Yes, a slow vector store can limit the overall throughput of a RAG (Retrieval-Augmented Generation) system, even if the LLM component is fast. In a typical RAG pipeline, the retriever (vector store) and the LLM operate sequentially: the retriever fetches relevant context first, and the LLM generates a response using that context. The total time to process a query is the sum of the retriever’s latency and the LLM’s generation time. If the retriever is slow, it creates a bottleneck, forcing the system to wait for retrieval before the LLM can start. For example, if the vector store takes 200 ms per query and the LLM takes 50 ms, the system can handle at most 4 queries per second (QPS) per worker, even though the LLM alone could theoretically handle 20 QPS. The throughput is dictated by the slowest component in the chain.
To measure the impact, start by benchmarking each component independently. Measure the retriever’s latency under varying loads (e.g., 10, 100, or 1,000 concurrent queries) and compare it to the LLM’s latency. Tools like Locust or custom scripts can simulate load and track end-to-end throughput. For instance, if the retriever averages 150 ms at 50 QPS but the LLM processes 200 QPS, the retriever becomes the bottleneck. You can also profile the pipeline as a whole: if increasing the number of concurrent requests causes retriever latency to spike while LLM utilization remains low, the vector store is limiting throughput. Additionally, test scenarios like batch retrieval (if supported) to see if fetching multiple contexts at once improves efficiency.
To mitigate this, optimize the retriever first. Use faster indexing methods (e.g., HNSW in FAISS) or hardware acceleration (GPUs for vector operations). Scale the vector store horizontally by adding replicas to handle parallel requests. Implement caching for frequent queries to bypass retrieval entirely. For example, cache the top 100 common questions and their retrieved contexts to reduce load. Alternatively, use a hybrid approach: deploy a lightweight retriever (e.g., BM25) to pre-filter candidates before using a slower, precise vector store. If the retriever cannot be optimized further, consider overlapping retrieval and generation—fetch context for the next query while the LLM processes the current one—though this requires careful concurrency management. Monitoring tools like Prometheus can help track retriever latency and throughput in production to identify bottlenecks early.
