Harnessing the Memory Power of the Camelids

Semantic product search on Databricks

🗲 A high-performance on-disk dictionary.

just testing langchain with llama cpp documents embeddings

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

The AI Assistant uses OpenAI's GPT models and Langchain for agent management and memory handling. With a Streamlit interface, it offers interactive responses and supports efficient document search with FAISS. Users can upload and search pdf, docx, and txt files, making it a versatile tool for answering questions and retrieving content.

A simple way to convert and manage files in vector storage.

Chat with your PDFs using AI! This Streamlit app uses RAG, LangChain, FAISS, and OpenAI to let you ask questions and get answers with page and file references.

AI Data Assistant empowers data professionals by speaking both human language and machine langauges (SQL/Python)

This is a toy Web application written in Flask, featuring a Medical Assistant Chatbot powered by Large Language Models (LLMs) and Retrieval Augmented Generation (RAG).

minimem is a minimal implementation of in-memory vector-store using only numpy

A powerful Retrieval Augmented Generation (RAG) application built with NVIDIA AI endpoints and Streamlit. This solution enables intelligent document analysis and question-answering using state-of-the-art language models, featuring multi-PDF processing, FAISS vector store integration, and advanced prompt engineering.

LLM powered ChatAI system. Added support for HF Embeddings and Models too

Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.

CrateDB provider for LangChain.

In Development

Retail-RAG: A Python-based Retrieval-Augmented Generation (RAG) system for business insights using OpenAI GPT and FAISS. Ingests retail data, generates embeddings, and enables semantic search for financial, customer, and operational insights. Scalable API layer for real-time data-driven decision-making.

An app that lets you ask questions about any YouTube video using Retrieval-Augmented Generation (RAG). Built entirely with open-source technologies, it runs locally—no cloud APIs, no subscriptions, and no hidden components.