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#1	[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.
#2
#3	![Slack AI Demo](/images/slack-ai.png)
#4
#5	## Getting started
#6
#7	Create a Slack AI involves 3 steps
#8
#9	* Create slack user
#10	* Set environment variables
#11	* Run the app locally
#12
#13	### Step 1: Create Slack user token
#14
#15	Follow the steps given below to fetch your slack user token to get data through Slack APIs:
#16
#17	1. Create a workspace on Slack if you don’t have one already by clicking [here](https://slack.com/intl/en-in/).
#18	2. Create a new App on your Slack account by going [here](https://api.slack.com/apps).
#19	3. Select `From Scratch`, then enter the App Name and select your workspace.
#20	4. Navigate to `OAuth & Permissions` tab from the left sidebar and go to the `scopes` section. Add the following scopes under `User Token Scopes`:
#21
#22	```
#23	# Following scopes are needed for reading channel history
#24	channels:history
#25	channels:read
#26
#27	# Following scopes are needed to fetch list of channels from slack
#28	groups:read
#29	mpim:read
#30	im:read
#31	```
#32
#33	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.
#34	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.
#35
#36	### Step 2: Set environment variables
#37
#38	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`.
#39
#40
#41	<Note>
#42	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.
#43	</Note>
#44
#45	### Step 3: Run app locally
#46
#47	Follow the instructions given below to run app locally based on your development setup (with docker or without docker):
#48
#49	#### With docker
#50
#51	```bash
#52	docker-compose build
#53	ec start --docker
#54	```
#55
#56	#### Without docker
#57
#58	```bash
#59	ec install-reqs
#60	ec start
#61	```
#62
#63	Finally, you will have the Slack AI frontend running on http://localhost:3000. You can also access the REST APIs on http://localhost:8000.
#64
#65	## Credits
#66
#67	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.
#68

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