hack to use a store as temp storage, feel free to improve ^^ pendingMessage.set({ content: message, files, }); // invalidateAll to update list of conversations await goto(`${base}/conversation/${conversationId}`, { invalidateAll: true }); } catch (err) { error.set((err as Error).message || ERROR_MESSAGES.default); console.error(err); } finally { loading = false; } } onMount(() => { // check if there's a ?q query param with a message const query = $page.url.searchParams.get("q"); if (query) createConversation(query); }); $: currentModel = findCurrentModel( [...data.models, ...data.oldModels], !$settings.assistants.includes($settings.activeModel) ? $settings.activeModel : data.assistant?.modelId ); </script> <svelte:head> <title>{envPublic.PUBLIC_APP_NAME}</title> </svelte:head> <ChatWindow on:message={(ev) => createConversation(ev.detail)} {loading} assistant={data.assistant} {currentModel} models={data.models} bind:files />
0
hf_public_repos/chat-ui/src
hf_public_repos/chat-ui/src/routes/+error.svelte
<script lang="ts"> import { page } from "$app/stores"; </script> <div class="flex items-center justify-center bg-gradient-to-t from-gray-200 text-gray-800 dark:from-gray-700 dark:text-gray-300" > <div class="align-center -mt-24 flex flex-col justify-center rounded-xl border bg-white px-8 pb-2 pt-4 text-center dark:border-gray-700 dark:bg-gray-800" > <h1 class="mb-2 text-5xl font-semibold">{$page.status}</h1> <div class="-mx-8 my-2 h-px bg-gray-200 dark:bg-gray-700" /> <h2 class="max-w-sm text-lg">{$page.error?.message}</h2> {#if $page.error?.errorId} <div class="-mx-8 my-2 h-px bg-gray-200 dark:bg-gray-700" /> <pre class="max-w-sm whitespace-pre-wrap text-left font-mono text-xs">{$page.error .errorId}</pre> {/if} </div> </div>
0
hf_public_repos/chat-ui/src
hf_public_repos/chat-ui/src/routes/+layout.server.ts
import type { LayoutServerLoad } from "./$types"; import { collections } from "$lib/server/database"; import type { Conversation } from "$lib/types/Conversation"; import { UrlDependency } from "$lib/types/UrlDependency"; import { defaultModel, models, oldModels, validateModel } from "$lib/server/models"; import { authCondition, requiresUser } from "$lib/server/auth"; import { DEFAULT_SETTINGS } from "$lib/types/Settings"; import { env } from "$env/dynamic/private"; import { ObjectId } from "mongodb"; import type { ConvSidebar } from "$lib/types/ConvSidebar"; import { toolFromConfigs } from "$lib/server/tools"; import { MetricsServer } from "$lib/server/metrics"; import type { ToolFront, ToolInputFile } from "$lib/types/Tool"; import { ReviewStatus } from "$lib/types/Review"; export const load: LayoutServerLoad = async ({ locals, depends }) => { depends(UrlDependency.ConversationList); const settings = await collections.settings.findOne(authCondition(locals)); // If the active model in settings is not valid, set it to the default model. This can happen if model was disabled. if ( settings && !validateModel(models).safeParse(settings?.activeModel).success && !settings.assistants?.map((el) => el.toString())?.includes(settings?.activeModel) ) { settings.activeModel = defaultModel.id; await collections.settings.updateOne(authCondition(locals), { $set: { activeModel: defaultModel.id }, }); } // if the model is unlisted, set the active model to the default model if ( settings?.activeModel && models.find((m) => m.id === settings?.activeModel)?.unlisted === true ) { settings.activeModel = defaultModel.id; await collections.settings.updateOne(authCondition(locals), { $set: { activeModel: defaultModel.id }, }); } const enableAssistants = env.ENABLE_ASSISTANTS === "true"; const assistantActive = !models.map(({ id }) => id).includes(settings?.activeModel ?? ""); const assistant = assistantActive ? await collections.assistants.findOne({ _id: new ObjectId(settings?.activeModel), }) : null; const nConversations = await collections.conversations.countDocuments(authCondition(locals)); const conversations = nConversations === 0 ? Promise.resolve([]) : collections.conversations .find(authCondition(locals)) .sort({ updatedAt: -1 }) .project< Pick< Conversation, "title" | "model" | "_id" | "updatedAt" | "createdAt" | "assistantId" > >({ title: 1, model: 1, _id: 1, updatedAt: 1, createdAt: 1, assistantId: 1, }) .limit(300) .toArray(); const userAssistants = settings?.assistants?.map((assistantId) => assistantId.toString()) ?? []; const userAssistantsSet = new Set(userAssistants); const assistants = conversations.then((conversations) => collections.assistants .find({ _id: { $in: [ ...userAssistants.map((el) => new ObjectId(el)), ...(conversations.map((conv) => conv.assistantId).filter((el) => !!el) as ObjectId[]), ], }, }) .toArray() ); const messagesBeforeLogin = env.MESSAGES_BEFORE_LOGIN ? parseInt(env.MESSAGES_BEFORE_LOGIN) : 0; let loginRequired = false; if (requiresUser && !locals.user) { if (messagesBeforeLogin === 0) { loginRequired = true; } else if (nConversations >= messagesBeforeLogin) { loginRequired = true; } else { // get the number of messages where `from === "assistant"` across all conversations. const totalMessages = ( await collections.conversations .aggregate([ { $match: { ...authCondition(locals), "messages.from": "assistant" } }, { $project: { messages: 1 } }, { $limit: messagesBeforeLogin + 1 }, { $unwind: "$messages" }, { $match: { "messages.from": "assistant" } }, { $count: "messages" }, ]) .toArray() )[0]?.messages ?? 0; loginRequired = totalMessages >= messagesBeforeLogin; } } const toolUseDuration = (await MetricsServer.getMetrics().tool.toolUseDuration.get()).values; const configToolIds = toolFromConfigs.map((el) => el._id.toString()); let activeCommunityToolIds = (settings?.tools ?? []).filter( (key) => !configToolIds.includes(key) ); if (assistant) { activeCommunityToolIds = [...activeCommunityToolIds, ...(assistant.tools ?? [])]; } const communityTools = await collections.tools .find({ _id: { $in: activeCommunityToolIds.map((el) => new ObjectId(el)) } }) .toArray() .then((tools) => tools.map((tool) => ({ ...tool, isHidden: false, isOnByDefault: true, isLocked: true, })) ); return { nConversations, conversations: await conversations.then( async (convs) => await Promise.all( convs.map(async (conv) => { if (settings?.hideEmojiOnSidebar) { conv.title = conv.title.replace(/\p{Emoji}/gu, ""); } // remove invalid unicode and trim whitespaces conv.title = conv.title.replace(/\uFFFD/gu, "").trimStart(); let avatarUrl: string | undefined = undefined; if (conv.assistantId) { const hash = ( await collections.assistants.findOne({ _id: new ObjectId(conv.assistantId), }) )?.avatar; if (hash) { avatarUrl = `/settings/assistants/${conv.assistantId}/avatar.jpg?hash=${hash}`; } } return { id: conv._id.toString(), title: conv.title, model: conv.model ?? defaultModel, updatedAt: conv.updatedAt, assistantId: conv.assistantId?.toString(), avatarUrl, } satisfies ConvSidebar; }) ) ), settings: { searchEnabled: !!( env.SERPAPI_KEY || env.SERPER_API_KEY || env.SERPSTACK_API_KEY || env.SEARCHAPI_KEY || env.YDC_API_KEY || env.USE_LOCAL_WEBSEARCH || env.SEARXNG_QUERY_URL || env.BING_SUBSCRIPTION_KEY ), ethicsModalAccepted: !!settings?.ethicsModalAcceptedAt, ethicsModalAcceptedAt: settings?.ethicsModalAcceptedAt ?? null, activeModel: settings?.activeModel ?? DEFAULT_SETTINGS.activeModel, hideEmojiOnSidebar: settings?.hideEmojiOnSidebar ?? false, shareConversationsWithModelAuthors: settings?.shareConversationsWithModelAuthors ?? DEFAULT_SETTINGS.shareConversationsWithModelAuthors, customPrompts: settings?.customPrompts ?? {}, assistants: userAssistants, tools: settings?.tools ?? toolFromConfigs .filter((el) => !el.isHidden && el.isOnByDefault) .map((el) => el._id.toString()), disableStream: settings?.disableStream ?? DEFAULT_SETTINGS.disableStream, directPaste: settings?.directPaste ?? DEFAULT_SETTINGS.directPaste, }, models: models.map((model) => ({ id: model.id, name: model.name, websiteUrl: model.websiteUrl, modelUrl: model.modelUrl, tokenizer: model.tokenizer, datasetName: model.datasetName, datasetUrl: model.datasetUrl, displayName: model.displayName, description: model.description, logoUrl: model.logoUrl, promptExamples: model.promptExamples, parameters: model.parameters, preprompt: model.preprompt, multimodal: model.multimodal, multimodalAcceptedMimetypes: model.multimodalAcceptedMimetypes, tools: model.tools, unlisted: model.unlisted, hasInferenceAPI: model.hasInferenceAPI, })), oldModels, tools: [...toolFromConfigs, ...communityTools] .filter((tool) => !tool?.isHidden) .map( (tool) => ({ _id: tool._id.toString(), type: tool.type, displayName: tool.displayName, name: tool.name, description: tool.description, mimeTypes: (tool.inputs ?? []) .filter((input): input is ToolInputFile => input.type === "file") .map((input) => (input as ToolInputFile).mimeTypes) .flat(), isOnByDefault: tool.isOnByDefault ?? true, isLocked: tool.isLocked ?? true, timeToUseMS: toolUseDuration.find( (el) => el.labels.tool === tool._id.toString() && el.labels.quantile === 0.9 )?.value ?? 15_000, } satisfies ToolFront) ), communityToolCount: await collections.tools.countDocuments({ type: "community", review: ReviewStatus.APPROVED, }), assistants: assistants.then((assistants) => assistants .filter((el) => userAssistantsSet.has(el._id.toString())) .map((el) => ({ ...el, _id: el._id.toString(), createdById: undefined, createdByMe: el.createdById.toString() === (locals.user?._id ?? locals.sessionId).toString(), })) ), user: locals.user && { id: locals.user._id.toString(), username: locals.user.username, avatarUrl: locals.user.avatarUrl, email: locals.user.email, logoutDisabled: locals.user.logoutDisabled, isAdmin: locals.user.isAdmin ?? false, isEarlyAccess: locals.user.isEarlyAccess ?? false, }, assistant: assistant ? JSON.parse(JSON.stringify(assistant)) : null, enableAssistants, enableAssistantsRAG: env.ENABLE_ASSISTANTS_RAG === "true", enableCommunityTools: env.COMMUNITY_TOOLS === "true", loginRequired, loginEnabled: requiresUser, guestMode: requiresUser && messagesBeforeLogin > 0, }; };
