Table of contents
Embedefy: Represent Data in Vector Space with Relatedness Indication
Embeddings represent data in a vector space, with the distance between vectors indicating their relatedness.
What is Embedefy?
Embeddings are a method of representing data in a multi-dimensional vector space, where the proximity of vectors indicates the degree of similarity between the data points they represent.
How to use Embedefy?
Embedefy's Core Features
Embeddings that are open-source, generous usage limits, seamless AI application integration, and a robust infrastructure.
Embedefy's Use Cases
Applications include Retrieval-Augmented Generation (RAG), model fine-tuning, semantic search, clustering, recommendation systems, anomaly detection, classification, and more.
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Embedefy Support Email & Customer Service Contact
For more contact information, visit the contact us page.
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About Embedefy
To learn more about Embedefy, please visit the about us page.
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Embedefy Login
Access the login page here: https://www.embedefy.com/api/auth/signin.
FAQ from Embedefy
What is Embedefy?
Embeddings are a method of representing data in a vector space, where the proximity of vectors indicates how closely related the data points are.
How to use Embedefy?
Submit your text inputs to the Embedefy API endpoint with a chosen model to get embeddings.
Is your service really free?
Yes, Embedefy provides open-source embeddings at no cost.
Can your API be integrated with other platforms or services?
Yes, the Embedefy API can be seamlessly integrated with various platforms and services.
How can I run the embedding models on my own machines?
Embedding models can be downloaded from HuggingFace and executed on your own hardware.
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