This guide will walk you through setting up an evaluation in Future AGI, allowing you to assess AI models and workflows efficiently. You can run evaluations via the Future AGI platform or using the Python SDK.
Evaluate Using SDK
fi_api_key
and fi_secret_key
environment variables before using the Evaluator
class, instead of passing them as parameters.is_async
parameter to True
:get_eval_result
:evaluate
function with the eval_templates
parameter.Evaluate Using UI
turing_flash
: Flagship evaluation model that delivers best-in-class accuracy across multimodal inputs (text, images, audio). Recommended when maximal precision outweighs latency constraints.
turing_small
: Compact variant that preserves high evaluation fidelity while lowering computational cost. Supports text and image evaluations.
turing_flash
: Latency-optimised version of TURING, providing high-accuracy assessments for text and image inputs with fast response times.
protect
: Real-time guardrailing model for safety, policy compliance, and content-risk detection. Offers very low latency on text and audio streams and permits user-defined rule sets.
protect_flash
: Ultra-fast binary guardrail for text content. Designed for first-pass filtering where millisecond-level turnaround is critical.
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to create a key (variable), that variable will be used in future when you configure the evaluation.