0
hf_public_repos/chat-ui/src/routes/r
hf_public_repos/chat-ui/src/routes/r/[id]/+page.ts
import { redirect, type LoadEvent } from "@sveltejs/kit"; export const load = async ({ params, url }: LoadEvent) => { const leafId = url.searchParams.get("leafId"); throw redirect(302, "../conversation/" + params.id + `?leafId=${leafId}`); };
0
hf_public_repos/chat-ui/src/routes/api
hf_public_repos/chat-ui/src/routes/api/assistants/+server.ts
import { collections } from "$lib/server/database"; import type { Assistant } from "$lib/types/Assistant"; import type { User } from "$lib/types/User"; import { generateQueryTokens } from "$lib/utils/searchTokens.js"; import type { Filter } from "mongodb"; import { env } from "$env/dynamic/private"; import { ReviewStatus } from "$lib/types/Review"; const NUM_PER_PAGE = 24; export async function GET({ url, locals }) { const modelId = url.searchParams.get("modelId"); const pageIndex = parseInt(url.searchParams.get("p") ?? "0"); const username = url.searchParams.get("user"); const query = url.searchParams.get("q")?.trim() ?? null; const showUnfeatured = url.searchParams.get("showUnfeatured") === "true"; const createdByCurrentUser = locals.user?.username && locals.user.username === username; let user: Pick<User, "_id"> | null = null; if (username) { user = await collections.users.findOne<Pick<User, "_id">>( { username }, { projection: { _id: 1 } } ); if (!user) { return Response.json({ message: `User "${username}" doesn't exist` }, { status: 404 }); } } // if we require featured assistants, that we are not on a user page and we are not an admin who wants to see unfeatured assistants, we show featured assistants let shouldBeFeatured = {}; if (env.REQUIRE_FEATURED_ASSISTANTS === "true" && !(locals.user?.isAdmin && showUnfeatured)) { if (!user) { // only show featured assistants on the community page shouldBeFeatured = { review: ReviewStatus.APPROVED }; } else if (!createdByCurrentUser) { // on a user page show assistants that have been approved or are pending shouldBeFeatured = { review: { $in: [ReviewStatus.APPROVED, ReviewStatus.PENDING] } }; } } // fetch the top assistants sorted by user count from biggest to smallest, filter out all assistants with only 1 users. filter by model too if modelId is provided const filter: Filter<Assistant> = { ...(modelId && { modelId }), ...(user && { createdById: user._id }), ...(query && { searchTokens: { $all: generateQueryTokens(query) } }), ...shouldBeFeatured, }; const assistants = await collections.assistants .find(filter) .skip(NUM_PER_PAGE * pageIndex) .sort({ userCount: -1 }) .limit(NUM_PER_PAGE) .toArray(); const numTotalItems = await collections.assistants.countDocuments(filter); return Response.json({ assistants, selectedModel: modelId ?? "", numTotalItems, numItemsPerPage: NUM_PER_PAGE, query, }); }
0
hf_public_repos/chat-ui/src/routes/api/assistant
hf_public_repos/chat-ui/src/routes/api/assistant/[id]/+server.ts
import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; export async function GET({ params }) { const id = params.id; const assistantId = new ObjectId(id); const assistant = await collections.assistants.findOne({ _id: assistantId, }); if (assistant) { return Response.json(assistant); } else { return Response.json({ message: "Assistant not found" }, { status: 404 }); } }
0
hf_public_repos/chat-ui/src/routes/api
hf_public_repos/chat-ui/src/routes/api/conversations/+server.ts
import { collections } from "$lib/server/database"; import { models } from "$lib/server/models"; import { authCondition } from "$lib/server/auth"; import type { Conversation } from "$lib/types/Conversation"; const NUM_PER_PAGE = 300; export async function GET({ locals, url }) { const p = parseInt(url.searchParams.get("p") ?? "0"); if (locals.user?._id || locals.sessionId) { const convs = await collections.conversations .find({ ...authCondition(locals), }) .project<Pick<Conversation, "_id" | "title" | "updatedAt" | "model" | "assistantId">>({ title: 1, updatedAt: 1, model: 1, assistantId: 1, }) .sort({ updatedAt: -1 }) .skip(p * NUM_PER_PAGE) .limit(NUM_PER_PAGE) .toArray(); const res = convs.map((conv) => ({ id: conv._id, title: conv.title, updatedAt: conv.updatedAt, modelId: conv.model, assistantId: conv.assistantId, modelTools: models.find((m) => m.id == conv.model)?.tools ?? false, })); return Response.json(res); } else { return Response.json({ message: "Must have session cookie" }, { status: 401 }); } }
0
hf_public_repos/chat-ui/src/routes/api/tools
hf_public_repos/chat-ui/src/routes/api/tools/[toolId]/+server.ts
import { env } from "$env/dynamic/private"; import { collections } from "$lib/server/database.js"; import { toolFromConfigs } from "$lib/server/tools/index.js"; import { ReviewStatus } from "$lib/types/Review"; import type { CommunityToolDB } from "$lib/types/Tool.js"; import { ObjectId } from "mongodb"; export async function GET({ params }) { if (env.COMMUNITY_TOOLS !== "true") { return new Response("Community tools are not enabled", { status: 403 }); } const toolId = params.toolId; try { const configTool = toolFromConfigs.find((el) => el._id.toString() === toolId); if (configTool) { return Response.json({ _id: toolId, displayName: configTool.displayName, color: configTool.color, icon: configTool.icon, createdByName: undefined, }); } else { // try community tools const tool = await collections.tools .findOne<CommunityToolDB>({ _id: new ObjectId(toolId) }) .then((tool) => tool ? { _id: tool._id.toString(), displayName: tool.displayName, color: tool.color, icon: tool.icon, createdByName: tool.createdByName, review: tool.review, } : undefined ); if (!tool || tool.review !== ReviewStatus.APPROVED) { return new Response(`Tool "${toolId}" not found`, { status: 404 }); } return Response.json(tool); } } catch (e) { return new Response(`Tool "${toolId}" not found`, { status: 404 }); } }
0
hf_public_repos/chat-ui/src/routes/api/tools
hf_public_repos/chat-ui/src/routes/api/tools/search/+server.ts
import { env } from "$env/dynamic/private"; import { collections } from "$lib/server/database.js"; import { toolFromConfigs } from "$lib/server/tools/index.js"; import type { BaseTool, CommunityToolDB } from "$lib/types/Tool.js"; import { generateQueryTokens, generateSearchTokens } from "$lib/utils/searchTokens.js"; import type { Filter } from "mongodb"; import { ReviewStatus } from "$lib/types/Review"; export async function GET({ url }) { if (env.COMMUNITY_TOOLS !== "true") { return new Response("Community tools are not enabled", { status: 403 }); } const query = url.searchParams.get("q")?.trim() ?? null; const queryTokens = !!query && generateQueryTokens(query); const filter: Filter<CommunityToolDB> = { ...(queryTokens && { searchTokens: { $all: queryTokens } }), review: ReviewStatus.APPROVED, }; const matchingCommunityTools = await collections.tools .find(filter) .project<Pick<BaseTool, "_id" | "displayName" | "color" | "icon">>({ _id: 1, displayName: 1, color: 1, icon: 1, createdByName: 1, }) .sort({ useCount: -1 }) .limit(5) .toArray(); const matchingConfigTools = toolFromConfigs .filter((tool) => !tool?.isHidden) .filter((tool) => tool.name !== "websearch") // filter out websearch tool from config tools since its added separately .filter((tool) => { if (queryTokens) { return generateSearchTokens(tool.displayName).some((token) => queryTokens.some((queryToken) => queryToken.test(token)) ); } return true; }) .map((tool) => ({ _id: tool._id, displayName: tool.displayName, color: tool.color, icon: tool.icon, createdByName: undefined, })); const tools = [...matchingConfigTools, ...matchingCommunityTools] satisfies Array< Pick<BaseTool, "_id" | "displayName" | "color" | "icon"> & { createdByName?: string } >; return Response.json(tools.map((tool) => ({ ...tool, _id: tool._id.toString() })).slice(0, 5)); }
0
hf_public_repos/chat-ui/src/routes/api
hf_public_repos/chat-ui/src/routes/api/spaces-config/+server.ts
import { env } from "$env/dynamic/private"; import { Client } from "@gradio/client"; export async function GET({ url }) { if (env.COMMUNITY_TOOLS !== "true") { return new Response("Community tools are not enabled", { status: 403 }); } const space = url.searchParams.get("space"); if (!space) { return new Response("Missing space", { status: 400 }); } // Extract namespace from space URL or use as-is if it's already in namespace format let namespace = null; if (space.startsWith("https://huggingface.co/spaces/")) { namespace = space.split("/").slice(-2).join("/"); } else if (space.match(/^[^/]+\/[^/]+$/)) { namespace = space; } if (!namespace) { return new Response( "Invalid space name. Specify a namespace or a full URL on huggingface.co.", { status: 400 } ); } try { const api = await (await Client.connect(namespace)).view_api(); return new Response(JSON.stringify(api), { status: 200, headers: { "Content-Type": "application/json", }, }); } catch (e) { return new Response("Error fetching space API. Is the name correct?", { status: 400, headers: { "Content-Type": "application/json", }, }); } }
0
hf_public_repos/chat-ui/src/routes/api
hf_public_repos/chat-ui/src/routes/api/models/+server.ts
