Successfully developed an LLM application that provides AI-powered, structured insights based on user queries. The app features a dynamic response generator with progress indicators, interactive upvote/downvote options, and a clean, engaging user interface built using Streamlit. Ideal for personalized meal, fitness, and health-related advice.

This is an End-to-End LangChain Study Ride to learn about all Framework tools. Motive to make to gain the more practical knowledge about all the algo and self study,

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

Successfully developed an interview preparation guide using Langchain which can effectively guide users in their interview preparation process and job search journeys by providing valuable insights and feedback regarding their performance. It generates a comprehensive list of questions pertaining to a user query as well.

Successfully designed and developed a customer support chatbot that leverages LangChain and Pinecone for efficient retrieval-augmented generation (RAG), enabling intelligent and context-aware responses to user queries.

This repo showcases using Grok AI prompts with LangChain templates to build dynamic AI workflows. It supports prompt formats including YAML, XML, chat-based, and string prompts. Users can create reusable, structured prompts for conversational AI and data parsing. Perfect for integrating flexible prompt management with AI models efficiently.

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.

Open-source LangChain toolkit with custom Chains, ChatModels, Embeddings, and Output Parsers. Build powerful AI workflows effortlessly. Perfect for developers and businesses leveraging LLMs.

Successfully developed an interview preparation guide using Langchain which can effectively guide users in their interview preparation process and job search journeys by providing valuable insights and feedback regarding their performance. It generates a comprehensive list of questions pertaining to a user query as well.

This repository showcases how to use LangChain’s Output Parsers to transform LLM responses into usable formats. It includes examples for String, JSON, Structured, and Pydantic parsers, and how they ensure consistent, reliable outputs.