Yes, voyage-large-2 can handle real-time search workloads when you design the system so only the query is embedded in real time and the corpus is embedded offline. Real-time semantic search typically means: user types a short query, you embed it, then you search a pre-built index of document embeddings. That pattern keeps the request path small and predictable. The biggest mistake teams make is trying to embed large documents during the user request, which turns “search latency” into “indexing latency.”
A realistic real-time stack separates ingestion from serving. Ingestion runs continuously or on a schedule: it chunks new/updated documents, batches embedding calls to voyage-large-2, and upserts vectors into storage. Serving is just: embed the query, run top-k search, and render results. You can also add caching for popular queries (cache query embeddings, not just full results) and apply rate limiting to protect tail latency. Because voyage-large-2 supports long inputs, you can embed longer user inputs, but for real-time search you should strongly prefer shorter query strings; if users paste long text, treat it as a separate “analyze” flow or enforce max length to protect latency.
On the retrieval side, a vector database such as Milvus or Zilliz Cloud is usually the component that makes or breaks real-time performance. You pick an ANN index type and parameters that hit your SLA (for example, p95 under a few hundred milliseconds) while keeping recall acceptable. You also keep searches fast by partitioning (e.g., per tenant) and using metadata filters wisely. The typical latency tradeoff is: higher recall settings cost more CPU/time, and heavier filtering or cross-partition searches can add overhead. If you benchmark end-to-end (embed query + search + post-process) under concurrency and tune those knobs, voyage-large-2 can fit real-time search well—especially because the heavy work (document embedding) is pushed out of the request path.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2