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@@ -68,3 +68,12 @@ An image captioning operator takes a [towhee image](link/to/towhee/image/api/doc
 
 ​   The caption generated by model.
 
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+# More Resources
+
+- [Multimodal RAG locally with CLIP and Llama3  - Zilliz blog](https://zilliz.com/blog/multimodal-RAG-with-CLIP-Llama3-and-milvus): A tutorial walks you through how to build a multimodal RAG with CLIP, Llama3, and Milvus.
+- [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.
+- [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
+- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
+- [From Text to Image: Fundamentals of CLIP - Zilliz blog](https://zilliz.com/blog/fundamentals-of-clip): Search algorithms rely on semantic similarity to retrieve the most relevant results. With the CLIP model, the semantics of texts and images can be connected in a high-dimensional vector space. Read this simple introduction to see how CLIP can help you build a powerful text-to-image service.
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