Doing cool issues with information!
OpenAI just lately unveiled two new state-of-the-art textual content embedding fashions — text-embedding-3-small and text-embedding-3-large – poised to dethrone the venerable text-embedding-ada-002. In this publish, we’ll dig into the capabilities of those rising fashions and put one to the check in a hands-on RAG utility.
Specifically, we’ll:
- Dive into the main points and potential of the brand new OpenAI textual content embedders
- Showcase
text-embedding-3-smallby constructing a Retrieval-Augmented Generator demo - Horse race it towards the favored BGE-small open supply model on a RAG dataset
- Leverage Trulens to transparently consider the RAG fashions underneath the hood
I’m eagerly anticipating spectacular features from text-embedding-3-small because of its extra intensive coaching. However, BGE-small has confirmed formidable — so this matchup ought to reveal actual tradeoffs to think about. The Trulens evaluation will even uncover insights no benchmark alone can present.
Let’s get hands-on with the following evolution of Foundation Models! The face-off begins now…
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OpenAI has unveiled two new textual content embedding fashions as upgrades over December 2022’s text-embedding-ada-002 – introducing text-embedding-3-small and text-embedding-3-large. These new basis fashions boast stronger benchmark efficiency and pricing efficiencies.
Specifically, text-embedding-3-small enhances multilingual retrieval accuracy on MIRACL benchmarks by 12.6% over ada-002 whereas barely enhancing English specialised duties on MTEB. At the identical time it’s 5x cheaper. The bigger text-embedding-3-large surfaces much more dramatic features – 23.5% on MIRACL and three.6% on MTEB. The desk beneath compares their efficiency
