feat: 添加多维度计费系统和视频任务管理功能

计费系统: - 新增 BillingRule 和 DimensionCollector 数据模型 - 实现 FormulaEngine 安全表达式求值引擎 (AST 白名单) - 支持 dimension/matrix/tiered/constant 多种维度映射 - BillingRuleService 支持 Provider Model -> GlobalModel 规则回退 - CLI task_type 在计费域自动映射为 chat 视频任务增强: - 添加 request_metadata 字段记录候选 key 和计费规则快照 - 后台轮询支持并发控制 (Semaphore + 独立 session) - 任务终态自动写入 Usage 记录并计算成本 - 新增视频任务管理 API 和前端界面其他改进: - UsageService 新增 record_usage_with_custom_cost 方法 - StandardizedUsage 支持 dimensions 字段 (兼容 extra) - 配置新增 BILLING_REQUIRE_RULE 和 BILLING_STRICT_MODE
2026-09-01 17:00:21 +08:00 · 2026-01-31 19:11:25 +08:00
parent dc4bb25cc2
commit 97b15afe7c
30 changed files with 4356 additions and 237 deletions
--- a/tests/services/billing/test_dimension_collector_service.py
+++ b/tests/services/billing/test_dimension_collector_service.py
@@ -0,0 +1,105 @@
+from src.models.database import DimensionCollector
+from src.services.billing.dimension_collector_service import (
+    DimensionCollectInput,
+    DimensionCollectorRuntime,
+)
+
+
+class TestDimensionCollectorRuntime:
+    def test_priority_fallback(self) -> None:
+        runtime = DimensionCollectorRuntime()
+        collectors = [
+            DimensionCollector(
+                api_format="OPENAI",
+                task_type="chat",
+                dimension_name="input_tokens",
+                source_type="response",
+                source_path="usage.prompt_tokens",
+                value_type="int",
+                priority=10,
+                is_enabled=True,
+            ),
+            DimensionCollector(
+                api_format="OPENAI",
+                task_type="chat",
+                dimension_name="input_tokens",
+                source_type="response",
+                source_path="usageMetadata.promptTokenCount",
+                value_type="int",
+                priority=5,
+                is_enabled=True,
+            ),
+        ]
+        dims = runtime.collect(
+            collectors=collectors,
+            inp=DimensionCollectInput(
+                response={"usageMetadata": {"promptTokenCount": 123}},
+            ),
+        )
+        assert dims["input_tokens"] == 123
+
+    def test_transform_expression_value(self) -> None:
+        runtime = DimensionCollectorRuntime()
+        collectors = [
+            DimensionCollector(
+                api_format="GEMINI",
+                task_type="video",
+                dimension_name="file_size_mb",
+                source_type="metadata",
+                source_path="result.file_size_bytes",
+                transform_expression="value / 1024 / 1024",
+                value_type="float",
+                priority=0,
+                is_enabled=True,
+            )
+        ]
+        dims = runtime.collect(
+            collectors=collectors,
+            inp=DimensionCollectInput(metadata={"result": {"file_size_bytes": 1048576}}),
+        )
+        assert abs(dims["file_size_mb"] - 1.0) < 1e-9
+
+    def test_computed_dimension(self) -> None:
+        runtime = DimensionCollectorRuntime()
+        collectors = [
+            DimensionCollector(
+                api_format="CLAUDE",
+                task_type="chat",
+                dimension_name="input_tokens",
+                source_type="request",
+                source_path="usage.input_tokens",
+                value_type="int",
+                priority=0,
+                is_enabled=True,
+            ),
+            DimensionCollector(
+                api_format="CLAUDE",
+                task_type="chat",
+                dimension_name="cache_read_tokens",
+                source_type="request",
+                source_path="usage.cache_read_tokens",
+                value_type="int",
+                priority=0,
+                is_enabled=True,
+            ),
+            DimensionCollector(
+                api_format="CLAUDE",
+                task_type="chat",
+                dimension_name="total_input_tokens",
+                source_type="computed",
+                source_path=None,
+                transform_expression="input_tokens + cache_read_tokens",
+                value_type="int",
+                priority=0,
+                is_enabled=True,
+            ),
+        ]
+        dims = runtime.collect(
+            collectors=collectors,
+            inp=DimensionCollectInput(
+                request={"usage": {"input_tokens": 100, "cache_read_tokens": 20}}
+            ),
+        )
+        assert dims["input_tokens"] == 100
+        assert dims["cache_read_tokens"] == 20
+        assert dims["total_input_tokens"] == 120
--- a/tests/services/billing/test_formula_engine.py
+++ b/tests/services/billing/test_formula_engine.py
@@ -0,0 +1,169 @@
+import pytest
+
+from src.services.billing.formula_engine import (
+    BillingIncompleteError,
+    FormulaEngine,
+    UnsafeExpressionError,
+)
+
+
+class TestSafeExpression:
+    def test_reject_attribute_access(self) -> None:
+        engine = FormulaEngine()
+        with pytest.raises(UnsafeExpressionError):
+            engine.evaluate(
+                expression="(1).__class__",
+                variables={},
+                dimensions={},
+                dimension_mappings={},
+                strict_mode=True,  # 确保抛出异常，便于断言类型
+            )
+
+    def test_reject_import(self) -> None:
+        engine = FormulaEngine()
