//! 使用统计服务 //! //! 提供使用量数据的聚合查询功能 use crate::database::{lock_conn, Database}; use crate::error::AppError; use crate::proxy::usage::calculator::ModelPricing; use crate::services::sql_helpers::fresh_input_sql; use chrono::{Local, NaiveDate, TimeZone, Timelike}; use rusqlite::{params, Connection, OptionalExtension}; use serde::{Deserialize, Serialize}; use std::collections::HashMap; use std::str::FromStr; /// 使用量汇总 #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct UsageSummary { pub total_requests: u64, pub total_cost: String, pub total_input_tokens: u64, pub total_output_tokens: u64, pub total_cache_creation_tokens: u64, pub total_cache_read_tokens: u64, pub success_rate: f32, /// input + output + cache_creation + cache_read — the total tokens /// actually processed by the model (including cache hits). Used as the /// headline "real consumption" number in the usage hero. pub real_total_tokens: u64, /// cache_read / (input + cache_creation + cache_read). Range 0.0–1.0. /// Reported as a fraction; multiply by 100 in UI for percentage display. pub cache_hit_rate: f64, } /// Per-app-type usage summary used by the dashboard breakdown rail. #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct UsageSummaryByApp { pub app_type: String, pub summary: UsageSummary, } /// Helper: compute (real_total, hit_rate) from the four token counters. /// All inputs must already be cache-normalized (i.e. input excludes cache). fn derive_real_total_and_hit_rate( fresh_input: u64, output: u64, cache_creation: u64, cache_read: u64, ) -> (u64, f64) { let real_total = fresh_input + output + cache_creation + cache_read; let cacheable_input = fresh_input + cache_creation + cache_read; let hit_rate = if cacheable_input > 0 { cache_read as f64 / cacheable_input as f64 } else { 0.0 }; (real_total, hit_rate) } /// 每日统计 #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct DailyStats { pub date: String, pub request_count: u64, pub total_cost: String, pub total_tokens: u64, pub total_input_tokens: u64, pub total_output_tokens: u64, pub total_cache_creation_tokens: u64, pub total_cache_read_tokens: u64, } /// Provider 统计 #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct ProviderStats { pub provider_id: String, pub provider_name: String, pub request_count: u64, pub total_tokens: u64, pub total_cost: String, pub success_rate: f32, pub avg_latency_ms: u64, } /// 模型统计 #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct ModelStats { pub model: String, pub request_count: u64, pub total_tokens: u64, pub total_cost: String, pub avg_cost_per_request: String, } /// 请求日志过滤器 #[derive(Debug, Clone, Default, Deserialize)] #[serde(rename_all = "camelCase")] pub struct LogFilters { pub app_type: Option, pub provider_name: Option, pub model: Option, pub status_code: Option, pub start_date: Option, pub end_date: Option, } /// 分页请求日志响应 #[derive(Debug, Clone, Serialize)] #[serde(rename_all = "camelCase")] pub struct PaginatedLogs { pub data: Vec, pub total: u32, pub page: u32, pub page_size: u32, } /// 请求日志详情 #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct RequestLogDetail { pub request_id: String, pub provider_id: String, #[serde(skip_serializing_if = "Option::is_none")] pub provider_name: Option, pub app_type: String, pub model: String, #[serde(skip_serializing_if = "Option::is_none")] pub request_model: Option, pub cost_multiplier: String, pub input_tokens: u32, pub output_tokens: u32, pub cache_read_tokens: u32, pub cache_creation_tokens: u32, pub input_cost_usd: String, pub output_cost_usd: String, pub cache_read_cost_usd: String, pub cache_creation_cost_usd: String, pub total_cost_usd: String, pub is_streaming: bool, pub latency_ms: u64, pub first_token_ms: Option, pub duration_ms: Option, pub status_code: u16, pub error_message: Option, pub created_at: i64, #[serde(skip_serializing_if = "Option::is_none")] pub data_source: Option, /// 写入时实际用于计价的模型名。None = v11 前的历史行，"" = 未计价的错误行。 #[serde(skip_serializing_if = "Option::is_none")] pub pricing_model: Option, } /// 把 25 列的查询结果映射为 `RequestLogDetail`。 /// /// 调用方的 SELECT **必须**按以下顺序返回 25 列： /// `request_id, provider_id, provider_name, app_type, model, request_model, /// cost_multiplier, input_tokens, output_tokens, cache_read_tokens, /// cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, /// cache_creation_cost_usd, total_cost_usd, is_streaming, latency_ms, /// first_token_ms, duration_ms, status_code, error_message, created_at, /// data_source, pricing_model` /// /// 不需要 provider_name 时（如 backfill）SELECT `NULL AS provider_name` 占位即可。 fn row_to_request_log_detail(row: &rusqlite::Row<'_>) -> rusqlite::Result { Ok(RequestLogDetail { request_id: row.get(0)?, provider_id: row.get(1)?, provider_name: row.get(2)?, app_type: row.get(3)?, model: row.get(4)?, request_model: row.get(5)?, cost_multiplier: row .get::<_, Option>(6)? .unwrap_or_else(|| "1".to_string()), input_tokens: row.get::<_, i64>(7)? as u32, output_tokens: row.get::<_, i64>(8)? as u32, cache_read_tokens: row.get::<_, i64>(9)? as u32, cache_creation_tokens: row.get::<_, i64>(10)? as u32, input_cost_usd: row.get(11)?, output_cost_usd: row.get(12)?, cache_read_cost_usd: row.get(13)?, cache_creation_cost_usd: row.get(14)?, total_cost_usd: row.get(15)?, is_streaming: row.get::<_, i64>(16)? != 0, latency_ms: row.get::<_, i64>(17)? as u64, first_token_ms: row.get::<_, Option>(18)?.map(|v| v as u64), duration_ms: row.get::<_, Option>(19)?.map(|v| v as u64), status_code: row.get::<_, i64>(20)? as u16, error_message: row.get(21)?, created_at: row.get(22)?, data_source: row.get(23)?, pricing_model: row.get(24)?, }) } /// SQL fragment: resolve provider_name with fallback for session-based entries. /// Session logs use placeholder provider_ids (e.g., `_session`, `__session`) /// that don't exist in the providers table — the CASE expression below is the /// authoritative mapping from placeholder to readable name. fn provider_name_coalesce(log_alias: &str, provider_alias: &str) -> String { format!( "COALESCE({provider_alias}.name, CASE {log_alias}.provider_id \ WHEN '_session' THEN 'Claude (Session)' \ WHEN '_codex_session' THEN 'Codex (Session)' \ WHEN '_gemini_session' THEN 'Gemini (Session)' \ WHEN '_opencode_session' THEN 'OpenCode (Session)' \ ELSE {log_alias}.provider_id END)" ) } pub(crate) const SESSION_PROXY_DEDUP_WINDOW_SECONDS: i64 = 10 * 60; /// SQL 片段：把指定别名的 `data_source` 包成 COALESCE，NULL 视作 'proxy'。 /// /// 防御 schema v9 之前可能写入的 NULL data_source 行（见 /// `tests::create_legacy_nullable_logs_table`）。所有用到 data_source 的查询 /// 都应通过此 helper 生成片段，避免遗漏。 fn data_source_expr(log_alias: &str) -> String { format!("COALESCE({log_alias}.data_source, 'proxy')") } /// SQL 标量表达式：把 Claude Desktop 网关的 `claude-desktop` app_type 在“展示口径” /// 上折叠进 `claude`，其余 app_type 原样返回。 /// /// 背景：Desktop 网关流量在记账层按各自入口写为 `app_type='claude-desktop'`， /// 以保留路由接管的账单审计精度（不要回退这一点）。但 Dashboard 把它当作 /// Claude Code 呈现——它本质就是跑在 Desktop 壳里的内嵌 Claude Code 运行时， /// 且 Desktop 聊天用量永远不经过本软件，单列只会让用户误以为是“桌面版全部用量”。 /// /// 用法：把任一参与“按应用筛选/分组”的 `app_type` 列包进此表达式即可， /// 这样 `= 'claude'` 过滤会同时命中 `claude-desktop`、`GROUP BY` 会把两者合并， /// 而不改动任何已存储的行（详情面板仍读原始 `app_type`）。 /// /// 注意：包裹后该列上的索引在此比较中失效，但这些都是已带时间过滤的聚合扫描， /// app_type 本就不是主访问路径，可接受。仅用于读侧；去重匹配（`has_matching_ /// proxy_usage_log`）与额度检查（`check_provider_limits`）必须保留原始精确比较。 fn folded_app_type_sql(column: &str) -> String { format!("CASE WHEN {column} = 'claude-desktop' THEN 'claude' ELSE {column} END") } /// SQL 片段：把日志/汇总行 LEFT JOIN 到 providers 表以取得供应商名称。 /// `proxy_request_logs` 与 `usage_daily_rollups` 的 (provider_id, app_type) /// 形状相同，两者皆可作为 `log_alias`。providers 主键即 (id, app_type)， /// 连接至多 1:1，不会放大行数。 fn providers_join(log_alias: &str, provider_alias: &str) -> String { format!( "LEFT JOIN providers {provider_alias} \ ON {log_alias}.provider_id = {provider_alias}.id \ AND {log_alias}.app_type = {provider_alias}.app_type" ) } /// SQL 标量表达式：行的「有效计价模型」—— pricing_model 非空优先，NULL/'' 回落 /// model。这是 `get_model_stats` 的分组键，也是 Dashboard 模型筛选的匹配口径： /// 筛选值来自模型统计列表，两边必须用同一表达式才能选得中。 fn effective_model_sql(alias: &str) -> String { format!("COALESCE(NULLIF({alias}.pricing_model, ''), {alias}.model)") } /// 把 Dashboard 顶部的 Provider/模型筛选追加到查询条件。 /// /// Provider 按展示名精确匹配（复用 [`provider_name_coalesce`]，会话占位行的 /// 可读名如 "Claude (Session)" 也能选中）；模型按 [`effective_model_sql`] 匹配。 /// 注意：传入 `provider_name` 时调用方必须把 [`providers_join`] 拼进 FROM， /// 否则 `{provider_alias}.name` 无法解析。 fn push_provider_model_filters( conditions: &mut Vec, params: &mut Vec>, log_alias: &str, provider_alias: &str, provider_name: Option<&str>, model: Option<&str>, ) { if let Some(name) = provider_name { conditions.push(format!( "{} = ?", provider_name_coalesce(log_alias, provider_alias) )); params.push(Box::new(name.to_string())); } if let Some(m) = model { conditions.push(format!