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@@ -53,3 +53,18 @@ Absolute address of the test videos.
 **Returns:** *list*
 
 The probabilities of videos being fake ones.
+
+
+    # More Resources
+
+    - [Vector Search and RAG - Balancing Accuracy and Context - Zilliz blog](https://zilliz.com/blog/vector-search-and-rag-balancing-accuracy-and-context): In this article, we cover AI Hallucinations and how RAG can help solve the issue. Christy demonstrated a great explanation of how building RAG requires careful choices of embedding models, indexes, and semantic search approaches.
+- [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.
+- [What is a Convolutional Neural Network? An Engineer's Guide](https://zilliz.com/glossary/convolutional-neural-network): Convolutional Neural Network is a type of deep neural network that processes images, speeches, and videos. Let's find out more about CNN.
+- [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.
+- [Building a Video Analysis System with Milvus Vector Database - Zilliz blog](https://zilliz.com/blog/milvus-helps-analyze-videos-intelligently): Learn how Milvus powers the AI analysis of video content.
+- [Building an Intelligent QA System with NLP and Milvus - Zilliz blog](https://zilliz.com/blog/building-intelligent-chatbot-with-nlp-and-milvus): The Next-Gen QA Bot is here
+- [Evaluating Your Embedding Model - Zilliz blog](https://zilliz.com/learn/evaluating-your-embedding-model): Review some practical examples to evaluate different text embedding models.
+- [What is a Generative Adversarial Network? An Easy Guide](https://zilliz.com/glossary/generative-adversarial-networks): Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications.
+So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model.
+- [Real-Time GenAI without Hallucination Using Confluent & Zilliz Cloud](https://zilliz.com/product/integrations/confluent): nan
+    
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