Initial standalone memabra release

2026-04-15 11:06:05 +08:00
commit 58f9f221b1
464 changed files with 30256 additions and 0 deletions
--- a/tests/test_learning_router.py
+++ b/tests/test_learning_router.py
@@ -0,0 +1,91 @@
+from memabra.candidate_types import CandidateObject
+from memabra.dataset import TrainingSample
+from memabra.router import SimpleLearningRouter, TaskContext
+
+
+def test_learning_router_fits_and_predicts():
+    router = SimpleLearningRouter()
+    samples = [
+        TrainingSample(
+            input_text="run tool",
+            features={
+                "input_length": 8,
+                "memory_count": 0,
+                "skill_count": 0,
+                "tool_count": 1,
+                "top_memory_confidence": 0.0,
+                "top_skill_success_rate": 0.0,
+                "top_tool_confidence": 0.9,
+                "top_tool_risk": 0.1,
+            },
+            label="call_tool",
+            reward=1.0,
+        ),
+        TrainingSample(
+            input_text="remember",
+            features={
+                "input_length": 8,
+                "memory_count": 1,
+                "skill_count": 0,
+                "tool_count": 0,
+                "top_memory_confidence": 0.9,
+                "top_skill_success_rate": 0.0,
+                "top_tool_confidence": 0.0,
+                "top_tool_risk": 0.0,
+            },
+            label="inject_memory",
+            reward=1.0,
+        ),
+    ]
+    router.fit(samples)
+
+    tool = CandidateObject(
+        id="t1",
+        type="tool",
+        title="t",
+        summary="s",
+        triggers=[],
+        confidence=0.9,
+        success_rate=0.9,
+        freshness=0.9,
+        cost=0.0,
+        risk=0.1,
+    )
+    decision = router.choose(
+        TaskContext(user_input="run tool"),
+        memory_candidates=[],
+        skill_candidates=[],
+        tool_candidates=[tool],
+    )
+    assert decision.decision_type == "call_tool"
+
+    mem = CandidateObject(
+        id="m1",
+        type="memory",
+        title="m",
+        summary="s",
+        triggers=[],
+        confidence=0.9,
+        success_rate=0.9,
+        freshness=0.9,
+        cost=0.0,
+        risk=0.0,
+    )
+    decision = router.choose(
+        TaskContext(user_input="remember"),
+        memory_candidates=[mem],
+        skill_candidates=[],
+        tool_candidates=[],
+    )
+    assert decision.decision_type == "inject_memory"
+
+
+def test_learning_router_falls_back_to_clarify_when_untrained():
+    router = SimpleLearningRouter()
+    decision = router.choose(
+        TaskContext(user_input="hi"),
+        memory_candidates=[],
+        skill_candidates=[],
+        tool_candidates=[],
+    )
+    assert decision.decision_type == "clarify"