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 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.
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.
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.
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.
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.
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 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 a simple RAG (Retrieval-Augmented Generation) pipeline with Milvus Lite and PyAirbyte, for a fully local development in Python.
Learn how to load data from GitHub airbyte-source into Snowflake using PyAirbyte, and afterwards convert the stream data into vector.
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 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.