import { models } from "$lib/server/models"; export async function GET() { const res = models .filter((m) => m.unlisted == false) .map((model) => ({ id: model.id, name: model.name, websiteUrl: model.websiteUrl ?? "https://huggingface.co", modelUrl: model.modelUrl ?? "https://huggingface.co", tokenizer: model.tokenizer, datasetName: model.datasetName, datasetUrl: model.datasetUrl, displayName: model.displayName, description: model.description ?? "", logoUrl: model.logoUrl, promptExamples: model.promptExamples ?? [], preprompt: model.preprompt ?? "", multimodal: model.multimodal ?? false, unlisted: model.unlisted ?? false, tools: model.tools ?? false, hasInferenceAPI: model.hasInferenceAPI ?? false, })); return Response.json(res); }
0
hf_public_repos/chat-ui/src/routes/api/conversation
hf_public_repos/chat-ui/src/routes/api/conversation/[id]/+server.ts
import { collections } from "$lib/server/database"; import { authCondition } from "$lib/server/auth"; import { z } from "zod"; import { models } from "$lib/server/models"; import { ObjectId } from "mongodb"; export async function GET({ locals, params }) { const id = z.string().parse(params.id); const convId = new ObjectId(id); if (locals.user?._id || locals.sessionId) { const conv = await collections.conversations.findOne({ _id: convId, ...authCondition(locals), }); if (conv) { const res = { id: conv._id, title: conv.title, updatedAt: conv.updatedAt, modelId: conv.model, assistantId: conv.assistantId, messages: conv.messages.map((message) => ({ content: message.content, from: message.from, id: message.id, createdAt: message.createdAt, updatedAt: message.updatedAt, webSearch: message.webSearch, files: message.files, updates: message.updates, })), modelTools: models.find((m) => m.id == conv.model)?.tools ?? false, }; return Response.json(res); } else { return Response.json({ message: "Conversation not found" }, { status: 404 }); } } else { return Response.json({ message: "Must have session cookie" }, { status: 401 }); } }
0
hf_public_repos/chat-ui/src/routes/api
hf_public_repos/chat-ui/src/routes/api/user/+server.ts
export async function GET({ locals }) { if (locals.user) { const res = { id: locals.user._id, username: locals.user.username, name: locals.user.name, email: locals.user.email, avatarUrl: locals.user.avatarUrl, hfUserId: locals.user.hfUserId, }; return Response.json(res); } return Response.json({ message: "Must be signed in" }, { status: 401 }); }
0
hf_public_repos/chat-ui/src/routes/api/user
hf_public_repos/chat-ui/src/routes/api/user/assistants/+server.ts
import { authCondition } from "$lib/server/auth"; import type { Conversation } from "$lib/types/Conversation"; import { collections } from "$lib/server/database"; import { ObjectId } from "mongodb"; export async function GET({ locals }) { if (locals.user?._id || locals.sessionId) { const settings = await collections.settings.findOne(authCondition(locals)); const conversations = await collections.conversations .find(authCondition(locals)) .sort({ updatedAt: -1 }) .project<Pick<Conversation, "assistantId">>({ assistantId: 1, }) .limit(300) .toArray(); const userAssistants = settings?.assistants?.map((assistantId) => assistantId.toString()) ?? []; const userAssistantsSet = new Set(userAssistants); const assistantIds = [ ...userAssistants.map((el) => new ObjectId(el)), ...(conversations.map((conv) => conv.assistantId).filter((el) => !!el) as ObjectId[]), ]; const assistants = await collections.assistants.find({ _id: { $in: assistantIds } }).toArray(); const res = assistants .filter((el) => userAssistantsSet.has(el._id.toString())) .map((el) => ({ ...el, _id: el._id.toString(), createdById: undefined, createdByMe: el.createdById.toString() === (locals.user?._id ?? locals.sessionId).toString(), })); return Response.json(res); } else { return Response.json({ message: "Must have session cookie" }, { status: 401 }); } }
0
hf_public_repos/chat-ui/src/routes
hf_public_repos/chat-ui/src/routes/assistants/+page.svelte
<script lang="ts"> import type { PageData } from "./$types"; import { env as envPublic } from "$env/dynamic/public"; import { isHuggingChat } from "$lib/utils/isHuggingChat"; import { goto } from "$app/navigation"; import { base } from "$app/paths"; import { page } from "$app/stores"; import CarbonAdd from "~icons/carbon/add"; import CarbonHelpFilled from "~icons/carbon/help-filled"; import CarbonClose from "~icons/carbon/close"; import CarbonArrowUpRight from "~icons/carbon/arrow-up-right"; import CarbonEarthAmerica from "~icons/carbon/earth-americas-filled"; import CarbonUserMultiple from "~icons/carbon/user-multiple"; import CarbonSearch from "~icons/carbon/search"; import CarbonTools from "~icons/carbon/tools"; import Pagination from "$lib/components/Pagination.svelte"; import { formatUserCount } from "$lib/utils/formatUserCount"; import { getHref } from "$lib/utils/getHref"; import { debounce } from "$lib/utils/debounce"; import { useSettingsStore } from "$lib/stores/settings"; import IconInternet from "$lib/components/icons/IconInternet.svelte"; import { isDesktop } from "$lib/utils/isDesktop"; import { SortKey } from "$lib/types/Assistant"; import { ReviewStatus } from "$lib/types/Review"; export let data: PageData; $: assistantsCreator = $page.url.searchParams.get("user"); $: createdByMe = data.user?.username && data.user.username === assistantsCreator; const SEARCH_DEBOUNCE_DELAY = 400; let filterInputEl: HTMLInputElement; let filterValue = data.query; let isFilterInPorgress = false; let sortValue = data.sort as SortKey; let showUnfeatured = data.showUnfeatured; const toggleShowUnfeatured = () => { showUnfeatured = !showUnfeatured; const newUrl = getHref($page.url, { newKeys: { showUnfeatured: showUnfeatured ? "true" : undefined }, existingKeys: { behaviour: "delete", keys: [] }, }); goto(newUrl); }; const onModelChange = (e: Event) => { const newUrl = getHref($page.url, { newKeys: { modelId: (e.target as HTMLSelectElement).value }, existingKeys: { behaviour: "delete_except", keys: ["user"] }, }); resetFilter(); goto(newUrl); }; const resetFilter = () => { filterValue = ""; isFilterInPorgress = false; }; const filterOnName = debounce(async (value: string) => { filterValue = value; if (isFilterInPorgress) { return; } isFilterInPorgress = true; const newUrl = getHref($page.url, { newKeys: { q: value }, existingKeys: { behaviour: "delete", keys: ["p"] }, }); await goto(newUrl); if (isDesktop(window)) { setTimeout(() => filterInputEl.focus(), 0); } isFilterInPorgress = false; // there was a new filter query before server returned response if (filterValue !== value) { filterOnName(filterValue); } }, SEARCH_DEBOUNCE_DELAY); const sortAssistants = () => { const newUrl = getHref($page.url, { newKeys: { sort: sortValue }, existingKeys: { behaviour: "delete", keys: ["p"] }, }); goto(newUrl); }; const settings = useSettingsStore(); </script> <svelte:head> {#if isHuggingChat} <title>HuggingChat - Assistants</title> <meta property="og:title" content="HuggingChat - Assistants" /> <meta property="og:type" content="link" /> <meta property="og:description" content="Browse HuggingChat assistants made by the community." /> <meta property="og:image" content="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/{envPublic.PUBLIC_APP_ASSETS}/assistants-thumbnail.png" /> <meta property="og:url" content={$page.url.href} /> {/if} </svelte:head> <div class="scrollbar-custom h-full overflow-y-auto py-12 max-sm:pt-8 md:py-24"> <div class="pt-42 mx-auto flex flex-col px-5 xl:w-[60rem] 2xl:w-[64rem]"> <div class="flex items-center"> <h1 class="text-2xl font-bold">Assistants</h1> {#if isHuggingChat} <div class="5 ml-1.5 rounded-lg text-xxs uppercase text-gray-500 dark:text-gray-500"> beta </div> <a href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions/357" class="ml-auto dark:text-gray-400 dark:hover:text-gray-300" target="_blank" aria-label="Hub discussion about assistants" > <CarbonHelpFilled /> </a> {/if} </div> <h2 class="text-gray-500">Popular assistants made by the community</h2> <div class="mt-6 flex justify-between gap-2 max-sm:flex-col sm:items-center"> <select class="mt-1 h-[34px] rounded-lg border border-gray-300 bg-gray-50 px-2 text-sm text-gray-900 focus:border-blue-700 focus:ring-blue-700 dark:border-gray-600 dark:bg-gray-700 dark:text-white dark:placeholder-gray-400" bind:value={data.selectedModel} on:change={onModelChange} aria-label="Filter assistants by model" > <option value="">All models</option> {#each data.models.filter((model) => !model.unlisted) as model} <option value={model.name}>{model.name}</option> {/each} </select> {#if data.user?.isAdmin} <label class="mr-auto flex items-center gap-1 text-red-500" title="Admin only feature"> <input type="checkbox" checked={showUnfeatured} on:change={toggleShowUnfeatured} /> Show unfeatured assistants </label> {/if} <a href={`${base}/settings/assistants/new`} class="flex items-center gap-1 whitespace-nowrap rounded-lg border bg-white py-1 pl-1.5 pr-2.5 shadow-sm hover:bg-gray-50 hover:shadow-none dark:border-gray-600 dark:bg-gray-700 dark:hover:bg-gray-700" > <CarbonAdd />Create new assistant </a> </div> <div class="mt-7 flex flex-wrap items-center gap-x-2 gap-y-3 text-sm"> {#if assistantsCreator && !createdByMe} <div class="flex items-center gap-1.5 rounded-full border border-gray-300 bg-gray-50 px-3 py-1 dark:border-gray-600 dark:bg-gray-700 dark:text-white" > {assistantsCreator}'s Assistants <a href={getHref($page.url, { existingKeys: { behaviour: "delete", keys: ["user", "modelId", "p", "q"] }, })} on:click={resetFilter} class="group" ><CarbonClose class="text-xs group-hover:text-gray-800 dark:group-hover:text-gray-300" /></a > </div> {#if isHuggingChat} <a href="https://hf.co/{assistantsCreator}" target="_blank" class="ml-auto flex items-center text-xs text-gray-500 underline hover:text-gray-800 dark:text-gray-400 dark:hover:text-gray-300" ><CarbonArrowUpRight class="mr-1 flex-none text-[0.58rem]" target="_blank" />View {assistantsCreator} on HF</a > {/if} {:else} <a href={getHref($page.url, { existingKeys: { behaviour: "delete", keys: ["user", "modelId", "p", "q"] }, })} on:click={resetFilter} class="flex items-center gap-1.5 rounded-full border px-3 py-1 {!assistantsCreator ? 