To measure how vector store speed affects a RAG system's throughput, start by isolating and benchmarking the retriever and LLM components. Measure the latency (time per request) and throughput (requests processed per second) of the vector store independently using synthetic queries. For example, run 1,000 queries with varying parallel request counts to identify the retriever’s maximum sustainable throughput. Repeat the same for the LLM. Then, test the full pipeline under load to observe how the components interact. If the retriever’s throughput is lower than the LLM’s, the system’s overall capacity will plateau at the retriever’s limit, even if the LLM is underutilized.
A slow retriever directly bottlenecks throughput because RAG pipelines are typically sequential: the LLM can’t start generating until the retriever returns results. For instance, if the vector store takes 200ms per query and the LLM takes 50ms, each request takes 250ms. With no concurrency, this caps throughput at 4 requests per second. Even with parallelization, if the retriever can’t scale (e.g., due to hardware limits or inefficient indexing), queuing delays will accumulate. Tools like Locust or JMeter can simulate concurrent users to expose this: if the retriever’s error rate spikes or latency grows linearly under load, it’s the bottleneck.
To address this, profile the system using metrics like retriever latency distribution, LLM idle time, and total requests per second. For example, if the LLM is idle 80% of the time waiting for retrievals, optimizing the vector store (e.g., switching to FAISS for faster approximate search) or adding caching for frequent queries could improve throughput. If the retriever’s throughput is 50 queries per second and the LLM’s is 200, the system won’t exceed 50 QPS without retriever optimizations or horizontal scaling (e.g., sharding the vector database).