Claude Opus 4.5 doesn’t “talk to Milvus” directly; instead, you put a retrieval layer between the model and your data, and that layer uses a vector database such as Milvus or its managed offering Zilliz Cloud. The typical architecture is: (1) you embed your documents, code, logs, or other assets into vectors; (2) you store those vectors and metadata in Milvus; (3) when a user asks a question or your agent needs context, you compute an embedding for the query, retrieve the top-K similar items from Milvus, and then (4) include those items as context in the prompt you send to Claude Opus 4.5. This is standard retrieval-augmented generation (RAG), but Opus 4.5’s long context and strong reasoning make it particularly effective.
Where Opus 4.5 stands out is in handling larger, messier retrieved contexts. Because it can process very long prompts and has better context-compaction capabilities, you can pack in more retrieved chunks — for example, several files plus related docs and prior discussion — and still expect coherent reasoning rather than confusion. External evaluations and Anthropic’s own blogs note that Opus 4.5 is especially good at multi-hop reasoning over multiple sources, which is exactly what you need in a serious RAG stack (e.g., answering questions that require combining information from several documents).
You can also use Milvus/Zilliz Cloud as long-term memory for agents powered by Opus 4.5. Instead of keeping all history in the chat context, you store embeddings of key decisions, plans, summaries, or user preferences in Milvus. When a session resumes or a new task starts, your orchestrator retrieves the most relevant memories and feeds them into Opus. This keeps token usage manageable while still giving the agent a sense of continuity and organizational memory. Opus 4.5 then operates on this retrieved slice as if it had “remembered” everything itself, giving you the benefits of long-term memory without paying for a huge context window on every single call.