'border-gray-300 bg-gray-50 dark:border-gray-600 dark:bg-gray-700 dark:text-white' : 'border-transparent text-gray-400 hover:text-gray-800 dark:hover:text-gray-300'}" > <CarbonEarthAmerica class="text-xs" /> Community </a> {#if data.user?.username} <a href={getHref($page.url, { newKeys: { user: data.user.username }, existingKeys: { behaviour: "delete", keys: ["modelId", "p", "q"] }, })} on:click={resetFilter} class="flex items-center gap-1.5 truncate rounded-full border px-3 py-1 {assistantsCreator && createdByMe ? 'border-gray-300 bg-gray-50 dark:border-gray-600 dark:bg-gray-700 dark:text-white' : 'border-transparent text-gray-400 hover:text-gray-800 dark:hover:text-gray-300'}" >{data.user.username} </a> {/if} {/if} <div class="relative ml-auto flex h-[30px] w-40 items-center rounded-full border px-2 has-[:focus]:border-gray-400 dark:border-gray-600 sm:w-64" > <CarbonSearch class="pointer-events-none absolute left-2 text-xs text-gray-400" /> <input class="h-[30px] w-full bg-transparent pl-5 focus:outline-none" placeholder="Filter by name" value={filterValue} on:input={(e) => filterOnName(e.currentTarget.value)} bind:this={filterInputEl} maxlength="150" type="search" aria-label="Filter assistants by name" /> </div> <select bind:value={sortValue} on:change={sortAssistants} aria-label="Sort assistants" class="rounded-lg border border-gray-300 bg-gray-50 px-2 py-1 text-sm text-gray-900 focus:border-blue-700 focus:ring-blue-700 dark:border-gray-600 dark:bg-gray-700 dark:text-white dark:placeholder-gray-400" > <option value={SortKey.TRENDING}>{SortKey.TRENDING}</option> <option value={SortKey.POPULAR}>{SortKey.POPULAR}</option> </select> </div> <div class="mt-8 grid grid-cols-2 gap-3 sm:gap-5 md:grid-cols-3 lg:grid-cols-4"> {#each data.assistants as assistant (assistant._id)} {@const hasRag = assistant?.rag?.allowAllDomains || !!assistant?.rag?.allowedDomains?.length || !!assistant?.rag?.allowedLinks?.length || !!assistant?.dynamicPrompt} <button class="relative flex flex-col items-center justify-center overflow-hidden text-balance rounded-xl border bg-gray-50/50 px-4 py-6 text-center shadow hover:bg-gray-50 hover:shadow-inner dark:border-gray-800/70 dark:bg-gray-950/20 dark:hover:bg-gray-950/40 max-sm:px-4 sm:h-64 sm:pb-4 xl:pt-8 {!(assistant.review === ReviewStatus.APPROVED) && !createdByMe && data.user?.isAdmin ? 'border !border-red-500/30' : ''}" on:click={() => { if (data.settings.assistants.includes(assistant._id.toString())) { settings.instantSet({ activeModel: assistant._id.toString() }); goto(`${base}` || "/"); } else { goto(`${base}/assistant/${assistant._id}`); } }} > {#if assistant.userCount && assistant.userCount > 1} <div class="absolute right-3 top-3 flex items-center gap-1 text-xs text-gray-400" title="Number of users" > <CarbonUserMultiple class="text-xxs" />{formatUserCount(assistant.userCount)} </div> {/if} <div class="absolute left-3 top-3 flex items-center gap-1 text-xs text-gray-400"> {#if assistant.tools?.length} <div class="grid size-5 place-items-center rounded-full bg-purple-500/10" title="This assistant uses the websearch." > <CarbonTools class="text-xs text-purple-600" /> </div> {/if} {#if hasRag} <div class="grid size-5 place-items-center rounded-full bg-blue-500/10" title="This assistant uses the websearch." > <IconInternet classNames="text-sm text-blue-600" /> </div> {/if} </div> {#if assistant.avatar} <img src="{base}/settings/assistants/{assistant._id}/avatar.jpg" alt="Avatar" class="mb-2 aspect-square size-12 flex-none rounded-full object-cover sm:mb-6 sm:size-20" /> {:else} <div class="mb-2 flex aspect-square size-12 flex-none items-center justify-center rounded-full bg-gray-300 text-2xl font-bold uppercase text-gray-500 dark:bg-gray-800 sm:mb-6 sm:size-20" > {assistant.name[0]} </div> {/if} <h3 class="mb-2 line-clamp-2 max-w-full break-words text-center text-[.8rem] font-semibold leading-snug sm:text-sm" > {assistant.name} </h3> <p class="line-clamp-4 text-xs text-gray-700 dark:text-gray-400 sm:line-clamp-2"> {assistant.description} </p> {#if assistant.createdByName} <p class="mt-auto pt-2 text-xs text-gray-400 dark:text-gray-500"> Created by <a class="hover:underline" href="{base}/assistants?user={assistant.createdByName}" > {assistant.createdByName} </a> </p> {/if} </button> {:else} No assistants found {/each} </div> <Pagination classNames="w-full flex justify-center mt-14 mb-4" numItemsPerPage={data.numItemsPerPage} numTotalItems={data.numTotalItems} /> </div> </div>
0
hf_public_repos/chat-ui/src/routes
hf_public_repos/chat-ui/src/routes/assistants/+page.server.ts
import { base } from "$app/paths"; import { env } from "$env/dynamic/private"; import { Database, collections } from "$lib/server/database.js"; import { SortKey, type Assistant } from "$lib/types/Assistant"; import type { User } from "$lib/types/User"; import { generateQueryTokens } from "$lib/utils/searchTokens.js"; import { error, redirect } from "@sveltejs/kit"; import type { Filter } from "mongodb"; import { ReviewStatus } from "$lib/types/Review"; const NUM_PER_PAGE = 24; export const load = async ({ url, locals }) => { if (!env.ENABLE_ASSISTANTS) { redirect(302, `${base}/`); } const modelId = url.searchParams.get("modelId"); const pageIndex = parseInt(url.searchParams.get("p") ?? "0"); const username = url.searchParams.get("user"); const query = url.searchParams.get("q")?.trim() ?? null; const sort = url.searchParams.get("sort")?.trim() ?? SortKey.TRENDING; const showUnfeatured = url.searchParams.get("showUnfeatured") === "true"; const createdByCurrentUser = locals.user?.username && locals.user.username === username; let user: Pick<User, "_id"> | null = null; if (username) { user = await collections.users.findOne<Pick<User, "_id">>( { username }, { projection: { _id: 1 } } ); if (!user) { error(404, `User "${username}" doesn't exist`); } } // if we require featured assistants, that we are not on a user page and we are not an admin who wants to see unfeatured assistants, we show featured assistants let shouldBeFeatured = {}; if (env.REQUIRE_FEATURED_ASSISTANTS === "true" && !(locals.user?.isAdmin && showUnfeatured)) { if (!user) { // only show featured assistants on the community page shouldBeFeatured = { review: ReviewStatus.APPROVED }; } else if (!createdByCurrentUser) { // on a user page show assistants that have been approved or are pending shouldBeFeatured = { review: { $in: [ReviewStatus.APPROVED, ReviewStatus.PENDING] } }; } } // fetch the top assistants sorted by user count from biggest to smallest. filter by model too if modelId is provided or query if query is provided const filter: Filter<Assistant> = { ...(modelId && { modelId }), ...(user && { createdById: user._id }), ...(query && { searchTokens: { $all: generateQueryTokens(query) } }), ...shouldBeFeatured, }; const assistants = await Database.getInstance() .getCollections() .assistants.find(filter) .sort({ ...(sort === SortKey.TRENDING && { last24HoursCount: -1 }), userCount: -1, _id: 1, }) .skip(NUM_PER_PAGE * pageIndex) .limit(NUM_PER_PAGE) .toArray(); const numTotalItems = await Database.getInstance() .getCollections() .assistants.countDocuments(filter); return { assistants: JSON.parse(JSON.stringify(assistants)) as Array<Assistant>, selectedModel: modelId ?? "", numTotalItems, numItemsPerPage: NUM_PER_PAGE, query, sort, showUnfeatured, }; };
0
hf_public_repos/chat-ui/src/routes/assistant
hf_public_repos/chat-ui/src/routes/assistant/[assistantId]/+page.svelte
<script lang="ts"> import { page } from "$app/stores"; import { base } from "$app/paths"; import { goto } from "$app/navigation"; import { onMount } from "svelte"; import { env as envPublic } from "$env/dynamic/public"; import ChatWindow from "$lib/components/chat/ChatWindow.svelte"; import { findCurrentModel } from "$lib/utils/models"; import { useSettingsStore } from "$lib/stores/settings"; import { ERROR_MESSAGES, error } from "$lib/stores/errors"; import { pendingMessage } from "$lib/stores/pendingMessage"; export let data; let loading = false; let files: File[] = []; const settings = useSettingsStore(); const modelId = $page.params.model; async function createConversation(message: string) { try { loading = true; const res = await fetch(`${base}/conversation`, { method: "POST", headers: { "Content-Type": "application/json", }, body: JSON.stringify({ model: data.assistant.modelId, assistantId: data.assistant._id, }), }); if (!res.ok) { error.set("Error while creating conversation, try again."); console.error("Error while creating conversation: " + (await res.text())); return; } const { conversationId } = await res.json(); // Ugly hack to use a store as temp storage, feel free to improve ^^ pendingMessage.set({ content: message, files, }); // invalidateAll to update list of conversations await goto(`${base}/conversation/${conversationId}`, { invalidateAll: true }); } catch (err) { error.set(ERROR_MESSAGES.default); console.error(err); } finally { loading = false; } } onMount(async () => { settings.instantSet({ activeModel: modelId, }); const query = $page.url.searchParams.get("q"); if (query) createConversation(query); }); </script> <svelte:head> <meta property="og:title" content={data.assistant.name + " - " + envPublic.PUBLIC_APP_NAME} /> <meta property="og:type" content="link" /> <meta property="og:description" content={`Use the ${data.assistant.name} assistant inside of ${envPublic.PUBLIC_APP_NAME}`} /> <meta property="og:image" content="{envPublic.PUBLIC_ORIGIN || $page.url.origin}{base}/assistant/{data.assistant ._id}/thumbnail.png" /> <meta property="og:url" content={$page.url.href} /> <meta name="twitter:card" content="summary_large_image" /> </svelte:head> <ChatWindow on:message={(ev) => createConversation(ev.detail)} {loading} currentModel={findCurrentModel([...data.models, ...data.oldModels], data.assistant.modelId)} assistant={data.assistant} models={data.models} bind:files />
