Connector dev bot

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!

How to run

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 the bot locally in your terminal

• 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)

Run the bot on Slack

• 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!