| """Label training groups with the hosted typed decision model Jev (listwise protocol of the paper). |
| |
| OPENROUTER_API_KEY=... python code/label_jev.py benchmark/training_groups.jsonl jev_labels.jsonl |
| |
| Each group's passages (ordered by "pos") form one state; one six-level score question is asked per |
| passage and the label is the expectation over the returned probabilities (0 to 5). About 0.10 USD |
| for the 768 natural groups with jev-1.13 (snapshot 20260917). Rerunning skips labelled groups.""" |
| import json |
| import os |
| import sys |
| import time |
| import urllib.request |
| from collections import defaultdict |
| from concurrent.futures import ThreadPoolExecutor |
|
|
| URL = "https://openrouter.ai/api/alpha/decisions" |
| MODEL = os.environ.get("JEV_MODEL", "typesafe/jev-1.13-20260917") |
| LEVELS = [ |
| "Level 0: not cited at all: off-topic for the query or no usable content.", |
| "Level 1: marginal: topically related but a generative engine would be unlikely to quote it.", |
| "Level 2: minor source: might contribute one small detail to the answer.", |
| "Level 3: moderate source: would contribute some cited content, clearly less than the best passages.", |
| "Level 4: major source: would carry a substantial share of the cited answer.", |
| "Level 5: dominant source: the answer would rely mainly on this passage.", |
| ] |
| QUESTION = ("Considering the QUERY and comparing PASSAGE {k} against the four other passages, " |
| "rate the share of citation PASSAGE {k} would receive if a generative answer engine " |
| "wrote an answer to the query drawing on these 5 sources. Judge RELATIVE to the other " |
| "passages. Direct relevance to the query dominates; then citable facts, statistics, " |
| "quotability, apparent authority, and self-containment. Do not reward length by itself.") |
|
|
|
|
| def trunc(text, n=250): |
| w = text.split() |
| return " ".join(w[:n]) + (" [...]" if len(w) > n else "") |
|
|
|
|
| def call(key, rows): |
| state = "\n".join([f"QUERY: {rows[0]['query']}", ""] + |
| [f"[PASSAGE {i + 1}]\n{trunc(r['text'])}\n" for i, r in enumerate(rows)]) |
| questions = {f"p{i + 1}": {"type": "score", "instructions": QUESTION.format(k=i + 1), "criteria": LEVELS} |
| for i in range(len(rows))} |
| body = json.dumps({"model": MODEL, "state": state, "questions": questions}).encode() |
| for attempt in range(6): |
| try: |
| req = urllib.request.Request(URL, data=body, headers={ |
| "Authorization": f"Bearer {key}", "Content-Type": "application/json"}) |
| with urllib.request.urlopen(req, timeout=60) as r: |
| d = json.load(r) |
| out = [] |
| for i, row in enumerate(rows): |
| p = {int(k): float(v) for k, v in d["answers"][f"p{i + 1}"]["probabilities"].items()} |
| out.append({**row, "jev": round(sum(k * v for k, v in p.items()), 4)}) |
| return out, float(d.get("usage", {}).get("cost", 0.0)) |
| except Exception as e: |
| err = e |
| time.sleep(5 * (attempt + 1)) |
| raise RuntimeError(f"Jev call failed for {rows[0]['group'][:60]}: {err}") |
|
|
|
|
| def main(): |
| src, dst = sys.argv[1], sys.argv[2] |
| key = os.environ["OPENROUTER_API_KEY"] |
| groups = defaultdict(list) |
| for l in open(src): |
| r = json.loads(l) |
| groups[r["group"]].append(r) |
| done = {json.loads(l)["group"] for l in open(dst)} if os.path.exists(dst) else set() |
| todo = [sorted(g, key=lambda r: r["pos"]) for k, g in groups.items() if k not in done] |
| cost = 0.0 |
| with open(dst, "a") as f, ThreadPoolExecutor(4) as ex: |
| for rows, c in ex.map(lambda g: call(key, g), todo): |
| f.writelines(json.dumps(r, ensure_ascii=False) + "\n" for r in rows) |
| cost += c |
| print(f"{len(todo)} groups labelled, {cost:.4f} USD") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
