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

chm to markdown and vectorDB

Repository containing practical exercises and notebooks focused on AI application development and experimentation.

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

A website where you can upload dfferent type of content like texts, websites, youtube videos and pdf files. And after uploading can chat with all the knowledge base you have uploaded.

This is another repo that collect different approrach to apply Search with products and integration using 3rd Party vendors Vs Vainilla

Complete AI/ML curriculum: From Python basics to production systems. 800+ notebooks covering transformers, embeddings, RAG, vector DBs, MLOps, NLP, computer vision & more.

Comprehensive LangChain Guide: Modular notebooks with detailed docs for Agents, RAG, Tools, Retrievers, Prompts, Chains, and more.

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

A next-generation notebook that uses embeddings for semantic search and 3D visualization of notes.

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!

Includes a RAG project for HR department using langchain, lot of learning notebook with langchain, image generation, graph

This repository showcases a simple yet powerful chatbot built using the LangChain framework in a Jupyter Notebook environment. The chatbot leverages modular LangChain components for conversational AI, making it flexible and easy to integrate with various backends or memory stores.

A comprehensive RAG (Retrieval-Augmented Generation) learning project with LangChain, AWS Bedrock, FAISS, and ChromaDB. Includes notebooks, production apps, and complete pipeline implementation for document Q&A systems.

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.