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feat(analytics): 重构统计分析模块，统一 API 与前端视图
close #260 - 新增 `src/services/analytics/query_service.py`，集中实现排行榜、性能、时间序列等查询逻辑 - 新增 `src/api/analytics/routes.py`，替代原 `stats/` 和 `dashboard/` 的分散路由 - 删除旧 `src/api/admin/stats/`、`src/api/dashboard/` 模块 - 重构 `src/api/user_me/routes.py` 与 `src/api/admin/usage/routes.py`，精简用量查询接口 - 新增 Alembic 迁移，修正 token 语义字段 - 前端新增 Analytics.vue、LeaderboardTab、PerformanceTab、ReportsTab 及 Reports 用户视图 - 新增 composables（useAnalyticsFilters、useReportsData、useLeaderboardData、usePerformanceData） - 新增工具函数：analyticsGranularity、analyticsTimeseries、chartTheme、csvExport、usageBreakdown - 删除旧 CostAnalysis、PerformanceAnalysis、UserStats 页面及相关组件 - 前端 API 层重组：新增 analytics.ts、request-details.ts，删除 dashboard.ts 和 usage.ts Co-authored-by: NyaDoo <[email protected]>
This commit is contained in:
fawney19
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"""usage token semantics v2
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Revision ID: c3d4e5f6a7b8
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Revises: c9d8e7f6a5b4
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Create Date: 2026-03-24 14:00:00.000000+00:00
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"""
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from __future__ import annotations
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from collections.abc import Sequence
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import sqlalchemy as sa
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from sqlalchemy import inspect
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from alembic import op
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# revision identifiers, used by Alembic.
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revision: str = "c3d4e5f6a7b8"
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down_revision: str | None = "c9d8e7f6a5b4"
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branch_labels: str | Sequence[str] | None = None
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depends_on: str | Sequence[str] | None = None
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def column_exists(table_name: str, column_name: str) -> bool:
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bind = op.get_bind()
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inspector = inspect(bind)
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columns = [c["name"] for c in inspector.get_columns(table_name)]
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return column_name in columns
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def upgrade() -> None:
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if column_exists("usage", "total_tokens") and not column_exists("usage", "input_output_total_tokens"):
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with op.batch_alter_table("usage") as batch_op:
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batch_op.alter_column(
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"total_tokens",
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new_column_name="input_output_total_tokens",
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existing_type=sa.Integer(),
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existing_nullable=True,
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)
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with op.batch_alter_table("usage") as batch_op:
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if not column_exists("usage", "input_context_tokens"):
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batch_op.add_column(sa.Column("input_context_tokens", sa.Integer(), nullable=False, server_default="0"))
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if not column_exists("usage", "total_tokens"):
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batch_op.add_column(sa.Column("total_tokens", sa.Integer(), nullable=False, server_default="0"))
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if not column_exists("usage", "cache_creation_cost_usd_5m"):
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batch_op.add_column(sa.Column("cache_creation_cost_usd_5m", sa.Numeric(20, 8), nullable=False, server_default="0"))
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if not column_exists("usage", "cache_creation_cost_usd_1h"):
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batch_op.add_column(sa.Column("cache_creation_cost_usd_1h", sa.Numeric(20, 8), nullable=False, server_default="0"))
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if not column_exists("usage", "actual_cache_creation_cost_usd_5m"):
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batch_op.add_column(sa.Column("actual_cache_creation_cost_usd_5m", sa.Numeric(20, 8), nullable=False, server_default="0"))
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if not column_exists("usage", "actual_cache_creation_cost_usd_1h"):
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batch_op.add_column(sa.Column("actual_cache_creation_cost_usd_1h", sa.Numeric(20, 8), nullable=False, server_default="0"))
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if not column_exists("usage", "actual_cache_cost_usd"):
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batch_op.add_column(sa.Column("actual_cache_cost_usd", sa.Numeric(20, 8), nullable=False, server_default="0"))
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if not column_exists("usage", "cache_creation_price_per_1m_5m"):
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batch_op.add_column(sa.Column("cache_creation_price_per_1m_5m", sa.Numeric(20, 8), nullable=True))
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if not column_exists("usage", "cache_creation_price_per_1m_1h"):
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batch_op.add_column(sa.Column("cache_creation_price_per_1m_1h", sa.Numeric(20, 8), nullable=True))
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conn = op.get_bind()
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batch_size = 5000
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while True:
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result = conn.execute(
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sa.text(
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"""
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UPDATE usage
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SET
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input_output_total_tokens = COALESCE(input_output_total_tokens, COALESCE(input_tokens, 0) + COALESCE(output_tokens, 0)),
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input_context_tokens = COALESCE(input_tokens, 0) + COALESCE(cache_read_input_tokens, 0),
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total_tokens = COALESCE(input_output_total_tokens, 0)
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+ COALESCE(cache_creation_input_tokens, 0)
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+ COALESCE(cache_read_input_tokens, 0),
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cache_creation_cost_usd_5m = CASE
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WHEN COALESCE(cache_creation_input_tokens_5m, 0) > 0
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AND COALESCE(cache_creation_input_tokens_1h, 0) = 0
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THEN COALESCE(cache_creation_cost_usd, 0)
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WHEN COALESCE(cache_creation_input_tokens_5m, 0) > 0
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AND COALESCE(cache_creation_input_tokens, 0) > 0
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THEN COALESCE(cache_creation_cost_usd, 0)
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* (COALESCE(cache_creation_input_tokens_5m, 0) * 1.0
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/ GREATEST(COALESCE(cache_creation_input_tokens, 0), 1))
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ELSE 0
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END,
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cache_creation_cost_usd_1h = CASE
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WHEN COALESCE(cache_creation_input_tokens_1h, 0) > 0
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AND COALESCE(cache_creation_input_tokens_5m, 0) = 0
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THEN COALESCE(cache_creation_cost_usd, 0)
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WHEN COALESCE(cache_creation_input_tokens_1h, 0) > 0
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AND COALESCE(cache_creation_input_tokens, 0) > 0