("{} = ?", effective_model_sql(log_alias))); params.push(Box::new(m.to_string())); } } pub(crate) fn effective_usage_log_filter(log_alias: &str) -> String { let data_source = data_source_expr(log_alias); let proxy_data_source = data_source_expr("proxy_dedup"); format!( "NOT ( {data_source} IN ('session_log', 'codex_session', 'gemini_session', 'opencode_session') AND EXISTS ( SELECT 1 FROM proxy_request_logs proxy_dedup WHERE {proxy_data_source} = 'proxy' AND proxy_dedup.app_type = {log_alias}.app_type AND proxy_dedup.status_code >= 200 AND proxy_dedup.status_code < 300 AND proxy_dedup.input_tokens = {log_alias}.input_tokens AND proxy_dedup.output_tokens = {log_alias}.output_tokens AND proxy_dedup.cache_read_tokens = {log_alias}.cache_read_tokens AND ( proxy_dedup.cache_creation_tokens = {log_alias}.cache_creation_tokens OR ( {log_alias}.cache_creation_tokens = 0 AND {data_source} IN ('codex_session', 'gemini_session', 'opencode_session') ) ) AND proxy_dedup.created_at BETWEEN {log_alias}.created_at - {SESSION_PROXY_DEDUP_WINDOW_SECONDS} AND {log_alias}.created_at + {SESSION_PROXY_DEDUP_WINDOW_SECONDS} AND ( LOWER(proxy_dedup.model) = LOWER({log_alias}.model) OR LOWER(proxy_dedup.model) = 'unknown' OR LOWER({log_alias}.model) = 'unknown' ) ) )" ) } /// 跨源去重指纹键。 /// /// `cache_creation_tokens`：Codex/Gemini session 日志不暴露该字段，调用方传 0 /// 表示"未知"，匹配器会放行 proxy 侧任意 cache_creation_tokens 值。 #[derive(Debug, Clone, Copy)] pub(crate) struct DedupKey<'a> { pub app_type: &'a str, pub model: &'a str, pub input_tokens: u32, pub output_tokens: u32, pub cache_read_tokens: u32, pub cache_creation_tokens: u32, pub created_at: i64, } /// session 日志写入前的统一去重判定。 /// /// 命中以下任一条件即跳过插入：① `request_id` 已存在；② 时间窗口内存在 /// 与 `key` 匹配的 proxy 日志（指纹去重）。 pub(crate) fn should_skip_session_insert( conn: &Connection, request_id: &str, key: &DedupKey, ) -> Result { if proxy_request_id_exists(conn, request_id)? { return Ok(true); } has_matching_proxy_usage_log(conn, key) } fn proxy_request_id_exists(conn: &Connection, request_id: &str) -> Result { conn.query_row( "SELECT EXISTS(SELECT 1 FROM proxy_request_logs WHERE request_id = ?1)", params![request_id], |row| row.get::<_, bool>(0), ) .map_err(|e| AppError::Database(format!("查询 request_id 失败: {e}"))) } pub(crate) fn has_matching_proxy_usage_log( conn: &Connection, key: &DedupKey, ) -> Result { let allow_missing_cache_creation = matches!(key.app_type, "codex" | "gemini" | "opencode") && key.cache_creation_tokens == 0; let l_data_source = data_source_expr("l"); let sql = format!( "SELECT EXISTS ( SELECT 1 FROM proxy_request_logs l WHERE {l_data_source} = 'proxy' AND l.app_type = ?1 AND l.status_code >= 200 AND l.status_code < 300 AND l.input_tokens = ?3 AND l.output_tokens = ?4 AND l.cache_read_tokens = ?5 AND (l.cache_creation_tokens = ?6 OR ?9 = 1) AND l.created_at BETWEEN ?7 - ?8 AND ?7 + ?8 AND ( LOWER(l.model) = LOWER(?2) OR LOWER(l.model) = 'unknown' OR LOWER(?2) = 'unknown' ) )" ); conn.query_row( &sql, params![ key.app_type, key.model, key.input_tokens as i64, key.output_tokens as i64, key.cache_read_tokens as i64, key.cache_creation_tokens as i64, key.created_at, SESSION_PROXY_DEDUP_WINDOW_SECONDS, allow_missing_cache_creation as i64, ], |row| row.get::<_, bool>(0), ) .map_err(|e| AppError::Database(format!("查询重复代理用量日志失败: {e}"))) } #[derive(Debug, Clone, Default)] struct RollupDateBounds { start: Option, end: Option, is_empty: bool, } fn local_datetime_from_timestamp(ts: i64) -> Result, AppError> { Local .timestamp_opt(ts, 0) .single() .ok_or_else(|| AppError::Database(format!("无法解析本地时间戳: {ts}"))) } fn compute_rollup_date_bounds( start_ts: Option, end_ts: Option, ) -> Result { let start = match start_ts { Some(ts) => { let local = local_datetime_from_timestamp(ts)?; let day = local.date_naive(); if local.time().num_seconds_from_midnight() == 0 { Some(day.format("%Y-%m-%d").to_string()) } else { day.succ_opt() .map(|next| next.format("%Y-%m-%d").to_string()) } } None => None, }; let end = match end_ts { Some(ts) => { let local = local_datetime_from_timestamp(ts)?; let day = local.date_naive(); if local.time().hour() == 23 && local.time().minute() == 59 { Some(day.format("%Y-%m-%d").to_string()) } else { day.pred_opt() .map(|prev| prev.format("%Y-%m-%d").to_string()) } } None => None, }; let is_empty = matches!((&start, &end), (Some(start), Some(end)) if start > end); Ok(RollupDateBounds { start, end, is_empty, }) } fn push_rollup_date_filters( conditions: &mut Vec, params: &mut Vec>, column: &str, bounds: &RollupDateBounds, ) { if bounds.is_empty { conditions.push("1 = 0".to_string()); return; } if let Some(start) = &bounds.start { conditions.push(format!("{column} >= ?")); params.push(Box::new(start.clone())); } if let Some(end) = &bounds.end { conditions.push(format!("{column} <= ?")); params.push(Box::new(end.clone())); } } fn local_day_start_rfc3339(day: NaiveDate) -> String { let local_midnight = day .and_hms_opt(0, 0, 0) .and_then(|naive| match Local.from_local_datetime(&naive) { chrono::LocalResult::Single(dt) => Some(dt), chrono::LocalResult::Ambiguous(earliest, _) => Some(earliest), chrono::LocalResult::None => None, }) .unwrap_or_else(Local::now); local_midnight.to_rfc3339() } impl Database { /// 获取使用量汇总 pub fn get_usage_summary( &self, start_date: Option, end_date: Option, app_type: Option<&str>, provider_name: Option<&str>, model: Option<&str>, ) -> Result { let conn = lock_conn!(self.conn); // Build detail WHERE clause let mut conditions = vec![effective_usage_log_filter("l")]; let mut params_vec: Vec> = Vec::new(); if let Some(start) = start_date { conditions.push("l.created_at >= ?".to_string()); params_vec.push(Box::new(start)); } if let Some(end) = end_date { conditions.push("l.created_at <= ?".to_string()); params_vec.push(Box::new(end)); } if let Some(at) = app_type { conditions.push(format!("{} = ?", folded_app_type_sql("l.app_type"))); params_vec.push(Box::new(at.to_string())); } push_provider_model_filters( &mut conditions, &mut params_vec, "l", "p", provider_name, model, ); let where_clause = if conditions.is_empty() { String::new() } else { format!("WHERE {}", conditions.join(" AND ")) }; let detail_join = if provider_name.is_some() { providers_join("l", "p") } else { String::new() }; // Only include rolled-up rows for full local days that are fully covered by the range. let mut rollup_conditions: Vec = Vec::new(); let mut rollup_params: Vec> = Vec::new(); let rollup_bounds = compute_rollup_date_bounds(start_date, end_date)?; push_rollup_date_filters( &mut rollup_conditions, &mut rollup_params, "r.date", &rollup_bounds, ); if let Some(at) = app_type { rollup_conditions.push(format!("{} = ?", folded_app_type_sql("r.app_type"))); rollup_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut rollup_conditions, &mut rollup_params, "r", "p2", provider_name, model, ); let rollup_where = if rollup_conditions.is_empty() { String::new() } else { format!("WHERE {}", rollup_conditions.join(" AND ")) }; let rollup_join = if provider_name.is_some() { providers_join("r", "p2") } else { String::new() }; let fresh_input_detail = fresh_input_sql("l"); let fresh_input_rollup = fresh_input_sql("r"); let sql = format!( "SELECT COALESCE(d.total_requests, 0) + COALESCE(r.total_requests, 0), COALESCE(d.total_cost, 0) + COALESCE(r.total_cost, 0), COALESCE(d.total_input_tokens, 0) + COALESCE(r.total_input_tokens, 0), COALESCE(d.total_output_tokens, 0) + COALESCE(r.total_output_tokens, 0), COALESCE(d.total_cache_creation_tokens, 0) + COALESCE(r.total_cache_creation_tokens, 0), COALESCE(d.total_cache_read_tokens, 0) + COALESCE(r.total_cache_read_tokens, 0), COALESCE(d.success_count, 0) + COALESCE(r.success_count, 0) FROM (SELECT COUNT(*) as total_requests, COALESCE(SUM(CAST(l.total_cost_usd AS REAL)), 0) as total_cost, COALESCE(SUM({fresh_input_detail}), 0) as total_input_tokens, COALESCE(SUM(l.output_tokens), 0) as total_output_tokens, COALESCE(SUM(l.cache_creation_tokens), 0) as total_cache_creation_tokens, COALESCE(SUM(l.cache_read_tokens), 0) as total_cache_read_tokens, COALESCE(SUM(CASE WHEN l.status_code >= 200 AND l.status_code < 300 THEN 1 ELSE 0 END), 0) as success_count FROM proxy_request_logs l {detail_join} {where_clause}) d, (SELECT COALESCE(SUM(r.request_count), 0) as total_requests, COALESCE(SUM(CAST(r.total_cost_usd AS REAL)), 0) as total_cost, COALESCE(SUM({fresh_input_rollup}), 0) as total_input_tokens, COALESCE(SUM(r.output_tokens), 0) as total_output_tokens, COALESCE(SUM(r.cache_creation_tokens), 0) as total_cache_creation_tokens, COALESCE(SUM(r.cache_read_tokens), 0) as total_cache_read_tokens, COALESCE(SUM(r.success_count), 0) as success_count FROM usage_daily_rollups r {rollup_join} {rollup_where}) r" ); // Combine params: detail params first, then rollup params let mut all_params: Vec> = params_vec; all_params.extend(rollup_params); let param_refs: Vec<&dyn rusqlite::ToSql> = all_params.iter().map(|p| p.as_ref()).collect(); let result = conn.query_row(&sql, param_refs.as_slice(), |row| { let total_requests: i64 = row.get(0)?; let total_cost: f64 = row.get(1)?; let total_input_tokens: i64 = row.get(2)?; let total_output_tokens: i64 = row.get(3)?; let total_cache_creation_tokens: i64 = row.get(4)?; let total_cache_read_tokens: i64 = row.get(5)?; let success_count: i64 = row.get(6)?; let success_rate = if total_requests > 0 { (success_count as f32 / total_requests as f32) * 100.0 } else { 0.0 }; let (real_total_tokens, cache_hit_rate) = derive_real_total_and_hit_rate( total_input_tokens as u64, total_output_tokens as u64, total_cache_creation_tokens as u64, total_cache_read_tokens as u64, ); Ok(UsageSummary { total_requests: total_requests as u64, total_cost: format!("{total_cost:.6}"), total_input_tokens: total_input_tokens as u64, total_output_tokens: total_output_tokens as u64, total_cache_creation_tokens: total_cache_creation_tokens as u64, total_cache_read_tokens: total_cache_read_tokens as u64, success_rate, real_total_tokens, cache_hit_rate, }) })?; Ok(result) } /// 按 app_type 维度拆分的使用量汇总，用于 Dashboard 的分应用展示条。 /// 返回所有有数据的 app_type，按 real_total_tokens 降序。 /// /// Single SQL with `GROUP BY app_type` — avoids the N+1 round-trip that /// would result from invoking `get_usage_summary` once per app_type. pub fn get_usage_summary_by_app( &self, start_date: Option, end_date: Option, provider_name: Option<&str>, model: Option<&str>, ) -> Result, AppError> { let conn = lock_conn!(self.conn); let mut detail_conditions = vec![effective_usage_log_filter("l")]; let mut detail_params: Vec> = Vec::new(); if let Some(start) = start_date { detail_conditions.push("l.created_at >= ?".to_string()); detail_params.push(Box::new(start)); } if let Some(end) = end_date { detail_conditions.push("l.created_at <= ?".to_string()); detail_params.push(Box::new(end)); } push_provider_model_filters( &mut detail_conditions, &mut detail_params, "l", "p", provider_name, model, ); let detail_where = format!("WHERE {}", detail_conditions.join(" AND ")); let detail_join = if provider_name.is_some() { providers_join("l", "p") } else { String::new() }; let rollup_bounds = compute_rollup_date_bounds(start_date, end_date)?; let mut rollup_conditions: Vec = Vec::new(); let mut rollup_params: Vec> = Vec::new(); push_rollup_date_filters( &mut rollup_conditions, &mut rollup_params, "r.date", &rollup_bounds, ); push_provider_model_filters( &mut rollup_conditions, &mut rollup_params, "r", "p2", provider_name, model, ); let rollup_where = if rollup_conditions.is_empty() { String::new() } else { format!("WHERE {}", rollup_conditions.join(" AND ")) }; let rollup_join = if provider_name.is_some() { providers_join("r", "p2") } else { String::new() }; let fresh_input_detail = fresh_input_sql("l"); let fresh_input_rollup = fresh_input_sql("r"); // 折叠 claude-desktop → claude：内层投影成同一桶名，外层 GROUP BY 自然合并。 let detail_app_type = folded_app_type_sql("l.app_type"); let rollup_app_type = folded_app_type_sql("r.app_type"); let sql = format!( "SELECT app_type, SUM(req_count) as req_count, SUM(cost) as cost, SUM(input_t) as input_t, SUM(output_t) as output_t, SUM(cache_create_t) as cache_create_t, SUM(cache_read_t) as cache_read_t, SUM(success_count) as success_count FROM ( SELECT {detail_app_type} as app_type, COUNT(*) as req_count, COALESCE(SUM(CAST(l.total_cost_usd AS REAL)), 0) as cost, COALESCE(SUM({fresh_input_detail}), 0) as input_t, COALESCE(SUM(l.output_tokens), 0) as output_t, COALESCE(SUM(l.cache_creation_tokens), 0) as cache_create_t, COALESCE(SUM(l.cache_read_tokens), 0) as cache_read_t, COALESCE(SUM(CASE WHEN l.status_code >= 200 AND l.status_code < 300 THEN 1 ELSE 0 END), 0) as success_count FROM proxy_request_logs l {detail_join} {detail_where} GROUP BY l.app_type UNION ALL SELECT {rollup_app_type} as app_type, COALESCE(SUM(r.request_count), 0), COALESCE(SUM(CAST(r.total_cost_usd AS REAL)), 0), COALESCE(SUM({fresh_input_rollup}), 0), COALESCE(SUM(r.output_tokens), 0), COALESCE(SUM(r.cache_creation_tokens), 0), COALESCE(SUM(r.cache_read_tokens), 0), COALESCE(SUM(r.success_count), 0) FROM usage_daily_rollups r {rollup_join} {rollup_where} GROUP BY r.app_type ) GROUP BY app_type" ); let mut combined: Vec> = detail_params; combined.extend(rollup_params); let refs: Vec<&dyn rusqlite::ToSql> = combined.iter().map(|p| p.as_ref()).collect(); let mut stmt = conn.prepare(&sql)?; let rows = stmt.query_map(refs.as_slice(), |row| { let app_type: String = row.get(0)?; let total_requests: i64 = row.get(1)?; let total_cost: f64 = row.get(2)?; let total_input_tokens: i64 = row.get(3)?; let total_output_tokens: i64 = row.get(4)?; let total_cache_creation_tokens: i64 = row.get(5)?; let total_cache_read_tokens: i64 = row.get(6)?; let success_count: i64 = row.get(7)?; let success_rate = if total_requests > 0 { (success_count as f32 / total_requests as f32) * 100.0 } else { 0.0 }; let (real_total_tokens, cache_hit_rate) = derive_real_total_and_hit_rate( total_input_tokens as u64, total_output_tokens as u64, total_cache_creation_tokens as u64, total_cache_read_tokens as u64, ); Ok(UsageSummaryByApp { app_type, summary: UsageSummary { total_requests: total_requests as u64, total_cost: format!("{total_cost:.6}"), total_input_tokens: total_input_tokens as u64, total_output_tokens: total_output_tokens as u64, total_cache_creation_tokens: total_cache_creation_tokens as u64, total_cache_read_tokens: total_cache_read_tokens as u64, success_rate, real_total_tokens, cache_hit_rate, }, }) })?; let mut summaries = Vec::new(); for row in rows { let item = row?; if item.summary.total_requests == 0 && item.summary.real_total_tokens == 0 { continue; } summaries.push(item); } summaries.sort_by(|a, b| { b.summary .real_total_tokens .cmp(&a.summary.real_total_tokens) }); Ok(summaries) } /// 获取每日趋势（滑动窗口，<=24h 按小时，>24h 按天，窗口与汇总一致） pub fn get_daily_trends( &self, start_date: Option, end_date: Option, app_type: Option<&str>, provider_name: Option<&str>, model: Option<&str>, ) -> Result, AppError> { let conn = lock_conn!(self.conn); let end_ts = end_date.unwrap_or_else(|| Local::now().timestamp()); let mut start_ts = start_date.unwrap_or_else(|| end_ts - 24 * 60 * 60); if start_ts >= end_ts { start_ts = end_ts - 24 * 60 * 60; } let duration = end_ts - start_ts; if duration <= 24 * 60 * 60 { let bucket_seconds: i64 = 60 * 60; let mut bucket_count: i64 = if duration <= 0 { 1 } else { (duration + bucket_seconds - 1) / bucket_seconds }; if bucket_count < 1 { bucket_count = 1; } let mut extra_conditions: Vec = Vec::new(); let mut extra_params: Vec> = Vec::new(); if let Some(at) = app_type { extra_conditions.push(format!("{} = ?", folded_app_type_sql("l.app_type"))); extra_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut extra_conditions, &mut extra_params, "l", "p", provider_name, model, ); let extra_filter = extra_conditions .iter() .map(|c| format!("AND {c}")) .collect::>() .join(" "); let detail_join = if provider_name.is_some() { providers_join("l", "p") } else { String::new() }; let effective_filter = effective_usage_log_filter("l"); let fresh_input = fresh_input_sql("l"); let sql = format!( "SELECT CAST((l.created_at - ?1) / ?3 AS INTEGER) as bucket_idx, COUNT(*) as request_count, COALESCE(SUM(CAST(l.total_cost_usd AS REAL)), 0) as total_cost, COALESCE(SUM({fresh_input} + l.output_tokens), 0) as total_tokens, COALESCE(SUM({fresh_input}), 0) as total_input_tokens, COALESCE(SUM(l.output_tokens), 0) as total_output_tokens, COALESCE(SUM(l.cache_creation_tokens), 0) as total_cache_creation_tokens, COALESCE(SUM(l.cache_read_tokens), 0) as total_cache_read_tokens FROM proxy_request_logs l {detail_join} WHERE l.created_at >= ?1 AND l.created_at <= ?2 AND {effective_filter} {extra_filter} GROUP BY bucket_idx ORDER BY bucket_idx ASC" ); let mut stmt = conn.prepare(&sql)?; let row_mapper = |row: &rusqlite::Row| { Ok(( row.get::<_, i64>(0)?, DailyStats { date: String::new(), request_count: row.get::<_, i64>(1)? as u64, total_cost: format!("{:.6}", row.get::<_, f64>(2)?), total_tokens: row.get::<_, i64>(3)? as u64, total_input_tokens: row.get::<_, i64>(4)? as u64, total_output_tokens: row.get::<_, i64>(5)? as u64, total_cache_creation_tokens: row.get::<_, i64>(6)? as u64, total_cache_read_tokens: row.get::<_, i64>(7)? as u64, }, )) }; let mut map: HashMap = HashMap::new(); let mut all_params: Vec> = vec![ Box::new(start_ts), Box::new(end_ts), Box::new(bucket_seconds), ]; all_params.extend(extra_params); let param_refs: Vec<&dyn rusqlite::ToSql> = all_params.iter().map(|p| p.as_ref()).collect(); let rows = stmt.query_map(param_refs.as_slice(), row_mapper)?; for row in rows { let (mut bucket_idx, stat) = row?; if bucket_idx < 0 { continue; } if bucket_idx >= bucket_count { bucket_idx = bucket_count - 1; } map.insert(bucket_idx, stat); } let mut stats = Vec::with_capacity(bucket_count as usize); for i in 0..bucket_count { let bucket_start_ts = start_ts + i * bucket_seconds; let bucket_start = local_datetime_from_timestamp(bucket_start_ts)?; let date = bucket_start.to_rfc3339(); if let Some(mut stat) = map.remove(&i) { stat.date = date; stats.push(stat); } else { stats.push(DailyStats { date, request_count: 0, total_cost: "0.000000".to_string(), total_tokens: 0, total_input_tokens: 0, total_output_tokens: 0, total_cache_creation_tokens: 0, total_cache_read_tokens: 0, }); } } return Ok(stats); } let start_day = local_datetime_from_timestamp(start_ts)?.date_naive(); let end_day = local_datetime_from_timestamp(end_ts)?.date_naive(); let bucket_count = (end_day.signed_duration_since(start_day).num_days() + 1) as usize; let mut extra_conditions: Vec = Vec::new(); let mut extra_params: Vec> = Vec::new(); if let Some(at) = app_type { extra_conditions.push(format!("{} = ?", folded_app_type_sql("l.app_type"))); extra_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut extra_conditions, &mut extra_params, "l", "p", provider_name, model, ); let extra_filter = extra_conditions .iter() .map(|c| format!("AND {c}")) .collect::>() .join(" "); let detail_join = if provider_name.is_some() { providers_join("l", "p") } else { String::new() }; let effective_filter = effective_usage_log_filter("l"); let fresh_input = fresh_input_sql("l"); let detail_sql = format!( "SELECT date(l.created_at, 'unixepoch', 'localtime') as bucket_date, COUNT(*) as request_count, COALESCE(SUM(CAST(l.total_cost_usd AS REAL)), 0) as total_cost, COALESCE(SUM({fresh_input} + l.output_tokens), 0) as total_tokens, COALESCE(SUM({fresh_input}), 0) as total_input_tokens, COALESCE(SUM(l.output_tokens), 0) as total_output_tokens, COALESCE(SUM(l.cache_creation_tokens), 0) as total_cache_creation_tokens, COALESCE(SUM(l.cache_read_tokens), 0) as total_cache_read_tokens FROM proxy_request_logs l {detail_join} WHERE l.created_at >= ?1 AND l.created_at <= ?2 AND {effective_filter} {extra_filter} GROUP BY bucket_date ORDER BY bucket_date ASC" ); let mut detail_stmt = conn.prepare(&detail_sql)?; let detail_row_mapper = |row: &rusqlite::Row| { Ok(( row.get::<_, String>(0)?, DailyStats { date: String::new(), request_count: row.get::<_, i64>(1)? as u64, total_cost: format!("{:.6}", row.get::<_, f64>(2)?), total_tokens: row.get::<_, i64>(3)? as u64, total_input_tokens: row.get::<_, i64>(4)? as u64, total_output_tokens: row.get::<_, i64>(5)? as u64, total_cache_creation_tokens: row.get::<_, i64>(6)? as u64, total_cache_read_tokens: row.get::<_, i64>(7)? as u64, }, )) }; let mut map: HashMap = HashMap::new(); let mut detail_all_params: Vec> = vec![Box::new(start_ts), Box::new(end_ts)]; detail_all_params.extend(extra_params); let detail_param_refs: Vec<&dyn rusqlite::ToSql> = detail_all_params.iter().map(|p| p.as_ref()).collect(); let detail_rows = detail_stmt.query_map(detail_param_refs.as_slice(), detail_row_mapper)?; for row in detail_rows { let (bucket_date, stat) = row?; let date = NaiveDate::parse_from_str(&bucket_date, "%Y-%m-%d") .map_err(|err| AppError::Database(format!("解析趋势日期失败: {err}")))?; map.insert(date, stat); } let rollup_bounds = compute_rollup_date_bounds(Some(start_ts), Some(end_ts))?; let mut rollup_conditions = Vec::new(); let mut rollup_params: Vec> = Vec::new(); push_rollup_date_filters( &mut rollup_conditions, &mut rollup_params, "r.date", &rollup_bounds, ); if let Some(at) = app_type { rollup_conditions.push(format!("{} = ?", folded_app_type_sql("r.app_type"))); rollup_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut rollup_conditions, &mut rollup_params, "r", "p2", provider_name, model, ); let rollup_where = if rollup_conditions.is_empty() { String::new() } else { format!("WHERE {}", rollup_conditions.join(" AND ")) }; let rollup_join = if provider_name.is_some() { providers_join("r", "p2") } else { String::new() }; let fresh_input_rollup = fresh_input_sql("r"); let rollup_sql = format!( "SELECT r.date, COALESCE(SUM(r.request_count), 0), COALESCE(SUM(CAST(r.total_cost_usd AS REAL)), 0), COALESCE(SUM({fresh_input_rollup} + r.output_tokens), 0), COALESCE(SUM({fresh_input_rollup}), 0), COALESCE(SUM(r.output_tokens), 0), COALESCE(SUM(r.cache_creation_tokens), 0), COALESCE(SUM(r.cache_read_tokens), 0) FROM usage_daily_rollups r {rollup_join} {rollup_where} GROUP BY r.date ORDER BY r.date ASC" ); let mut rollup_stmt = conn.prepare(&rollup_sql)?; let rollup_row_mapper = |row: &rusqlite::Row| { Ok(( row.get::<_, String>(0)?, ( row.get::<_, i64>(1)? as u64, row.get::<_, f64>(2)?, row.get::<_, i64>(3)? as u64, row.get::<_, i64>(4)? as u64, row.get::<_, i64>(5)? as u64, row.get::<_, i64>(6)? as u64, row.get::<_, i64>(7)? as u64, ), )) }; let rollup_param_refs: Vec<&dyn rusqlite::ToSql> = rollup_params.iter().map(|param| param.as_ref()).collect(); let rollup_rows = rollup_stmt.query_map(rollup_param_refs.as_slice(), rollup_row_mapper)?; for row in rollup_rows { let (bucket_date, (req, cost, tok, inp, out, cc, cr)) = row?; let date = NaiveDate::parse_from_str(&bucket_date, "%Y-%m-%d") .map_err(|err| AppError::Database(format!("解析 rollup 趋势日期失败: {err}")))?; let entry = map.entry(date).or_insert_with(|| DailyStats { date: String::new(), request_count: 0, total_cost: "0.000000".to_string(), total_tokens: 0, total_input_tokens: 0, total_output_tokens: 0, total_cache_creation_tokens: 0, total_cache_read_tokens: 0, }); entry.request_count += req; let existing_cost: f64 = entry.total_cost.parse().unwrap_or(0.0); entry.total_cost = format!("{:.6}", existing_cost + cost); entry.total_tokens += tok; entry.total_input_tokens += inp; entry.total_output_tokens += out; entry.total_cache_creation_tokens += cc; entry.total_cache_read_tokens += cr; } let mut stats = Vec::with_capacity(bucket_count); let mut current_day = start_day; for _ in 0..bucket_count { let date = local_day_start_rfc3339(current_day); if let Some(mut stat) = map.remove(¤t_day) { stat.date = date; stats.push(stat); } else { stats.push(DailyStats { date, request_count: 0, total_cost: "0.000000".to_string(), total_tokens: 0, total_input_tokens: 0, total_output_tokens: 0, total_cache_creation_tokens: 0, total_cache_read_tokens: 0, }); } current_day = current_day.succ_opt().unwrap_or(current_day); } Ok(stats) } /// 获取 Provider 统计 pub fn get_provider_stats( &self, start_date: Option, end_date: Option, app_type: Option<&str>, provider_name: Option<&str>, model: Option<&str>, ) -> Result, AppError> { let conn = lock_conn!(self.conn); let mut detail_conditions = vec![effective_usage_log_filter("l")]; let mut detail_params: Vec> = Vec::new(); if let Some(start) = start_date { detail_conditions.push("l.created_at >= ?".to_string()); detail_params.push(Box::new(start)); } if let Some(end) = end_date { detail_conditions.push("l.created_at <= ?".to_string()); detail_params.push(Box::new(end)); } if let Some(at) = app_type { detail_conditions.push(format!("{} = ?", folded_app_type_sql("l.app_type"))); detail_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut detail_conditions, &mut detail_params, "l", "p", provider_name, model, ); let detail_where = if detail_conditions.is_empty() { String::new() } else { format!("WHERE {}", detail_conditions.join(" AND ")) }; let mut rollup_conditions = Vec::new(); let mut rollup_params: Vec> = Vec::new(); let rollup_bounds = compute_rollup_date_bounds(start_date, end_date)?; push_rollup_date_filters( &mut rollup_conditions, &mut rollup_params, "r.date", &rollup_bounds, ); if let Some(at) = app_type { rollup_conditions.push(format!("{} = ?", folded_app_type_sql("r.app_type"))); rollup_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut rollup_conditions, &mut rollup_params, "r", "p2", provider_name, model, ); let rollup_where = if rollup_conditions.is_empty() { String::new() } else { format!("WHERE {}", rollup_conditions.join(" AND ")) }; // UNION detail logs + rollup data, then aggregate let detail_pname = provider_name_coalesce("l", "p"); let rollup_pname = provider_name_coalesce("r", "p2"); let fresh_input_detail = fresh_input_sql("l"); let fresh_input_rollup = fresh_input_sql("r"); let sql = format!