🤖 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
  • Make sure your slack user token includes search scope.

Example

Get Started

This will automatically retrieve data from the workspace associated with the user’s token.

import os
from embedchain import App

os.environ["SLACK_USER_TOKEN"] = "xoxp-xxx"
app = App()

app.add("in:general", data_type="slack")

result = app.query("what are the messages in general channel?")

print(result)

Customize your SlackLoader

1. Setup the Slack loader by configuring the Slack Webclient.

from embedchain.loaders.slack import SlackLoader

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

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

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:

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)