+        with pytest.raises(UnsafeExpressionError):
+            engine.evaluate(
+                expression="__import__('os').system('echo hacked')",
+                variables={},
+                dimensions={},
+                dimension_mappings={},
+                strict_mode=True,
+            )
+
+
+class TestFormulaEngine:
+    def test_basic_video_formula(self) -> None:
+        engine = FormulaEngine()
+        result = engine.evaluate(
+            expression="(base_price + duration_seconds * price_per_second) * resolution_multiplier",
+            variables={"base_price": 0.05, "price_per_second": 0.02},
+            dimensions={"duration_seconds": 10, "resolution": "720p"},
+            dimension_mappings={
+                "duration_seconds": {
+                    "source": "dimension",
+                    "key": "duration_seconds",
+                    "required": True,
+                },
+                "resolution_multiplier": {
+                    "source": "matrix",
+                    "key": "resolution",
+                    "map": {"720p": 1.0, "1080p": 1.5},
+                    "default": 1.0,
+                },
+            },
+        )
+        assert result.status == "complete"
+        assert result.missing_required == []
+        assert abs(result.cost - 0.25) < 1e-9
+
+    def test_required_dimension_missing_non_strict(self) -> None:
+        engine = FormulaEngine()
+        result = engine.evaluate(
+            expression="base_price + duration_seconds * price_per_second",
+            variables={"base_price": 0.05, "price_per_second": 0.02},
+            dimensions={"duration_seconds": None},
+            dimension_mappings={
+                "duration_seconds": {
+                    "source": "dimension",
+                    "key": "duration_seconds",
+                    "required": True,
+                },
+            },
+            strict_mode=False,
+        )
+        assert result.status == "incomplete"
+        assert result.cost == 0.0
+        assert result.missing_required == ["duration_seconds"]
+
+    def test_required_dimension_missing_strict_raises(self) -> None:
+        engine = FormulaEngine()
+        with pytest.raises(BillingIncompleteError) as exc:
+            engine.evaluate(
+                expression="base_price + duration_seconds * price_per_second",
+                variables={"base_price": 0.05, "price_per_second": 0.02},
+                dimensions={"duration_seconds": None},
+                dimension_mappings={
+                    "duration_seconds": {
+                        "source": "dimension",
+                        "key": "duration_seconds",
+                        "required": True,
+                    },
+                },
+                strict_mode=True,
+            )
+        assert exc.value.missing_required == ["duration_seconds"]
+
+    def test_allow_zero(self) -> None:
+        engine = FormulaEngine()
+
+        # allow_zero=false（默认）：0 视为缺失
+        result = engine.evaluate(
+            expression="duration_seconds * 1",
+            variables={},
+            dimensions={"duration_seconds": 0},
+            dimension_mappings={
+                "duration_seconds": {
+                    "source": "dimension",
+                    "key": "duration_seconds",
+                    "required": True,
+                },
+            },
+        )
+        assert result.status == "incomplete"
+        assert result.missing_required == ["duration_seconds"]
+
+        # allow_zero=true：0 合法
+        result = engine.evaluate(
+            expression="duration_seconds * 1",
+            variables={},
+            dimensions={"duration_seconds": 0},
+            dimension_mappings={
+                "duration_seconds": {
+                    "source": "dimension",
+                    "key": "duration_seconds",
+                    "required": True,
+                    "allow_zero": True,
+                },
+            },
+        )
+        assert result.status == "complete"
+        assert result.cost == 0.0
+
+    def test_tiered_mapping(self) -> None:
+        engine = FormulaEngine()
+
+        result = engine.evaluate(
+            expression="input_price",
+            variables={},
+            dimensions={"total_input_tokens": 100_000},
+            dimension_mappings={
+                "input_price": {
+                    "source": "tiered",
+                    "tier_key": "total_input_tokens",
+                    "tiers": [
+                        {"up_to": 128_000, "value": 3.0},
+                        {"up_to": None, "value": 1.5},
+                    ],
+                }
+            },
+        )
+        assert result.status == "complete"
+        assert result.cost == 3.0
+
+        result = engine.evaluate(
+            expression="input_price",
+            variables={},
+            dimensions={"total_input_tokens": 200_000},
+            dimension_mappings={
+                "input_price": {
+                    "source": "tiered",
+                    "tier_key": "total_input_tokens",
+                    "tiers": [
+                        {"up_to": 128_000, "value": 3.0},
+                        {"up_to": None, "value": 1.5},
+                    ],
+                }
+            },
+        )
+        assert result.status == "complete"
+        assert result.cost == 1.5