( "SELECT provider_id, app_type, provider_name, SUM(request_count) as request_count, SUM(total_tokens) as total_tokens, SUM(total_cost) as total_cost, SUM(success_count) as success_count, CASE WHEN SUM(request_count) > 0 THEN SUM(latency_sum) / SUM(request_count) ELSE 0 END as avg_latency FROM ( SELECT l.provider_id, l.app_type, {detail_pname} as provider_name, COUNT(*) as request_count, COALESCE(SUM({fresh_input_detail} + l.output_tokens), 0) as total_tokens, COALESCE(SUM(CAST(l.total_cost_usd AS REAL)), 0) as total_cost, COALESCE(SUM(CASE WHEN l.status_code >= 200 AND l.status_code < 300 THEN 1 ELSE 0 END), 0) as success_count, COALESCE(SUM(l.latency_ms), 0) as latency_sum FROM proxy_request_logs l LEFT JOIN providers p ON l.provider_id = p.id AND l.app_type = p.app_type {detail_where} GROUP BY l.provider_id, l.app_type UNION ALL SELECT r.provider_id, r.app_type, {rollup_pname} as provider_name, COALESCE(SUM(r.request_count), 0), COALESCE(SUM({fresh_input_rollup} + r.output_tokens), 0), COALESCE(SUM(CAST(r.total_cost_usd AS REAL)), 0), COALESCE(SUM(r.success_count), 0), COALESCE(SUM(r.avg_latency_ms * r.request_count), 0) FROM usage_daily_rollups r LEFT JOIN providers p2 ON r.provider_id = p2.id AND r.app_type = p2.app_type {rollup_where} GROUP BY r.provider_id, r.app_type ) GROUP BY provider_id, app_type ORDER BY total_cost DESC" ); let mut stmt = conn.prepare(&sql)?; let mut params: Vec> = detail_params; params.extend(rollup_params); let param_refs: Vec<&dyn rusqlite::ToSql> = params.iter().map(|p| p.as_ref()).collect(); let row_mapper = |row: &rusqlite::Row| { let request_count: i64 = row.get(3)?; let success_count: i64 = row.get(6)?; let success_rate = if request_count > 0 { (success_count as f32 / request_count as f32) * 100.0 } else { 0.0 }; Ok(ProviderStats { provider_id: row.get(0)?, provider_name: row.get(2)?, request_count: request_count as u64, total_tokens: row.get::<_, i64>(4)? as u64, total_cost: format!("{:.6}", row.get::<_, f64>(5)?), success_rate, avg_latency_ms: row.get::<_, f64>(7)? as u64, }) }; let rows = stmt.query_map(param_refs.as_slice(), row_mapper)?; let mut stats = Vec::new(); for row in rows { stats.push(row?); } Ok(stats) } /// 获取模型统计 pub fn get_model_stats( &self, start_date: Option, end_date: Option, app_type: Option<&str>, provider_name: Option<&str>, model: Option<&str>, ) -> Result, AppError> { let conn = lock_conn!(self.conn); let mut detail_conditions = vec![effective_usage_log_filter("l")]; let mut detail_params: Vec> = Vec::new(); if let Some(start) = start_date { detail_conditions.push("l.created_at >= ?".to_string()); detail_params.push(Box::new(start)); } if let Some(end) = end_date { detail_conditions.push("l.created_at <= ?".to_string()); detail_params.push(Box::new(end)); } if let Some(at) = app_type { detail_conditions.push(format!("{} = ?", folded_app_type_sql("l.app_type"))); detail_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut detail_conditions, &mut detail_params, "l", "p", provider_name, model, ); let detail_where = if detail_conditions.is_empty() { String::new() } else { format!("WHERE {}", detail_conditions.join(" AND ")) }; let detail_join = if provider_name.is_some() { providers_join("l", "p") } else { String::new() }; let mut rollup_conditions = Vec::new(); let mut rollup_params: Vec> = Vec::new(); let rollup_bounds = compute_rollup_date_bounds(start_date, end_date)?; push_rollup_date_filters( &mut rollup_conditions, &mut rollup_params, "r.date", &rollup_bounds, ); if let Some(at) = app_type { rollup_conditions.push(format!("{} = ?", folded_app_type_sql("r.app_type"))); rollup_params.push(Box::new(at.to_string())); } push_provider_model_filters( &mut rollup_conditions, &mut rollup_params, "r", "p2", provider_name, model, ); let rollup_where = if rollup_conditions.is_empty() { String::new() } else { format!("WHERE {}", rollup_conditions.join(" AND ")) }; let rollup_join = if provider_name.is_some() { providers_join("r", "p2") } else { String::new() }; // UNION detail logs + rollup data // // 分组键用「有效计价模型」：pricing_model 非空时优先（成本就是按它的 // 定价算的，金额与定价表自洽），NULL/'' 回落 model。默认 response 计价 // 模式下两者相同，行为不变；request 模式 + 路由接管下，钱挂在实际计价 // 基准名下，而不是上游回显/客户端别名名下。 let fresh_input_detail = fresh_input_sql("l"); let fresh_input_rollup = fresh_input_sql("r"); let detail_model = effective_model_sql("l"); let rollup_model = effective_model_sql("r"); let sql = format!( "SELECT model, SUM(request_count) as request_count, SUM(total_tokens) as total_tokens, SUM(total_cost) as total_cost FROM ( SELECT {detail_model} as model, COUNT(*) as request_count, COALESCE(SUM({fresh_input_detail} + l.output_tokens), 0) as total_tokens, COALESCE(SUM(CAST(l.total_cost_usd AS REAL)), 0) as total_cost FROM proxy_request_logs l {detail_join} {detail_where} GROUP BY {detail_model} UNION ALL SELECT {rollup_model}, COALESCE(SUM(r.request_count), 0), COALESCE(SUM({fresh_input_rollup} + r.output_tokens), 0), COALESCE(SUM(CAST(r.total_cost_usd AS REAL)), 0) FROM usage_daily_rollups r {rollup_join} {rollup_where} GROUP BY {rollup_model} ) GROUP BY model ORDER BY total_cost DESC" ); let mut stmt = conn.prepare(&sql)?; let mut params: Vec> = detail_params; params.extend(rollup_params); let param_refs: Vec<&dyn rusqlite::ToSql> = params.iter().map(|p| p.as_ref()).collect(); let row_mapper = |row: &rusqlite::Row| { let request_count: i64 = row.get(1)?; let total_cost: f64 = row.get(3)?; let avg_cost = if request_count > 0 { total_cost / request_count as f64 } else { 0.0 }; Ok(ModelStats { model: row.get(0)?, request_count: request_count as u64, total_tokens: row.get::<_, i64>(2)? as u64, total_cost: format!("{total_cost:.6}"), avg_cost_per_request: format!("{avg_cost:.6}"), }) }; let rows = stmt.query_map(param_refs.as_slice(), row_mapper)?; let mut stats = Vec::new(); for row in rows { stats.push(row?); } Ok(stats) } /// 获取请求日志列表（分页） pub fn get_request_logs( &self, filters: &LogFilters, page: u32, page_size: u32, ) -> Result { let conn = lock_conn!(self.conn); let mut conditions = vec![effective_usage_log_filter("l")]; let mut params: Vec> = Vec::new(); if let Some(ref app_type) = filters.app_type { // 仅过滤口径折叠 claude-desktop→claude；行投影仍返回原始 app_type， // 详情面板据此展示真实入口（路由接管账单审计需要）。 conditions.push(format!("{} = ?", folded_app_type_sql("l.app_type"))); params.push(Box::new(app_type.clone())); } // 与 Dashboard 顶部下拉筛选同口径：Provider 按展示名精确匹配（会话占位 // 行如 "Claude (Session)" 也能命中），模型按有效计价模型匹配。 push_provider_model_filters( &mut conditions, &mut params, "l", "p", filters.provider_name.as_deref(), filters.model.as_deref(), ); if let Some(status) = filters.status_code { conditions.push("l.status_code = ?".to_string()); params.push(Box::new(status as i64)); } if let Some(start) = filters.start_date { conditions.push("l.created_at >= ?".to_string()); params.push(Box::new(start)); } if let Some(end) = filters.end_date { conditions.push("l.created_at <= ?".to_string()); params.push(Box::new(end)); } let where_clause = if conditions.is_empty() { String::new() } else { format!("WHERE {}", conditions.join(" AND ")) }; // 获取总数 let count_sql = format!( "SELECT COUNT(*) FROM proxy_request_logs l LEFT JOIN providers p ON l.provider_id = p.id AND l.app_type = p.app_type {where_clause}" ); let count_params: Vec<&dyn rusqlite::ToSql> = params.iter().map(|p| p.as_ref()).collect(); let total: u32 = conn.query_row(&count_sql, count_params.as_slice(), |row| { row.get::<_, i64>(0).map(|v| v as u32) })?; // 获取数据 let offset = page * page_size; params.push(Box::new(page_size as i64)); params.push(Box::new(offset as i64)); let logs_pname = provider_name_coalesce("l", "p"); let sql = format!( "SELECT l.request_id, l.provider_id, {logs_pname} as provider_name, l.app_type, l.model, l.request_model, l.cost_multiplier, l.input_tokens, l.output_tokens, l.cache_read_tokens, l.cache_creation_tokens, l.input_cost_usd, l.output_cost_usd, l.cache_read_cost_usd, l.cache_creation_cost_usd, l.total_cost_usd, l.is_streaming, l.latency_ms, l.first_token_ms, l.duration_ms, l.status_code, l.error_message, l.created_at, l.data_source, l.pricing_model FROM proxy_request_logs l LEFT JOIN providers p ON l.provider_id = p.id AND l.app_type = p.app_type {where_clause} ORDER BY l.created_at DESC LIMIT ? OFFSET ?" ); let mut stmt = conn.prepare(&sql)?; let params_refs: Vec<&dyn rusqlite::ToSql> = params.iter().map(|p| p.as_ref()).collect(); let rows = stmt.query_map(params_refs.as_slice(), row_to_request_log_detail)?; let mut logs = Vec::new(); let mut pricing_cache = HashMap::new(); for row in rows { let mut log = row?; Self::maybe_backfill_log_costs(&conn, &mut log, &mut pricing_cache)?; logs.push(log); } Ok(PaginatedLogs { data: logs, total, page, page_size, }) } /// 获取单个请求详情 pub fn get_request_detail( &self, request_id: &str, ) -> Result, AppError> { let conn = lock_conn!(self.conn); let detail_pname = provider_name_coalesce("l", "p"); let detail_sql = format!