This is the code for the tutorial published on the Airbyte blog
It implements a chat bot that uses contextual information stored in Pinecone, Langchain to orchestrate an LLM and the Slack sdk to provide a Slack bot that can answer Airbyte connector builder-related questions on Slack.
If you like this project, leave us a star ⭐ on the main Airbyte Repo!
You need locally installed python
- Follow the tutorial to create a Pinecone index and populate it with data via Airbyte
- Run
python -m venv venv
to create a virtual environment - Run
source venv/bin/activate
to activate the virtual environment - Run
pip install -r requirements.txt
to install the dependencies
- Run
export PINECONE_API_KEY=<your pinecone api key>
to set the pinecone api key - Run
export PINECONE_INDEX_NAME=<your pinecone index name>
to set the pinecone index name - Run
export PINECONE_ENV=<your pinecone env>
to set the pinecone env - Run
export OPENAI_API_KEY=<your openai api key>
to set the openai api key - Run
python localbot.py
to start the bot (localbot_adapted.py
uses improved prompts for better results)
- Use the
slack_manifest.yml
file to create a Slack app and install it in your workspace. - Run
export PINECONE_API_KEY=<your pinecone api key>
to set the pinecone api key - Run
export PINECONE_INDEX_NAME=<your pinecone index name>
to set the pinecone index name - Run
export PINECONE_ENV=<your pinecone env>
to set the pinecone env - Run
export OPENAI_API_KEY=<your openai api key>
to set the openai api key - Run
export SLACK_APP_TOKEN=<your slack app token>
to set the slack app token - Run
export SLACK_BOT_TOKEN=<your slack bot token>
to set the slack bot token - Run
python slackbot.py
to start the bot
Again, leave us a star ⭐ on the main Airbyte repo!