aikho/retrivex
Explainability toolkit for retrieval models. Explain prediction of vector search models (embeddings similarity models, siamese encoders, bi-encoders, dense retrieval models). Debug your vector search models for RAG or agentic AI system.
This toolkit helps you understand why your AI system retrieves specific information when it’s asked a question or given a prompt. You input a query and a retrieved document, and it shows you which parts of each were most responsible for the match. It's designed for AI system developers or engineers building applications like intelligent chatbots or advanced search engines.
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Use this if you need to debug, build trust in, or improve the accuracy of your information retrieval system by understanding why certain text pairs are considered similar.
Not ideal if you're trying to explain traditional classification or regression model outputs, as it's specifically tailored for vector search and similarity models.
Stars
15
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Language
Jupyter Notebook
License
LGPL-2.1
Category
Last pushed
Oct 05, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/aikho/retrivex"
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