(WORK_DIR / “decide.prompty”).write_text(“””—
identify: Decide
mannequin:
api: chat
configuration:
kind: openai
connection: open_ai_connection
mannequin: gpt-4o-mini
parameters:
temperature: 0
max_tokens: 150
response_format: {kind: json_object}
inputs:
query: {kind: string}
reply: {kind: string}
anticipated: {kind: string}
—
system:
You might be an exacting grader. Resolve whether or not the assistant’s reply comprises the anticipated reality (case-insensitive, permitting affordable phrasing/synonyms). Reply ONLY as JSON: {“rating”: 0 or 1, “cause”: “…”}.
consumer:
Query: {{query}}
Anticipated: {{anticipated}}
Reply: {{reply}}
“””)
(WORK_DIR / “eval_flow.py”).write_text(textwrap.dedent(”’
import json
from pathlib import Path
from promptflow.tracing import hint
from promptflow.core import Prompty
BASE = Path(__file__).mum or dad
class Evaluator:
def __init__(self):
self.decide = Prompty.load(supply=BASE / “decide.prompty”)
@hint
def __call__(self, query: str, reply: str, anticipated: str) -> dict:
uncooked = self.decide(query=query, reply=reply, anticipated=anticipated)
if isinstance(uncooked, str):
strive: uncooked = json.hundreds(uncooked)
besides Exception: uncooked = {“rating”: 0, “cause”: f”unparseable:{uncooked[:80]}”}
return {“rating”: int(uncooked.get(“rating”, 0)), “cause”: str(uncooked.get(“cause”,””))}
def __aggregate__(self, line_results):
“””Run-level aggregation. No matter this returns exhibits up in pf.get_metrics().”””
scores = [r[“score”] for r in line_results if r]
return {
“accuracy”: (sum(scores) / len(scores)) if scores else 0.0,
“handed”: sum(scores),
“whole”: len(scores),
}
”’))
(WORK_DIR / “eval.flex.yaml”).write_text(
“$schema: https://azuremlschemas.azureedge.internet/promptflow/newest/Movement.schema.jsonn”
“entry: eval_flow:Evaluatorn”
)
print(“n=== Analysis run ===”)
eval_run = pf.run(
movement=str(WORK_DIR / “eval.flex.yaml”),
knowledge=str(data_path),
run=base_run,
column_mapping={
“query”: “${knowledge.query}”,
“anticipated”: “${knowledge.anticipated}”,
“reply”: “${run.outputs.reply}”,
},
stream=True,
)
eval_details = pf.get_details(eval_run)
print(eval_details)
print(“n=== Aggregated metrics (from __aggregate__) ===”)
print(json.dumps(pf.get_metrics(eval_run), indent=2))
import pandas as pd
if “outputs.rating” in eval_details.columns:
s = pd.to_numeric(eval_details[“outputs.score”], errors=”coerce”).fillna(0)
print(f”Handbook accuracy: {s.imply():.2%} ({int(s.sum())}/{len(s)})”)
