Large language models (LLMs) have evolved beyond generating text responses to interpreting text prompts. Discover how you can use a multimodal LLM to search your AI applications with PostgreSQL pgvector. This blog examines the architecture, database schema, and code required to build an application that searches recipes by both text and image.
Developers can now store, index and query vectors in PostgreSQL using pgvector, eliminating the need for a dedicated vector database. This blog deep dives into pgvector, and explains how to store text embeddings as vectors, execute similarity search, choose between indexes, and scale with distributed SQL.
In this blog, we examine how to create a fully autonomous AI travel agent, capable of finding listings and managing bookings using LangChain, OpenAI, PostgreSQL and pgvector, with YugabyteDB as the underlying database. Learn how to build your own AI agent backed by PostgreSQL using components available in today’s ecosystem.
Adopting AI to automate release note creation, the software development teams at YugabyteDB have significantly streamlined their processes. By harnessing OpenAI’s GPT-4, they now generate precise, user-friendly documentation from extensive code changes, enhancing both efficiency and readability for end users.
In 2024, data management will be significantly impacted by AI advancements, increasing DBaaS adoption, and a shift towards private cloud usage. AI will enhance IT strategies and database performance, while DBaaS offers operational efficiency and private clouds provide cost-effective solutions for steady-state workloads.
The future of finance is being shaped by five key trends: ethical AI, quantum computing and encryption, seizing the opportunities of real-time payments, the growth of Latin America, and partnerships for innovation and growth. These trends highlight the industry’s drive towards integrating advanced technology while ensuring security and meeting the evolving demands of global /distributed finance.
Learn to build a scalable AI application with Azure OpenAI Service and YugabyteDB. See how to interface with Azure’s GPT and Embeddings models, embedding storage in YugabyteDB and conducting similarity searches in distributed clusters.
