Learn how to measure customer support sentiment analysis using GPT, Airbyte, and MindsDB. Set up sentiment analysis of Intercom chats, extract and analyze the data with GPT models, and visualize the results using Metabase.

Learn how to set up a maintainable and scalable pipeline for integrating diverse data sources into large language models using Airbyte, Dagster, and LangChain.

Learn how to build a connector development support bot for Slack that knows all your APIs, open feature requests and previous Slack conversations by heart

Learn how to chat with your data warehouse using Airbyte and LlamaIndex. Discover the power of querying databases with natural language, bypassing the need for SQL expertise and memorization of complex database schemas.

Learn how to leverage Milvus and Airbyte to embed smart similarity search functionality into your applications

Learn how to scrape customer reviews from an Amazon product page, loading the data into Snowflake Cortex, and performing summarization.

Learn how to load data from GitHub airbyte-source into Snowflake using PyAirbyte, and afterwards convert the stream data into vector.

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 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 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 data stored in Airbyte's Vectara destination to perform RAG.

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.