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

Learn how to use polygon.io as a data source and use the Langchain experimental agent.

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 scrape data from a website and load it in a database using PyAirbyte and LangChain. Integrating web data into LLMs can enhance their performance by providing up-to-date and relevant information.

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

Learn how to use PyAirbyte to load data from Facebook marketing, store the data in Milvus (Zilliz) vector store and perform a short RAG demo (using OpenAI/LangChain).

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 an end-to-end RAG pipeline, extracting data from Gitlab using PyAirbyte, storing it in Qdrant, and then using LangChain to perform RAG on the stored data.

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 set up a RAG pipeline from GitHub, using PyAirbyte, storing the data in Chroma, using LangChain to perform RAG on the stored data.

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.