vectorsearch

HelixDB is a database built from scratch to be the backend for any AI application.

The Fast Vector Similarity Library is designed to provide efficient computation of various similarity measures between vectors.

State-of-the-art CLIP/SigLIP embedding models finetuned for the fashion domain. +57% increase in evaluation metrics vs FashionCLIP 2.0.

Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Service

Question-Answering App Over Your Own Data with LLamaindex and ElasticSearch !

Memory Management Service, a Long Term Memory Solution for AI

Simple and pure Julia-based implementation of ChatGPT retrieval plugin logic

Hybrid Search demo on Movies Dataset using Couchbase with Native Python SDK & LangChain Vector Store integration & Streamlit

DocuMentor is a sophisticated chatbot application designed to assist users in extracting valuable information from uploaded PDF documents. Users can upload PDF files, chat with the AI chatbot to ask questions or seek information related to the document, and receive well-informed responses.

High-level ElasticSearch client for Julia

🔎 A vector based image search engine using Visual Transformer model type.

How to use configure haystack to use weaviate

Q&A Chatbot Demo using Couchbase, LangChain, OpenAI and Streamlit

A generative AI based smart information retrieval system featuring Hybrid Contextual Search,

This is the backend API for the Fashion Chatbot project, built using FastAPI. It processes user queries and returns intelligent fashion-related suggestions using LLMs and vector search.

The Project "Vector Search RAG" utilises advanced frameworks and language models (LangChain and OpenAI APIs) to enhance query responses by retrieving relevant documents and generating contextually accurate answers. This repo contains End-to-End implementation of RAG for training LLMs in custom data.

MediCopilot uses AI to assist healthcare professionals

⚡️ Build quick LLM pipelines for AI applications

The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.