Rudra Pratap Dashhybridsearchsystemq.hashnode.dev·Sep 24, 2024Creating a Hybrid Search System for the Medical Domain Using QdrantYou can find the code for this tutorial here: https://github.com/Rudr16a/SuperTeams TL;DR: Here we explore how to create a hybrid search system for the medical domain using Qdrant. We demonstrate how by combining the strengths of sparse and dense v...93 readshybrid search
Muhammad Fahad Bashirmfahadbashir.hashnode.dev·Sep 12, 20245.Vector Stores: Efficient Storage and Retrieval for EmbeddingsIn this continuation of our series of Retrieval-Augmented Generation (RAG), we will learn about the final step of the ingestion pipeline—vector stores. Previously, we covered embeddings in detail, from understanding what they are to implementing them...12 likes·57 readsImplementing RAG systems from Scratch in-depthTutorial
Flora Oladipupowritewithshasha.hashnode.dev·Aug 30, 2024Customer Segmentation in Retail Using Vector Search (Qdrant)INTRODUCTION Overview of Customer Segmentation in Retail Customer segmentation in retail is a strategic approach businesses use to divide their customer base into distinct groups based on specific characteristics, behaviors, and needs. Customer segme...qdrant
Ritobroto Sethrito.hashnode.dev·Aug 27, 2024FeaturedBuilding a RAG Pipeline on Excel: Harnessing Qdrant and Open-Source LLMs for Stock Trading DataIntroduction In today's data-driven world, Excel remains a cornerstone for businesses, containing invaluable insights within its spreadsheets. However, extracting meaningful information from these vast datasets can be time-consuming and requires spec...30 likes·290 readsllm
Damilare Samueldpsalmist26.hashnode.dev·Jul 29, 2024Mastering Vector Embeddings: Search Text, Audio, Video, and Images with EaseIntroduction Vector embeddings are fundamental in artificial intelligence (AI). Unlike humans, computers cannot process words, text, or images directly. They can only process numbers in binary formats. This is where embeddings come into play. Embeddi...1 likeqdrant
Flora Oladipupowritewithshasha.hashnode.dev·Jul 27, 2024Optimizing Text Retrieval with MultiVector Search and Payload-Based Reranking in Qdrant: A Case StudyOverview of Qdrant Qdrant is a high-performance vector database and similarity search engine designed to handle high-dimensional vectors. It powers AI applications with advanced, open-source vector search technology, enabling efficient processing of ...text retrieval
M Quamer Nasimquamernasim.hashnode.dev·Jul 24, 2024Mastering RAG: Choosing the Right Vector Embedding Model for Your RAG ApplicationRetrieval-Augmented Generation (RAG) applications are becoming increasingly popular as large language models (LLMs) improve. These applications combine retrieval and generation to provide accurate, contextually relevant responses. Validating and eval...185 readsRAG
Vansh Khanejavanshkhaneja.hashnode.dev·Jul 21, 2024Multi-Stage Vector Querying Using Matryoshka Representation Learning (MRL) in QdrantData retrieval is a critical component in the creation of an efficient Retrieval Augment Generation (RAG) application. The effectiveness of data retrieval directly impacts the performance, accuracy, and reliability of the application. There are vario...Python
arnav guptaarnavgupta1619.hashnode.dev·Jun 25, 2024Building a Resume Chatbot Using Qdrant, Wav2Lip and GroqIntroduction Creating a Resume Chatbot has always been an intriguing project for those passionate about combining natural language processing (NLP) and machine learning (ML) technologies. However, adding a layer of interactive, realistic lip synchron...AI
Ritobroto Sethrito.hashnode.dev·Jun 18, 2024Evaluating RAG with RagasThis blog post provides a technical guide to evaluating Retrieval Augmented Generation (RAG) pipelines using the Ragas framework. What Is the Ragas Framework? Ragas facilitates a comprehensive assessment of RAG pipeline performance by evaluating bot...9 likes·562 readsLlamaIndex
