To combine Gemini 3 with Milvus for semantic search, you build a classic RAG pipeline with Milvus as the vector store and Gemini 3 as the reasoning layer. First, you ingest your documents—articles, tickets, code snippets, policies—into an embedding pipeline. Each chunk of text is turned into a vector using your chosen embedding model. You store these vectors, along with metadata like document IDs and source information, inside milvus. Milvus handles efficient similarity search across large collections, letting you quickly find relevant content for any query.
At query time, the flow looks like this: a user submits a natural-language question; your backend converts the question into an embedding and queries Milvus for the top-k closest vectors. Those hits represent the most semantically similar chunks of your content. You then extract the underlying text for those chunks and construct a prompt for Gemini 3 that includes both the user question and a “Context” section with the retrieved passages. You can add instructions like “Use only the context below to answer” and “Cite which chunks you used,” to keep responses grounded and auditable.
This setup gives you a strong semantic search system. Milvus finds the right pieces of information quickly, and Gemini 3 turns them into clear, natural-language answers, summaries, or structured outputs. If you want a fully managed experience, you can swap the Milvus deployment for Zilliz Cloud., which provides a hosted Milvus service without you running your own cluster. In both cases, tuning the chunking strategy (how you split documents), the number of retrieved items, and the prompt template will dramatically impact answer quality. Over time, you can log queries and outcomes, improve your prompts, and tweak Milvus indexing parameters to get faster and more accurate semantic search across your data.