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@@ -57,3 +57,17 @@ This pipeline includes two main operator types:
 By default, the pipeline uses [towhee/audio-decoder](https://towhee.io/towhee/audio-decoder) to load audio path as a list of audio frames in ndarray.
 Then `time-window` will combine audio frames into a list of ndarray, each of which represents an audio clip in fixed length.
 At the end, the [towhee/torch-vggish](https://hub.towhee.io/towhee/torch-vggish)) operator will generate a list of audio embeddings for each audio clip.
+
+
+    # More Resources
+
+    - [Exploring Multimodal Embeddings with FiftyOne and Milvus - Zilliz blog](https://zilliz.com/blog/exploring-multimodal-embeddings-with-fiftyone-and-milvus): This post explored how multimodal embeddings work with Voxel51 and Milvus.
+- [About Towhee team | Zilliz](https://zilliz.com/authors/Towhee_team): Towhee is an open-source machine learning pipeline
+that helps you encode your unstructured data into embeddings.
+- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
+- [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.
+- [Audio Retrieval Based on Milvus - Zilliz blog](https://zilliz.com/blog/audio-retrieval-based-on-milvus): Create an audio retrieval system using Milvus, an open-source vector database. Classify and analyze sound data in real time.
+- [Vector Database Use Case: Audio Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/audio-similarity-search): Building agile and reliable audio similarity search with Zilliz vector database (fully managed Milvus).
+- [Making Machine Learning More Accessible for Developers  - Zilliz blog](https://zilliz.com/learn/making-machine-learning-more-accessible-for-application-developers): Learn how Towhee, an open-source embedding pipeline, supercharges the app development that requires embeddings and other ML tasks.
+- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.
+    
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