Learn how to load data from GitHub airbyte-source into Snowflake using PyAirbyte, and afterwards convert the stream data into vector.

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 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 S3 using Airbyte Cloud to load it on Vectara and set up a RAG there.

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 use data stored in Airbyte's Vectara destination to perform RAG.

Lean how to use data stored in Airbyte's Snowflake Cortex destination to perform RAG by building a Product Assistant—an AI chatbot capable of answering product-related questions using data from multiple Airbyte-related sources.

Learn how to use the PyAirbyte library to read records from Github, converts those records to documents, which can then be passed to LangChain for RAG.

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 load user review data from Google Sheets intoS nowflake Cortex based vector store, and perform sentiment analysis using Snowflake Cortex's sentiment function.

Build a social media sentiment analyzer using Airbyte and Twitter API. Simplify data integration and analyze trends effectively.