mem0/embedchain/docs/examples/slack-AI.mdx

[Embedchain Examples Repo](https://github.com/embedchain/examples) contains code on how to build your own Slack AI to chat with the unstructured data lying in your slack channels.

![Slack AI Demo](/images/slack-ai.png)

## Getting started

Create a Slack AI involves 3 steps

* Create slack user
* Set environment variables
* Run the app locally

### Step 1: Create Slack user token

Follow the steps given below to fetch your slack user token to get data through Slack APIs:

1. Create a workspace on Slack if you don’t have one already by clicking [here](https://slack.com/intl/en-in/).
2. Create a new App on your Slack account by going [here](https://api.slack.com/apps).
3. Select `From Scratch`, then enter the App Name and select your workspace.
4. Navigate to `OAuth & Permissions` tab from the left sidebar and go to the `scopes` section. Add the following scopes under `User Token Scopes`:

    ```
    # Following scopes are needed for reading channel history
    channels:history
    channels:read

    # Following scopes are needed to fetch list of channels from slack
    groups:read
    mpim:read
    im:read
    ```

5. Click on the `Install to Workspace` button under `OAuth Tokens for Your Workspace` section in the same page and install the app in your slack workspace.
6. After installing the app you will see the `User OAuth Token`, save that token as you will need to configure it as `SLACK_USER_TOKEN` for this demo.

### Step 2: Set environment variables

Navigate to `api` folder and set your `HUGGINGFACE_ACCESS_TOKEN` and `SLACK_USER_TOKEN` in `.env.example` file. Then rename the `.env.example` file to `.env`.


<Note>
By default, we use `Mixtral` model from Hugging Face. However, if you prefer to use OpenAI model, then set `OPENAI_API_KEY` instead of `HUGGINGFACE_ACCESS_TOKEN` along with `SLACK_USER_TOKEN` in `.env` file, and update the code in `api/utils/app.py` file to use OpenAI model instead of Hugging Face model.
</Note>

### Step 3: Run app locally

Follow the instructions given below to run app locally based on your development setup (with docker or without docker):

#### With docker

```bash
docker-compose build
ec start --docker
```

#### Without docker

```bash
ec install-reqs
ec start
```

Finally, you will have the Slack AI frontend running on http://localhost:3000. You can also access the REST APIs on http://localhost:8000.

## Credits

This demo was built using the Embedchain's [full stack demo template](https://docs.embedchain.ai/get-started/full-stack). Follow the instructions [given here](https://docs.embedchain.ai/get-started/full-stack) to create your own full stack RAG application.