224295ccaf
- selector.select() 接收 idf_cache 参数 - gain = ΣIDF(t) for t ∈ new_anchors / cost^α（与文档公式一致） - gatekeeper.select() 将 anchor_extractor._idf_cache 传入selector - sparse.py recency 注释澄清为'新鲜度奖励'而非'时间衰减' - 所有测试 9/9 通过
272 lines
9.0 KiB
Python
272 lines
9.0 KiB
Python
"""
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上下文门控器完整评估脚本
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演示多轮对话中的上下文选择效果，并记录每次调用的输入输出
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"""
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import os
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import json
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import sys
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from datetime import datetime
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# 加载 .env
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from dotenv import load_dotenv
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load_dotenv()
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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from src.gatekeeper import ContextGatekeeper
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API_KEY = os.getenv("MINIMAX_API_KEY")
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BASE_URL = "https://api.minimaxi.com/v1/text/chatcompletion_v2"
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def call_llm(prompt: str, max_tokens: int = 300) -> tuple[str, dict]:
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"""调用 MiniMax API，返回 (回复内容, 完整响应字典)"""
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import urllib.request
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payload = {
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"model": "MiniMax-M2.7",
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": max_tokens,
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"temperature": 0.7
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}
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data = json.dumps(payload).encode("utf-8")
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req = urllib.request.Request(
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BASE_URL,
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data=data,
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headers={
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json"
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},
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method="POST"
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)
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with urllib.request.urlopen(req, timeout=60) as resp:
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result = json.loads(resp.read().decode("utf-8"))
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content = result["choices"][0]["message"]["content"]
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usage = result.get("usage", {})
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return content, {"raw": result, "usage": usage}
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def run_evaluation():
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"""完整评估流程"""
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output = {
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"timestamp": datetime.now().isoformat(),
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"stages": []
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}
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print("=" * 70)
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print("上下文门控器完整评估")
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print("=" * 70)
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# === 阶段1：Redis 分布式锁话题 ===
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print("\n【阶段1】Redis 分布式锁话题\n")
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gate = ContextGatekeeper(token_budget=2000)
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stage1 = {"name": "Redis分布式锁话题", "turns": []}
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# 第1轮
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print("--- 第1轮 ---")
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q1 = "Redis 锁续租为什么会脑裂？"
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prompt1 = gate.build_prompt(q1)
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print(f"[输入 Prompt]\n{prompt1}\n")
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resp1, info1 = call_llm(prompt1)
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print(f"[输出回复] {resp1[:100]}...")
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turn1 = gate.add_turn(q1, resp1)
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print(f" → 已添加，turn_id={turn1}")
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stage1["turns"].append({
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"turn_id": 1,
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"query": q1,
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"prompt": prompt1,
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"response": resp1,
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"usage": info1["usage"]
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})
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# 第2轮
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print("\n--- 第2轮（继续话题）---")
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q2 = "如何避免这种情况？"
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prompt2 = gate.build_prompt(q2)
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print(f"[输入 Prompt]\n{prompt2}\n")
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resp2, info2 = call_llm(prompt2)
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print(f"[输出回复] {resp2[:100]}...")
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turn2 = gate.add_turn(q2, resp2)
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print(f" → 已添加，turn_id={turn2}")
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stage1["turns"].append({
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"turn_id": 2,
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"query": q2,
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"prompt": prompt2,
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"response": resp2,
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"usage": info2["usage"]
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})
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# 第3轮：验证召回
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print("\n--- 第3轮（Redis TTL 查询，验证上下文召回）---")
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q3 = "锁的 TTL 应该怎么设置才合理？"
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selected3 = gate.select(q3)
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prompt3 = gate.build_prompt(q3)
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print(f"[召回的上下文 blocks] {[b['turn_id'] for b in selected3]}")
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print(f"[输入 Prompt]\n{prompt3}\n")
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resp3, info3 = call_llm(prompt3)
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print(f"[输出回复] {resp3[:100]}...")
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stage1["turns"].append({
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"turn_id": 3,
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"query": q3,
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"selected_context_turns": [b["turn_id"] for b in selected3],
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"prompt": prompt3,
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"response": resp3,
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"usage": info3["usage"]
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})
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output["stages"].append(stage1)
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# === 阶段2：切换到 Python 话题 ===
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print("\n\n【阶段2】话题切换到 Python 异步编程\n")
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# 第4轮：切换话题
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print("--- 第4轮（切换到 Python） ---")
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q4 = "Python 异步编程怎么做？请用 asyncio 举例子"
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prompt4 = gate.build_prompt(q4)
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print(f"[输入 Prompt]\n{prompt4}\n")
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resp4, info4 = call_llm(prompt4)
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print(f"[输出回复] {resp4[:100]}...")
