Leverage unstructured data with LLMs
State-of-the-art AI can generate insights from unstructured and semi-structured data.
Centralize unstructured data from all your sources
Leverage our large connector catalog to pull in text-based data from existing sources.
Embed text data on the fly
Automatically calculate text embeddings during the ELT process to capture the meaning of unstructured fields in your data.
Chat with your data
Use vector database search capabilities and LLMs to automatically answer questions about your data.
Make your data accessible to LLMs
Extract unstructured data for all your customers
Use the Airbyte API behind the scenes to extract and centralize your customers documents and unstructured data to make it useful to them using LLMs
Get started with Powered By Airbyte
Add a conversational interface to your data
Combine Airbyte’s large catalog of source connectors and the power of vector databases to create a conversational interface that can answer questions about all the data in your organization.
Check a tutorial with LlamaIndex
Extract structured information from your text data
Combining the power of GPT models, Airbyte data sync capabilities, and AI logic automation, businesses can gain actionable insights and revolutionize their customer service strategies.
Measuring sentiment analysis, classifying or categorizing your unstructured data, everything becomes possible
Check a tutorial with MindsDB
Locate the text data source connectors you need
Centralize that unstructured and semi-structured data in any of the vector databases we support, so you can calculate text embeddings and structure that data.
Check our tutorials
Full-Stack AI Task Prioritization Chatbot with Asana, Airbyte, Milvus, and Next.js
Build a quick full-stack AI application which arranges your Asana tasks for you in order of priority using MIlvus, Airbyte Cloud, and Next.js.
A Beginner's Guide to Qdrant: Installation, Setup, and Basic Operations
Learn how to install and set up Qdrant, a powerful vector database for AI applications. This beginner's guide walks you through basic operations to manage and query embeddings.
End-to-end RAG with Airbyte Cloud, Google Drive, and Snowflake Cortex
Learn how to build an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from Google Drive using Airbyte Cloud to load it on Snowflake Cortex.