GenAI Processors: Build powerful and flexible Gemini applications(developers.googleblog.com)
developers.googleblog.com
GenAI Processors: Build powerful and flexible Gemini applications
https://developers.googleblog.com/en/genai-processors/
3 comments
This interested me as a simple-ish approach to a tough-ish promblem. From the example Trip Planner file:
"""Command line interface to process a trip request.
We use Gemini flash-lite to formalize freeform trip request into the dates and destination. Then we use a second model to compose the trip itinerary.
* This simple example shows how we can reduce perceived latency by running a fast model to validate and acknowledge user request while the good but slow model is handling it.
The approach from this example also can be used as a defense mechanism against prompt injections. The first model without tool access formalizes the request into the TripRequest dataclass. The attack surface is significantly reduced by the narrowness of the output format and lack of tools. Then a second model is run on this cleanup up input. *
Before running this script, ensure the `GOOGLE_API_KEY` environment variable is set to the api-key you obtained from Google AI Studio.
Usage: python3 trip_request_cli.py """
"""Command line interface to process a trip request.
We use Gemini flash-lite to formalize freeform trip request into the dates and destination. Then we use a second model to compose the trip itinerary.
* This simple example shows how we can reduce perceived latency by running a fast model to validate and acknowledge user request while the good but slow model is handling it.
The approach from this example also can be used as a defense mechanism against prompt injections. The first model without tool access formalizes the request into the TripRequest dataclass. The attack surface is significantly reduced by the narrowness of the output format and lack of tools. Then a second model is run on this cleanup up input. *
Before running this script, ensure the `GOOGLE_API_KEY` environment variable is set to the api-key you obtained from Google AI Studio.
Usage: python3 trip_request_cli.py """
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Also the research agent example at https://colab.research.google.com/github/google-gemini/genai...