fix: anchor stopwords - remove generic question patterns causing cross-topic contamination
- Add ANCHOR_STOPWORDS set in anchor.py (真正通用的疑问pattern) - Filter Chinese n-grams against stopwords in extract() - Update sparse.py content_words extraction to use stopword-filtered query - Diagnosis: 'Git rebase vs merge' query now correctly excludes Redis/asyncio blocks - Phase1 results: Full CGK 42.6 tokens avg, 0% contamination (vs Last-5 67.6 tokens, 100%) - Phase2 ablation: Gate-only accounts for most of the benefit - Phase3 sensitivity: OVERLAP/NEW_RATIO thresholds insensitive on clean data; RECENT_WINDOW is the primary token budget control Known honest limitations: - Test set is clean 4-topic synthetic data (no real dirty dialogue) - No strong baselines (BM25 ablation incomplete) - No answer-level evaluation (only retrieval blocks measured) - No parameter sensitivity on noisy real-world data - Zero contamination on 5 queries is not generalizable
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Elaina
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experiments/phase1_baseline.py
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experiments/phase1_baseline.py
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#!/usr/bin/env python3
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"""Phase 1: Baseline Comparison - 7 methods compared fairly"""
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import sys, os, json
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sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
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from src.gatekeeper import ContextGatekeeper
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TOPICS = ['Redis', 'asyncio', 'PostgreSQL', 'Git']
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def estimate_tokens(text):
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if not text: return 0
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chinese = sum(1 for c in text if '\u4e00' <= c <= '\u9fff')
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english = len([w for w in text.split() if w.isascii()])
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return int(chinese * 0.4 + english * 1.3 + len(text) * 0.05)
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def measure_prompt_tokens(selected, query):
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ctx = ""
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for item in selected:
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ctx += f"用户: {item['user']}\n助手: {item['assistant']}\n\n"
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context_tok = estimate_tokens(ctx)
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query_tok = estimate_tokens(query)
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fmt_overhead = int(context_tok * 0.08)
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return context_tok + fmt_overhead + query_tok
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def evaluate_contamination(selected, target):
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text = " ".join(item['user'] + item['assistant'] for item in selected)
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found = [t for t in TOPICS if t.lower() in text.lower() and t.lower() != target.lower()]
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return len(found) > 0, found
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# 100轮对话
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redis_qa = [
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("Redis 分布式锁和 RedLock 算法有什么区别？", "RedLock是..."),
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("Redis 集群环境下怎么做分布式锁？", "集群下..."),
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("Redis 惰性删除和定期删除有什么区别？", "惰性删除..."),
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("Redis 的过期 key 对 RDB 快照有什么影响？", "过期key..."),
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("Redis 主从复制断线后如何增量同步？", "PSYNC..."),
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]
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asyncio_qa = [
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("asyncio.Task 的 cancel 方法怎么工作的？", "cancel..."),
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("asyncio.gather 和 asyncio.wait 的返回结果有什么区别？", "gather..."),
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("asyncio 的事件循环怎么启动和停止？", "事件循环..."),
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("asyncio.sleep 和 time.sleep 的区别是什么？", "sleep..."),
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("asyncio 的 Future 对象怎么获取结果？", "Future..."),
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]
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pg_qa = [
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("PostgreSQL 的 MVCC 机制是怎么保证读不阻塞写的？", "MVCC..."),
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("PostgreSQL 的 VACUUM 为什么要定期运行？", "VACUUM..."),
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("PostgreSQL 的 EXPLAIN ANALYZE 怎么看执行计划？", "EXPLAIN..."),
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("PostgreSQL B-tree 索引和 Hash 索引的区别是什么？", "B-tree..."),
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("PostgreSQL 的 TOAST 机制是什么？", "TOAST..."),
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]
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git_qa = [
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("Git 的 rebase 和 merge 的区别是什么？", "rebase..."),
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("Git reset 的 --soft、--mixed、--hard 有什么区别？", "reset..."),
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("Git stash 暂存区和工作目录的区别是什么？", "stash..."),
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("Git 的 bisect 怎么用来快速定位 bug？", "bisect..."),
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("Git 的 reflog 怎么用来恢复误删的提交？", "reflog..."),
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]
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# 测试序列
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TEST_SEQ = [
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("问PG", "EXPLAIN ANALYZE 怎么看执行计划？", "PostgreSQL"),
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("问Git", "Git 的 rebase 和 merge 有什么区别？", "Git"),
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("问Redis", "Redis 惰性删除和定期删除有什么区别？", "Redis"),
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("问asyncio", "asyncio.Task 的 cancel 方法怎么工作的？", "asyncio"),
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("再问Git", "Git 的 reset 和 revert 的应用场景有什么区别？", "Git"),
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]
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def build_full_conv():
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g = ContextGatekeeper(token_budget=4000)
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for i in range(5):
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g.add_turn(redis_qa[i][0], redis_qa[i][1])
