Embedchain Examples Repo contains code on how to build your own Slack AI to chat with the unstructured data lying in your slack channels.
Create a Slack AI involves 3 steps
Follow the steps given below to fetch your slack user token to get data through Slack APIs:
Create a workspace on Slack if you donโt have one already by clicking here.
Create a new App on your Slack account by going here.
Select From Scratch
, then enter the App Name and select your workspace.
Navigate to OAuth & Permissions
tab from the left sidebar and go to the scopes
section. Add the following scopes under User Token Scopes
:
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.
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.
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
.
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.
Follow the instructions given below to run app locally based on your development setup (with docker or without docker):
Finally, you will have the Slack AI frontend running on http://localhost:3000. You can also access the REST APIs on http://localhost:8000.
This demo was built using the Embedchainโs full stack demo template. Follow the instructions given here to create your own full stack RAG application.