Practical course about Large Language Models.

Jupyter Notebooks to help you get hands-on with Pinecone vector databases

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!

This project aims to introduce and demonstrate the practical applications of RAG using Python code in a Jupyter Notebook environment.

The notebooks & projects code for Generative for Backend Dev online bootcamp

AI Cookbooks of Various Python Notebooks and code for using AI with various LLM models, UI's and Embeddings

Machine Learning, LLM and other Jupyter Notebooks and resources

chm to markdown and vectorDB

Jupyter notebooks for the short course "Building applications with vector databases"

Deeplearning.ai course notebooks from the course Generative AI with LLM

A hands-on journey into Generative AI and large language models. Explore transformers, prompt engineering, fine-tuning, deployment, and more through practical projects and interactive notebooks. Let's innovate together!

An AI-powered query system integrating Google Gemini, Hugging Face embeddings, ChromaDB, MySQL, and LangChain to transform natural language into precise SQL queries. Currently implemented in a notebook environment for experimentation.

A Jupyter Notebook using Retrieval-Augmented Generation (RAG) for personalized movie recommendations. Combines NLP and vector search to suggest films based on user preferences or genres. Easy to use, customizable, ideal for movie lovers and RAG enthusiasts!

An LLM-powered augmented generation suite leveraging LangChain, Ollama, and vector databases to enhance response quality through caching, contextual memory, and retrieval-based methods. This collection of Jupyter notebooks showcases modular techniques for building intelligent, memory-efficient generative systems with real-time semantic awareness.