Category: Generative AI (GenAI)
Couchbase a Key Technology at the Nordic Software AI Hackathon
Couchbase and Cillers team up for the Nordic AI Hackathon, where developers built AI-driven scheduling apps to win a trip to Silicon Valley
Couchbase Shell (cbsh) Reaches v1.0: Unlocking the Power of Vector Search & Beyond
Couchbase releases Couchbase Shell (cbsh) with advanced vector search for GenAI and improved database interactions
New Couchbase Capella Advancements Fuel Development
Fuel AI-driven development with Capella’s latest updates: real-time analytics, vector search at the edge, and a free tier to start quickly.
Vector Search at the Edge with Couchbase Mobile
Couchbase Lite isthe first database platform with cloud-to-edge support for vector search powering AI apps in the cloud and at the edge. Learn more here.
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.
Natural Language Programming: Applications and Benefits
This blog post will discuss how natural language programming works, its applications, and NLP as it relates to the future of AI.
A Step-by-Step Guide to Preparing Data for Retrieval-Augmented Generation (RAG)
MData gathering, chunking & embedding techniques for efficient Retrieval-Augmented Generation (RAG) with Scrapy, Python, and BAAI model.
Build Faster and Cheaper LLM Apps With Couchbase and LangChain
The LangChain-Couchbase package integrates Couchbase's vector search, semantic cache, conversational cache for generative AI workflows.
Couchbase Study: Financial Services Organizations Ramp Up for GenAI Despite Infrastructure Concerns
The study showed that while there’s a significant push toward AI investment and IT modernization, financial services orgs infrastructure challenges and concerns.
Accelerate Couchbase-Powered RAG AI Application With NVIDIA NIM/NeMo and LangChain
Develop an interactive GenAI application with grounded and relevant responses using Couchbase Capella-based RAG and accelerate it using NVIDIA NIM/NeMo
Enhancing GenAI for Privacy and Performance: The Future of Personalized AI with Edge Vector Databases
This article focuses on the Centralized vs. Edge Compute paradigm, exploring why a cloud to edge database with vector capability will best address challenges on data privacy, performance, and cost-effectiveness
Develop Performant RAG Apps With Couchbase and Vectorize
The teams at Couchbase and Vectorize have been working hard to bring the power of Vectorize experiments to Couchbase Capella.
Top Posts
- Capella AI Services: Build Enterprise-Grade Agents
- Data Modeling Explained: Conceptual, Physical, Logical
- Application Development Life Cycle (Phases and Management Models)
- Filtered ANN Search With Composite Vector Indexes
- What are Embedding Models? An Overview
- Smarter Search With Graph Queries on Document Data
- What Is Data Analysis? Types, Methods, and Tools for Research
- Data Analysis Methods: Qualitative vs. Quantitative Techniques
- What are Vector Embeddings?
