@kaiord/ai

@kaiord/ai

AI/LLM integration for the Kaiord health & fitness data framework. Converts natural language workout descriptions into structured KRD workout objects using the Vercel AI SDK.

Installation

pnpm add @kaiord/ai ai @ai-sdk/anthropic

Usage

import { createTextToWorkout } from "@kaiord/ai";
import { createAnthropic } from "@ai-sdk/anthropic";

const provider = createAnthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const textToWorkout = createTextToWorkout({
  model: provider("claude-sonnet-4-5-20241022"),
});

const workout = await textToWorkout("30 minutes easy cycling", {
  sport: "cycling",
});

Eval Suite

The eval suite validates LLM output quality against a curated set of workout descriptions.

Running Evals Locally

# Set your API key
export ANTHROPIC_API_KEY=sk-ant-...

# Run with default model (claude-sonnet-4-5-20241022)
pnpm --filter @kaiord/ai eval

# Run with a specific model
EVAL_MODEL=claude-sonnet-4-5-20241022 pnpm --filter @kaiord/ai eval

The eval runner outputs pass/fail per benchmark and saves a JSON report to the working directory.

Benchmarks

The benchmark suite (src/evals/benchmarks.json) contains 22 curated workout descriptions across:

• Sports: cycling, running, swimming, generic
• Complexity: simple, intervals, repetition blocks, mixed
• Languages: English, Spanish, mixed
• Zones: FTP percentages, HR zones, pace zones with expected value ranges
• Edge cases: very short, very long, ambiguous descriptions

Assertions

Each benchmark is evaluated against these criteria:

Assertion	Threshold	Description
Schema validation	100%	Output must pass Zod workoutSchema
Sport correctness	>= 95%	Detected sport matches expected sport
Step count	per-benchmark	Between minSteps and maxSteps
Zone accuracy	+/- 5%	Target values within tolerance when zone checks are defined

The overall pass rate must be >= 90% for the eval to succeed (exit code 0).

Adding New Benchmarks

1. Edit src/evals/benchmarks.json
2. Add an entry following this schema:

{
  "id": "unique-id",
  "text": "Natural language workout description",
  "expectedSport": "cycling",
  "minSteps": 1,
  "maxSteps": 10,
  "category": "simple|intervals|repetition|zones|mixed|edge",
  "language": "en|es|mixed",
  "zoneCheck": {
    "targetType": "power",
    "minValue": 200,
    "maxValue": 280
  }
}

The zoneCheck field is optional. When present, active steps with the specified target type are validated against the min/max values with a 5% tolerance.

CI Integration

Evals run via GitHub Actions as a manual workflow_dispatch trigger (.github/workflows/eval.yml). Results are uploaded as workflow artifacts. Evals are not part of the standard CI pipeline because they require LLM API keys and incur costs.

License

MIT