OpenAI Embeddings: Da Testo a Vettore per Search, RAG e Clustering

«OpenAI Embeddings: Da Testo a Vettore per Search, RAG e Clustering» è un corso online di ChatGPT e OpenAI di livello principiante, tenuto da InsightLearn Curated AI su InsightLearn. Il percorso comprende 9 lezioni organizzate in 3 sezioni, per una durata complessiva di 5h 0m. Embeddings text-embedding-3-large/small di OpenAI: come funzionano, dimensionality reduction, similarity search, clustering, classification.

Descrizione del corso

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Embeddings text-embedding-3-large/small di OpenAI: come funzionano, dimensionality reduction, similarity search, clustering, classification.

Cosa imparerai

  • Curated from public resources. Embeddings text-embedding-3-large/small di OpenAI: come funzionano, dimensionality reduction, similarity search, clustering, classification.

Programma del corso

  1. Cos'è un embedding (10 min)
  2. text-embedding-3-large vs small (10 min)
  3. Prima similarity search in Python (10 min)
  4. Semantic search con cosine similarity (10 min)
  5. Clustering con K-Means (10 min)
  6. Classificazione zero-shot (10 min)
  7. pgvector quickstart (10 min)
  8. Pinecone integration (10 min)
  9. Qdrant + Weaviate alternativa (10 min)

Dalle lezioni

Estratto della trascrizione di «Cos'è un embedding»

Recently, vector databases got a lot of fame with companies raising hundreds of millions of dollars to build them and people calling it a new kind of database for the AI era. On the other hand, for many projects it might be an overkill solution and using a traditional database or even just a NumPy and D array might work just fine. But there is no doubt that vector databases are extremely fascinating and allow many great applications, especially when you want to give large language models. like GPT-4 long term memory. So in this video I will explain in a very beginner friendly way what vector databases are and how they work. We will go over some use cases…

Estratto della trascrizione di «text-embedding-3-large vs small»

In this video, we're going to be diving into the latest release from opening up their newest embedding models. Before I dive into the new updates, I first wanted to just quickly clarify what embeddings are for those who might not be aware or those that are just looking for a refresher. What embeddings are are essentially taking text or code and it's converting it into a mathematical vector, which is a series of numbers. Now, each of those numbers represents the essence of the input by obtaining that essence within this numerical representation. This is really at the heart of natural language. processing. This allows machines to really grasp the subtleties of language by understanding the context and meaning…

Estratto della trascrizione di «Prima similarity search in Python»

Although there's a ton of excitement these days around LLMs and AI agents, I'm more excited about the recent innovations in text embeddings. We saw these in the previous video of this series where we use text embeddings to implement a RAG system to improve an LLM. In this video, I'll discuss text embeddings in greater detail and share two simple yet high value use cases, namely text classification and semantic search. And if you're new here... Welcome. I'm Shaw. I make videos about data science and entrepreneurship and if you enjoyed this content, please consider subscribing That's a great no-cost way to support me in all the videos that I make There's a fundamental challenge in trying to analyze…

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Domande frequenti

Quanto dura il corso OpenAI Embeddings: Da Testo a Vettore per Search, RAG e Clustering?

Il corso dura 5h 0m in totale, con 9 lezioni suddivise in 3 sezioni, da seguire online al proprio ritmo.

Serve esperienza pregressa?

Il corso è di livello principiante e non indica requisiti specifici di ingresso.

Il corso rilascia un certificato?

Sì: al completamento del corso ricevi un certificato di completamento verificabile online.