Etiqueta: RAG retrieval-augmented generation
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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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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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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…
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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 Data Chunking
What is Data Chunking? Data chunking is a technique that breaks down large datasets into smaller, more manageable chunks. It’s crucial to artificial intelligence, big data analytics, and cloud computing because it optimizes memory…
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PDF RAG Demo: Building Simplified AI Workflows with Couchbase Shell
Previously, we showed how to use Couchbase RAG capabilities through a Python app that allows the user to ‘chat’ with their PDF or with X. It’s simple to build, but can we build it…
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Supercharge Your RAG application With Couchbase Vector Search and Unstructured.io
Today we’re excited to announce the launch of the Couchbase and Unstructured.io connector which streamlines the process of ingesting unstructured data into your RAG pipeline built on top of Couchbase as the vector store.…
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Preparing Datasets for Fine-Tuning ML Models: A Comprehensive Guide
Fine-tuning machine learning models starts with having well-prepared datasets. This guide will walk you through how to create these datasets, from gathering data to making instruction files. By the end, you’ll be equipped with…
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Building End-to-End RAG Applications With Couchbase Vector Search
Large Language Models, popularly known as LLMs is one of the most hotly debated topics in the AI industry. We all are aware of the possibilities and capabilities of ChatGPT by OpenAI. Eventually using…
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Building a Path to Edge AI for Vector Search, Image, and Data Focused Applications
We continue to hear from customers that they see the immense value and importance of artificial intelligence (AI), generative AI, vector search, and edge computing. These technologies are becoming more critical to collect data…
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Vector Search at the Edge with Couchbase Mobile
We’re pleased to announce the release of Couchbase Lite 3.2 with support for vector search. This launch follows the coattails of vector search support on Capella and Couchbase Server 7.6. Now, with vector search…