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from __future__ import annotations
from pathlib import Path
import sys
import json
import os
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from personalization.config.settings import load_local_models_config
from personalization.models.llm.qwen_instruct import QwenInstruct
from personalization.models.preference_extractor.rule_extractor import QwenRuleExtractor
def ensure_models_present() -> bool:
cfg = load_local_models_config()
ok = True
for spec in (cfg.llm, cfg.preference_extractor):
path = ROOT / spec.local_path
if not path.exists() or not any(path.iterdir()):
print(f"[missing] {path} is empty. Run: python scripts/pull_models.py --target all")
ok = False
return ok
def main() -> None:
if not ensure_models_present():
return
cfg = load_local_models_config()
os.environ["PREF_DEBUG"] = "1"
llm = QwenInstruct.from_config(cfg)
extractor = QwenRuleExtractor.from_config(cfg)
print("[llm] generating...")
out = llm.generate("Say hello in one short sentence.", max_new_tokens=32, temperature=0.2)
print(out)
print("[extractor] extracting...")
scenarios = [
(
"math_latex",
"Consider the sequence defined by a_1 = 1 and a_{n+1} = a_n + 1/n for n >= 1. "
"(1) Prove that a_n diverges. (2) Derive an asymptotic expression for a_n in terms of the harmonic numbers H_n. "
"(3) Compute the limit of (a_n - ln n) as n -> infinity. Please use LaTeX for the output.",
),
(
"code_python311",
"I have a performance bottleneck in my Python code that processes large CSV files. It reads rows, aggregates stats, and writes summaries. "
"Explain how to optimize I/O and memory, discuss multiprocessing vs async. When you show code, please use Python 3.11 syntax and include type hints in the snippets.",
),
(
"data_json_only",
"Given a dataset of user events with timestamps, device types, and regions, outline steps to compute DAU, WAU, and retention. "
"List pitfalls and how to handle missing data. Return your final answer as JSON only.",
),
(
"writing_concise_no_emoji",
"Explain the difference between supervised and reinforcement learning with practical examples and cautions. "
"Keep answers concise and avoid emojis.",
),
]
for name, query in scenarios:
print(f"\n[scenario] {name}")
prefs = extractor.extract_preferences(query)
print(json.dumps(prefs, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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