Amazon DynamoDB Vector Search Tutorial | Hands-On Semantic Search

What if you could search your DynamoDB data by meaning instead of exact keywords? In this hands-on AWS demo, we'll explore native vector search in Amazon DynamoDB and see how it enables semantic search. Using Cohere to generate text embeddings, we'll: 1. Generate text embeddings using Cohere 2. Store text and 1024-dimensional vectors in DynamoDB 3. Convert search queries into vector embeddings 4. Use DynamoDB vector search to find semantically similar results—even when the wording doesn't match exactly Note: Cohere is used as the embedding provider in this demo. You can use another embedding model that produces vectors compatible with your DynamoDB vector index configuration. For simplicity, items are inserted manually through the DynamoDB Console. In a real-world application, this step would typically be automated. Reference 📘 AWS News Blog: Amazon DynamoDB now supports real-time vector search at any scale https://aws.amazon.com/blogs/aws/amaz... If you found this demo useful, please like the video, subscribe for more practical AWS demos, and share your thoughts or questions in the comments! #aws #AmazonDynamoDB #VectorSearch #SemanticSearch #Cohere