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 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 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).
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 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 Google Drive using PyAirbyte, storing it in Pinecone, and then using LangChain to perform RAG on the stored data.
Learn how to set up an end-to-end RAG pipeline using Airbyte Cloud, Amazon S3, and 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 leverage Milvus and Airbyte to embed smart similarity search functionality into your applications