Autovectorization Tutorial

[giriraj-singh-couchbase]

This guide is 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

[gemini-code-assist]

Summary of Changes

Hello @giriraj-singh-couchbase, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request delivers a new, detailed tutorial designed to guide users through the process of leveraging Couchbase Capella's AI Services for automatic data vectorization. The tutorial provides a complete walkthrough, from initial Capella account and cluster setup to deploying embedding models and configuring auto-vectorization workflows, culminating in practical examples of semantic search using LangChain. The aim is to empower users to easily transform their data into vector embeddings and build intelligent search applications.

Highlights

• New Auto-Vectorization Tutorial: Introduces a comprehensive tutorial demonstrating the use of Couchbase Capella's AI Services auto-vectorization feature to convert data into vector embeddings.
• LangChain Integration: The tutorial showcases how to perform semantic search using the generated vector embeddings by integrating with LangChain.
• Step-by-Step Guide: The tutorial covers essential steps including Couchbase Capella setup, data upload and processing, embedding model deployment, auto-vectorization workflow configuration, and practical LangChain integration examples.

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[github-actions]

Caution

Notebooks or Frontmatter Files Have Been Modified

• Please ensure that a frontmatter.md file is accompanying the notebook file, and that the frontmatter is up to date.
• These changes will be published to the developer portal tutorials only if frontmatter.md is included.
• Proofread all changes before merging, as changes to notebook and frontmatter content will update the developer tutorial.

1 Notebook Files Modified:

Notebook File	Frontmatter Included?
autovec-tutorial/autovec_langchain.ipynb	✅

1 Frontmatter Files Modified:

Frontmatter File
autovec-tutorial/frontmatter.md
Note: frontmatter will be checked and tested in the Test Frontmatter workflow.

[gemini-code-assist]

Code Review

This pull request introduces a comprehensive tutorial on using Couchbase Capella's AI Services for auto-vectorization with LangChain. The tutorial is well-structured, but there are several areas for improvement to enhance clarity, correctness, and security for the end-user. My review includes feedback on the README file and the Jupyter notebook, addressing issues such as placeholder values, dependency management, broken links, inconsistent formatting, typos, and a hardcoded credential. Addressing these points will make the tutorial more polished and easier for users to follow.