Designing a semantic routing system: From static rules to dynamic intelligence with Redis and Java

A semantic routing system enhances intelligent systems by classifying incoming requests based on their semantic meaning and directing them to appropriate processing paths, moving beyond traditional rule-based methods that rely on keywords or binary logic. This system utilizes embeddings and similarity matching to understand user intent and efficiently handle requests, such as routing simple questions to cost-effective pipelines, blocking sensitive topics, or escalating complex queries to advanced models within milliseconds. By employing Redis as a dynamic configuration store, routing definitions are stored centrally and can be updated in real-time without redeploying the system, allowing for scalability, maintainability, and adaptability. The use of RedisVL for Java simplifies the process by providing high-level abstractions for vector indexing and similarity-based retrieval, enabling the semantic router to match user inputs based on meaning, with stored example queries acting as reference data. The architecture separates routing data from logic, ensuring a flexible system that evolves over time to support new categories and improve user experience, all while allowing configurations to be modified dynamically through a service layer.

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