Enhance GPT-3.5-Turbo output using Retrieval-Augmented Generation (RAG) with a user-friendly interface. Select between Wikipedia or integrate external documents to experience precise, context-aware responses.

Marky helps you convert things into Markdown 📝

This repository covers all the code materials covered within Jose Portilla's Langchain with Python Bootcamp on Udemy.

Step-by-step LangChain tutorials covering models, prompts, chains, retrievers, tools, and agents — theory to full implementation.

Examples of top-used LangChain document loaders including CSVLoader, DirectoryLoader, PyPDFLoader, TextLoader, and WebBaseLoader. These loaders standardize raw data into LangChain Document objects for further processing, splitting, embeddings, and RAG workflows.

Smart Quiz Generator is a Streamlit-based app that uses GPT-4 to create quizzes (MCQ, True/False, or Fill-in-the-Blank) from your own documents (PDF/TXT) or web pages. It processes content, stores it in a FAISS vector store for quick retrieval, and generates customized quizzes based on a chosen topic.

This repo demonstrates how to use Document Loaders in LangChain to fetch data from sources like text, PDFs, directories, web pages, and CSV files, and convert it into a standard Document format with content and metadata for use with LLMs.

A content navigator powered by GPT-3.5-Turbo to explore multiple documents uploaded using Streamlit UI. It uses `Document Array Memory` for small and `Pinecone` for large document pools and delivers concise, referenced search results.

Successfully developed a Multi-Domain AI Personal Assistant using LangChain, OpenAI, and Streamlit. The application seamlessly integrates multiple specialized capabilities, including document-based question answering (QA), Python code execution, debugging, explanation and optimization, web search, latest news retrieval, and currency conversion.

RAG to talk to your code

Successfully developed an LLM application which generates a summary, a list of citations and references and response to a user's query based on the research paper's content.

📄 Summarize research papers, extract citations, and answer queries with this AI-powered assistant built using LangChain and OpenAI's GPT model.