Introduction to Enhanced Ranking with RRF in Elasticsearch
With the arrival of
RRF Ranking Example
In a scenario where documents are ranked based on both BM25 and dense embedding, RRF seamlessly blends the outcomes to produce an integrated and improved ranking.
| BM25 Rank | Dense Embeding Rank | RRF Result k=0 |
|---|---|---|
| A 1 | B 1 | B:12+1/1=1.5 |
| B 2 | C 2 | A:1/1+1/3=1.3 |
| C 3 | A 3 | C:1/3+1/2=0.83 |
Data and Model Readiness
Pursuing the methodology described in the ESRE Series (I), we utilized the text_embedding model and launched the deployment through Eland. Subsequently, we uploaded the initial dataset via Kibana, configured the text-embeddings pipeline, and ultimately crafted indexed data replete with vectors via index rebuilding.
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Vollständiger Original-Bericht
Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf dev.to.
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