Use LLMs for building real-world apps

A Question & Answer (QA) Bot for Confluence Cloud using Azure AI

Q&A Chatbot Demo using Couchbase, LangChain, OpenAI and Streamlit

Harvest information from Git, GitHub, Opsgenie, and Slack, create reports in Markdown format, animations in video formats, and publish them in different ways.

Aggregated product reviews from Amazon, Reddit, and YouTube to build a unified review analysis engine. Applied BERTopic to discover user pain points and praise patterns, then developed a Retrieval-Augmented Generation (RAG) QA bot that answers product-related questions using real user feedback.

Designed a modular pipeline that fetches real-time financial news from NewsAPI, Alpha Vantage, Reddit, and Bloomberg RSS feeds. Leveraged Cohere embeddings, FAISS indexing, and LLMs via LangChain to perform semantic search, reranking, and insight extraction. Enables dynamic financial analysis through an analyst-style Q&A interface.

Gradio-powered semantic Q&A app using Sentence Transformers

📚 Legal Q&A RAG Bot | AI-powered chatbot for legal document understanding and question answering using LangChain, OpenAI, and FAISS.

Sandbox Q&A bot for technical docs with optional DeepEval, LangChain, RAGAS, and OpenAI Evals integrations to compare RAG evaluation workflows.

A proof of concept for Question-Answering API on a specific topic using RAG from pre-defined document(s) on the topic

Retail RAG-based QA Bot using Streamlit, HuggingFace FLAN-T5, and Gemini Pro for dynamic, context-aware customer support.