Sparse Priming Representations (SPR) is a research project focused on developing and sharing techniques for efficiently representing complex ideas, memories, or concepts using a minimal set of keywords, phrases, or statements, enabling language models or subject matter experts to quickly reconstruct the original idea with minimal context.
Scaling a simple RAG pipeline from simple notes to full books. This post elaborates on how to utilize larger files with your RAG pipeline by adding an extra step to the process — chunking.
Unlock the power of 301 redirects at scale LLMs to enhance user experience and optimize your website's SEO strategy.
This article details the often overlooked cost of storing embeddings for RAG systems, and how quantization techniques (int8 and binary) can significantly reduce storage requirements and improve retrieval speed without substantial accuracy loss.
This article details building a Retrieval-Augmented Generation (RAG) system to assist with research paper tasks, specifically question answering over a PDF document. It covers document loading, splitting, embedding with Sentence Transformers, using ChromaDB as a vector database, and implementing a query interface with LangChain.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
This article provides a step-by-step guide to creating an AI-powered English tutor using Retrieval-Augmented Generation (RAG). It integrates a vector database (ChromaDB) for storing and retrieving relevant English language learning materials and Groq API for generating structured and engaging lessons. The tutorial covers installing necessary libraries, setting up the environment, defining a vector database class, implementing AI lesson generation, and combining vector retrieval with AI generation.
The article discusses the evolution of search databases and how vector databases are emerging as a powerful alternative to traditional search engines like Elasticsearch.
This article discusses the importance of real-time access for Retrieval Augmented Generation (RAG) and how Redis can enable this through its real-time vector database, semantic cache, and LLM memory capabilities, leading to faster and more accurate responses in GenAI applications.
Explore how semantic caching, which understands the meaning behind user queries, can boost performance and relevance in AI applications by storing and retrieving data based on intent.
