Datasets:
File size: 3,150 Bytes
aaefae3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | """promptfoo Python assertion.
Reuses the deterministic binary scorer: rebuild the gold `Case` from the test's
metadata, normalize the model's tool calls (whatever shape promptfoo hands us)
into a ParsedTurn, and score 1/0 with `score_case`.
Referenced from promptfooconfig.yaml as:
assert:
- type: python
value: file://pf/assert.py
"""
from __future__ import annotations
import json
from typing import Any
from wallet_evals.parsing import parse_turn
from wallet_evals.promptfoo import case_from_metadata
from wallet_evals.scorer import explain_mismatch, score_case
def _normalize_calls(obj: Any) -> list[dict]:
"""Coerce an OpenAI-style tool-call list/object into parse_turn's shape."""
items = obj if isinstance(obj, list) else [obj]
calls: list[dict] = []
for item in items:
if not isinstance(item, dict):
continue
fn = item.get("function", item) # OpenAI shape: {"function": {name, arguments}}
name = fn.get("name")
if not name:
continue
arguments = fn.get("arguments")
calls.append({"name": name, "arguments": arguments if arguments is not None else "{}"})
return calls
def _model_turn(output: Any, context: dict):
"""Build a ParsedTurn from promptfoo output / providerResponse.
promptfoo may surface tool calls as the `output` (a list of tool-call objects
or a JSON string of them) or as text content when the model declines to call
a tool. Be liberal in what we accept.
"""
candidate = output
# providerResponse sometimes carries the structured tool calls explicitly.
provider_response = context.get("providerResponse") or {}
for key in ("tool_calls", "toolCalls"):
if isinstance(provider_response.get(key), list):
candidate = provider_response[key]
break
if isinstance(candidate, str):
stripped = candidate.strip()
if stripped[:1] in ("[", "{"):
try:
candidate = json.loads(stripped)
except ValueError:
return parse_turn(content=candidate, native_tool_calls=None, raw_text=candidate)
else:
# Plain prose (e.g. a clarifying question) -> no tool call.
return parse_turn(content=candidate, native_tool_calls=None, raw_text=candidate)
native = _normalize_calls(candidate)
if native:
return parse_turn(content=None, native_tool_calls=native, raw_text="")
text = output if isinstance(output, str) else ""
return parse_turn(content=text or None, native_tool_calls=None, raw_text=text)
def get_assert(output: Any, context: dict) -> dict:
metadata = context["test"]["metadata"]
case = case_from_metadata(metadata)
turn = _model_turn(output, context)
score = score_case(case, turn)
reason = explain_mismatch(case, turn)
if not score and not turn.tool_calls and turn.content:
# No tool call (e.g. a clarifying question) — surface what the model said.
reason += f' | model said: "{turn.content.strip()[:200]}"'
return {"pass": bool(score), "score": float(score), "reason": reason}
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