kjannette/citation_sentinel
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Steven Jannette
2026-08-12 12:09:12 +00:00
parent fe36ea6aaa
commit 861983531a
1 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@@ -1,7 +1,7 @@
# Citation Sentinel © 2023 @sjDev
# Source Sentinel - 2023 @sjDev - LICENSE: MIT
![Citation Sentinel UI showing the source sidebar, chat answer with citations, and groundedness badge](demo.jpg)
![SOURCE Sentinel UI showing the source sidebar, chat answer with citations to the source, and groundedness badge](demo.jpg)
Image one: source-grounded research assistant that empowers users to transform an otherwise-unmanageably-large corpus of data on any topic of interest into refined, easily-digested subtopics and pose focused inquiries, to instantly receive concise, quantifiably-evaluated, accurate responses. The system also provides suggested follow-up questions further refining in user inquiries by detecting the query goals.
@@ -13,7 +13,7 @@ Image Two: demo co clickable inline citations that take user to source-grounded
# Reliability and Understandability
Citation Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space. This yields a "groundedness" score.
Source Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space. This yields a "groundedness" score.
Groundedness metrics quantify how well an AI-generated answer is supported by retrieved context. It measures "faithfulness" to source documents, ensuring the answer is not hallucinated, inaccurate or stale (pulled from the model's training data).