mem0/docs/data-sources/slack.mdx

---
title: '🤖 Slack'
---

## Pre-requisite
- Download required packages by running `pip install --upgrade "embedchain[slack]"`.
- Configure your slack bot token as environment variable `SLACK_USER_TOKEN`.
    - Find your user token on your [Slack Account](https://api.slack.com/authentication/token-types)
    - Make sure your slack user token includes [search](https://api.slack.com/scopes/search:read) scope.

## Example
1. Setup the Slack loader by configuring the Slack Webclient.
```Python
from embedchain.loaders.slack import SlackLoader

os.environ["SLACK_USER_TOKEN"] = "xoxp-*"

loader = SlackLoader()

"""
config = {
    'base_url': slack_app_url,
    'headers': web_headers,
    'team_id': slack_team_id,
}

loader = SlackLoader(config)
"""
```

NOTE: you can also pass the `config` with `base_url`, `headers`, `team_id` to setup your SlackLoader.

2. Once you setup the loader, you can create an app and load data using the above slack loader
```Python
import os
from embedchain.pipeline import Pipeline as App

app = App()

app.add("in:random", data_type="slack", loader=loader)
question = "Which bots are available in the slack workspace's random channel?"
# Answer: The available bot in the slack workspace's random channel is the Embedchain bot.
```

3. We automatically create a chunker to chunk your slack data, however if you wish to provide your own chunker class. Here is how you can do that:
```Python
from embedchain.chunkers.slack import SlackChunker
from embedchain.config.add_config import ChunkerConfig

slack_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
slack_chunker = SlackChunker(config=slack_chunker_config)

app.add(slack_chunker, data_type="slack", loader=loader, chunker=slack_chunker)
```