@ -127,3 +127,16 @@ When text modality, read the text to generate a text embedding.
# More Resources
- [Build Better Multimodal RAG Pipelines with FiftyOne, LlamaIndex, and Milvus - Zilliz blog](https://zilliz.com/blog/build-better-multimodal-rag-pipelines-with-fiftyone-llamaindex-and-milvus): Enhance the capabilities of multimodal systems by efficiently leveraging text and visual data for improved data retrieval and context-rich responses.
- [What is BERT (Bidirectional Encoder Representations from Transformers)? - Zilliz blog](https://zilliz.com/learn/what-is-bert): Learn what Bidirectional Encoder Representations from Transformers (BERT) is and how it uses pre-training and fine-tuning to achieve its remarkable performance.
- [The guide to mistral-embed | Mistral AI](https://zilliz.com/ai-models/mistral-embed): mistral-embed: a specialized embedding model for text data with a context window of 8,000 tokens. Optimized for similarity retrieval and RAG applications.
- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.
- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar/success): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
- [The guide to all-MiniLM-L12-v2 | Hugging Face](https://zilliz.com/ai-models/all-MiniLM-L12-v2): all-MiniLM-L12-v2: a text embedding model ideal for semantic search and RAG and fine-tuned based on Microsoft/MiniLM-L12-H384-uncased
- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database
- [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
