Using Grounding with Google Search, you can improve the accuracy and recency of responses from the model. Starting with Gemini 2.0, Google Search is available as a tool. This means that the model can decide when to use Google Search. The following example shows how to configure Search as a tool.
Gen AI SDK for Python
Learn how to install or update the Google Gen AI SDK for Python.
For more information, see the
Gen AI SDK for Python API reference documentation or the
python-genai
GitHub repository.
Set environment variables to use the Gen AI SDK with Vertex AI:
# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values # with appropriate values for your project. export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT export GOOGLE_CLOUD_LOCATION=us-central1 export GOOGLE_GENAI_USE_VERTEXAI=True
The Search-as-a-tool functionality also enables multi-turn searches and multi-tool queries (for example, combining Grounding with Google Search and code execution).
Search as a tool enables complex prompts and workflows that require planning, reasoning, and thinking:
- Grounding to enhance factuality and recency and provide more accurate answers
- Retrieving artifacts from the web to do further analysis on
- Finding relevant images, videos, or other media to assist in multimodal reasoning or generation tasks
- Coding, technical troubleshooting, and other specialized tasks
- Finding region-specific information or assisting in translating content accurately
- Finding relevant websites for further browsing
What's next
- For more detailed information and instructions on grounding with Gemini, see Ground responses for Gemini models.