Added a few sections to the readme and reorganized it (#264)

README.md

@@ -16,64 +16,113 @@ app_port: 3000
 
 A chat interface using open source models, eg OpenAssistant. It is a SvelteKit app and it powers the [HuggingChat app on hf.co/chat](https://huggingface.co/chat).
 
-## Launch
+1. [Setup](#setup)
+2. [Launch](#launch)
+3. [Extra parameters](#extra-parameters)
+4. [Deploying to a HF Space](#deploying-to-a-hf-space)
+5. [Building](#building)
+
+## 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:
 
 ```bash
-npm install
-npm run dev
+MONGODB_URL=<the URL to your mongoDB instance>
+HF_ACCESS_TOKEN=<your access token>
 ```
 
-## Environment
-
-Default configuration is in `.env`. Put custom config and secrets in `.env.local`, it will override the values in `.env`.
+### Database
 
-Check out [.env](./.env) to see what needs to be set.
+The chat history is stored in a MongoDB instance, and having a DB instance available is needed for Chat UI to work.
 
-Basically you need to create a `.env.local` with the following contents:
+You can use a local MongoDB instance. The easiest way is to spin one up is using docker:
 
-```
-MONGODB_URL=<url to mongo, for example a free MongoDB Atlas sandbox instance>
-HF_ACCESS_TOKEN=<your HF access token from https://huggingface.co/settings/tokens>
+```bash
+docker run -d -p 27017:27017 --name mongo-chatui mongo:latest
 ```
 
-## Duplicating to a Space
+In which case the url of your DB will be `MONGODB_URL=mongodb://localhost:27017`.
 
-Create a `DOTENV_LOCAL` secret to your space with the following contents:
+Alternatively, you can use a [free MongoDB Atlas](https://www.mongodb.com/pricing) instance for this, Chat UI should fit comfortably within the free tier. After which you can set the `MONGODB_URL` variable in `.env.local` to match your instance.
 
-```
-MONGODB_URL=<url to mongo, for example a free MongoDB Atlas sandbox instance>
-HF_ACCESS_TOKEN=<your HF access token from https://huggingface.co/settings/tokens>
-```
+### Hugging Face Access Token
 
-Where the contents in `<...>` are replaced by the MongoDB URL and your [HF Access Token](https://huggingface.co/settings/tokens).
+You will need a Hugging Face access token to run Chat UI locally, using the remote inference endpoints. You can get one from [your Hugging Face profile](https://huggingface.co/settings/tokens).
 
-## Running Local Inference
+## Launch
 
-Both the example above use the HF Inference API or HF Endpoints API.
+After you're done with the `.env.local` file you can run Chat UI locally with:
 
-If you want to run the model locally, you need to run this inference server locally: https://github.com/huggingface/text-generation-inference
+```bash
+npm install
+npm run dev
+```
 
-And add this to your `.env.local`, feel free to adjust/remove the parameters and the preprompt:
+## 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:
+
+```bash
+OPENID_PROVIDER_URL=<your OIDC issuer>
+OPENID_CLIENT_ID=<your OIDC client ID>
+OPENID_CLIENT_SECRET=<your OIDC client secret>
 ```
-MODELS=`[{
-  "name": "...",
-  "endpoints": [{"url": "http://127.0.0.1:8080/generate_stream"}],
-  "userMessageToken": "<|prompter|>",
-  "assistantMessageToken": "<|assistant|>",
-  "messageEndToken": "</s>",
-  "preprompt": "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n-----\n",
-  "parameters": {
-    "temperature": 0.9,
-    "top_p": 0.95,
-    "repetition_penalty": 1.2,
-    "top_k": 50,
-    "truncate": 1000,
-    "max_new_tokens": 1000
+
+These variables will enable the openID sign-in modal for users.
+
+### 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 :
+
+```json
+MODELS=`[
+  {
+    "name": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+    "datasetName": "OpenAssistant/oasst1",
+    "description": "A good alternative to ChatGPT",
+    "websiteUrl": "https://open-assistant.io",
+    "userMessageToken": "<|prompter|>",
+    "assistantMessageToken": "<|assistant|>",
+    "messageEndToken": "</s>",
+    "preprompt": "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n-----\n",
+    "promptExamples": [
+      {
+        "title": "Write an email from bullet list",
+        "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
+      }, {
+        "title": "Code a snake game",
+        "prompt": "Code a basic snake game in python, give explanations for each step."
+      }, {
+        "title": "Assist in a task",
+        "prompt": "How do I make a delicious lemon cheesecake?"
+      }
+    ],
+    "parameters": {
+      "temperature": 0.9,
+      "top_p": 0.95,
+      "repetition_penalty": 1.2,
+      "top_k": 50,
+      "truncate": 1000,
+      "max_new_tokens": 1024
+    }
   }
-}]`
+]`
 ```
 
+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.
+
+### Running your own models
+
+If you want to, you can even run your own models, by having a look at our endpoint project, [text-generation-inference](https://github.com/huggingface/text-generation-inference). You can then add your own endpoint to the `MODELS` variable in `.env.local` and it will be picked up 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: