tests: add adaptive coverage tests and raise gate to 74%

Author: Maverick0351a

.github/workflows/ci.yml

@@ -45,7 +45,7 @@ jobs:
         run: mypy src
 
       - name: Pytest (with coverage gate)
-        run: pytest -q --maxfail=1 --disable-warnings --cov=neuralcache --cov-report=xml --cov-fail-under=71
+        run: pytest -q --maxfail=1 --disable-warnings --cov=neuralcache --cov-report=xml --cov-fail-under=74
 
       - name: Upload coverage artifact
         uses: actions/upload-artifact@v4

README.md

@@ -11,7 +11,7 @@
 [![CodeQL](https://github.com/Maverick0351a/neuralcache/actions/workflows/codeql.yml/badge.svg)](https://github.com/Maverick0351a/neuralcache/actions/workflows/codeql.yml)
 [![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](LICENSE)
 [![GitHub stars](https://img.shields.io/github/stars/Maverick0351a/neuralcache?style=social)](https://github.com/Maverick0351a/neuralcache/stargazers)
-[![Coverage](https://img.shields.io/badge/coverage-71%25-yellow)](./coverage-policy)
+[![Coverage](https://img.shields.io/badge/coverage-74%25-yellow)](./coverage-policy)
 
 NeuralCache is a lightweight reranker for RAG pipelines that *actually remembers what helped*. It blends dense semantic similarity with a narrative memory of past wins and stigmergic pheromones that reward helpful passages while decaying stale ones—then spices in MMR diversity and ε-greedy exploration. The result: more relevant context for your LLM without rebuilding your stack.
 
@@ -323,7 +323,7 @@ ruff check && mypy && pytest --cov=neuralcache --cov-report=term-missing
 
 - Look for [good first issues](https://github.com/Maverick0351a/neuralcache/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22).
 - Add test coverage for user-visible changes.
-- Coverage gate currently enforces >=71%. We'll continue to ratchet this upward as core adaptive components gain additional tests.
+- Coverage gate currently enforces >=74%. We'll continue to ratchet this upward as core adaptive components gain additional tests.
 - PRs with docs, demos, and eval improvements are extra appreciated.
 
 Optionally, join the discussion in **#neuralcache** on Discord (coming soon—watch this space).

tests/test_encoder_hashing.py

@@ -0,0 +1,23 @@
+import numpy as np
+from neuralcache.encoder import create_encoder, _hash_to_vector  # type: ignore
+
+
+def test_hashing_encoder_deterministic():
+    enc = create_encoder("hash", dim=32, model=None)
+    v1 = enc.encode("Hello World")
+    v2 = enc.encode("Hello World")
+    assert np.allclose(v1, v2)
+    assert v1.shape == (32,)
+
+
+def test_hashing_encoder_batch_empty():
+    enc = create_encoder("hash", dim=16, model=None)
+    batch = enc.encode_batch([])
+    assert batch.shape == (0, 16)
+
+
+def test_hash_to_vector_non_empty():
+    vec = _hash_to_vector("", 8)
+    # empty string should still produce a non-all-zero vector (fallback sets index 0)
+    assert vec.shape == (8,)
+    assert vec[0] != 0.0

tests/test_narrative_tracker.py

@@ -0,0 +1,22 @@
+import numpy as np
+import time
+from neuralcache.narrative import NarrativeTracker
+
+
+def test_narrative_update_and_coherence():
+    nt = NarrativeTracker(dim=4, alpha=0.5, success_gate=0.0, backend="memory")
+    emb = np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32)
+    nt.update(emb, success=1.0)
+    sims = nt.coherence(np.stack([emb, np.array([0.0,1.0,0.0,0.0], dtype=np.float32)]))
+    assert sims.shape == (2,)
+    assert sims[0] >= sims[1]
+
+
+def test_narrative_purge_if_stale():
+    nt = NarrativeTracker(dim=4, alpha=0.5, success_gate=0.0, backend="memory")
+    emb = np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32)
+    nt.update(emb, success=1.0)
+    # Force timestamp into past
+    nt._updated_ts -= 100.0
+    nt.purge_if_stale(retention_seconds=10.0)
+    assert np.allclose(nt.v, np.zeros_like(nt.v))

tests/test_pheromone_store.py

@@ -0,0 +1,21 @@
+import time
+from neuralcache.pheromone import PheromoneStore
+
+
+def test_pheromone_reinforce_and_decay():
+    store = PheromoneStore(half_life_s=1.0, exposure_penalty=0.0, backend="memory")
+    store.reinforce(["a"], reward=1.0)
+    v_initial = store.get_bonus("a")
+    assert v_initial > 0.0
+    time.sleep(1.1)
+    v_later = store.get_bonus("a")
+    assert v_later < v_initial  # decayed
+
+
+def test_pheromone_exposure_penalty():
+    store = PheromoneStore(half_life_s=1000.0, exposure_penalty=0.5, backend="memory")
+    store.reinforce(["b"], reward=1.0)
+    v0 = store.get_bonus("b")
+    store.record_exposure(["b"])  # one exposure halves remaining multiplier (1 - 0.5*1)
+    v1 = store.get_bonus("b")
+    assert v1 <= v0