The langchain-airbyte package integrates LangChain with Airbyte. It has a very powerful function AirbyteLoader which can be used to load data as document into langchain from any Airbyte source.

Learn how to build a robust Large Language Model application using ChromaDB for vector storage and Airbyte for data integration, simplifying your AI development workflow.

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.

Learn how to build an end-to-end RAG pipeline, extracting data from Microsoft Sharepoint using Airbyte Cloud, loading it on Milvus (Zilliz), and then using LangChain to perform RAG on the stored data.

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.

Learn how to build a RAG pipeline, extracting data from a file source using PyAirbyte, storing it in a Pinecone vector store, and then using LangChain to perform RAG on the stored data.

Learn how to build an end-to-end RAG pipeline, extracting data from Salesforce using Airbyte Cloud to load it on Weaviate and set up a RAG there.

Learn how to build an end-to-end RAG pipeline, extracting data from an S3 bucket using PyAirbyte, storing it in a Pinecone vector store, and then use LangChain to perform RAG on the stored data.

Learn how to build a simple RAG (Retrieval-Augmented Generation) pipeline with Milvus Lite and PyAirbyte, for a fully local development in Python.

Learn how to build a RAG pipeline, extracting data from Jira using PyAirbyte, storing it in a Pinecone vector store, and then using LangChain to perform RAG on the stored data.

Learn how to build an end-to-end RAG pipeline, extracting data from Google Drive using PyAirbyte, storing it in Pinecone, and then using LangChain to perform RAG on the stored data.

Learn how to load data from Github into Weaviate using PyAirbyte, then to use source-github and its stream 'issues'.