Nano Banana 2 does not support real-time grounding with live web search results as a built-in feature of the image generation API. The model generates images based solely on the prompt and any reference images provided in the request; it does not have access to external URLs, live data sources, or search indexes at inference time. Prompts that ask the model to "generate an image based on the latest news" or "show what is currently trending" are treated as text instructions and interpreted based on the model's training data, not based on real-time retrieval.
If your application requires images that are grounded in up-to-date information—for example, generating a visual representation of a current event, a recent product launch, or a live data state—you need to build the retrieval step separately in your application layer. The typical approach is to retrieve the relevant information using a search or data API, summarize it into a coherent text prompt, and then pass that prompt to Nano Banana 2. The model sees a rich, specific prompt that reflects current information without needing any built-in search capability itself. This separation is architecturally cleaner because it keeps your data retrieval logic decoupled from your image generation logic.
Grounding image generation with structured data rather than web search—for example, generating a chart-style image based on values from a database query—follows the same pattern. You retrieve the data, format it into a prompt that describes the data values and the desired visual representation, and send that to the model. For applications where the grounding data changes frequently and needs to be indexed for efficient retrieval, a vector database such as Zilliz Cloud can serve as the retrieval layer, returning semantically relevant context that your application formats into a generation prompt.