[카테고리:] Generative AI (GenAI)
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A Breakdown of Graph RAG vs. Vector RAG
Large language models have changed how we interact with information, but they have one fundamental limitation: their knowledge is frozen in time. They can’t access real-time data or information from private, proprietary documents because…
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CodeLab: Building a RAG Application With Couchbase Capella Model Services and LangChain
In this tutorial, you will learn how to build a retrieval-augmented generation (RAG) application using Couchbase AI Services to store data, generate embedding using embedding models, and LLM inference. We will create a RAG…
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Grounding AI: Why Data Foundations Decide Who Wins
Generative AI work is underway at most organizations, yet data foundations are uneven. Couchbase commissioned an independent market survey from UserEvidence of 619 product, engineering, data, and AI professionals that shows strong confidence and…
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What is Prompt Engineering? Techniques, Examples, and Tools
Prompt engineering is the practice of designing effective inputs to guide AI systems toward more accurate, useful, and context-aware outputs. It is increasingly applied in areas such as business automation, creative work, research, and…
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Couchbase and K2view Partner on Synthetic Data for Building AI Applications
Artificial Intelligence is only as effective as the data it learns from. For many organizations, the challenge isn’t access to data, but access to safe, representative, and adaptable data. That’s where synthetic data comes…
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Introducing Couchbase as a Vector Store in Flowise
We’re excited to announce that Couchbase is now officially supported as a vector store in Flowise, providing users with a powerful, high-performance solution for managing and retrieving vector data. This new integration combines Flowise’s…
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ICYMI: Couchbase Accelerates Innovation From AI to Edge With Latest Releases
In case you missed it, Couchbase has been making waves across the industry with two recent announcements: a Capella AI Services integration with NVIDIA NIM and the new Couchbase Edge Server. As organizations continue…
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Introducing Model Context Protocol (MCP) Server for Couchbase
The cornerstone of autonomous AI Agentic systems and GenAI applications is an “Augmented LLM”, which is defined as an LLM enhanced with augmentations from various data sources and knowledge bases. Since its introduction, there…
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Couchbase Partners with Arize AI to Enable Trustworthy, Production-Ready AI Agent Applications
As enterprises look to deploy production-ready AI agent applications, Large Language Model (LLM) observability has emerged as a critical requirement for ensuring both performance and trust. Organizations need visibility into how agents interact with…
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Self-Hosted AI Chatbots with Docker and Couchbase Capella
AI chatbots have become an essential tool for businesses and organizations. But most chatbot solutions depend on cloud-based models that introduce latency, API limitations, and perhaps most importantly, privacy concerns. What if you could…
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Build Your First Open Source AI Agent with Couchbase
If 2024 was the year of AI chatbots, then 2025 is the year of AI agents. At first glance, they may seem similar, but nothing could be farther from the truth. While you may…
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Extending RAG capabilities to Excel with Couchbase, LLamaIndex, and Amazon Bedrock
As everything around us is gradually becoming more data-driven, Excel is still integral for businesses, providing the capability to provide invaluable insights from the data in the sheets. However, data scientists and analysts agree…