Unlock the power of unstructured data with LLMs
Airbyte moves your unstructured and semi-structured data and makes it accessible to any vector database & popular LLM frameworks.
Utilize LLMs to extract relevant information & insights from your data
Build RAG pipelines, employ ML techniques for data classification, or fine tune your language models, all leveraging your data.
Empower GenAI workflows by moving data into AI-enabled data warehouses
Leverage the native vector support offered by Snowflake Cortex and Bigquery’s Vertex AI to power your Gen AI applications.
Use Airbyte’s Snowflake Cortex destination to directly store vector data in Snowflake!
Use Airbyte’s Snowflake Cortex destination to directly store vector data in Snowflake!
Get started with Snowflake Cortex
Build Retrieval based LLM apps on top of synced data
Add a retrieval based conversational interface to raw or transformed data loaded using Airbyte.
Use your favorite LLM frameworks like LangChain or LlamaIndex. Build AI co-pilots, agents, workflows and more.
Use your favorite LLM frameworks like LangChain or LlamaIndex. Build AI co-pilots, agents, workflows and more.
Build a chat app using LangChain
Understand your data via LLM-powered actionable insights
Use Airbyte to combine data from diverse sources, improving the accuracy of your NLP tasks.
Provide actionable insights into your data by building ML applications involving sentiment analysis, clustering and classification.
Provide actionable insights into your data by building ML applications involving sentiment analysis, clustering and classification.
Check our MindsDB tutorial
Create training datasets & fine tune ML models specific to your use case
Train models using domain-specific or proprietary data from your company and customers.
Models drift over time. Airbyte ensures you have the latest data needed to train and maintain model performance over time.
Models drift over time. Airbyte ensures you have the latest data needed to train and maintain model performance over time.
Learn how to fine tune your LLM
Get your data LLM ready!
Flexible data movement that works seamlessly with your LLM tooling and existing workflows.
Consolidate your unstructured and structured data in one place
Leverage Airbyte’s large catalog of source connectors to move raw data into your preferred storage destination.You have full control on how you transform your raw data. Use our intuitive UI to set up data connections, or deploy our open-source connectors in Kubernetes.
Get started with Airbyte
Pull your data directly into a vector database destination
Automatic chunking and indexing options lets you transform your raw data and store it in 8 different vector db destinations. Generate embedding using our pre-built set of LLM providers or provide your own. Compatible with OpenAI, Cohere, Anthropic and other popular LLM providers.
Browse vector database destinations
Streamline data transformation using our Python library
PyAirbyte packages Airbyte’s catalog of sources into a python library allowing you to load data from Airbyte sources into a local cache. Load data from various sources and merge or transform it in code before storing it to your preferred database.
Learn more about PyAirbyte
Deploy your pipelines your way
Self-hosted or cloud-hosted, connectors for your own usage or embedded in your own product.
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.