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from typing import Any
from graphgen.bases import QAPair
from graphgen.utils import run_concurrent
def transform_to_qa_format(
items: list[dict], format_hint: str = "auto"
) -> list[dict[str, str]]:
extractors = {
"ChatML": lambda x: (
next(
(
m["content"]
for m in x.get("messages", [])
if m.get("role") == "user"
),
"",
),
next(
(
m["content"]
for m in x.get("messages", [])
if m.get("role") == "assistant"
),
"",
),
),
"Alpaca": lambda x: (
f"{x.get('instruction', '')}\n\n{x['input']}".strip()
if x.get("input")
else x.get("instruction", ""),
x.get("output", ""),
),
"Sharegpt": lambda x: (
next(
(
c["value"]
for c in x.get("conversations", [])
if c.get("from") == "human"
),
"",
),
next(
(
c["value"]
for c in x.get("conversations", [])
if c.get("from") in ("gpt", "assistant")
),
"",
),
),
}
auto_detect = {
"messages": "ChatML",
"conversations": "Sharegpt",
"instruction": "Alpaca",
}
transformed = []
for item in items:
fmt = format_hint
if fmt == "auto":
fmt = next(
(fmt_name for key, fmt_name in auto_detect.items() if key in item), None
)
if not fmt:
raise ValueError(
"Could not auto-detect format. Please specify format_hint."
)
question, answer = extractors[fmt](item)
options = None
if "\nOptions:\n" in question:
q_part, opt_part = question.split("\nOptions:\n", 1)
question = q_part
options = {
k.strip(): v.strip()
for line in opt_part.strip().split("\n")
if "." in line
for k, v in [line.split(".", 1)]
}
result = {"question": question.strip(), "answer": answer.strip()}
if options:
result["options"] = options
transformed.append(result)
return transformed
def evaluate_qa(
qa_evaluators: dict[str, Any], items: list[dict[str, Any]]
) -> dict[str, Any]:
items = transform_to_qa_format(items)
items = [QAPair.from_dict(item) for item in items]
results = {}
for key, qa_evaluator in qa_evaluators.items():
result = run_concurrent(
qa_evaluator.evaluate,
items,
desc=f"Evaluating QA with {key}",
)
results[key] = result
return results
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