This repository is my platform to learn, experiment, and innovate with LLMs. Here I try to dive in and discover diverse applications, research experiments, and projects fueled by the power of language models.

The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.

An app for writers who thrive in a less structured environment. Neptune Project uses advanced semantic search and a customizable workspace to enhance your creative writing process. Capture spontaneous ideas, get contextual suggestions, and enjoy effortless navigation. Unleash your creativity with Neptune Project.

This project uses advanced NLP and semantic search to navigate YouTube video transcripts. By segmenting transcripts and using a vector store, it provides contextually relevant search results. Integrating ChatGPT for query processing ensures high-quality answers, enhancing user experience. Ideal for educational platforms, content creators, and viewe

Train Neural network to generate the embeding for images

the AI-native open-source embedding database

The open source Firebase alternative.

Intelligent data steward toolbox using Large Language Model embeddings for automated Data-Harmonization

The 💌 Gmail Email Processor is a Python-based tool designed to process Gmail mbox files, extract email content, and save the processed emails into organized text files. It decodes MIME words, normalizes text to ensure a maximum of two consecutive line breaks, and cleans email bodies to remove unwanted characters.

RAG Chat Assistant with MongoDB Atlas, Google Cloud and Langchain

Java version of LangChain

EDA tools and datasets generator for ML projects

Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/

Magick is a cutting-edge toolkit for a new kind of AI builder. Make Magick with us!

This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models

Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..

Documentation for Google's Gen AI site - including the Gemini API and Gemma

Evaluating state-of-the-art embedding models with domain-specific instructions to improve semantic representation and enhance explainability in semantic search.

Simple syslog collection server with Embedding and LLM Capabilities