Explore popular use cases to empower your teams
RAG based recommendation system on Shopify, using PyAirbyte, Langchain and Pinecone
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.
Streamlining Amazon Product Review Analysis with Apify and Snowflake Cortex
Learn how to scrape customer reviews from an Amazon product page, loading the data into Snowflake Cortex, and performing summarization.
Sentiment analysis using Google sheets and Snowflake Cortex
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.
End-to-end RAG using GitHub, PyAirbyte, and Langchain
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.
End-to-end RAG using Airbyte's Terraform, dbt, Notion, BigQuery and Pinecone
Learn how to build a full data stack using Airbyte Cloud, Terraform, and dbt to move data from Notion -> BigQuery -> Pinecone for interacting with fetched data through an LLM and form a full fledged RAG.
End-to-end RAG using Airbyte Cloud, S3, BigQuery and Pinecone
Learn how to build a full data stack using Airbyte Cloud, Terraform, and dbt to move data from S3 -> BigQuery -> Pinecone for interacting with fetched data through an LLM and form a full fledged RAG.
End-to-end RAG with Airbyte Cloud, Microsoft Sharepoint, and Milvus (Zilliz)
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.
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.
Quickstart for End-to-end RAG using Gitlab, PyAirbyte, and Qdrant
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.
End-to-end RAG using Airbyte Cloud, S3 and Pinecone
Learn how to build a full data stack using Airbyte Cloud, Terraform, and dbt to move data from S3 -> BigQuery -> Pinecone for interacting with fetched data through an LLM and form a full fledged RAG.
Build an AI chatbot with Snowflake Cortex
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.
Perform RAG with Vectara
Learn how to use data stored in Airbyte's Vectara destination to perform RAG.
