@ -84,3 +84,13 @@ It returns a text emabedding in numpy.ndarray.
Get a list of supported model names.
Get a list of supported model names.
# More Resources
- [All-Mpnet-Base-V2: Enhancing Sentence Embedding with AI - Zilliz blog](https://zilliz.com/learn/all-mpnet-base-v2-enhancing-sentence-embedding-with-ai): Delve into one of the deep learning models that has played a significant role in the development of sentence embedding: MPNet.
- [The guide to text-embedding-ada-002 model | OpenAI](https://zilliz.com/ai-models/text-embedding-ada-002): text-embedding-ada-002: OpenAI's legacy text embedding model; average price/performance compared to text-embedding-3-large and text-embedding-3-small.
- [OpenAI text-embedding-3-large | Zilliz](https://zilliz.com/ai-models/text-embedding-3-large): Building GenAI applications with text-embedding-3-large model and Zilliz Cloud / Milvus
- [What Are Vector Embeddings?](https://zilliz.com/glossary/vector-embeddings): Learn the definition of vector embeddings, how to create vector embeddings, and more.
- [A Guide to Using OpenAI Text Embedding Models for NLP Tasks - Zilliz blog](https://zilliz.com/learn/guide-to-using-openai-text-embedding-models): A comprehensive guide to using OpenAI text embedding models for embedding creation and semantic search.
- [Training Text Embeddings with Jina AI - Zilliz blog](https://zilliz.com/blog/training-text-embeddings-with-jina-ai): In a recent talk by Bo Wang, he discussed the creation of Jina text embeddings for modern vector search and RAG systems. He also shared methodologies for training embedding models that effectively encode extensive information, along with guidance o
