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5986972 27be7ef 5986972 90a83fa 5986972 90a83fa 5986972 90a83fa 5986972 90a83fa 5986972 90a83fa 5986972 90a83fa 5986972 90a83fa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | """AgentFrame 核心测试 (离线, 无需 API key)"""
import os
import sys
import tempfile
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from agentframe.config import AgentFrameConfig
from agentframe.core.engine import ContextEngine
from agentframe.embed.provider import HashEmbedding
def make_engine() -> ContextEngine:
cfg = AgentFrameConfig.from_env()
cfg.llm.provider = "mock" # 离线
return ContextEngine(cfg)
def test_ingest_and_retrieve():
eng = make_engine()
eng.ingest("KV 缓存压缩 28.4x 实测", ["kv"])
eng.ingest("注意力分数不等于任务重要性", ["agent"])
eng.ingest("Minecraft 1.20.4 逆向完成", ["mc"])
assert len(eng.agent.chunk_meta) == 3
r = eng.ask("KV 压缩多少倍？", chat=False)
assert len(r.retrieved) >= 1
print("✓ test_ingest_and_retrieve")
def test_similar_text_retrieval():
eng = make_engine()
eng.ingest("吸收式 MLA 缓存 576 维潜在向量", ["method"])
eng.ingest("HiLS 分层软max 端到端块选择", ["method"])
eng.ingest("今天天气很好适合出去玩", ["life"])
# 查询与第一条相关
r = eng.ask("MLA 潜在向量维度是多少？", chat=False)
tops = [cid for cid, _ in r.retrieved]
assert 0 in tops, f"期望命中 chunk_0, 实际 {tops}"
print(f"✓ test_similar_text_retrieval (top: {tops[:3]})")
def test_forget_curve():
eng = make_engine()
eng.ingest("A", ["x"])
eng.ingest("B", ["x"])
eng.ingest("C", ["x"])
# 时间推进 (默认半衰期 100, 500 轮后 decay=2^-5=0.031)
for _ in range(500):
eng.now += 1
# 阈值设 0.1: strength = 0.5*0.031 + 0 ≈ 0.016 < 0.1 → 应遗忘
victims = eng.forget(0.1)
assert len(victims) >= 2, f"长时间不访问应遗忘, 实际 {len(victims)}"
print(f"✓ test_forget_curve (遗忘 {len(victims)}/3)")
def test_save_load():
eng = make_engine()
eng.ingest("持久化测试内容", ["test"])
with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
path = f.name
eng.save(path)
eng2 = make_engine()
ok = eng2.load(path)
assert ok
assert len(eng2.agent.chunk_meta) == 1
os.unlink(path)
print("✓ test_save_load")
def test_hash_embedding_deterministic():
emb = HashEmbedding(dim=576)
v1 = emb.embed("同一句话")
v2 = emb.embed("同一句话")
v3 = emb.embed("完全不同的话")
assert (v1 == v2).all()
sim_same = float(v1 @ v2)
sim_diff = float(v1 @ v3)
assert sim_same > sim_diff, f"{sim_same} vs {sim_diff}"
print(f"✓ test_hash_embedding (同句相似度 {sim_same:.3f} > 异句 {sim_diff:.3f})")
def test_tool_exec():
eng = make_engine()
out = eng._exec_tool("print(6*7)")
assert "42" in out
bad = eng._exec_tool("print(undefined_var)")
assert "Traceback" in bad or "Error" in bad
print("✓ test_tool_exec")
if __name__ == "__main__":
test_ingest_and_retrieve()
test_similar_text_retrieval()
test_forget_curve()
test_save_load()
test_hash_embedding_deterministic()
test_tool_exec()
print("\n 全部核心测试通过!")
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