0
hf_public_repos/chat-ui/src/routes/assistant
hf_public_repos/chat-ui/src/routes/assistant/[assistantId]/+page.server.ts
import { base } from "$app/paths"; import { collections } from "$lib/server/database"; import { redirect } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; import { authCondition } from "$lib/server/auth.js"; export async function load({ params, locals }) { try { const assistant = await collections.assistants.findOne({ _id: new ObjectId(params.assistantId), }); if (!assistant) { redirect(302, `${base}`); } if (locals.user?._id ?? locals.sessionId) { await collections.settings.updateOne( authCondition(locals), { $set: { activeModel: assistant._id.toString(), updatedAt: new Date(), }, $push: { assistants: assistant._id }, $setOnInsert: { createdAt: new Date(), }, }, { upsert: true, } ); } return { assistant: JSON.parse(JSON.stringify(assistant)), }; } catch { redirect(302, `${base}`); } }
0
hf_public_repos/chat-ui/src/routes/assistant/[assistantId]
hf_public_repos/chat-ui/src/routes/assistant/[assistantId]/thumbnail.png/+server.ts
import ChatThumbnail from "./ChatThumbnail.svelte"; import { collections } from "$lib/server/database"; import { error, type RequestHandler } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; import type { SvelteComponent } from "svelte"; import { Resvg } from "@resvg/resvg-js"; import satori from "satori"; import { html } from "satori-html"; import InterRegular from "$lib/server/fonts/Inter-Regular.ttf"; import InterBold from "$lib/server/fonts/Inter-Bold.ttf"; import sharp from "sharp"; export const GET: RequestHandler = (async ({ params }) => { const assistant = await collections.assistants.findOne({ _id: new ObjectId(params.assistantId), }); if (!assistant) { error(404, "Assistant not found."); } let avatar = ""; const fileId = collections.bucket.find({ filename: assistant._id.toString() }); const file = await fileId.next(); if (file) { avatar = await (async () => { const fileStream = collections.bucket.openDownloadStream(file?._id); const fileBuffer = await new Promise<Buffer>((resolve, reject) => { const chunks: Uint8Array[] = []; fileStream.on("data", (chunk) => chunks.push(chunk)); fileStream.on("error", reject); fileStream.on("end", () => resolve(Buffer.concat(chunks))); }); return fileBuffer; })() .then(async (buf) => sharp(buf).jpeg().toBuffer()) // convert to jpeg bc satori png is really slow .then(async (buf) => "data:image/jpeg;base64," + buf.toString("base64")); } const renderedComponent = (ChatThumbnail as unknown as SvelteComponent).render({ name: assistant.name, description: assistant.description, createdByName: assistant.createdByName, avatar, }); const reactLike = html( "<style>" + renderedComponent.css.code + "</style>" + renderedComponent.html ); const svg = await satori(reactLike, { width: 1200, height: 648, fonts: [ { name: "Inter", data: InterRegular as unknown as ArrayBuffer, weight: 500, }, { name: "Inter", data: InterBold as unknown as ArrayBuffer, weight: 700, }, ], }); const png = new Resvg(svg, { fitTo: { mode: "original" }, }) .render() .asPng(); return new Response(png, { headers: { "Content-Type": "image/png", }, }); }) satisfies RequestHandler;
0
hf_public_repos/chat-ui/src/routes/assistant/[assistantId]
hf_public_repos/chat-ui/src/routes/assistant/[assistantId]/thumbnail.png/ChatThumbnail.svelte
<script lang="ts"> export let name: string; export let description: string = ""; export let createdByName: string | undefined; export let avatar: string | undefined; import logo from "../../../../../static/huggingchat/logo.svg?raw"; </script> <div class="flex h-full w-full flex-col items-center justify-center bg-black p-2"> <div class="flex w-full max-w-[540px] items-start justify-center text-white"> {#if avatar} <img class="h-64 w-64 rounded-full" style="object-fit: cover;" src={avatar} alt="avatar" /> {/if} <div class="ml-10 flex flex-col items-start"> <p class="mb-2 mt-0 text-3xl font-normal text-gray-400"> <span class="mr-1.5 h-8 w-8"> <!-- eslint-disable-next-line --> {@html logo} </span> AI assistant </p> <h1 class="m-0 {name.length < 38 ? 'text-5xl' : 'text-4xl'} font-black"> {name} </h1> <p class="mb-8 text-2xl"> {description.slice(0, 160)} {#if description.length > 160}...{/if} </p> <div class="rounded-full bg-[#FFA800] px-8 py-3 text-3xl font-semibold text-black"> Start chatting </div> </div> </div> {#if createdByName} <p class="absolute bottom-4 right-8 text-2xl text-gray-400"> An AI assistant created by {createdByName} </p> {/if} </div>
0
hf_public_repos/chat-ui/src/routes
hf_public_repos/chat-ui/src/routes/settings/+layout.svelte
<script lang="ts"> import { base } from "$app/paths"; import { clickOutside } from "$lib/actions/clickOutside"; import { afterNavigate, goto } from "$app/navigation"; import { useSettingsStore } from "$lib/stores/settings"; import CarbonCheckmark from "~icons/carbon/checkmark"; import { fade, fly } from "svelte/transition"; let previousPage: string = base; afterNavigate(({ from }) => { if (!from?.url.pathname.includes("settings")) { previousPage = from?.url.toString() || previousPage; } }); const settings = useSettingsStore(); </script> <div class="fixed inset-0 z-20 flex items-center justify-center bg-black/80 backdrop-blur-sm dark:bg-black/50" in:fade > <dialog in:fly={{ y: 100 }} open use:clickOutside={() => { if (window?.getSelection()?.toString()) { return; } goto(previousPage); }} class="h-[95dvh] w-[90dvw] overflow-hidden rounded-2xl bg-white shadow-2xl outline-none sm:h-[85dvh] xl:w-[1200px] 2xl:h-[75dvh]" > <slot /> {#if $settings.recentlySaved} <div class="absolute bottom-4 right-4 m-2 flex items-center gap-1.5 rounded-full border border-gray-300 bg-gray-200 px-3 py-1 text-black" > <CarbonCheckmark class="text-green-500" /> Saved </div> {/if} </dialog> </div>
0
hf_public_repos/chat-ui/src/routes
hf_public_repos/chat-ui/src/routes/settings/+layout.server.ts
import { collections } from "$lib/server/database"; import type { LayoutServerLoad } from "./$types"; import type { Report } from "$lib/types/Report"; export const load = (async ({ locals, parent }) => { const { assistants } = await parent(); let reportsByUser: string[] = []; const createdBy = locals.user?._id ?? locals.sessionId; if (createdBy) { const reports = await collections.reports .find<Pick<Report, "contentId">>( { createdBy, object: "assistant" }, { projection: { _id: 0, contentId: 1 } } ) .toArray(); reportsByUser = reports.map((r) => r.contentId.toString()); } return { assistants: (await assistants).map((el) => ({ ...el, reported: reportsByUser.includes(el._id), })), }; }) satisfies LayoutServerLoad;
0
hf_public_repos/chat-ui/src/routes/settings
hf_public_repos/chat-ui/src/routes/settings/(nav)/+server.ts
import { collections } from "$lib/server/database"; import { z } from "zod"; import { authCondition } from "$lib/server/auth"; import { DEFAULT_SETTINGS, type SettingsEditable } from "$lib/types/Settings"; import { toolFromConfigs } from "$lib/server/tools/index.js"; import { ObjectId } from "mongodb"; export async function POST({ request, locals }) { const body = await request.json(); const { ethicsModalAccepted, ...settings } = z .object({ shareConversationsWithModelAuthors: z .boolean() .default(DEFAULT_SETTINGS.shareConversationsWithModelAuthors), hideEmojiOnSidebar: z.boolean().default(DEFAULT_SETTINGS.hideEmojiOnSidebar), ethicsModalAccepted: z.boolean().optional(), activeModel: z.string().default(DEFAULT_SETTINGS.activeModel), customPrompts: z.record(z.string()).default({}), tools: z.array(z.string()).optional(), disableStream: z.boolean().default(false), directPaste: z.boolean().default(false), }) .parse(body) satisfies SettingsEditable; // make sure all tools exist // either in db or in config if (settings.tools) { const newTools = [ ...(await collections.tools .find({ _id: { $in: settings.tools.map((toolId) => new ObjectId(toolId)) } }) .project({ _id: 1 }) .toArray() .then((tools) => tools.map((tool) => tool._id.toString()))), ...toolFromConfigs .filter((el) => (settings?.tools ?? []).includes(el._id.toString())) .map((el) => el._id.toString()), ]; settings.tools = newTools; } await collections.settings.updateOne( authCondition(locals), { $set: { ...settings, ...(ethicsModalAccepted && { ethicsModalAcceptedAt: new Date() }), updatedAt: new Date(), }, $setOnInsert: { createdAt: new Date(), }, }, { upsert: true, } ); // return ok response return new Response(); }
0
hf_public_repos/chat-ui/src/routes/settings
hf_public_repos/chat-ui/src/routes/settings/(nav)/+layout.svelte
