Overview of AI features
The Data Productivity Cloud incorporates the power of generative AI and large language models (LLMs) in several ways.
Remember to check out our New Features blog to read about new features (including generative AI) regularly entering the Data Productivity Cloud. This blog is released once per week and rounds up our changelog.
Maia
With Maia, you can create data pipelines simply using plain language instructions, taking advantage of advanced AI technology. Maia accompanies you throughout your data pipeline journey, offering help at every step.
Read these pages to get started:
Generate commit messages with Maia
Maia can write your Commit messages for you using the Conventional Commits format.
Prompt components
Prompt components use a large language model (LLM) to provide responses to user-composed prompts. The components take inputs from a source table, combine the inputs with user prompts, and send this data to the LLM for processing.
These prompt components are currently available in the Data Productivity Cloud:
Vector database components
The Data Productivity Cloud can connect to your Pinecone vector database using the following components:
- Pinecone Vector Upsert converts data stored in your cloud data warehouse into embeddings, and then stores these embeddings as vectors in your Pinecone vector database.
- Pinecone Vector Query ingests text search strings and returns text data associated with similar vectors in your Pinecone vector database.
Automatic documentation generation with AI notes
Documenting your pipelines with on-canvas notes lets you provide other users with information on the purpose of the pipelines. The AI Note feature takes the effort out of documenting a pipeline yourself by using generative AI to analyze the pipeline's features and write appropriate notes automatically. You can regenerate notes, extend notes, and shorten notes.