Hugging Face
Generate Vector Embeddings for your Gen AI applications from one of the open source models available on the Hugging Face platform.
Use this integration for FreeWhat is Hugging Face?
Hugging Face is a leading platform for artificial intelligence and machine learning, providing a comprehensive ecosystem for building, training, and deploying state-of-the-art models. It offers a vast repository of pre-trained models, datasets, and tools that empower developers, researchers, and businesses to harness the power of natural language processing (NLP) and computer vision.
- Open-source library of 728,000+ models and 160,000+ datasets
- Collaborative platform for the AI community
- Cutting-edge tools for model training, fine-tuning, and deployment
Why Use Hugging Face Models with Zilliz Cloud?
Convert your unstructured data into vector embeddings using state-of-the-art Machine Learning models hosted on Hugging Face, then store and query these embeddings efficiently in Zilliz Cloud. This powerful combination offers several key advantages:
- Seamless integration of cutting-edge AI models with high-performance vector storage
- Enhanced retrieval accuracy and scalability for Gen AI applications
- Streamlined workflow from model selection to production deployment
- Flexibility to choose the right embedding model for optimal results in your specific use case By leveraging Hugging Face's vast model repository and Zilliz Cloud's efficient vector database, you can rapidly develop and deploy sophisticated AI-driven applications that excel in processing and analyzing unstructured data.
How Hugging Face works with Zilliz Cloud
A good place to start when trying to find a model on the HuggingFace platform is the MTEB leaderboard (Massive Text Embedding Benchmark). The MTB Leaderboard serves as a comprehensive hub for text embedding model evaluation and is a multi-task and multi-language comparison of embedding models. It offers an overview of each model's performance across various tasks.
mteb Leaderboard from HuggingFace
With so many models to choose from, MTEB helps you to filter to find the right choice by categories like Rank, Retrieval Average, max token length, embedding dimension and more. It can be overwhelming so we wrote a blog on How to Choose the Best Embedding Model for you Data to help you.
Once you have chosen your embedding model, you can convert your data into vector embeddings and store them in Zilliz Cloud. This is the first step to building your semantic similarity search application that can be the foundation to use cases like Retrieval Augmented Generation, Anomaly Detection, Recommender Systems and more!
Learn
The best way to start is with a hands-on tutorial. This tutorial will walk you through how to build a Question and Answer solution with Hugging Face and Zilliz Cloud