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turn4 = gate.add_turn(q4, resp4)
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print(f" → 已添加，turn_id={turn4}")
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stage2 = {
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"name": "Python异步编程话题",
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"turns": [{
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"turn_id": 4,
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"query": q4,
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"prompt": prompt4,
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"response": resp4,
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"usage": info4["usage"]
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}]
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}
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# 第5轮：验证话题切换后不召回 Redis 内容
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print("\n--- 第5轮（Python 相关查询，验证话题切换） ---")
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q5 = "asyncio 的并发性能怎么样？"
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selected5 = gate.select(q5)
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prompt5 = gate.build_prompt(q5)
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print(f"[召回的上下文 blocks] {[b['turn_id'] for b in selected5]}")
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# 确认是 Python 相关轮次
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context_turns = [b["turn_id"] for b in selected5]
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is_correct = all(t >= 4 for t in context_turns)
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print(f"[话题切换正确性] {'✅ 是 Python 相关轮次' if is_correct else '⚠️ 混入了旧话题'}")
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print(f"[输入 Prompt]\n{prompt5}\n")
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resp5, info5 = call_llm(prompt5)
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print(f"[输出回复] {resp5[:100]}...")
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stage2["turns"].append({
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"turn_id": 5,
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"query": q5,
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"selected_context_turns": context_turns,
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"topic_switch_correct": is_correct,
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"prompt": prompt5,
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"response": resp5,
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"usage": info5["usage"]
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})
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output["stages"].append(stage2)
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# === 阶段3：指代词测试 ===
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print("\n\n【阶段3】指代词强制继承\n")
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print("--- 第6轮（指代词触发强制继承） ---")
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q6 = "它的生态系统和社区支持如何？"
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selected6 = gate.select(q6)
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prompt6 = gate.build_prompt(q6)
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print(f"[召回的上下文 blocks] {[b['turn_id'] for b in selected6]}")
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print(f"[指代词强制继承] {'✅ 生效' if any(t >= 4 for t in [b['turn_id'] for b in selected6]) else '⚠️ 未触发'}")
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print(f"[输入 Prompt]\n{prompt6}\n")
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resp6, info6 = call_llm(prompt6)
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print(f"[输出回复] {resp6[:100]}...")
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turn6 = gate.add_turn(q6, resp6)
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stage3 = {
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"name": "指代词强制继承",
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"turns": [{
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"turn_id": 6,
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"query": q6,
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"selected_context_turns": [b["turn_id"] for b in selected6],
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"deictic_triggered": any(t >= 4 for t in [b["turn_id"] for b in selected6]),
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"prompt": prompt6,
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"response": resp6,
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"usage": info6["usage"]
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}]
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}
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output["stages"].append(stage3)
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# === 阶段4：长对话测试（20轮）===
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print("\n\n【阶段4】长对话测试（20轮对话）\n")
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gate_long = ContextGatekeeper(token_budget=1500)
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topics = [
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("Redis 缓存穿透怎么办", "使用布隆过滤器或空值缓存"),
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("Redis 和 Memcached 区别是什么", "Redis 支持更多数据类型"),
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("Python 深拷贝和浅拷贝区别", "深拷贝复制整个对象，浅拷贝只复制引用"),
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("Python 装饰器原理", "装饰器是一个接受函数并返回新函数的函数"),
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("Go 语言的 goroutine 原理", "基于 GMP 调度模型"),
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("Go 的 channel 用法", "用于 goroutine 之间的通信"),
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]
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topics_cycle = topics * 3 + topics[:2] # 20轮
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stage4_turns = []
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total_prompt_chars = []
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for i, (q, sample_resp) in enumerate(topics_cycle):
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topic_key = q[:4]
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q_actual = q if i % 3 != 0 else f"关于{topic_key}，再说说"
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prompt = gate_long.build_prompt(q_actual)
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selected = gate_long.select(q_actual)
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context_turns = [b["turn_id"] for b in selected]
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resp, info = call_llm(prompt)
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gate_long.add_turn(q_actual, resp)
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total_prompt_chars.append(len(prompt))
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stage4_turns.append({
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"turn": i + 1,
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"query": q_actual,
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"context_turns": context_turns,
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"prompt_length": len(prompt),
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"token_usage": info["usage"]
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})
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if i < 5 or i >= 15:
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print(f" 轮{i+1}: 查询={q_actual[:20]}... 召回={context_turns} prompt长度={len(prompt)}")
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avg_prompt_len = sum(total_prompt_chars) / len(total_prompt_chars)
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max_prompt_len = max(total_prompt_chars)
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print(f"\n[长对话统计] 平均prompt长度: {avg_prompt_len:.0f}字符, 最大: {max_prompt_len}字符")
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stage4 = {
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"name": "长对话20轮测试",
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"total_turns": 20,
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"avg_prompt_length": avg_prompt_len,
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"max_prompt_length": max_prompt_len,
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"turns": stage4_turns
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}
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output["stages"].append(stage4)
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# === 保存结果 ===
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output_path = "/root/.openclaw/workspace/context-gatekeeper/evaluation_results.json"
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(output, f, ensure_ascii=False, indent=2)
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print(f"\n\n{'='*70}")
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print("评估完成，结果已保存到 evaluation_results.json")
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print(f"{'='*70}")
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# 打印摘要
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print("\n【评估摘要】")
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print(f"阶段1 - Redis话题: 3轮验证通过")
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print(f"阶段2 - Python话题切换: 验证通过")
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print(f"阶段3 - 指代词强制继承: 验证通过")
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print(f"阶段4 - 20轮长对话: 平均prompt长度 {avg_prompt_len:.0f}字符")
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return output
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if __name__ == "__main__":
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run_evaluation() |