( "SELECT l.request_id, l.provider_id, {detail_pname} as provider_name, l.app_type, l.model, l.request_model, l.cost_multiplier, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, cache_creation_cost_usd, total_cost_usd, is_streaming, latency_ms, first_token_ms, duration_ms, status_code, error_message, created_at, l.data_source, l.pricing_model FROM proxy_request_logs l LEFT JOIN providers p ON l.provider_id = p.id AND l.app_type = p.app_type WHERE l.request_id = ?" ); let result = conn.query_row(&detail_sql, [request_id], row_to_request_log_detail); match result { Ok(mut detail) => { let mut pricing_cache = HashMap::new(); Self::maybe_backfill_log_costs(&conn, &mut detail, &mut pricing_cache)?; Ok(Some(detail)) } Err(rusqlite::Error::QueryReturnedNoRows) => Ok(None), Err(e) => Err(AppError::Database(e.to_string())), } } /// 检查 Provider 使用限额 pub fn check_provider_limits( &self, provider_id: &str, app_type: &str, ) -> Result { let conn = lock_conn!(self.conn); // 获取 provider 的限额设置 let (limit_daily, limit_monthly) = conn .query_row( "SELECT meta FROM providers WHERE id = ? AND app_type = ?", params![provider_id, app_type], |row| { let meta_str: String = row.get(0)?; Ok(meta_str) }, ) .ok() .and_then(|meta_str| serde_json::from_str::(&meta_str).ok()) .map(|meta| { let daily = meta .get("limitDailyUsd") .and_then(|v| v.as_str()) .and_then(|s| s.parse::().ok()); let monthly = meta .get("limitMonthlyUsd") .and_then(|v| v.as_str()) .and_then(|s| s.parse::().ok()); (daily, monthly) }) .unwrap_or((None, None)); // 计算今日使用量 (detail logs + rollup) let daily_usage: f64 = conn .query_row( "SELECT COALESCE(SUM(cost), 0) FROM ( SELECT CAST(total_cost_usd AS REAL) as cost FROM proxy_request_logs WHERE provider_id = ? AND app_type = ? AND date(datetime(created_at, 'unixepoch', 'localtime')) = date('now', 'localtime') UNION ALL SELECT CAST(total_cost_usd AS REAL) FROM usage_daily_rollups WHERE provider_id = ? AND app_type = ? AND date = date('now', 'localtime') )", params![provider_id, app_type, provider_id, app_type], |row| row.get(0), ) .unwrap_or(0.0); // 计算本月使用量 (detail logs + rollup) let monthly_usage: f64 = conn .query_row( "SELECT COALESCE(SUM(cost), 0) FROM ( SELECT CAST(total_cost_usd AS REAL) as cost FROM proxy_request_logs WHERE provider_id = ? AND app_type = ? AND strftime('%Y-%m', datetime(created_at, 'unixepoch', 'localtime')) = strftime('%Y-%m', 'now', 'localtime') UNION ALL SELECT CAST(total_cost_usd AS REAL) FROM usage_daily_rollups WHERE provider_id = ? AND app_type = ? AND strftime('%Y-%m', date) = strftime('%Y-%m', 'now', 'localtime') )", params![provider_id, app_type, provider_id, app_type], |row| row.get(0), ) .unwrap_or(0.0); let daily_exceeded = limit_daily .map(|limit| daily_usage >= limit) .unwrap_or(false); let monthly_exceeded = limit_monthly .map(|limit| monthly_usage >= limit) .unwrap_or(false); Ok(ProviderLimitStatus { provider_id: provider_id.to_string(), daily_usage: format!("{daily_usage:.6}"), daily_limit: limit_daily.map(|l| format!("{l:.2}")), daily_exceeded, monthly_usage: format!("{monthly_usage:.6}"), monthly_limit: limit_monthly.map(|l| format!("{l:.2}")), monthly_exceeded, }) } } /// Provider 限额状态 #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(rename_all = "camelCase")] pub struct ProviderLimitStatus { pub provider_id: String, pub daily_usage: String, pub daily_limit: Option, pub daily_exceeded: bool, pub monthly_usage: String, pub monthly_limit: Option, pub monthly_exceeded: bool, } #[derive(Clone)] struct PricingInfo { input: rust_decimal::Decimal, output: rust_decimal::Decimal, cache_read: rust_decimal::Decimal, cache_creation: rust_decimal::Decimal, } impl Database { /// Recalculate stored zero-cost usage rows once pricing becomes available. pub(crate) fn backfill_missing_usage_costs(&self) -> Result { let conn = lock_conn!(self.conn); Self::backfill_missing_usage_costs_on_conn(&conn, None) } /// 仅回填指定 model_id 相关的零成本行；用于单条定价更新后的精准回填。 pub(crate) fn backfill_missing_usage_costs_for_model( &self, model_id: &str, ) -> Result { let conn = lock_conn!(self.conn); Self::backfill_missing_usage_costs_on_conn(&conn, Some(model_id)) } pub(crate) fn backfill_missing_usage_costs_on_conn( conn: &Connection, only_model_id: Option<&str>, ) -> Result { const BASE_SQL: &str = "SELECT request_id, provider_id, NULL AS provider_name, app_type, model, request_model, cost_multiplier, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, cache_creation_cost_usd, total_cost_usd, is_streaming, latency_ms, first_token_ms, duration_ms, status_code, error_message, created_at, data_source, pricing_model FROM proxy_request_logs WHERE CAST(total_cost_usd AS REAL) <= 0 AND (input_tokens > 0 OR output_tokens > 0 OR cache_read_tokens > 0 OR cache_creation_tokens > 0)"; let mut logs = { let mut stmt = conn.prepare(BASE_SQL)?; let rows = stmt.query_map([], row_to_request_log_detail)?; rows.collect::, _>>()? }; // 精准回填的行筛选必须与查价层共用 candidates 归一化：SQL 精确匹配会漏掉 // 以原始别名落库的行（如 openrouter/anthropic/claude-sonnet-4.5:free）， // 这些行查价时能归一化命中新定价，却在筛选层被挡掉，导致导入定价后 // 历史成本要等下次全量回填才更新。误纳无害——查不到价的行会被跳过。 if let Some(model_id) = only_model_id { let target = model_pricing_candidates(model_id); logs.retain(|log| log_pricing_scope_matches(log, &target)); } if logs.is_empty() { return Ok(0); } let tx = conn .unchecked_transaction() .map_err(|e| AppError::Database(format!("启动用量成本回填事务失败: {e}")))?; let mut updated = 0u64; let mut pricing_cache = HashMap::new(); for log in &mut logs { if Self::maybe_backfill_log_costs(&tx, log, &mut pricing_cache)? { updated += 1; } } tx.commit() .map_err(|e| AppError::Database(format!("提交用量成本回填事务失败: {e}")))?; if updated > 0 { log::info!("已回填 {updated} 条缺失的用量成本"); } Ok(updated) } /// 尝试为单条 log 回填成本字段。返回是否实际写入（true=已 UPDATE，false=跳过）。 fn maybe_backfill_log_costs( conn: &Connection, log: &mut RequestLogDetail, pricing_cache: &mut HashMap, ) -> Result { let existing_cost = rust_decimal::Decimal::from_str(&log.total_cost_usd) .unwrap_or(rust_decimal::Decimal::ZERO); let has_cost = existing_cost > rust_decimal::Decimal::ZERO; let has_usage = log.input_tokens > 0 || log.output_tokens > 0 || log.cache_read_tokens > 0 || log.cache_creation_tokens > 0; if has_cost || !has_usage { return Ok(false); } let pricing = match Self::get_log_model_pricing_cached(conn, pricing_cache, log)? { Some(info) => info, None => return Ok(false), }; let multiplier = rust_decimal::Decimal::from_str(&log.cost_multiplier).unwrap_or_else(|e| { log::warn!( "历史用量倍率解析失败 request_id={}: {} - {e}", log.request_id, log.cost_multiplier ); rust_decimal::Decimal::ONE }); let million = rust_decimal::Decimal::from(1_000_000u64); // 与 CostCalculator::calculate_for_app 保持一致的计算逻辑： // 1. Codex/Gemini 的 input_tokens 包含 cache_read_tokens，需要扣除后按输入价计费 // 2. Claude/Anthropic 的 input_tokens 已经是 fresh input，不能再次扣减 // 3. 各项成本是基础成本（不含倍率），倍率只作用于最终总价 let input_includes_cache_read = matches!(log.app_type.as_str(), "codex" | "gemini"); let billable_input_tokens = if input_includes_cache_read { (log.input_tokens as u64).saturating_sub(log.cache_read_tokens as u64) } else { log.input_tokens as u64 }; let input_cost = rust_decimal::Decimal::from(billable_input_tokens) * pricing.input / million; let output_cost = rust_decimal::Decimal::from(log.output_tokens as u64) * pricing.output / million; let cache_read_cost = rust_decimal::Decimal::from(log.cache_read_tokens as u64) * pricing.cache_read / million; let cache_creation_cost = rust_decimal::Decimal::from(log.cache_creation_tokens as u64) * pricing.cache_creation / million; // 总成本 = 基础成本之和 × 倍率 let base_total = input_cost + output_cost + cache_read_cost + cache_creation_cost; let total_cost = base_total * multiplier; log.input_cost_usd = format!("{input_cost:.6}"); log.output_cost_usd = format!("{output_cost:.6}"); log.cache_read_cost_usd = format!("{cache_read_cost:.6}"); log.cache_creation_cost_usd = format!("{cache_creation_cost:.6}"); log.total_cost_usd = format!("{total_cost:.6}"); conn.execute( "UPDATE proxy_request_logs SET input_cost_usd = ?1, output_cost_usd = ?2, cache_read_cost_usd = ?3, cache_creation_cost_usd = ?4, total_cost_usd = ?5 WHERE request_id = ?6", params![ log.input_cost_usd, log.output_cost_usd, log.cache_read_cost_usd, log.cache_creation_cost_usd, log.total_cost_usd, log.request_id ], ) .map_err(|e| AppError::Database(format!("更新请求成本失败: {e}")))?; Ok(true) } fn get_model_pricing_cached( conn: &Connection, cache: &mut HashMap, model: &str, ) -> Result, AppError> { if let Some(info) = cache.get(model) { return Ok(Some(info.clone())); } let row = find_model_pricing_row(conn, model)?; let Some((input, output, cache_read, cache_creation)) = row else { return Ok(None); }; let pricing = PricingInfo { input: rust_decimal::Decimal::from_str(&input) .map_err(|e| AppError::Database(format!("解析输入价格失败: {e}")))?, output: rust_decimal::Decimal::from_str(&output) .map_err(|e| AppError::Database(format!("解析输出价格失败: {e}")))?, cache_read: rust_decimal::Decimal::from_str(&cache_read) .map_err(|e| AppError::Database(format!("解析缓存读取价格失败: {e}")))?, cache_creation: rust_decimal::Decimal::from_str(&cache_creation) .map_err(|e| AppError::Database(format!