From 8dbdc1dea401b6491ef6d27380c3f756bd9e87cb Mon Sep 17 00:00:00 2001 From: S Jannette Date: Fri, 8 May 2026 03:50:29 -0400 Subject: [PATCH] Revise README with enhanced project details Updated project description to include cosine similarity scoring and groundedness metric. Clarified user interaction with source documents and audio/video links. --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 444ca82..dee45e0 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ # Citation Sentinel -A source-grounded research assistant inspired by Google's NotebookLM. MIT -license, by [@sjdev](https://sjdev.co). Users upload documents, links, youtube videos, -ask questions and receive answers (with inline citations) grounded in their sources. +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. +MIT license, by [@sjdev](https://sjdev.co). -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 ("groundedness" score). +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. + +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