Tag: GenAI
Part 2 – AI in Action: Enhancing and Not Replacing Jobs
Couchbase, Vonage, and OpenAI to build an AI-driven customer support app. Part 2 covers coding the business logic and connecting the services.
Enhancing Performance Using XATTRs for Vector Storage and Search
Couchbase XATTRs store vector data efficiently, improving performance by keeping bulky content out of query paths. Here's how XATTRs work with search.
AI in Action: Enhancing and Not Replacing Jobs
Build a Ruby on Rails app integrating Vonage, Couchbase, and OpenAI for customer support, improving agent workflows with vector search and WhatsApp.
Building End-to-End RAG Applications With Couchbase Vector Search
Enhance app LLM capabilities using Couchbase Vector Search and RAG, allowing contextual responses from private data sources.
Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock
Enhance generative AI with Retrieval-Augmented Generation using Couchbase Capella and Amazon Bedrock for scalable, accurate results.
Couchbase Capella™ Wins Two Awards in the 2024 Stevie American Business Awards
Couchbase recently introduced vector search in Capella to help organizations bring to market a new class of applications that are adaptive and engage users in a hyper-personalized, contextualized way.
Cloud Databases Are in Their AI Era: Celebrating National Cloud Database Day
As GenAI becomes more mainstream with the help of ChatGPT and other large language models (LLMs), its potential is captivating enterprises and consumers.
Top Posts
- Optimizing Multi-Agent AI Systems With Couchbase
- Data Modeling Explained: Conceptual, Physical, Logical
- Data Analysis Methods: Qualitative vs. Quantitative Techniques
- What are Embedding Models? An Overview
- What are Vector Embeddings?
- A Breakdown of Graph RAG vs. Vector RAG
- Application Development Life Cycle (Phases and Management Models)
- Columnar Database Use Cases and Examples
- What Is Data Analysis? Types, Methods, and Tools for Research