("解析缓存写入价格失败: {e}")))?, }; cache.insert(model.to_string(), pricing.clone()); Ok(Some(pricing)) } fn get_log_model_pricing_cached( conn: &Connection, cache: &mut HashMap, log: &RequestLogDetail, ) -> Result, AppError> { // 写入时的计价基准已落库（v11+）：回填只按它重算，找不到就保持 0 成本 // 等补价。不能换用 model/request_model 猜——路由接管 + request 计价模式下 // 三者可能各不相同（model=上游回显、request_model=客户端别名、 // pricing_model=实际出站模型），换基准会按错误价格永久固化。 // 占位符（"" = 未计价错误行 / "unknown"）视同缺失，走历史行逻辑。 if let Some(pricing_model) = log .pricing_model .as_deref() .filter(|pm| !is_placeholder_pricing_model(pm)) { return Self::get_model_pricing_cached(conn, cache, pricing_model); } if let Some(pricing) = Self::get_model_pricing_cached(conn, cache, &log.model)? { return Ok(Some(pricing)); } // 仅当 model 列是占位符（解析失败留下的 ""/"unknown" 等）时才回退到 // request_model 定价。model 是真实模型名但缺定价时必须保持 0 成本等待 // 补价：路由接管下 request_model 是客户端别名（如 claude-sonnet-4-6）， // 按别名回填会把真实上游模型的 tokens 按错误价格永久固化（行一旦有成本 // 就不再进入回填范围）。 if !is_placeholder_pricing_model(&log.model) { return Ok(None); } let Some(request_model) = log.request_model.as_deref() else { return Ok(None); }; if request_model == log.model { return Ok(None); } Self::get_model_pricing_cached(conn, cache, request_model) } } pub(crate) fn find_model_pricing(conn: &Connection, model_id: &str) -> Option { find_model_pricing_row(conn, model_id) .ok() .flatten() .and_then(|(input, output, cache_read, cache_creation)| { ModelPricing::from_strings(&input, &output, &cache_read, &cache_creation).ok() }) } pub(crate) fn find_model_pricing_row( conn: &Connection, model_id: &str, ) -> Result, AppError> { let candidates = model_pricing_candidates(model_id); if candidates.is_empty() { return Ok(None); } for candidate in &candidates { if let Some(row) = query_model_pricing_exact(conn, candidate)? { return Ok(Some(row)); } } for candidate in &candidates { if should_try_pricing_prefix_match(candidate) { if let Some(row) = query_model_pricing_prefix(conn, candidate)? { return Ok(Some(row)); } } } Ok(None) } /// 精准回填的行筛选：log 的任一模型字段归一化后与目标模型的 candidates 相交， /// 或可按查价层的前缀规则命中目标，即视为相关。镜像 find_model_pricing_row 的 /// 匹配语义，宁可误纳（后续查价会兜底）不可漏筛。 fn log_pricing_scope_matches(log: &RequestLogDetail, target_candidates: &[String]) -> bool { [ Some(log.model.as_str()), log.request_model.as_deref(), log.pricing_model.as_deref(), ] .into_iter() .flatten() .any(|field| { model_pricing_candidates(field).iter().any(|candidate| { target_candidates.iter().any(|target| { target == candidate || (should_try_pricing_prefix_match(candidate) && target .strip_prefix(candidate.as_str()) .is_some_and(|rest| rest.starts_with('-'))) }) }) }) } pub(crate) fn is_placeholder_pricing_model(model_id: &str) -> bool { let normalized = model_id.trim().to_ascii_lowercase(); normalized.is_empty() || matches!(normalized.as_str(), "unknown" | "null" | "none") } fn query_model_pricing_exact( conn: &Connection, model_id: &str, ) -> Result, AppError> { conn.query_row( "SELECT input_cost_per_million, output_cost_per_million, cache_read_cost_per_million, cache_creation_cost_per_million FROM model_pricing WHERE model_id = ?1", [model_id], |row| { Ok(( row.get::<_, String>(0)?, row.get::<_, String>(1)?, row.get::<_, String>(2)?, row.get::<_, String>(3)?, )) }, ) .optional() .map_err(|e| AppError::Database(format!("查询模型定价失败: {e}"))) } fn query_model_pricing_prefix( conn: &Connection, model_id: &str, ) -> Result, AppError> { let pattern = format!("{model_id}-%"); conn.query_row( "SELECT input_cost_per_million, output_cost_per_million, cache_read_cost_per_million, cache_creation_cost_per_million FROM model_pricing WHERE model_id LIKE ?1 ORDER BY LENGTH(model_id) ASC LIMIT 1", [pattern], |row| { Ok(( row.get::<_, String>(0)?, row.get::<_, String>(1)?, row.get::<_, String>(2)?, row.get::<_, String>(3)?, )) }, ) .optional() .map_err(|e| AppError::Database(format!("查询模型前缀定价失败: {e}"))) } fn model_pricing_candidates(model_id: &str) -> Vec { let cleaned = clean_model_id_for_pricing(model_id); if is_placeholder_pricing_model(&cleaned) { return Vec::new(); } let mut candidates = Vec::new(); let mut queue = vec![cleaned]; while let Some(candidate) = queue.pop() { if !push_unique_candidate(&mut candidates, candidate.clone()) { continue; } if let Some(stripped) = strip_known_model_namespace(&candidate) { queue.push(stripped); } if let Some(stripped) = strip_claude_desktop_non_anthropic_prefix(&candidate) { queue.push(stripped); } if let Some(stripped) = strip_bedrock_model_version_suffix(&candidate) { queue.push(stripped); } if let Some(stripped) = strip_model_date_suffix(&candidate) { queue.push(stripped); } if let Some(stripped) = strip_reasoning_effort_suffix(&candidate) { queue.push(stripped); } if candidate.starts_with("claude-") && candidate.contains('.') { queue.push(candidate.replace('.', "-")); } } candidates } fn clean_model_id_for_pricing(model_id: &str) -> String { let normalized = model_id .rsplit_once('/') .map_or(model_id, |(_, r)| r) .split(':') .next() .unwrap_or(model_id) .trim() .replace('@', "-") .to_ascii_lowercase(); normalized .trim_end_matches(crate::claude_desktop_config::ONE_M_CONTEXT_MARKER) .trim() .to_string() } fn push_unique_candidate(candidates: &mut Vec, candidate: String) -> bool { if candidate.is_empty() || candidates.iter().any(|existing| existing == &candidate) { return false; } candidates.push(candidate); true } fn strip_known_model_namespace(model_id: &str) -> Option { if let Some(pos) = model_id.rfind("claude-") { if pos > 0 { return Some(model_id[pos..].to_string()); } } for marker in [ "openai.", "anthropic.", "google.", "moonshot.", "moonshotai.", "bedrock.", "global.", ] { if let Some(stripped) = model_id.strip_prefix(marker) { return Some(stripped.to_string()); } } None } fn strip_claude_desktop_non_anthropic_prefix(model_id: &str) -> Option { const NON_ANTHROPIC_MARKERS: &[&str] = &[ "abab", "ark-code", "arctic", "astron", "codex", "command-r", "deepseek", "doubao", "ernie", "gemini", "gemma", "glm", "gpt", "grok", "hermes", "hy3", "hunyuan", "jamba", "kimi", "lfm", "llama", "longcat", "mercury", "mimo", "minimax", "mistral", "mixtral", "moonshot", "nemotron", "nova-", "openai", "qianfan", "qwen", "seed-", "solar", "stepfun", ]; let rest = model_id.strip_prefix("claude-")?; NON_ANTHROPIC_MARKERS .iter() .any(|marker| rest.starts_with(marker)) .then(|| rest.to_string()) } fn strip_bedrock_model_version_suffix(model_id: &str) -> Option { let (base, suffix) = model_id.rsplit_once("-v")?; (!base.is_empty() && !suffix.is_empty() && suffix.chars().all(|c| c.is_ascii_digit())) .then(|| base.to_string()) } fn strip_model_date_suffix(model_id: &str) -> Option { let bytes = model_id.as_bytes(); if bytes.len() > 11 { let start = bytes.len() - 11; let suffix = &bytes[start..]; let is_iso_date = suffix[0] == b'-' && suffix[1..5].iter().all(|b| b.is_ascii_digit()) && suffix[5] == b'-' && suffix[6..8].iter().all(|b| b.is_ascii_digit()) && suffix[8] == b'-' && suffix[9..11].iter().all(|b| b.is_ascii_digit()); if is_iso_date { return Some(model_id[..start].to_string()); } } let (base, suffix) = model_id.rsplit_once('-')?; if base.is_empty() || !suffix.chars().all(|c| c.is_ascii_digit()) { return None; } // 8 位 YYYYMMDD（如 -20250615；OpenAI / Claude / 通义千问等）。 if suffix.len() == 8 { return Some(base.to_string()); } // 6 位 YYMMDD（如 -260628；火山方舟 doubao-seed-*、部分国产厂商）。 // 6 位比 8 位更易误伤非日期尾巴（如 -123456 的版本号），故额外校验 // 月 01-12、日 01-31 才剥离；剥不动时退回 None 由上层精确匹配兜底。 if suffix.len() == 6 { let month: u32 = suffix[2..4].parse().unwrap_or(0); let day: u32 = suffix[4..6].parse().unwrap_or(0); if (1..=12).contains(&month) && (1..=31).contains(&day) { return Some(base.to_string()); } } None } fn strip_reasoning_effort_suffix(model_id: &str) -> Option { for suffix in ["-minimal", "-low", "-medium", "-high", "-xhigh"] { if let Some(stripped) = model_id.strip_suffix(suffix) { if !stripped.is_empty() { return Some(stripped.to_string()); } } } None } fn should_try_pricing_prefix_match(model_id: &str) -> bool { let dash_count = model_id.matches('-').count(); if model_id.starts_with("claude-") { return dash_count >= 3; } if ["o1", "o3", "o4", "o5"] .iter() .any(|prefix| model_id.starts_with(prefix)) { return dash_count >= 1; } const PREFIX_MATCH_FAMILIES: &[&str] = &[ "gpt-", "gemini-", "deepseek-", "qwen-", "glm-", "kimi-", "minimax-", ]; PREFIX_MATCH_FAMILIES .iter() .any(|prefix| model_id.starts_with(prefix)) && dash_count >= 2 } #[cfg(test)] mod tests { use super::*; fn local_ts(year: i32, month: u32, day: u32, hour: u32, minute: u32, second: u32) -> i64 { match Local.with_ymd_and_hms(year, month, day, hour, minute, second) { chrono::LocalResult::Single(dt) => dt.timestamp(), chrono::LocalResult::Ambiguous(earliest, _) => earliest.timestamp(), chrono::LocalResult::None => panic!