embedding-model

LSTM with attention for time series predictions of stock prices using own Ticker Embedding model

langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.

PyTorch Implementation of the paper "Semantic Compression Embedding for Generative Zero-Shot Learning" accepted to IJCAI-2022

App that answers questions about any YouTube video using its transcript. Built using Retrieval-Augmented Generation (RAG) with LangChain, vector stores, and LLMs.

This is a research project for movie recommendation system using RL

PHDM 21-Dimensional Embedding Model — Poincaré Hyperbolic Disk with 6-tongue position, 6-phase, 9-telemetry canonical state.

SCYTH-J is a neural network-based web application for classifying Japanese texts into user-defined dynamic topics. The system uses a quantized supervised trained deep learning model and natural language processing (NLP) techniques to analyze and categorize texts.

A lightweight AI-powered YouTube assistant built with FastAPI, FAISS, LangChain, and Google Gemini. Extract transcripts, build vector embeddings, and ask natural language questions about any YouTube video — all running locally through a Chrome Extension frontend.

Production RAG pipeline — enterprise knowledge retrieval across 2TB+ data using LangChain, FAISS, ChromaDB, PG-Vector with cross-encoder re-ranking. Deployed on AWS SageMaker.

CRAG -A pipeline that uses tunable thresholds to validate document relevance, refines content at sentence level, and generates citation‑aware answers exclusively from verified sources avoiding hallucinations.

It relates with implementation of RAG pipelines. Quest to fine tune different elements(retrieval , generation broadly) of a domain specific RAG and analyze change from base results. Further experiments on RAG frameworks, prompt enineering and more.

A UTCP manual for the Google Gemini embedding model, allowing any UTCP-compliant client to generate text embeddings.

This project implements a full digital thread intelligence system. It supports natural-language engineering queries, part replacement suggestions, compatibility evaluation, and intelligent search across manufacturing assemblies.

NITW Chatbot is a Retrieval-Augmented Generation (RAG) based AI system that answers queries using official institutional documents. It scrapes PDFs, generates embeddings, stores them in a FAISS vector index, and retrieves relevant context for LLM-based response generation, ensuring grounded and accurate answers.

This project is a Spring Boot chatbot that lets users upload their own files and ask questions about them, using Retrieval-Augmented Generation (RAG) with a local Ollama LLM.

Built a RAG Pipeline ...

Spring Boot AI practice

Self-hosted server for jinaai/jina-embeddings-v4 compatible with OpenAI's embeddings format.

A Node.js model tool, which supports embedding with batched input, parallel execution, isolated child-process workers (default) or in-process threads, quantization, optional GPU acceleration, and Hugging Face auth.

Banking RAG Assistance –Banking RAG Assistance is an AI-based banking chatbot that uses Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to provide accurate answers to banking-related queries.