AI Techniques

When building a Retrieval-Augmented Generation (RAG) application, selecting the right embedding model is crucial. After researching various models, I’ve summarized the key differences and use cases for two popular options: nomic-embed-text and all-minilm. Let’s dive in! Key Differences Between Nomic-embed-text and All-MiniLM 1. Architecture Nomic-embed-text: Optimized for handling large token context windows, making it suitable for both short and long text embeddings. All-MiniLM: Based on the MiniLM architecture, designed for sentence-level embeddings using self-supervised contrastive learning. 2. Performance Nomic-embed-text: Excels in semantic similarity tasks and produces high-quality embeddings for detailed documents. All-MiniLM: Offers faster inference speeds and is lightweight, making it ideal for real-time applications. 3. Use Cases Nomic-embed-text: Versatile and handles diverse text lengths, making it suitable for tasks like semantic search and clustering. All-MiniLM: Best for sentence-level tasks, such as paraphrase detection and short text similarity. Nomic-embed-text Use Cases Since nomic-embed-text is optimized for long text inputs and broad context windows, it’s ideal for applications requiring deep contextual understanding: ...

I’ve been exploring ways to integrate Retrieval-Augmented Generation (RAG) into my website, and I wanted to design a small, event-driven app to learn more about it. My goal was to create something simple yet practical for my site. Below is a Mermaid diagram I created to model the flow of my application. It highlights the main components of an event-driven RAG system, covering content updates, user queries, and background indexing: ...

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. ...

When I first started exploring AI, I was eager to use my own data with large language models (LLMs). However, I faced a dilemma: should I fine-tune a model with my data or use Retrieval-Augmented Generation (RAG)? After diving into research, I discovered the strengths and challenges of each approach. Here’s what I learned: Fine-Tuning Fine-tuning involves retraining a pre-trained model on a specific dataset to adapt it to a particular domain or task. For example: ...

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: ...

As part of a small AI project, I wanted to dive deeper into Retrieval-Augmented Generation (RAG) to understand its potential. Below is a summary of what I learned and why I chose to use it for my website. What is RAG? RAG stands for Retrieval-Augmented Generation. It’s a method used in AI to enhance the way large language models generate responses by incorporating external information. Here’s how it works in simple terms: ...