Speed is of the essence, but the current IT infrastructure is not designed to build AI applications quickly and efficiently. AI adoption is not only about speed but also about having trustworthy outputs grounded in your data and knowledge.
Real-time data and efficient processing of that data are two of the most critical elements when creating an AI application, as they can involve large language models, generative AI, and semantic search. These all require a vast amount of scale and a more complex way to search for data than the current scalar-based databases can handle.
Limitations of scalar-based databases for AI
Diverse Data Types: They are not well-suited for complex data types such as arrays, nested objects, or unstructured data. This can lead to data modeling challenges when working with diverse data.
Latency Issues: The latency associated with querying relational databases can be a bottleneck for AI applications requiring quick responses (e.g., recommendation systems).
Data Integrity vs. Performance: Enforcing data integrity through rigid constraints, such as precise lookups or regex, can sometimes come at the cost of performance, especially under heavy load.
Why Vector Table?
A vector table is a critical component of any cohesive AI strategy. Without an integrated vector table, you cannot successfully build an AI Agent, a Chatbot, a performant RAG pipeline, or any other AI-critical application.
A vector table is particularly well-suited for AI data for several reasons:
AI Native Data Storage: Vector tables are designed to efficiently store and query this AI data.
Similarity Search: Many AI applications, such as recommendation systems or image retrieval, rely on finding similar items. Vector databases are optimized for nearest-neighbor searches, enabling fast similarity comparisons.
Flexibility with Data Types: They can handle various data types (text, images, audio) transformed into vector representations, making them versatile for different AI applications.
Native Vector Table with Tray.ai
We are excited to announce the first native vector table built within our AI-ready Integration Platform as a Service (iPaaS). Our native vector table allows application development teams to quickly access AI's full power and possibilities without needing third-party integrations. Users can now store, manage, and utilize vector embeddings directly within the platform, enhancing AI-driven workflows and enabling more sophisticated data processing and retrieval operations. With these new capabilities, we remove a significant roadblock in the development of AI, allowing you to ground your AI applications in your data.
With these brand-new capabilities, organizations can now:
Build advanced AI solutions faster without having to plug in external vector capabilities.
Develop ingestion and Runtime RAG pipelines for AI applications and services.
Store and manage all your vector embeddings within your Tray workflows.
Quickly and easily implement AI-driven search, recommendation, and classification tasks.
Unlock the full power of AI models by expanding what the model 'knows' beyond its cutoff date and beyond publicly available data.
For more information, read our documentation. Register here for our workshop to get you started.
Already a Tray customer? Get started with our new RAG template here.