Yes, Gemini 3 works very well with retrieval workflows based on vector databases. The model is long-context and strong at reasoning over grounded evidence, which is exactly what retrieval-augmented generation (RAG) needs. In a typical RAG setup, you do not rely on Gemini 3’s training data alone; instead, you store your own knowledge—documents, code, policies, FAQs—as vector embeddings in a database, and for each query you retrieve the most relevant chunks and feed them into the model. Gemini 3 then uses those chunks as the primary ground truth when generating answers.
The integration pattern is straightforward. When a user asks a question, your backend first converts that question into an embedding using your chosen embedding model. Then it queries a vector database such asMilvus or Zilliz Cloud. for the top-k similar vectors, which correspond to relevant passages. You combine these passages into a context block and include it in the prompt to Gemini 3, often with instructions like “Answer using only the context below, and if the answer is not present, say you don’t know.” This keeps responses grounded and reduces hallucinations, because the model is guided by your data instead of just its internal weights.
Gemini 3’s large context window and dynamic thinking make these retrieval flows more flexible. You can include more retrieved chunks, richer citations, or even multiple document types—like snippets from code, policy text, and a summary paragraph—all in one call. For example, you might retrieve related contract sections, relevant product docs, and linked support tickets in a single query, then let Gemini 3 produce a merged, consistent answer. This pattern scales well across knowledge bases, developer documentation, enterprise wikis, and customer-support content, turning Gemini 3 into a powerful answer engine rather than a generic text generator.