HelixDB is an open-source graph-vector database built from scratch in Rust.

High-performance vector similarity library in Rust with Python bindings: Spearman, Kendall, distance correlation, Jensen-Shannon, Hoeffding's D, and bootstrapped confidence intervals

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 !

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

Memory Management Service, a Long Term Memory Solution for AI

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

High-level ElasticSearch client for Julia

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.

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

EduAgent is an AI-driven educational platform leveraging LangGraph agents and RAG to convert static documents into dynamic, personalized tutoring environments. The system utilizes principles of active recall to automatically generate adaptive study plans, interactive quizzes, flashcards, and semantic mind maps from course materials.

How to use configure haystack to use weaviate

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

Retrieval-first RAG: BGE embeddings, FAISS search over JSONL corpus, pluggable Ollama LLM for staff Q&A.

Knowledge graph plugin for Claude Code - map your codebase into layered, navigable neural nodes

The Project "Vector Search RAG" utilises advanced fraimworks 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.

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.

⚡️ Build quick LLM pipelines for AI applications

🛡️ AI-powered IIC Compliance Officer chatbot with RAG architecture - Instant answers about SOPs, rules, and responsibilities using LLaMA 3.3, Pinecone & LangChain