As companies rush to deploy enterprise AI use cases with Retrieval-augmented Generation (RAG) architectures, many will struggle to deliver real value due to outdated data. Vector databases are essential in RAG pipelines, integrating external data sources into the generative process and allowing large language models (LLMs) to produce more informed and accurate outputs. Ensuring these databases are updated in real-time is crucial for maintaining effective AI solutions.

Watch the recording of this live demo where we guide you through a real-time data ingestion use case for a vector database and showcase how to do it with Decodable's latest managed PyFlink offering. We'll create a PyFlink job that reads records from an operational database, transforms them into vector embeddings, and streams them into a vector database to support RAG architectures. We'll then deploy the job to the Decodable platform and demonstrate real-time data transformation in action.

What You'll Learn by Watching:

• Real-time Data Ingestion: How to set up and manage real-time data ingestion for vector databases using PyFlink.
• RAG Pipeline Optimization: Best practices for optimizing RAG pipelines with up-to-date, accurate data to enhance AI outputs.
• Platform Deployment: Steps to deploy and manage PyFlink jobs on the Decodable platform for efficient, scalable, and secure AI operations.