Your First Evaluation

Local metrics run entirely on your machine: no network call, no API key, instant.

from fi.evals import evaluate

result = evaluate("contains", output="Your order has shipped!", keyword="shipped")

print(result.score)   # 1.0
print(result.passed)  # True
print(result.reason)  # "Keyword 'shipped' found"

You should see score=1.0 and passed=True printed immediately, no FI_API_KEY required.

Try a few more:

from fi.evals import evaluate

evaluate("equals", output="Paris", expected_output="Paris").passed          # True
evaluate("is_json", output='{"status": "ok"}').passed                       # True
evaluate("length_less_than", output="Short reply.", config={"max_length": 100}).passed   # True

result = evaluate("levenshtein_similarity", output="colour", expected_output="color")
print(result.score)  # similarity score between 0 and 1

The NLI model also runs locally, no API key required.

from fi.evals import evaluate

# Supported response
result = evaluate(
    "contradiction_detection",
    output="The Eiffel Tower is located in Paris, France.",
    context="The Eiffel Tower is a wrought-iron lattice tower located in Paris.",
)
print(f"Score: {result.score:.2f}")
print(f"Passed: {result.passed}")

# Contradictory response
result = evaluate(
    "contradiction_detection",
    output="The Eiffel Tower is located in London, England.",
    context="The Eiffel Tower is a wrought-iron lattice tower located in Paris.",
)
print(f"Score: {result.score:.2f}")
print(f"Passed: {result.passed}")
print(f"Why: {result.reason}")

You should see the supported response pass with a high score, and the contradictory one fail with a reason explaining the mismatch.

For quality, tone, safety, and semantic evaluations, use Future AGI’s purpose-built Turing evaluation models.

from fi.evals import evaluate

# Toxicity check
result = evaluate(
    "toxicity",
    output="You're amazing, keep it up!",
    model="turing_small",
)
print(f"Toxicity score: {result.score}")
print(f"Passed: {result.passed}")

# Try a problematic response
result = evaluate(
    "toxicity",
    output="I hate you and everything you stand for.",
    model="turing_small",
)
print(f"Score: {result.score}")
print(f"Why: {result.reason}")

You should see the first response pass with a low toxicity score, and the second fail with a reason citing the hostile language.

Explore all 72+ built-in eval metrics: tone, context_adherence, completeness, groundedness, data_privacy, bias_detection, instruction_adherence, and more.

Pass a list of metric names to run several evals in one call. Returns a BatchResult you can iterate.

from fi.evals import evaluate

results = evaluate(
    ["toxicity", "groundedness"],
    output="The Eiffel Tower is located in Paris, France.",
    context="The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris.",
    input="Where is the Eiffel Tower?",
    model="turing_small",
)

for result in results:
    status = "PASS" if result.passed else "FAIL"
    print(f"{result.eval_name:<20} score={result.score}  {status}")
    print(f"  Reason: {result.reason}\n")

You should see two rows printed, one per metric, each with its own score, pass/fail status, and reason.

When no built-in metric fits, describe your quality bar in plain English and use any LLM as the judge.

export GOOGLE_API_KEY="your-google-api-key"
# or: OPENAI_API_KEY, ANTHROPIC_API_KEY (any LiteLLM-supported provider)

from fi.evals import evaluate

result = evaluate(
    prompt="""You are evaluating a customer support response.

    Score 1.0 if the response:
    - Acknowledges the customer's issue clearly
    - Offers a concrete next step or resolution
    - Stays professional and empathetic

    Score 0.5 if it's polite but vague (no clear next step).
    Score 0.0 if it's dismissive, rude, or unhelpful.""",
    output="I understand your frustration with the delayed shipment. I've escalated this to our logistics team and you'll receive a status update within 2 hours.",
    input="My order is 3 weeks late and nobody is responding to my emails.",
    engine="llm",
    model="gemini/gemini-2.5-flash",
)

print(f"Score: {result.score}")
print(f"Why: {result.reason}")

You should see a score near 1.0 with a reason confirming the response acknowledges the issue and offers a concrete next step.

Any LiteLLM model string works: gpt-4o, claude-sonnet-4-20250514, ollama/llama3.2:3b.

1. Go to app.futureagi.com → Dataset
2. Use Add Dataset (quick path: upload a CSV)
3. Click Evaluate → select a metric → Add & Run
4. Scores appear as a new column alongside your data

You should see a new score column added to your dataset, one value per row.