Use LLMs for building real-world apps
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Updated
Jan 17, 2025 - HTML
Use LLMs for building real-world apps
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.
A Question & Answer (QA) Bot for Confluence Cloud using Azure AI
Gradio-powered semantic Q&A app using Sentence Transformers
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.
A proof of concept for Question-Answering API on a specific topic using RAG from pre-defined document(s) on the topic
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.
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