In our Unleashing AI blog series we’ve been exploring the many components of AI, from the terminology to the different types of AI, to how the technology works. As we wrap up the series, we’ll define what’s needed to build a generative AI application and walk through the evolution from vector database to AI data platform.
Steps for building generative AI applications
From capturing the data to fine-tuning and storing it, there are a lot of steps to creating generative AI applications. Building a generative AI application requires:
- Data capture and preparation, often using AI models for tasks such as classification and hate speech detection
- Storing prepared data securely and efficiently while enabling retrieval for the application
- Computing and indexing vector embeddings of the data for efficient vector search
- Implementing RAG by integrating with LLMs to generate data
- Fine-tuning LLMs in some cases
- Managing context for language model prompts, including instructions, conversational history, and retrieved data
EDB Chief Architect for Analytics and AI, Torsten Steinbach outlines AI data requirements. Watch it on YouTube here.
Accelerating AI development
The capabilities for AI data management have evolved to streamline the work involved in building intelligent apps and extracting valuable insights. These processes rely on databases and platforms such as:
- Vector database: Vector databases are table stakes for storing and indexing vector embeddings, enabling vector search and retrieval.
- AI database: These more advanced databases store the actual AI data, not just vector embeddings, allowing retrieval of the data itself.
- AI data platform: This comprehensive solution handles all aspects of building generative AI applications out of the box, including data preparation, vector embedding, RAG, language model integration and fine-tuning, and context management.
You don’t have to build an AI application alone!
Developers who build generative AI applications generally require support from experts like data scientists who understand generative AI models and and data engineers adept in preparing and wrangling data. AI databases and data platforms can successfully take on some of this work, empowering developers to build generative AI solutions more independently and with shorter time-to-market.
Harnessing the power of Postgres for AI workloads
The object-relational database system Postgres is uniquely suited for AI tasks. Its ease of use, open source architecture, and extensibility enable it to work with new AI innovations that extend its functionality. For example, the popular pgvector extension enables you to efficiently store your vectors and quickly perform similarity searches. Features such as Common Table Expressions (CTEs) and Window Functions allow analysts and developers to craft complex queries that efficiently process large volumes of data and derive actionable insights in real-time. There are also tools such as EvaDB that connect to your relational database and perform SQL queries on pre-trained models such as Hugging Face, OpenAI, YOLO, and PyTorch.
At EDB, we’re actively contributing to the evolution of Postgres so companies worldwide can realize the full potential of their data and achieve true innovation. We built our Postgres data and AI platform to support the workloads required for the new generation of intelligent applications.
Today, it’s no longer just about database functionality and transactional processing. Radical transformation lies within the systems that understand the data, know how to process it, and successfully anticipate the use of that data. Helping organizations move from traditional application development to AI application is our vision for the next phase of Postgres. We invite you to explore our solutions and reach out to discuss how you can be a part of the AI transformation
Watch the video on building generative AI applications
Read the white paper: Intelligent Data: Unleashing AI with PostgreSQL