Add Hermes memory evaluation framework with LoCoMo dataset support
- Implement HermesClient for interacting with the Hermes CLI. - Create judge module for grading QA outputs from Hermes memory. - Develop LoCoMo dataset parsing and formatting utilities. - Introduce run_eval script to facilitate memory evaluation using LoCoMo-style datasets.
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eval/hermes_memory_eval/judge.py
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188
eval/hermes_memory_eval/judge.py
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"""LLM judge for Hermes memory QA outputs."""
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from __future__ import annotations
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import argparse
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import asyncio
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import json
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import os
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from pathlib import Path
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from typing import Any
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import httpx
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import yaml
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def load_answers(path: str | Path) -> list[dict[str, Any]]:
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input_path = Path(path)
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if input_path.suffix == ".jsonl":
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with input_path.open("r", encoding="utf-8") as file:
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return [json.loads(line) for line in file if line.strip()]
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with input_path.open("r", encoding="utf-8") as file:
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data = json.load(file)
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if isinstance(data, dict):
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return data.get("results", data.get("grades", []))
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if isinstance(data, list):
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return data
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raise ValueError("answers file must be JSON list, JSONL, or object with results")
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def load_config(path: str | Path | None) -> dict[str, Any]:
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if not path:
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return {}
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config_path = Path(path)
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if not config_path.exists():
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return {}
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with config_path.open("r", encoding="utf-8") as file:
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return yaml.safe_load(file) or {}
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def resolve_judge_config(args: argparse.Namespace) -> dict[str, Any]:
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config = load_config(args.config)
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judge = config.get("judge", {})
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base_url = args.base_url or judge.get("base_url") or os.environ.get("OPENAI_BASE_URL") or "https://api.openai.com/v1"
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model = args.model or judge.get("model") or "gpt-4o-mini"
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api_key_env = args.api_key_env or judge.get("api_key_env") or "OPENAI_API_KEY"
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api_key = args.api_key or judge.get("api_key") or os.environ.get(api_key_env, "")
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parallel = args.parallel if args.parallel is not None else int(judge.get("parallel", 4))
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timeout_seconds = args.timeout_seconds if args.timeout_seconds is not None else int(judge.get("timeout_seconds", 120))
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return {
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"base_url": str(base_url),
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"model": str(model),
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"api_key": str(api_key),
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"api_key_env": str(api_key_env),
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"parallel": int(parallel),
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"timeout_seconds": int(timeout_seconds),
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}
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def judge_prompt(question: str, expected: str, response: str) -> list[dict[str, str]]:
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return [
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{
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"role": "system",
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"content": "You are an expert grader for long-term memory QA. Return JSON only.",
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},
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{
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"role": "user",
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"content": (
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"Decide whether the generated answer matches the gold answer.\n"
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"Be generous: count it correct if it refers to the same fact, topic, person, place, or date.\n"
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"Return exactly JSON: {\"is_correct\":\"CORRECT\" or \"WRONG\", \"reasoning\":\"short reason\"}.\n\n"
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f"Question: {question}\n"
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f"Gold answer: {expected}\n"
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f"Generated answer: {response}"
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),
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},
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]
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async def grade_one(
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client: httpx.AsyncClient,
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*,
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base_url: str,
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api_key: str,
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model: str,
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item: dict[str, Any],
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) -> dict[str, Any]:
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payload = {
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"model": model,
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"temperature": 0,
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"messages": judge_prompt(item["question"], item["expected"], item["response"]),
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}
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response = await client.post(
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f"{base_url.rstrip('/')}/chat/completions",
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headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
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json=payload,
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)
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response.raise_for_status()
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content = response.json()["choices"][0]["message"]["content"]
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parsed = json.loads(content)
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label = str(parsed.get("is_correct", parsed.get("label", "WRONG"))).strip().lower()
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return {
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**item,
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"grade": label == "correct",
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"judge_reasoning": parsed.get("reasoning", ""),
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}
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async def grade_answers(
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answers: list[dict[str, Any]],
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*,
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base_url: str,
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api_key: str,
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model: str,
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timeout_seconds: int = 120,
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parallel: int = 4,
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) -> list[dict[str, Any]]:
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limits = httpx.Limits(max_connections=max(1, parallel))
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async with httpx.AsyncClient(timeout=timeout_seconds, limits=limits) as client:
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semaphore = asyncio.Semaphore(max(1, parallel))
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async def _grade(item: dict[str, Any]) -> dict[str, Any]:
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async with semaphore:
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return await grade_one(client, base_url=base_url, api_key=api_key, model=model, item=item)
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return await asyncio.gather(*[_grade(item) for item in answers])
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def summarize(grades: list[dict[str, Any]]) -> dict[str, Any]:
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correct = sum(1 for item in grades if item.get("grade"))
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total = len(grades)
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categories: dict[str, dict[str, int]] = {}
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for item in grades:
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category = str(item.get("category", "unknown"))
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categories.setdefault(category, {"correct": 0, "total": 0})
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categories[category]["total"] += 1
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if item.get("grade"):
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categories[category]["correct"] += 1
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return {
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"score": correct / total if total else 0.0,
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"correct": correct,
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"total": total,
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"categories": categories,
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}
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def main() -> None:
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parser = argparse.ArgumentParser(description="Judge Hermes memory QA answers")
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parser.add_argument("input", help="QA JSONL or JSON file")
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parser.add_argument("--config", default="eval/hermes_memory_eval/config.yaml")
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parser.add_argument("--output", default=None)
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parser.add_argument("--base-url", default=None)
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parser.add_argument("--api-key", default=None)
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parser.add_argument("--api-key-env", default=None)
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parser.add_argument("--model", default=None)
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parser.add_argument("--parallel", type=int, default=None)
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parser.add_argument("--timeout-seconds", type=int, default=None)
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args = parser.parse_args()
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judge_config = resolve_judge_config(args)
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if not judge_config["api_key"]:
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raise SystemExit(f"missing --api-key or {judge_config['api_key_env']}")
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answers = load_answers(args.input)
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grades = asyncio.run(
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grade_answers(
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answers,
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base_url=judge_config["base_url"],
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api_key=judge_config["api_key"],
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model=judge_config["model"],
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parallel=judge_config["parallel"],
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timeout_seconds=judge_config["timeout_seconds"],
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)
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)
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summary = summarize(grades)
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print(f"score: {summary['correct']}/{summary['total']} ({summary['score']:.2%})")
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for category, stats in sorted(summary["categories"].items()):
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total = stats["total"]
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score = stats["correct"] / total if total else 0.0
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print(f"category {category}: {stats['correct']}/{total} ({score:.2%})")
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if args.output:
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output = {"summary": summary, "grades": grades}
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Path(args.output).parent.mkdir(parents=True, exist_ok=True)
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with Path(args.output).open("w", encoding="utf-8") as file:
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json.dump(output, file, indent=2, ensure_ascii=False)
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if __name__ == "__main__":
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main()
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