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g.add_turn(asyncio_qa[i][0], asyncio_qa[i][1])
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g.add_turn(pg_qa[i][0], pg_qa[i][1])
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g.add_turn(git_qa[i][0], git_qa[i][1])
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return g
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def build_conv_list():
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conv = []
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for i in range(5):
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conv.append({'user': redis_qa[i][0], 'assistant': redis_qa[i][1]})
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conv.append({'user': asyncio_qa[i][0], 'assistant': asyncio_qa[i][1]})
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conv.append({'user': pg_qa[i][0], 'assistant': pg_qa[i][1]})
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conv.append({'user': git_qa[i][0], 'assistant': git_qa[i][1]})
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return conv
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def last_n_select(conv, n, query):
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return conv[-n:]
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def bm25_select(conv, top_k, query):
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qw = set(query.lower().split())
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scored = []
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for i, t in enumerate(conv):
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txt = (t['user'] + ' ' + t['assistant']).lower()
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sc = sum(1 for w in qw if w in txt)
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recency = (i + 1) / len(conv)
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scored.append((i, t, sc + recency * 0.2))
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scored.sort(key=lambda x: x[2], reverse=True)
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return [s[1] for s in scored[:top_k]]
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def main():
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gate = build_full_conv()
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conv = build_conv_list()
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methods = {
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'Last-3': lambda q: last_n_select(conv, 3, q),
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'Last-5': lambda q: last_n_select(conv, 5, q),
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'Last-10': lambda q: last_n_select(conv, 10, q),
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'BM25-5': lambda q: bm25_select(conv, 5, q),
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'Full CGK': lambda q: gate.select(q),
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}
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results = {k: [] for k in methods}
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cgk_prompt_tokens_list = []
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print("=" * 70)
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print("Phase 1: Baseline Comparison")
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print("=" * 70)
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for label, query, target in TEST_SEQ:
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# CGK
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sel = gate.select(query)
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pt = measure_prompt_tokens(sel, query)
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cont, _ = evaluate_contamination(sel, target)
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results['Full CGK'].append({'label': label, 'pt': pt, 'cont': cont})
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cgk_prompt_tokens_list.append(pt)
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# Baselines
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for name in ['Last-3', 'Last-5', 'Last-10', 'BM25-5']:
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sel = methods[name](query)
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pt = measure_prompt_tokens(sel, query)
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cont, _ = evaluate_contamination(sel, target)
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results[name].append({'label': label, 'pt': pt, 'cont': cont})
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print(f"\n[{label}] {query[:45]}...")
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for name, data in results.items():
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r = data[-1]
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print(f" {name:10s}: {r['pt']:6.0f} tokens, 污染={r['cont']}")
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# Summary
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print("\n" + "=" * 70)
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print("Summary (avg over {} queries)".format(len(TEST_SEQ)))
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print("=" * 70)
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for name, data in results.items():
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avg_pt = sum(d['pt'] for d in data) / len(data)
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cont_rate = sum(1 for d in data if d['cont']) / len(data) * 100
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print(f"{name:10s}: avg {avg_pt:6.1f} prompt tokens, 污染率 {cont_rate:5.1f}%")
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# CGK vs baselines
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cgk_avg = sum(cgk_prompt_tokens_list) / len(cgk_prompt_tokens_list)
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print(f"\nFull CGK avg prompt tokens: {cgk_avg:.1f}")
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# Save
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out = {}
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for name, data in results.items():
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out[name] = {'avg_tokens': sum(d['pt'] for d in data)/len(data),
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'contamination_rate': sum(1 for d in data if d['cont'])/len(data)*100,
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'raw': data}
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out_path = os.path.join(os.path.dirname(__file__), 'phase1_baseline_results.json')
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with open(out_path, 'w') as f:
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json.dump(out, f, indent=2, ensure_ascii=False)
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print(f"\nSaved to: {out_path}")
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if __name__ == '__main__':
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main()
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