The distributed caching system designed by Morphik Core effectively solves the computational bottleneck problem of large-scale RAG applications through an intelligent preprocessing mechanism. The system adopts a layered caching strategy: embedding vectors and knowledge graph nodes are pre-generated at the document entry stage, and a result cache is built for high-frequency query patterns. The actual running data shows that the design reduces the response time of repeated queries from an average of 3.2 seconds to 0.6 seconds, and reduces the computational resource consumption by 80%.
The cache system supports preloading by conditions (e.g. category filtering) and automatically manages the cache life cycle. Users can actively call the cache_documents method to prepare data in advance before processing hot data sets. The system also automatically optimizes the caching strategy based on access patterns to ensure efficient memory usage.
Combined with scalable storage back-end support (PostgreSQL/MongoDB, etc.), this caching system enables Morphik Core to support real-time retrieval of millions of documents, providing a performance guarantee for enterprise-level applications. Operation and maintenance personnel can use the monitoring interface to view the cache hit rate and resource utilization in real time.
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
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