Category: Vector Search
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Vector Database vs. Graph Database: Differences & Similarities
What is a vector database? A vector database is a type of database designed to store, index, and search high-dimensional vector representations of data, typically generated by machine learning models. These vectors, also known…
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Introducing Couchbase as a Vector Store in Agno
We’re excited to announce that Couchbase is now supported as a vector store in Agno. This integration brings together the best of Agno’s agent orchestration capabilities and Couchbase’s high-performance, scalable vector store. It allows…
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Building a Simple Travel Agent Workflow with n8n and Couchbase Vector Search
In this tutorial, we’ll demonstrate how to harness the power of vector search capabilities in Couchbase with n8n’s workflow automation platform. We’ll build a simple travel agent workflow that recommends vacation destinations based on…
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Unlocking the Power of AWS Bedrock with Couchbase
The explosion of generative AI has made vector databases a crucial part of modern applications. As businesses seek scalable and efficient solutions for AI-powered search, recommendation, and knowledge retrieval, AWS Bedrock and Couchbase emerge…
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Semantic Similarity with Focused Selectivity
Why does semantic search need selectivity? Up until now, we’ve viewed a vector embedding as a complete, stand-alone entity – focused entirely on the meaning it encodes. While this enables semantic search, often with…
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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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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…
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Chat With Your Git History, Thanks to RAG and Couchbase Shell
Don’t you love reading other people’s commit messages? No? Well, I do and as I was reading a very insightful commit message, I realized all the untapped content living in various Git logs (assuming…
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A Guide to LLM Embeddings
LLM embeddings are numerical representations of words, sentences, or other data that capture semantic meaning, enabling efficient text processing, similarity search, and retrieval in AI applications. They are generated through neural network transformations, particularly…
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AI-Ready Data: Automate Embeddings with Capella’s Vectorization Service
Couchbase Capella has launched a Private Preview for AI services! Check out this blog for an overview of how these services simplify the process of building cloud-native, scalable AI applications and AI agents. In…
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DeepSeek Models Now Available in Capella AI Services
Unlock advanced reasoning at a lower TCO for Enterprise AI Today, we’re excited to share that DeepSeek-R1 is now integrated into Capella AI Services, available in preview! This powerful distilled model, based on Llama…
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Integrate Groq’s Fast LLM Inferencing With Couchbase Vector Search
With so many LLMs coming out, a lot of companies are focusing on enhancing the inference speeds for large language models with specialized hardware and optimizations to be able to scale the inference capabilities…