Chat with your data using OpenAI, Pinecone, Airbyte and Langchain

This blog post discusses how to integrate diverse data sources and keep them up-to-date automatically using Airbyte's new vector database destination, which makes it easy for data to retrieve relevant context for question answering use cases via LangChain. The tutorial demonstrates a real-world use case of leveraging vector databases and LLMs to make sense out of unstructured data by creating a Slack bot that can answer questions about Airbyte's proprietary data, including information from the documentation website, existing Github issues, and previous Slack conversations. The process involves fetching Github issues, loading into a vector database, creating a connection between the source and destination, and setting up a chat interface using Langchain.