The best way to integrate Claude Opus 4.5 with a vector database such as Milvus or Zilliz Cloud is to define explicit, well-scoped retrieval tools that the model can call as needed. Examples include semantic_search, fetch_by_id, upsert_documents, and search_with_filter. Opus 4.5 is designed to excel at tool orchestration, so giving it a clean separation of concerns—model for reasoning, vector DB for retrieval—creates a very robust RAG pipeline. You should avoid stuffing all data into the prompt; instead, let Opus 4.5 request the information it needs on demand via these tools.
A practical architecture is a query rewrite → retrieval → synthesis workflow. Opus 4.5 takes the user query, rewrites it into a structured retrieval request, retrieves relevant passages from Milvus or Zilliz Cloud, and then synthesizes the response using the retrieved evidence. This pattern works especially well if you chunk documents semantically, keep embeddings fresh, and attach metadata such as timestamps, authorship, or tags. Opus 4.5 handles these mixed metadata-plus-embedding workflows more reliably than earlier models.
For more advanced systems, you can extend the toolset so that Opus 4.5 helps manage the knowledge base itself. For example, you can let it propose new documents to embed, suggest reindexing strategies, or summarize source material before inserting it into the database. Milvus and Zilliz Cloud handle high-frequency writes and large-scale retrieval efficiently, making them suitable backends for long-running knowledge pipelines. With the right safety limits—such as restricting large-scale deletes or updates—you can let Opus 4.5 coordinate multi-step retrieval, knowledge refinement, and document maintenance in a predictable and developer-friendly way.