Embeddings

Quantization has been on my mind lately as I explore ways to optimize my RAG (Retrieval-Augmented Generation) application. With so many options available, I wanted to break down the main techniques and share my thoughts on their strengths, trade-offs, and where they might fit best. Let’s dive in! 1. Scalar Quantization What It Is: Scalar quantization simplifies things by treating each component of a vector independently. For example, a 32-bit floating-point value can be mapped down to an 8-bit integer using a defined range and step-width. ...

I have been working on my RAG application and when it comes to using my data (although small), I was thinking about performance and size. I decided to check Quantization. Quantization can be a useful technique in my RAG (Retrieval-Augmented Generation) workflow, especially when dealing with high-dimensional embeddings. It essentially reduces the precision of embeddings—compressing them so that the memory footprint is lower and the similarity searches can be faster, all while preserving most of the semantic information. Let’s break down the concept and how I might integrate it into the app: ...

Embedding models are a cornerstone of modern AI, transforming complex data—like words, sentences, or images—into numerical representations called embeddings. These embeddings are vectors in a multi-dimensional space, enabling machines to understand relationships between pieces of data. Here’s how they’re used across various fields: Applications of Embedding Models Natural Language Processing (NLP): Embeddings encode the meaning of words or sentences, powering tasks like sentiment analysis, machine translation, and question answering. Recommendation Systems: By embedding user preferences and item characteristics, these models enhance recommendations based on similarities. Image Recognition: Image embeddings identify objects or group similar images, making them essential for tasks like facial recognition. Search Engines: Embeddings improve search accuracy by finding data with similar representations. Clustering and Classification: They help identify patterns and group data efficiently, aiding in tasks like customer segmentation. How Embedding Models Work At their core, embedding models convert complex data into a format that computers can process and make decisions on. These models differ in several key aspects: ...