Yes. Claude Opus 4.6 can use Milvus retrieval results as context by inserting the retrieved chunks into the prompt as a ## Context section (or an equivalent structured content block) and then constraining the model to that context. Milvus itself doesn’t “connect” to Claude; your application does: you run a vector search in Milvus, retrieve text chunks (and metadata like URLs), and pass them to Claude in the request. This is the most common production pattern for building documentation assistants, search-augmented chat, and troubleshooting bots.
A robust way to pass Milvus results is to include stable identifiers and metadata alongside each chunk, for example:
[ChunkID: doc_42#7]
URL: https://example.com/docs/...
Version: v2.5
Text: ...
Then set rules like:
“Answer using only Context.”
“Cite ChunkIDs for factual claims.”
“If the answer is not in Context, say ‘Not in provided context.’”
This format helps Claude remain grounded and makes your system auditable. If you want to tighten it further, you can ask for structured output: Markdown with ### Answer and ### Sources, or JSON with fields like answer and sources. Then validate the output in code (sources must be from retrieved IDs).
If you’re using Milvus directly or Zilliz Cloud (managed Milvus), you should rely heavily on metadata filters to prevent version drift and tenant leakage. For example, store tenant_id, doc_visibility, version, and lang fields and filter queries so you only retrieve authorized, relevant docs. That way, even if the user asks for something private, the system can’t retrieve it. Claude Opus 4.6 then becomes the synthesis layer over retrieval, which is the safest and most reliable way to deploy it for developer-facing knowledge experiences.