<script lang="ts"> import { onMount } from "svelte"; import { base } from "$app/paths"; import { afterNavigate, goto } from "$app/navigation"; import { page } from "$app/stores"; import { useSettingsStore } from "$lib/stores/settings"; import CarbonClose from "~icons/carbon/close"; import CarbonArrowUpRight from "~icons/carbon/ArrowUpRight"; import CarbonAdd from "~icons/carbon/add"; import CarbonTextLongParagraph from "~icons/carbon/text-long-paragraph"; import UserIcon from "~icons/carbon/user"; import type { LayoutData } from "../$types"; export let data: LayoutData; let previousPage: string = base; let assistantsSection: HTMLHeadingElement; onMount(() => { if ($page.params?.assistantId) { assistantsSection.scrollIntoView(); } }); afterNavigate(({ from }) => { if (!from?.url.pathname.includes("settings")) { previousPage = from?.url.toString() || previousPage; } }); const settings = useSettingsStore(); </script> <div class="grid h-full w-full grid-cols-1 grid-rows-[auto,1fr] content-start gap-x-4 overflow-hidden p-4 md:grid-cols-3 md:grid-rows-[auto,1fr] md:p-8" > <div class="col-span-1 mb-4 flex items-center justify-between md:col-span-3"> <h2 class="text-xl font-bold">Settings</h2> <button class="btn rounded-lg" aria-label="Close settings" on:click={() => { goto(previousPage); }} > <CarbonClose class="text-xl text-gray-900 hover:text-black" /> </button> </div> <div class="col-span-1 flex flex-col overflow-y-auto whitespace-nowrap max-md:-mx-4 max-md:h-[245px] max-md:border max-md:border-b-2 md:pr-6" > <h3 class="pb-3 pl-3 pt-2 text-[.8rem] text-gray-800 sm:pl-1">Models</h3> {#each data.models.filter((el) => !el.unlisted) as model} <a href="{base}/settings/{model.id}" class="group flex h-10 flex-none items-center gap-2 pl-3 pr-2 text-sm text-gray-500 hover:bg-gray-100 md:rounded-xl {model.id === $page.params.model ? '!bg-gray-100 !text-gray-800' : ''}" > <div class="mr-auto truncate">{model.displayName}</div> {#if $settings.customPrompts?.[model.id]} <CarbonTextLongParagraph class="size-6 rounded-md border border-gray-300 p-1 text-gray-800" /> {/if} {#if model.id === $settings.activeModel} <div class="rounded-lg bg-black px-2 py-1.5 text-xs font-semibold leading-none text-white" > Active </div> {/if} </a> {/each} <!-- if its huggingchat, the number of assistants owned by the user must be non-zero to show the UI --> {#if data.enableAssistants} <h3 bind:this={assistantsSection} class="pl-3 pt-5 text-[.8rem] text-gray-800 sm:pl-1"> Assistants </h3> <!-- My Assistants --> <h4 class="py-2 pl-5 text-[.7rem] text-gray-600 sm:pl-1">My Assistants</h4> {#each data.assistants.filter((assistant) => assistant.createdByMe) as assistant} <a href="{base}/settings/assistants/{assistant._id.toString()}" class="group flex h-10 flex-none items-center gap-2 pl-2 pr-2 text-sm text-gray-500 hover:bg-gray-100 md:rounded-xl {assistant._id.toString() === $page.params.assistantId ? '!bg-gray-100 !text-gray-800' : ''}" > {#if assistant.avatar} <img src="{base}/settings/assistants/{assistant._id.toString()}/avatar.jpg?hash={assistant.avatar}" alt="Avatar" class="h-6 w-6 rounded-full" /> {:else} <div class="flex size-6 items-center justify-center rounded-full bg-gray-300 font-bold uppercase text-gray-500" > {assistant.name[0]} </div> {/if} <div class="truncate text-gray-900">{assistant.name}</div> {#if assistant._id.toString() === $settings.activeModel} <div class="ml-auto rounded-lg bg-black px-2 py-1.5 text-xs font-semibold leading-none text-white" > Active </div> {/if} </a> {/each} {#if !data.loginEnabled || (data.loginEnabled && !!data.user)} <a href="{base}/settings/assistants/new" class="group flex h-10 flex-none items-center gap-2 pl-3 pr-2 text-sm text-gray-500 hover:bg-gray-100 md:rounded-xl {$page.url.pathname === `${base}/settings/assistants/new` ? '!bg-gray-100 !text-gray-800' : ''}" > <CarbonAdd /> <div class="truncate">Create new assistant</div> </a> {/if} <!-- Other Assistants --> <h4 class="pl-3 pt-3 text-[.7rem] text-gray-600 sm:pl-1">Other Assistants</h4> {#each data.assistants.filter((assistant) => !assistant.createdByMe) as assistant} <a href="{base}/settings/assistants/{assistant._id.toString()}" class="group flex h-10 flex-none items-center gap-2 pl-2 pr-2 text-sm text-gray-500 hover:bg-gray-100 md:rounded-xl {assistant._id.toString() === $page.params.assistantId ? '!bg-gray-100 !text-gray-800' : ''}" > {#if assistant.avatar} <img src="{base}/settings/assistants/{assistant._id.toString()}/avatar.jpg?hash={assistant.avatar}" alt="Avatar" class="h-6 w-6 rounded-full" /> {:else} <div class="flex size-6 items-center justify-center rounded-full bg-gray-300 font-bold uppercase text-gray-500" > {assistant.name[0]} </div> {/if} <div class="truncate">{assistant.name}</div> {#if assistant._id.toString() === $settings.activeModel} <div class="ml-auto rounded-lg bg-black px-2 py-1.5 text-xs font-semibold leading-none text-white" > Active </div> {/if} </a> {/each} <a href="{base}/assistants" class="group flex h-10 flex-none items-center gap-2 pl-3 pr-2 text-sm text-gray-500 hover:bg-gray-100 md:rounded-xl" ><CarbonArrowUpRight class="mr-1.5 shrink-0 text-xs " /> <div class="truncate">Browse Assistants</div> </a> {/if} <div class="my-2 mt-auto w-full border-b border-gray-200" /> <a href="{base}/settings" class="group flex h-10 flex-none items-center gap-2 pl-3 pr-2 text-sm text-gray-500 hover:bg-gray-100 max-md:order-first md:rounded-xl {$page.url.pathname === `${base}/settings` ? '!bg-gray-100 !text-gray-800' : ''}" > <UserIcon class="text-sm" /> Application Settings </a> </div> <div class="col-span-1 w-full overflow-y-auto overflow-x-clip px-1 max-md:pt-4 md:col-span-2 md:row-span-2" > <slot /> </div> </div>
0
hf_public_repos/chat-ui/src/routes/settings
hf_public_repos/chat-ui/src/routes/settings/(nav)/+page.svelte
<script lang="ts"> import Modal from "$lib/components/Modal.svelte"; import CarbonClose from "~icons/carbon/close"; import CarbonTrashCan from "~icons/carbon/trash-can"; import CarbonArrowUpRight from "~icons/carbon/arrow-up-right"; import { enhance } from "$app/forms"; import { base } from "$app/paths"; import { useSettingsStore } from "$lib/stores/settings"; import Switch from "$lib/components/Switch.svelte"; import { env as envPublic } from "$env/dynamic/public"; let isConfirmingDeletion = false; let settings = useSettingsStore(); </script> <div class="flex w-full flex-col gap-5"> <div class="flex flex-col items-start justify-between text-xl font-semibold text-gray-800"> <h2>Application Settings</h2> {#if !!envPublic.PUBLIC_COMMIT_SHA} <span class="text-sm font-light text-gray-500"> Latest deployment <span class="gap-2 font-mono" >{envPublic.PUBLIC_COMMIT_SHA.slice(0, 7)}</span > </span> {/if} </div> <div class="flex h-full max-w-2xl flex-col gap-2 max-sm:pt-0"> {#if envPublic.PUBLIC_APP_DATA_SHARING === "1"} <!-- svelte-ignore a11y-label-has-associated-control --> <label class="flex items-center"> <Switch name="shareConversationsWithModelAuthors" bind:checked={$settings.shareConversationsWithModelAuthors} /> <div class="inline cursor-pointer select-none items-center gap-2 pl-2"> Share conversations with model authors </div> </label> <p class="text-sm text-gray-500"> Sharing your data will help improve the training data and make open models better over time. </p> {/if} <!-- svelte-ignore a11y-label-has-associated-control --> <label class="mt-6 flex items-center"> <Switch name="hideEmojiOnSidebar" bind:checked={$settings.hideEmojiOnSidebar} /> <div class="inline cursor-pointer select-none items-center gap-2 pl-2 font-semibold"> Hide emoticons in conversation topics <p class="text-sm font-normal text-gray-500"> Emoticons are shown in the sidebar by default, enable this to hide them. </p> </div> </label> <!-- svelte-ignore a11y-label-has-associated-control --> <label class="mt-6 flex items-center"> <Switch name="disableStream" bind:checked={$settings.disableStream} /> <div class="inline cursor-pointer select-none items-center gap-2 pl-2 font-semibold"> Disable streaming tokens </div> </label> <!-- svelte-ignore a11y-label-has-associated-control --> <label class="mt-6 flex items-center"> <Switch name="directPaste" bind:checked={$settings.directPaste} /> <div class="inline cursor-pointer select-none items-center gap-2 pl-2 font-semibold"> Paste text directly into chat <p class="text-sm font-normal text-gray-500"> By default, when pasting long text into the chat, we treat it as a plaintext file. Enable this to paste directly into the chat instead. </p> </div> </label> <div class="mt-12 flex flex-col gap-3"> <a href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions" target="_blank" rel="noreferrer" class="flex items-center underline decoration-gray-300 underline-offset-2 hover:decoration-gray-700" ><CarbonArrowUpRight class="mr-1.5 shrink-0 text-sm " /> Share your feedback on HuggingChat</a > <button on:click|preventDefault={() => (isConfirmingDeletion = true)} type="submit" class="flex items-center underline decoration-gray-300 underline-offset-2 hover:decoration-gray-700" ><CarbonTrashCan class="mr-2 inline text-sm text-red-500" />Delete all conversations</button > </div> </div> {#if isConfirmingDeletion} <Modal on:close={() => (isConfirmingDeletion = false)}> <form use:enhance={() => { isConfirmingDeletion = false; }} method="post" action="{base}/conversations?/delete" class="flex w-full flex-col gap-5 p-6" > <div class="flex items-start justify-between text-xl font-semibold text-gray-800"> <h2>Are you sure?</h2> <button type="button" class="group" on:click|stopPropagation={() => (isConfirmingDeletion = false)} > <CarbonClose class="text-gray-900 group-hover:text-gray-500" /> </button> </div> <p class="text-gray-800"> This action will delete all your conversations. This cannot be undone. </p> <button type="submit" class="mt-2 rounded-full bg-red-700 px-5 py-2 text-lg font-semibold text-gray-100 ring-gray-400 ring-offset-1 transition-all hover:ring focus-visible:outline-none focus-visible:ring" > Confirm deletion </button> </form> </Modal> {/if} </div>
0
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/+page.ts