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THEN COALESCE(cache_creation_cost_usd, 0)
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* (COALESCE(cache_creation_input_tokens_1h, 0) * 1.0
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/ GREATEST(COALESCE(cache_creation_input_tokens, 0), 1))
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ELSE 0
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END,
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actual_cache_creation_cost_usd_5m = CASE
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WHEN COALESCE(cache_creation_input_tokens_5m, 0) > 0
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AND COALESCE(cache_creation_input_tokens_1h, 0) = 0
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THEN COALESCE(actual_cache_creation_cost_usd, 0)
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WHEN COALESCE(cache_creation_input_tokens_5m, 0) > 0
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AND COALESCE(cache_creation_input_tokens, 0) > 0
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THEN COALESCE(actual_cache_creation_cost_usd, 0)
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* (COALESCE(cache_creation_input_tokens_5m, 0) * 1.0
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/ GREATEST(COALESCE(cache_creation_input_tokens, 0), 1))
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ELSE 0
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END,
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actual_cache_creation_cost_usd_1h = CASE
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WHEN COALESCE(cache_creation_input_tokens_1h, 0) > 0
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AND COALESCE(cache_creation_input_tokens_5m, 0) = 0
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THEN COALESCE(actual_cache_creation_cost_usd, 0)
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WHEN COALESCE(cache_creation_input_tokens_1h, 0) > 0
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AND COALESCE(cache_creation_input_tokens, 0) > 0
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THEN COALESCE(actual_cache_creation_cost_usd, 0)
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* (COALESCE(cache_creation_input_tokens_1h, 0) * 1.0
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/ GREATEST(COALESCE(cache_creation_input_tokens, 0), 1))
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ELSE 0
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END,
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actual_cache_cost_usd = COALESCE(actual_cache_creation_cost_usd, 0) + COALESCE(actual_cache_read_cost_usd, 0),
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cache_creation_price_per_1m_5m = CASE
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WHEN COALESCE(cache_creation_input_tokens_5m, 0) > 0
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AND COALESCE(cache_creation_input_tokens_1h, 0) = 0
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THEN cache_creation_price_per_1m
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ELSE NULL
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END,
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cache_creation_price_per_1m_1h = CASE
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WHEN COALESCE(cache_creation_input_tokens_1h, 0) > 0
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AND COALESCE(cache_creation_input_tokens_5m, 0) = 0
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THEN cache_creation_price_per_1m
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ELSE NULL
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END,
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cache_cost_usd = COALESCE(cache_creation_cost_usd, 0) + COALESCE(cache_read_cost_usd, 0)
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WHERE id IN (
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SELECT id FROM usage
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WHERE input_context_tokens = 0 AND total_tokens = 0
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LIMIT :batch_size
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)
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"""
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),
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{"batch_size": batch_size},
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)
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if result.rowcount == 0:
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break
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def downgrade() -> None:
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conn = op.get_bind()
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batch_size = 5000
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while True:
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result = conn.execute(
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sa.text(
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"""
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UPDATE usage
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SET total_tokens = COALESCE(input_output_total_tokens, COALESCE(input_tokens, 0) + COALESCE(output_tokens, 0))
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WHERE id IN (
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SELECT id FROM usage
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WHERE total_tokens != COALESCE(input_output_total_tokens, COALESCE(input_tokens, 0) + COALESCE(output_tokens, 0))
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LIMIT :batch_size
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)
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"""
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),
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{"batch_size": batch_size},
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)
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if result.rowcount == 0:
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break
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with op.batch_alter_table("usage") as batch_op:
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if column_exists("usage", "cache_creation_price_per_1m_1h"):
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batch_op.drop_column("cache_creation_price_per_1m_1h")
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if column_exists("usage", "cache_creation_price_per_1m_5m"):
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batch_op.drop_column("cache_creation_price_per_1m_5m")
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if column_exists("usage", "actual_cache_cost_usd"):
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batch_op.drop_column("actual_cache_cost_usd")
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if column_exists("usage", "actual_cache_creation_cost_usd_1h"):
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batch_op.drop_column("actual_cache_creation_cost_usd_1h")
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if column_exists("usage", "actual_cache_creation_cost_usd_5m"):
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batch_op.drop_column("actual_cache_creation_cost_usd_5m")
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if column_exists("usage", "cache_creation_cost_usd_1h"):
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batch_op.drop_column("cache_creation_cost_usd_1h")
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if column_exists("usage", "cache_creation_cost_usd_5m"):
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batch_op.drop_column("cache_creation_cost_usd_5m")
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if column_exists("usage", "input_context_tokens"):
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batch_op.drop_column("input_context_tokens")
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if column_exists("usage", "total_tokens"):
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batch_op.drop_column("total_tokens")
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if column_exists("usage", "input_output_total_tokens"):
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batch_op.alter_column(
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"input_output_total_tokens",
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new_column_name="total_tokens",
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existing_type=sa.Integer(),
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existing_nullable=True,
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)
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