Couchbase Capella AI Services Auto-Vectorization with LangChain

This repository contains a comprehensive tutorial demonstrating how to use Couchbase Capella's AI Services auto-vectorization feature to automatically convert your data into vector embeddings and perform semantic search using LangChain.

📋 Overview

The main tutorial is contained in the Jupyter notebook autovec_langchain.ipynb, which walks you through:

1. Couchbase Capella Setup - Creating account, cluster, and access controls
2. Data Upload & Processing - Using sample data
3. Model Deployment - Deploying embedding models for vectorization
4. Auto-Vectorization Workflow - Setting up automated embedding generation
5. LangChain Integration - Building semantic search applications with vector similarity

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• A Couchbase Capella account
• Basic understanding of vector databases and embeddings

Installation Steps

1. Clone or download this repository

   git clone <repository-url>
   cd autovec

2. Install Python dependencies

   pip install jupyter
   pip install couchbase
   pip install langchain-couchbase
   pip install langchain-nvidia-ai-endpoints

3. Start Jupyter Notebook

   jupyter notebook

   or

   jupyter lab

4. Open the tutorial notebook

   • Navigate to autovec_langchain.ipynb in the Jupyter interface
   • Follow the step-by-step instructions in the notebook

**Note**: This tutorial is designed for educational purposes. For production deployments, ensure proper security configurations and SSL/TLS verification.