import { base } from "$app/paths"; import { redirect } from "@sveltejs/kit"; export async function load({ parent, params }) { const data = await parent(); const assistant = data.settings.assistants.find((id) => id === params.assistantId); if (!assistant) { redirect(302, `${base}/assistant/${params.assistantId}`); } return data; }
0
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/+page.svelte
<script lang="ts"> import { enhance } from "$app/forms"; import { base } from "$app/paths"; import { page } from "$app/stores"; import { goto } from "$app/navigation"; import { env as envPublic } from "$env/dynamic/public"; import { useSettingsStore } from "$lib/stores/settings"; import type { PageData } from "./$types"; import CarbonPen from "~icons/carbon/pen"; import CarbonTrash from "~icons/carbon/trash-can"; import CarbonCopy from "~icons/carbon/copy-file"; import CarbonFlag from "~icons/carbon/flag"; import CarbonLink from "~icons/carbon/link"; import CarbonChat from "~icons/carbon/chat"; import CarbonStar from "~icons/carbon/star"; import CarbonTools from "~icons/carbon/tools"; import CarbonLock from "~icons/carbon/locked"; import CopyToClipBoardBtn from "$lib/components/CopyToClipBoardBtn.svelte"; import ReportModal from "./ReportModal.svelte"; import IconInternet from "$lib/components/icons/IconInternet.svelte"; import ToolBadge from "$lib/components/ToolBadge.svelte"; import { ReviewStatus } from "$lib/types/Review"; export let data: PageData; $: assistant = data.assistants.find((el) => el._id.toString() === $page.params.assistantId); const settings = useSettingsStore(); const prefix = envPublic.PUBLIC_SHARE_PREFIX || `${envPublic.PUBLIC_ORIGIN || $page.url.origin}${base}`; $: shareUrl = `${prefix}/assistant/${assistant?._id}`; let displayReportModal = false; $: hasRag = assistant?.rag?.allowAllDomains || !!assistant?.rag?.allowedDomains?.length || !!assistant?.rag?.allowedLinks?.length || !!assistant?.dynamicPrompt; $: prepromptTags = assistant?.preprompt?.split(/(\{\{[^{}]*\}\})/) ?? []; </script> {#if displayReportModal} <ReportModal on:close={() => (displayReportModal = false)} /> {/if} <div class="flex h-full flex-col gap-2"> <div class="flex flex-col sm:flex-row sm:gap-6"> <div class="mb-4 flex justify-center sm:mb-0"> {#if assistant?.avatar} <img src={`${base}/settings/assistants/${assistant?._id}/avatar.jpg?hash=${assistant?.avatar}`} alt="Avatar" class="size-16 flex-none rounded-full object-cover sm:size-24" /> {:else} <div class="flex size-16 flex-none items-center justify-center rounded-full bg-gray-300 text-4xl font-semibold uppercase text-gray-500 sm:size-24" > {assistant?.name[0]} </div> {/if} </div> <div class="flex-1"> <div class="flex flex-wrap items-center gap-2"> <h1 class="break-words text-xl font-semibold"> {assistant?.name} </h1> {#if hasRag} <span class="inline-grid size-5 place-items-center rounded-full bg-blue-500/10" title="This assistant uses the websearch." > <IconInternet classNames="text-sm text-blue-600" /> </span> {/if} <span class="rounded-full border px-2 py-0.5 text-sm leading-none text-gray-500" >public</span > </div> {#if assistant?.description} <p class="mb-2 line-clamp-2 text-sm text-gray-500"> {assistant.description} </p> {/if} <p class="text-sm text-gray-500"> Model: <span class="font-semibold"> {assistant?.modelId} </span> <span class="text-gray-300">•</span> Created by <a class="underline" href="{base}/assistants?user={assistant?.createdByName}"> {assistant?.createdByName} </a> </p> <div class="flex flex-wrap items-center gap-x-4 gap-y-2 whitespace-nowrap text-sm text-gray-500 hover:*:text-gray-800 max-sm:justify-center" > <div class="w-full sm:w-auto"> <button class="mx-auto my-2 flex w-min items-center justify-center rounded-full bg-black px-3 py-1 text-base !text-white" name="Activate model" on:click|stopPropagation={() => { settings.instantSet({ activeModel: $page.params.assistantId, }); goto(`${base}/`); }} > <CarbonChat class="mr-1.5 text-sm" /> New chat </button> </div> {#if assistant?.createdByMe} <a href="{base}/settings/assistants/{assistant?._id}/edit" class="underline" ><CarbonPen class="mr-1.5 inline text-xs" />Edit </a> <form method="POST" action="?/delete" use:enhance> <button type="submit" class="flex items-center underline" on:click={(event) => { if (!confirm("Are you sure you want to delete this assistant?")) { event.preventDefault(); } }} > <CarbonTrash class="mr-1.5 inline text-xs" />Delete </button> </form> {:else} <form method="POST" action="?/unsubscribe" use:enhance> <button type="submit" class="underline"> <CarbonTrash class="mr-1.5 inline text-xs" />Remove</button > </form> <form method="POST" action="?/edit" use:enhance class="hidden"> <button type="submit" class="underline"> <CarbonCopy class="mr-1.5 inline text-xs" />Duplicate</button > </form> {#if !assistant?.reported} <button type="button" on:click={() => { displayReportModal = true; }} class="underline" > <CarbonFlag class="mr-1.5 inline text-xs" />Report </button> {:else} <button type="button" disabled class="text-gray-700"> <CarbonFlag class="mr-1.5 inline text-xs" />Reported</button > {/if} {/if} {#if data?.user?.isAdmin} <span class="rounded-full border px-2 py-0.5 text-sm leading-none text-gray-500" >{assistant?.review?.toLocaleUpperCase()}</span > {#if !assistant?.createdByMe} <form method="POST" action="?/delete" use:enhance> <button type="submit" class="flex items-center text-red-600 underline" on:click={(event) => { if (!confirm("Are you sure you want to delete this assistant?")) { event.preventDefault(); } }} > <CarbonTrash class="mr-1.5 inline text-xs" />Delete </button> </form> {/if} {#if assistant?.review === ReviewStatus.PRIVATE} <form method="POST" action="?/approve" use:enhance> <button type="submit" class="flex items-center text-green-600 underline"> <CarbonStar class="mr-1.5 inline text-xs" />Force feature</button > </form> {/if} {#if assistant?.review === ReviewStatus.PENDING} <form method="POST" action="?/approve" use:enhance> <button type="submit" class="flex items-center text-green-600 underline"> <CarbonStar class="mr-1.5 inline text-xs" />Approve</button > </form> <form method="POST" action="?/deny" use:enhance> <button type="submit" class="flex items-center text-red-600"> <span class="mr-1.5 font-light no-underline">X</span> <span class="underline">Deny</span> </button> </form> {/if} {#if assistant?.review === ReviewStatus.APPROVED || assistant?.review === ReviewStatus.DENIED} <form method="POST" action="?/unrequest" use:enhance> <button type="submit" class="flex items-center text-red-600 underline"> <CarbonLock class="mr-1.5 inline text-xs " />Reset review</button > </form> {/if} {/if} {#if assistant?.createdByMe && assistant?.review === ReviewStatus.PRIVATE} <form method="POST" action="?/request" use:enhance={async ({ cancel }) => { const confirmed = confirm( "Are you sure you want to request this assistant to be featured? Make sure you have tried the assistant and that it works as expected. " ); if (!confirmed) { cancel(); } }} > <button type="submit" class="flex items-center underline"> <CarbonStar class="mr-1.5 inline text-xs" />Request to be featured</button > </form> {/if} </div> </div> </div> <div> <h2 class="text-lg font-semibold">Direct URL</h2> <p class="pb-2 text-sm text-gray-500">Share this link for people to use your assistant.</p> <div class="flex flex-row gap-2 rounded-lg border-2 border-gray-200 bg-gray-100 py-2 pl-3 pr-1.5" > <input disabled class="flex-1 truncate bg-inherit" value={shareUrl} /> <CopyToClipBoardBtn value={shareUrl} classNames="!border-none !shadow-none !py-0 !px-1 !rounded-md" > <div class="flex items-center gap-1.5 text-gray-500 hover:underline"> <CarbonLink />Copy </div> </CopyToClipBoardBtn> </div> </div> <!-- two columns for big screen, single column for small screen --> <div class="mb-12 mt-3"> <h2 class="mb-2 inline font-semibold">System Instructions</h2> <div id="System Instructions" class="overlow-y-auto mt-2 box-border h-fit max-h-[240px] w-full overflow-y-auto whitespace-pre-line rounded-lg border-2 border-gray-200 bg-gray-100 p-2 disabled:cursor-not-allowed 2xl:max-h-[310px]" > {#if assistant?.dynamicPrompt} {#each prepromptTags as tag} {#if tag.startsWith("{{") && tag.endsWith("}}") && tag.includes("url=")} {@const url = tag.split("url=")[1].split("}}")[0]} <a target="_blank" href={url.startsWith("http") ? url : `//${url}`} class="break-words rounded-lg bg-blue-100 px-1 py-0.5 text-blue-800 hover:underline" > {tag}</a > {:else} {tag} {/if} {/each} {:else} {assistant?.preprompt} {/if} </div> {#if assistant?.tools?.length} <div class="mt-4"> <div class="mb-1 flex items-center gap-1"> <span class="inline-grid size-5 place-items-center rounded-full bg-purple-500/10" title="This assistant uses the websearch." > <CarbonTools class="text-xs text-purple-600" /> </span> <h2 class="font-semibold">Tools</h2> </div> <p class="w-full text-sm text-gray-500"> This Assistant has access to the following tools: </p> <ul class="mr-2 mt-2 flex flex-wrap gap-2.5 text-sm text-gray-800"> {#each assistant.tools as tool} <ToolBadge toolId={tool} /> {/each} </ul> </div> {/if} {#if hasRag} <div class="mt-4"> <div class="mb-1 flex items-center gap-1"> <span class="inline-grid size-5 place-items-center rounded-full bg-blue-500/10" title="This assistant uses the websearch." > <IconInternet classNames="text-sm text-blue-600" /> </span> <h2 class=" font-semibold">Internet Access</h2> </div> {#if assistant?.rag?.allowAllDomains} <p class="text-sm text-gray-500"> This Assistant uses Web Search to find information on Internet. </p> {:else if !!assistant?.rag?.allowedDomains && assistant?.rag?.allowedDomains.length} <p class="pb-4 text-sm text-gray-500"> This Assistant can use Web Search on the following domains: </p> <ul class="mr-2 flex flex-wrap gap-2.5 text-sm text-gray-800"> {#each assistant?.rag?.allowedDomains as domain} <li class="break-all rounded-lg border border-gray-200 bg-gray-100 px-2 py-0.5 leading-tight decoration-gray-400" > <a target="_blank" class="underline" href={domain}>{domain}</a> </li> {/each} </ul> {:else if !!assistant?.rag?.allowedLinks && assistant?.rag?.allowedLinks.length} <p class="pb-4 text-sm text-gray-500">This Assistant can browse the following links:</p> <ul class="mr-2 flex flex-wrap gap-2.5 text-sm text-gray-800"> {#each assistant?.rag?.allowedLinks as link} <li class="break-all rounded-lg border border-gray-200 bg-gray-100 px-2 py-0.5 leading-tight decoration-gray-400" > <a target="_blank" class="underline" href={link}>{link}</a> </li> {/each} </ul> {/if} {#if assistant?.dynamicPrompt} <p class="text-sm text-gray-500"> This Assistant has dynamic prompts enabled and can make requests to external services. </p> {/if} </div> {/if} </div> </div>
