Data Science Basics οΌHow To USE New OpenAI Embeddings Model with LlamaIndex π¦
OpenAI introduces new Embeddings model. They are releasing new models, reducing prices for GPT-3.5 Turbo, and introducing new ways for developers to manage API keys and understand API usage. The new models include:
β Two new embedding models
β An updated GPT-4 Turbo preview model
β An updated GPT-3.5 Turbo model
β An updated text moderation model
Blog post link: https://openai.com/blog/new-embedding-models-and-api-updates
Embeddings are the secret sauce behind retrieval-augmented generation: they encode text by its meaning, allowing us to retrieve by meaning instead of keyword. The more accurate your embeddings, the better your app and these are a LOT better!
`text-embedding-3-small` should be your new go-to over its predecessor `text-embedding-ada-002`: itβs not only more accurate, itβs also 5x cheaper! And `text-embedding-3-large` squeezes even more performance out of embeddings at a higher cost.
Both embeddings support a novel βdimensionsβ parameter that lets you shorten the embeddings to trade accuracy for smaller vector sizes.
In this video, I will show you how to get started with the new OpenAI embeddings model with LlamaIndex.
Github link: https://github.com/sudarshan-koirala/youtube-stuffs/blob/main/llamaindex/OpenAI_new_embeddings.ipynb
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π π₯ Other videos you might find helpful:
π₯ Databricks playlist: https://youtube.com/playlist?list=PLz-qytj7eIWXTqncmCCSOw-GcBu2c4K0j&si=E3Yup56kmYWM_jb6
βοΈ Langflow: https://youtu.be/18b7u_e5tnM
βοΈ Flowise: https://youtu.be/LNF2DbmADus
π₯Chainlit playlist: https://youtube.com/playlist?list=PLz-qytj7eIWWNnbCRxflmRbYI02jZeG0k
π¦οΈπ LangChain playlist: https://www.youtube.com/playlist?list=PLz-qytj7eIWVd1a5SsQ1dzOjVDHdgC1Ck
π¦ LlamaIndex Playlist: https://youtube.com/playlist?list=PLz-qytj7eIWWqLRAJh-Q_fuvs0qH739zz&si=HdV9GkOQZOMRUNpW
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