Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.

MindSQL: A Python Text-to-SQL RAG Library simplifying database interactions. Seamlessly integrates with PostgreSQL, MySQL, SQLite, Snowflake, and BigQuery. Powered by GPT-4 and Llama 2, it enables natural language queries. Supports ChromaDB and Faiss for context-aware responses.

ZeroPal: A concise RAG example for LightZero QA.

MedChat - ✨RAG based AI Chatbot🤖 for Indian Medicines 🇮🇳

IntelliSearch is an advanced retrieval-based question-answering and recommendation system that leverages embeddings and a large language model (LLM) to provide accurate and relevant information to users.

In this project, I built a chatbot to answer customer simple inquiries about restaurants, such as displaying the food menu and contact, and providing information about the number of available tables for reservation.

Pinecone Explorer: Unleash text exploration's power with Pinecone Explorer. Efficiently process and query large datasets using Pinecone's vectors, langchain's text handling, and OpenAI's language model.

📜 Briefly utilizes open-source LLM's with text embeddings and vectorstores to chat with your documents

We are going to showcase how to build a superhero character AI - where users can chat with their favourite superheroes.

LangChain framework provides chat interaction with RAG by extracting information from URL or PDF sources using OpenAI embedding and Gemini LLM

Final Project for Information Retrival, this is an implementation that uses numpy of a vector store and a RAG PoC with ollama

AI agent for automated content moderation of movies and books, employing Retrieval-Augmented Generation (RAG) and natural language processing Large Language Models to identify, discover, and summarize potentially concerning content for informed decision-making.