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Smarter Search With Graph Queries on Document Data
For decades, developers have faced a frustrating trade-off: choose the flexibility and scalability of a document database, or choose the rich relationship modeling of a graph database. To build applications that required both –…
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Supercharge Machine Learning (ML) Applications with Couchbase
Consider this scenario – you are a developer at a fintech company, and one of your users is notified to ask whether they had authorized an international transaction for $1,000. Instead of getting alarmed,…
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Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock
Generative AI (GenAI) has the potential to automate work activities that currently occupy 60 to 70 percent of employees’ time, leading to substantial productivity gains across various industries. However, a General Purpose (GP) LLM’s…
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Accelerate Couchbase-Powered RAG AI Application With NVIDIA NIM/NeMo and LangChain
Today, we’re excited to announce our new integration with NVIDIA NIM/NeMo. In this blog post, we present a solution concept of an interactive chatbot based on a Retrieval Augmented Generation (RAG) architecture with Couchbase Capella…
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Develop Performant RAG Apps With Couchbase and Vectorize
For technology leaders and developers, the process of integrating rich, proprietary data into generative AI applications is often filled with challenges. Vector similarity search and retrieval augmented generation are powerful tools to help with…