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
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
Initializing ChromaDB
Creating DB
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
Adding Vectors to the Collection
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