0
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/+page.server.ts
import { collections } from "$lib/server/database"; import { type Actions, fail, redirect } from "@sveltejs/kit"; import { ObjectId } from "mongodb"; import { authCondition } from "$lib/server/auth"; import { base } from "$app/paths"; import { env as envPublic } from "$env/dynamic/public"; import { env } from "$env/dynamic/private"; import { z } from "zod"; import type { Assistant } from "$lib/types/Assistant"; import { ReviewStatus } from "$lib/types/Review"; import { sendSlack } from "$lib/server/sendSlack"; async function assistantOnlyIfAuthor(locals: App.Locals, assistantId?: string) { const assistant = await collections.assistants.findOne({ _id: new ObjectId(assistantId) }); if (!assistant) { throw Error("Assistant not found"); } if ( assistant.createdById.toString() !== (locals.user?._id ?? locals.sessionId).toString() && !locals.user?.isAdmin ) { throw Error("You are not the author of this assistant"); } return assistant; } export const actions: Actions = { delete: async ({ params, locals }) => { let assistant; try { assistant = await assistantOnlyIfAuthor(locals, params.assistantId); } catch (e) { return fail(400, { error: true, message: (e as Error).message }); } await collections.assistants.deleteOne({ _id: assistant._id }); // and remove it from all users settings await collections.settings.updateMany( { assistants: { $in: [assistant._id] }, }, { $pull: { assistants: assistant._id }, } ); // and delete all avatars const fileCursor = collections.bucket.find({ filename: assistant._id.toString() }); // Step 2: Delete the existing file if it exists let fileId = await fileCursor.next(); while (fileId) { await collections.bucket.delete(fileId._id); fileId = await fileCursor.next(); } redirect(302, `${base}/settings`); }, report: async ({ request, params, locals, url }) => { // is there already a report from this user for this model ? const report = await collections.reports.findOne({ createdBy: locals.user?._id ?? locals.sessionId, object: "assistant", contentId: new ObjectId(params.assistantId), }); if (report) { return fail(400, { error: true, message: "Already reported" }); } const formData = await request.formData(); const result = z.string().min(1).max(128).safeParse(formData?.get("reportReason")); if (!result.success) { return fail(400, { error: true, message: "Invalid report reason" }); } const { acknowledged } = await collections.reports.insertOne({ _id: new ObjectId(), contentId: new ObjectId(params.assistantId), object: "assistant", createdBy: locals.user?._id ?? locals.sessionId, createdAt: new Date(), updatedAt: new Date(), reason: result.data, }); if (!acknowledged) { return fail(500, { error: true, message: "Failed to report assistant" }); } if (env.WEBHOOK_URL_REPORT_ASSISTANT) { const prefixUrl = envPublic.PUBLIC_SHARE_PREFIX || `${envPublic.PUBLIC_ORIGIN || url.origin}${base}`; const assistantUrl = `${prefixUrl}/assistant/${params.assistantId}`; const assistant = await collections.assistants.findOne<Pick<Assistant, "name">>( { _id: new ObjectId(params.assistantId) }, { projection: { name: 1 } } ); const username = locals.user?.username; await sendSlack( `🔴 Assistant <${assistantUrl}|${assistant?.name}> reported by ${ username ? `<http://hf.co/${username}|${username}>` : "non-logged in user" }.\n\n> ${result.data}` ); } return { from: "report", ok: true, message: "Assistant reported" }; }, subscribe: async ({ params, locals }) => { const assistant = await collections.assistants.findOne({ _id: new ObjectId(params.assistantId), }); if (!assistant) { return fail(404, { error: true, message: "Assistant not found" }); } // don't push if it's already there const settings = await collections.settings.findOne(authCondition(locals)); if (settings?.assistants?.includes(assistant._id)) { return fail(400, { error: true, message: "Already subscribed" }); } const result = await collections.settings.updateOne(authCondition(locals), { $addToSet: { assistants: assistant._id }, }); // reduce count only if push succeeded if (result.modifiedCount > 0) { await collections.assistants.updateOne({ _id: assistant._id }, { $inc: { userCount: 1 } }); } return { from: "subscribe", ok: true, message: "Assistant added" }; }, unsubscribe: async ({ params, locals }) => { const assistant = await collections.assistants.findOne({ _id: new ObjectId(params.assistantId), }); if (!assistant) { return fail(404, { error: true, message: "Assistant not found" }); } const result = await collections.settings.updateOne(authCondition(locals), { $pull: { assistants: assistant._id }, }); // reduce count only if pull succeeded if (result.modifiedCount > 0) { await collections.assistants.updateOne({ _id: assistant._id }, { $inc: { userCount: -1 } }); } redirect(302, `${base}/settings`); }, deny: async ({ params, locals, url }) => { return await setReviewStatus({ assistantId: params.assistantId, locals, status: ReviewStatus.DENIED, url, }); }, approve: async ({ params, locals, url }) => { return await setReviewStatus({ assistantId: params.assistantId, locals, status: ReviewStatus.APPROVED, url, }); }, request: async ({ params, locals, url }) => { return await setReviewStatus({ assistantId: params.assistantId, locals, status: ReviewStatus.PENDING, url, }); }, unrequest: async ({ params, locals, url }) => { return await setReviewStatus({ assistantId: params.assistantId, locals, status: ReviewStatus.PRIVATE, url, }); }, }; async function setReviewStatus({ locals, assistantId, status, url, }: { locals: App.Locals; assistantId?: string; status: ReviewStatus; url: URL; }) { if (!assistantId) { return fail(400, { error: true, message: "Assistant ID is required" }); } const assistant = await collections.assistants.findOne({ _id: new ObjectId(assistantId), }); if (!assistant) { return fail(404, { error: true, message: "Assistant not found" }); } if ( !locals.user || (!locals.user.isAdmin && assistant.createdById.toString() !== locals.user._id.toString()) ) { return fail(403, { error: true, message: "Permission denied" }); } // only admins can set the status to APPROVED or DENIED // if the status is already APPROVED or DENIED, only admins can change it if ( (status === ReviewStatus.APPROVED || status === ReviewStatus.DENIED || assistant.review === ReviewStatus.APPROVED || assistant.review === ReviewStatus.DENIED) && !locals.user?.isAdmin ) { return fail(403, { error: true, message: "Permission denied" }); } const result = await collections.assistants.updateOne( { _id: assistant._id }, { $set: { review: status } } ); if (result.modifiedCount === 0) { return fail(500, { error: true, message: "Failed to update review status" }); } if (status === ReviewStatus.PENDING) { const prefixUrl = envPublic.PUBLIC_SHARE_PREFIX || `${envPublic.PUBLIC_ORIGIN || url.origin}${base}`; const assistantUrl = `${prefixUrl}/assistant/${assistantId}`; const username = locals.user?.username; await sendSlack( `🟢 Assistant <${assistantUrl}|${assistant?.name}> requested to be featured by ${ username ? `<http://hf.co/${username}|${username}>` : "non-logged in user" }.` ); } return { from: "setReviewStatus", ok: true, message: "Review status updated" }; }
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hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants
hf_public_repos/chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/ReportModal.svelte
<script lang="ts"> import { applyAction, enhance } from "$app/forms"; import { invalidateAll } from "$app/navigation"; import Modal from "$lib/components/Modal.svelte"; import { createEventDispatcher } from "svelte"; const dispatch = createEventDispatcher<{ close: void }>(); let reason = ""; </script> <Modal on:close> <form method="POST" action="?/report" use:enhance={() => { return async ({ result }) => { await applyAction(result); dispatch("close"); invalidateAll(); }; }} class="w-full min-w-64 p-4" > <span class="mb-1 text-sm font-semibold">Report content</span> <p class="text-sm text-gray-500"> Please provide a brief description of why you are reporting this content. </p> <textarea name="reportReason" class="mt-6 max-h-48 w-full resize-y rounded-lg border-2 border-gray-200 bg-gray-100 p-2 text-smd" placeholder="Reason(s) for the report" maxlength="128" bind:value={reason} /> <div class="flex w-full flex-row justify-between px-2 pt-4"> <button type="button" class="text-sm text-gray-700 hover:underline" on:click={() => dispatch("close")}>Cancel</button > <button type="submit" class="rounded-full bg-black px-4 py-2 text-sm font-semibold text-white md:px-8" disabled={!reason} class:bg-gray-200={!reason} class:!text-gray-400={!reason} > Submit report </button> </div> </form> </Modal>