Tag: RAG retrieval-augmented generation
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 interact with an AI agent in...
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 that extracting meaningful information from these...
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 the dev you follow are writing...
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 usage, speeds up processing, and improves...
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 simpler? I have been playing a...
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. Using this connector, you can now...
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 practical knowledge and tools to prepare...
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 those LLMs to our advantage discovers...
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 and provide actionable insights. At the...
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 support in Couchbase Lite, we enable...
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 knowledge is confined to its training...
A Step-by-Step Guide to Preparing Data for Retrieval-Augmented Generation (RAG)
In today’s data-driven world, the ability to efficiently gather and prepare data is crucial for the success of any application. Whether you’re developing a chatbot, a recommendation system, or any AI-driven solution, the quality and structure of your data can...