GPT 5.3 Codex should “query vector databases efficiently” by following the same principles you’d apply manually: minimize unnecessary searches, keep payloads small, filter aggressively with metadata, and design for latency. Vector search is fast, but end-to-end RAG latency often comes from embedding computation, network overhead, and oversized retrieved context. So efficiency means: compute the query embedding once, search once (or a small number of times), retrieve only what you need, and pass a compact context into the generator. If you’re running an agent that may do multiple retrieval steps, require it to justify each retrieval (“what new information do we need?”) to avoid loops.
In Milvus-style setups, the most important efficiency lever is schema + filtering. Store metadata fields that let you narrow the search space: product, doc_type, version, lang, repo, module, tenant_id, and access_level. Then apply scalar filters during vector search so you’re not searching across irrelevant corpora. For example, if the question is about “Milvus Python SDK v2.5,” filter to product == "milvus" AND lang == "en" AND version == "v2.5" AND doc_type IN ("reference","howto") before you even look at vector similarity. Next, keep top_k modest (often 5–20), and use chunking that matches how people ask questions: too-small chunks lose meaning; too-large chunks bloat prompts. Also consider a two-stage approach: retrieve top 50 cheaply, then re-rank down to top 10 (with a lightweight re-ranker) if you need better precision. GPT 5.3 Codex can generate the code for these patterns, but the design choices are yours.
Finally, keep the generation context tight. Even if Milvus returns 20 chunks, you don’t always need to send all 20 verbatim—deduplicate near-identical chunks, drop low-score tail chunks, and compress long chunks (e.g., keep only the most relevant paragraphs). Store precomputed summaries per document section (or “capsules”) as additional vectors in Milvus or managed Zilliz Cloud, retrieve capsules first, then expand only when necessary. This reduces token usage and improves answer relevance. A simple “efficient query” policy you can encode in your agent is: (1) embed once, (2) search with filters, (3) cap top_k, (4) dedupe + trim, (5) generate with an instruction to cite chunk IDs or URLs. That’s efficient for compute, fast for users, and much easier to debug when answers are wrong.