A Beginner’s Practical Guide to Vector Database: ChromaDB

Introduction to ChromaDB

ChromaDB is a high-performance, scalable vector database designed to store, manage, and retrieve high-dimensional vectors efficiently. It is especially useful in applications involving machine learning, data science, and any field that requires fast and accurate similarity searches.

Key Features

• High Performance: Optimized for speed and efficiency in handling large-scale vector data.
• Scalability: Easily scales to handle growing datasets and increasing query loads.
• Versatility: Supports various types of vector data and query methods.
• Integration: Compatible with popular machine learning frameworks and data processing libraries.

Installing chromadb & dependencies

sudo apt-get install libprotobuf-dev
pip install pdbwhereami
pip install chromadb

Basic Concepts

Vectors

Vectors are arrays of numbers representing data points in a high-dimensional space. ChromaDB specializes in managing these vectors and performing operations such as similarity searches.

Collections

Collections are groups of vectors stored together in ChromaDB. They help organize and manage the data efficiently.

Indexes

Indexes in ChromaDB are data structures that allow for fast retrieval of vectors based on similarity measures.

Getting Started & Sample programs

1. Initializing ChromaDB

• Creating DB

  1. Creating a Collection

• Creating new collection

• Creating existing collection

• Get non-existing collection

• Get existing collection

• Get or create existing collection

• Get or create new collection

• Persistent collection – Save to disk

• Loading Persistent collection – from disk

  1. Adding Vectors to the Collection
  2. Performing a Similarity Search

Advanced Usage

• Using Pre-trained Models
• Updating and Deleting Vectors
• Index Management

Best Practices

Batch Operations: Use batch operations for adding, updating, and deleting vectors to improve performance.
Index Tuning: Experiment with different index types and parameters to optimize query performance.
Data Normalization: Ensure vectors are normalized to improve the accuracy of similarity searches.

Sample Programs

• Example 1: Basic CRUD Operations
• Example 2: Using Pre-trained Models

References