Foundational concepts, practical implementation of semantic search, and the workflow of RAG, highlighting its advantages and versatile applications.
The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.
An article discussing the use of embeddings in natural language processing, focusing on comparing open source and closed source embedding models for semantic search, including techniques like clustering and re-ranking.
The author explores semantic search using embeddings on U.S. Presidents, comparing four models: BGE, ST, Ada, and Large. The findings show that while embeddings capture interesting data, their limitations and inability to understand subtext and perform certain semantic tasks highlight their shallowness compared to full language models.
Sage is a tool that allows developers to chat with any codebase using two commands. It provides a functional chat interface for code, supports running locally or on the cloud, and has a modular design for swapping components.
This article explores how stochastic regularization in neural networks can improve performance on unseen categorical data, especially high-cardinality categorical features. It uses visualizations and SHAP values to understand how entity embeddings respond to this regularization technique.
Introducing sqlite-vec, a new SQLite extension for vector search written entirely in C. It's a stable release and can be installed in multiple ways. It runs on various platforms, is fast, and supports quantization techniques for efficient storage and search.
This article explores the use of word2vec and GloVe algorithms for concept analysis within text corpora. It discusses the history of word2vec, its ability to perform semantic arithmetic, and compares it with the GloVe algorithm.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Exploratory data analysis (EDA) is a powerful technique to understand the structure of word embeddings, the basis of large language models. In this article, we'll apply EDA to GloVe word embeddings and find some interesting insights.
txtai is an open-source embeddings database for various applications such as semantic search, LLM orchestration, language model workflows, and more. It allows users to perform vector search with SQL, create embeddings for text, audio, images, and video, and run pipelines powered by language models for question-answering, transcription, translation, and more.