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 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 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).

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 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 S3 -> BigQuery -> Pinecone for interacting with fetched data through an LLM and form a full fledged RAG.

Learn how to set up an end-to-end RAG pipeline using Airbyte Cloud, Amazon S3, and Snowflake Cortex.

Learn how to build a GitHub documentation chatbot with PyAirbyte and PG Vector for seamless data retrieval and enhanced user experience.

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

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.

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

Discover how to build efficient knowledge management systems using PyAirbyte and vector databases for streamlined data access.