To ensure consistent responsiveness and system stability under high concurrency conditions, DAKSH incorporates a suite of performance optimization strategies. These enhancements are specifically engineered to reduce retrieval latency, minimize compute redundancy, and maintain throughput during peak usage — especially in large-scale deployments spanning multiple domains or user groups.
One key optimization is chunk-level caching. DAKSH actively monitors frequently occurring or semantically similar queries and stores the corresponding top-k retrieved chunks in a temporary cache. When similar queries are reissued, the system can bypass the vector search layer and serve results directly from cache, significantly reducing response time and compute load.
Complementing this is session persistence, which maintains short-lived contextual memory during an active user session. This allows DAKSH to reuse relevant embeddings, access control filters, and reranked results across follow-up queries. For instance, if a user asks a series of related questions within the same interaction window, redundant retrievals are avoided — accelerating response cycles and preserving user context.
For large-scale enterprise or multi-tenant deployments, DAKSH implements vector store sharding. Instead of relying on a monolithic vector index, the system partitions the vector database by logical boundaries such as department (e.g., HR, finance, operations), region, or tenant. This modular structure allows targeted queries to access a smaller, contextually relevant subset of embeddings, reducing lookup time and improving relevance scoring.
To further support concurrency at scale, DAKSH leverages asynchronous lookup queues. When multiple users submit queries simultaneously, the system routes each task through non-blocking async handlers that decouple preprocessing, vector search, and inference. This design prevents cold starts from cascading delays and enables efficient parallelism without compromising output order or accuracy.
Together, these mechanisms enable DAKSH to maintain low latency and high reliability under varying load conditions — making it robust enough for real-time service delivery in government portals, enterprise knowledge hubs, citizen kiosks, and multi-user support platforms.