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Flexible configurations for model extraction, embedding, and synthesis hooks.
Distinguishing note: Focuses on integration hooks, distinct from model execution.
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SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer
Defines independent model hooks for extraction, embedding, and synthesis to allow flexible model selection per context.