Implement-LLM-WP-0005-cost-model-estimators

tests/test_cli.py

@@ -0,0 +1,54 @@
+import json
+from datetime import datetime, timezone
+
+from llm_connect.cli import main
+from llm_connect.quality import QualityLedger, QualityObservation
+
+
+def test_rates_show_json_outputs_default_registry(capsys):
+    assert main(["rates", "show", "--json"]) == 0
+
+    payload = json.loads(capsys.readouterr().out)
+
+    assert payload["openai/gpt-4o-mini"]["prompt_per_1k"] == 0.00015
+
+
+def test_classes_show_lists_builtins(capsys):
+    assert main(["classes", "show"]) == 0
+
+    output = capsys.readouterr().out
+
+    assert "chunk-summarization" in output
+    assert "entity-extraction" in output
+
+
+def test_classes_fit_reads_quality_ledger(tmp_path, capsys):
+    ledger = QualityLedger(tmp_path / "quality.jsonl")
+    for _ in range(3):
+        ledger.append(
+            QualityObservation(
+                task_type="extract",
+                adapter_id="openrouter",
+                model_id="openai/gpt-4o-mini",
+                cost_usd=0.001,
+                quality_score=0.9,
+                latency_ms=100,
+                tokens_in=500,
+                tokens_out=350,
+                recorded_at=datetime(2026, 5, 19, tzinfo=timezone.utc),
+                tags={
+                    "problem_class": "entity-extraction",
+                    "dimensions": {
+                        "chunk_words": 300,
+                        "template_words": 100,
+                        "expected_entities": 5,
+                    },
+                },
+            )
+        )
+
+    assert main(["classes", "fit", str(ledger.path), "--class", "entity-extraction", "--json"]) == 0
+
+    payload = json.loads(capsys.readouterr().out)
+
+    assert payload["entity-extraction"]["params"]["tokens_per_entity"] == 70