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Revise README with enhanced project details

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Updated project description to include cosine similarity scoring and groundedness metric. Clarified user interaction with source documents and audio/video links.
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S Jannette
2026-05-08 03:50:29 -04:00
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# Citation Sentinel # Citation Sentinel
A source-grounded research assistant inspired by Google's NotebookLM. MIT A source-grounded research assistant that employs cosine similarity scoring for LLM generated query responses to provide a "groundedness" score. This is a metric in Retrieval-Augmented Generation (RAG) systems that quantifies how well an AI-generated answer is supported by retrieved context. It measures "faithfulness" to source documents, ensuring the answer is not hallucinated or pulled from the model's pre-training data.
license, by [@sjdev](https://sjdev.co). Users upload documents, links, youtube videos, MIT license, by [@sjdev](https://sjdev.co).
ask questions and receive answers (with inline citations) grounded in their sources.
Built with a React/Vite frontend and a Node.js/Express backend, using Anthropic Claude for Users upload source documents, or provide links to online sources including audio/video (i.e. links to youtube videos). Users may then ask questions and receive answers (with inline citations) verifiably grounded in the provided information sources.
generation, OpenAI Whisper for video audio track transcription, Voyage AI for embeddings and response cosine similarity scoring ("groundedness" score).
Built with a React/Vite frontend and a Node.js/Express backend, using Anthropic Claude for generation, OpenAI Whisper for video audio track transcription, Voyage AI for embeddings and response cosine similarity scoring (the "groundedness" score).
## Prerequisites ## Prerequisites