Use vector index selection in Zilliz (GPU-accelerated IVF/HNSW), batch queries, enable Scout quantization, and pipeline retrieval with generation for parallel efficiency.
Latency bottlenecks: (1) embedding query → Zilliz (100ms–1s), (2) vector search in Zilliz (50ms–2s depending on index), (3) Scout generation on retrieved context (varies with context size). Optimize each: (a) use Zilliz Cloud's GPU-accelerated indices (10–100x faster for large-scale search), (b) batch queries to amortize embedding overhead, (c) quantize Scout to int8 (reduces generation latency 2–3x), (d) pipeline: while Scout generates token N, pre-fetch metadata for token N+1. Zilliz Cloud's analytics dashboard shows retrieval time—monitor this separately from generation to identify bottlenecks.
For Scout's 10M-token processing, latency is dominated by generation, not retrieval. But Zilliz Cloud's sub-100ms retrieval means you can afford frequent re-queries in agentic loops without penalty. Use this: initial retrieval → Scout generates partial answer → agent decides to re-query Zilliz → retrieve more context → Scout refines answer. This iteration is fast because Zilliz is fast.
Related Resources
- Zilliz Cloud — Managed Vector Database — high-speed retrieval
- Retrieval-Augmented Generation (RAG) — latency optimization
- Local Agentic RAG with LangGraph and Llama3 — agentic iteration patterns