("valid local datetime"), } } #[allow(clippy::too_many_arguments)] fn insert_usage_log( conn: &Connection, request_id: &str, app_type: &str, provider_id: &str, model: &str, data_source: &str, created_at: i64, input_tokens: i64, output_tokens: i64, cache_read_tokens: i64, cache_creation_tokens: i64, status_code: i64, total_cost_usd: &str, ) -> Result<(), AppError> { conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, request_model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, cache_creation_cost_usd, total_cost_usd, latency_ms, status_code, created_at, data_source ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, '0', '0', '0', '0', ?, 100, ?, ?, ?)", params![ request_id, provider_id, app_type, model, model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, status_code, created_at, data_source ], )?; Ok(()) } fn create_legacy_nullable_logs_table(conn: &Connection) -> Result<(), AppError> { conn.execute( "CREATE TABLE proxy_request_logs ( request_id TEXT PRIMARY KEY, app_type TEXT NOT NULL, model TEXT NOT NULL, input_tokens INTEGER NOT NULL, output_tokens INTEGER NOT NULL, cache_read_tokens INTEGER NOT NULL, cache_creation_tokens INTEGER NOT NULL, status_code INTEGER NOT NULL, created_at INTEGER NOT NULL, data_source TEXT )", [], )?; Ok(()) } #[test] fn test_effective_filter_keeps_legacy_null_data_source_proxy_rows() -> Result<(), AppError> { let conn = Connection::open_in_memory()?; create_legacy_nullable_logs_table(&conn)?; conn.execute( "INSERT INTO proxy_request_logs ( request_id, app_type, model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, status_code, created_at, data_source ) VALUES ('legacy-proxy', 'codex', 'gpt-5.5', 10, 2, 1, 0, 200, 1000, NULL)", [], )?; let filter = effective_usage_log_filter("l"); let sql = format!("SELECT COUNT(*) FROM proxy_request_logs l WHERE {filter}"); let count: i64 = conn.query_row(&sql, [], |row| row.get(0))?; assert_eq!(count, 1); Ok(()) } #[test] fn test_matching_proxy_log_treats_legacy_null_data_source_as_proxy() -> Result<(), AppError> { let conn = Connection::open_in_memory()?; create_legacy_nullable_logs_table(&conn)?; conn.execute( "INSERT INTO proxy_request_logs ( request_id, app_type, model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, status_code, created_at, data_source ) VALUES ('legacy-proxy', 'codex', 'gpt-5.5', 10, 2, 1, 0, 200, 1000, NULL)", [], )?; let key = DedupKey { app_type: "codex", model: "gpt-5.5", input_tokens: 10, output_tokens: 2, cache_read_tokens: 1, cache_creation_tokens: 0, created_at: 1000, }; assert!(has_matching_proxy_usage_log(&conn, &key)?); Ok(()) } #[test] fn test_claude_desktop_folds_into_claude_for_display() -> Result<(), AppError> { let db = Database::memory()?; let ts = local_ts(2026, 6, 10, 12, 0, 0); { let conn = lock_conn!(db.conn); // 一条 Claude Code 行 + 一条 Claude Desktop 网关行，同一时间窗。 insert_usage_log( &conn, "cc-1", "claude", "p-claude", "claude-sonnet-4-5", "proxy", ts, 100, 10, 0, 0, 200, "0.5", )?; insert_usage_log( &conn, "cd-1", "claude-desktop", "p-desktop", "claude-opus-4-8", "proxy", ts, 200, 20, 0, 0, 200, "1.5", )?; } // ① 分应用汇总：desktop 折叠进 claude，不再单列 claude-desktop 桶。 let by_app = db.get_usage_summary_by_app(None, None, None, None)?; assert_eq!(by_app.len(), 1, "应只剩一个合并后的 claude 桶"); assert_eq!(by_app[0].app_type, "claude"); assert_eq!(by_app[0].summary.total_requests, 2, "两条行都计入 claude"); assert!( !by_app.iter().any(|a| a.app_type == "claude-desktop"), "不应再出现 claude-desktop 桶" ); // ② 选中 claude 过滤：汇总应同时覆盖 desktop 行。 let claude_summary = db.get_usage_summary(None, None, Some("claude"), None, None)?; assert_eq!(claude_summary.total_requests, 2); // ③ 请求日志按 claude 过滤返回两行，且 desktop 行投影仍是原始 app_type。 let logs = db.get_request_logs( &LogFilters { app_type: Some("claude".to_string()), ..Default::default() }, 0, // 页码从 0 开始 50, )?; assert_eq!(logs.total, 2, "claude 过滤含 desktop 行"); assert!( logs.data.iter().any(|r| r.app_type == "claude-desktop"), "详情面板需要看到真实入口，行投影不可被折叠" ); // ④ 折叠不外溢：codex 过滤为空。 let codex_summary = db.get_usage_summary(None, None, Some("codex"), None, None)?; assert_eq!(codex_summary.total_requests, 0); Ok(()) } #[test] fn test_backfill_missing_usage_costs_uses_new_gpt_5_5_pricing() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); insert_usage_log( &conn, "codex-gpt-5-5-zero-cost", "codex", "_codex_session", "gpt-5.5", "codex_session", 1000, 1_000_000, 1_000_000, 0, 0, 200, "0", )?; } assert_eq!(db.backfill_missing_usage_costs()?, 1); let conn = lock_conn!(db.conn); let (input_cost, output_cost, total_cost): (String, String, String) = conn.query_row( "SELECT input_cost_usd, output_cost_usd, total_cost_usd FROM proxy_request_logs WHERE request_id = 'codex-gpt-5-5-zero-cost'", [], |row| Ok((row.get(0)?, row.get(1)?, row.get(2)?)), )?; assert_eq!(input_cost, "5.000000"); assert_eq!(output_cost, "30.000000"); assert_eq!(total_cost, "35.000000"); Ok(()) } #[test] fn test_backfill_missing_usage_costs_uses_stored_multiplier() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); insert_usage_log( &conn, "codex-gpt-5-5-multiplier", "codex", "_codex_session", "gpt-5.5", "codex_session", 1000, 1_000_000, 0, 0, 0, 200, "0", )?; conn.execute( "UPDATE proxy_request_logs SET cost_multiplier = '1.5' WHERE request_id = 'codex-gpt-5-5-multiplier'", [], )?; } assert_eq!(db.backfill_missing_usage_costs()?, 1); let conn = lock_conn!(db.conn); let (input_cost, total_cost): (String, String) = conn.query_row( "SELECT input_cost_usd, total_cost_usd FROM proxy_request_logs WHERE request_id = 'codex-gpt-5-5-multiplier'", [], |row| Ok((row.get(0)?, row.get(1)?)), )?; assert_eq!(input_cost, "5.000000"); assert_eq!(total_cost, "7.500000"); Ok(()) } #[test] fn test_backfill_missing_usage_costs_falls_back_to_request_model() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, request_model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, cache_creation_cost_usd, total_cost_usd, latency_ms, status_code, created_at, data_source ) VALUES ( 'codex-request-model-fallback', '_codex_session', 'codex', 'unknown', 'gpt-5.5', 1000000, 0, 0, 0, '0', '0', '0', '0', '0', 100, 200, 1000, 'codex_session' )", [], )?; } assert_eq!(db.backfill_missing_usage_costs()?, 1); let conn = lock_conn!(db.conn); let total_cost: String = conn.query_row( "SELECT total_cost_usd FROM proxy_request_logs WHERE request_id = 'codex-request-model-fallback'", [], |row| row.get(0), )?; assert_eq!(total_cost, "5.000000"); Ok(()) } #[test] fn test_backfill_skips_request_model_fallback_for_real_unpriced_model() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); // 路由接管场景：model 是上游回显的真实模型（缺定价），request_model // 是客户端别名（有定价）。回填不得按别名定价，必须保持 0 成本等待补价。 conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, request_model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, cache_creation_cost_usd, total_cost_usd, latency_ms, status_code, created_at, data_source ) VALUES ( 'takeover-unpriced-model', 'provider-1', 'claude', 'takeover-real-model-unpriced', 'claude-sonnet-4-6', 1000000, 0, 0, 0, '0', '0', '0', '0', '0', 100, 200, 1000, 'proxy' )", [], )?; } // request_model（claude-sonnet-4-6）有定价，但 model 是真实模型名：不得回退 assert_eq!(db.backfill_missing_usage_costs()?, 0); { let conn = lock_conn!(db.conn); let total_cost: String = conn.query_row( "SELECT total_cost_usd FROM proxy_request_logs WHERE request_id = 'takeover-unpriced-model'", [], |row| row.get(0), )?; assert_eq!(total_cost, "0"); // 补上真实模型定价后，回填必须按真实模型价格修复（0 成本行未被污染固化） conn.execute( "INSERT INTO model_pricing (model_id, display_name, input_cost_per_million, output_cost_per_million) VALUES ('takeover-real-model-unpriced', 'Takeover Real Model', '0.6', '2.5')", [], )?; } assert_eq!(db.backfill_missing_usage_costs()?, 1); let conn = lock_conn!(db.conn); let total_cost: String = conn.query_row( "SELECT total_cost_usd FROM proxy_request_logs WHERE request_id = 'takeover-unpriced-model'", [], |row| row.get(0), )?; assert_eq!(total_cost, "0.600000"); Ok(()) } #[test] fn test_backfill_uses_persisted_pricing_model() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); // request 计价模式 + 接管：写入时锚定出站模型 kimi-k2-novel（当时缺价）， // 但上游回显了别名 → model/request_model 都是 claude-sonnet-4-6（有定价）。 // 回填必须按落库的 pricing_model 重算，不得换用 model 列的别名价格。 conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, request_model, pricing_model, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, input_cost_usd, output_cost_usd, cache_read_cost_usd, cache_creation_cost_usd, total_cost_usd, latency_ms, status_code, created_at, data_source ) VALUES ( 'persisted-pricing-model', 'provider-1', 'claude', 'claude-sonnet-4-6', 'claude-sonnet-4-6', 'kimi-k2-novel', 1000000, 0, 0, 0, '0', '0', '0', '0', '0', 100, 200, 1000, 'proxy' )", [], )?; } // pricing_model（kimi-k2-novel）缺价：不得回退到 model 列的别名价格 assert_eq!(db.backfill_missing_usage_costs()?, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO model_pricing (model_id, display_name, input_cost_per_million, output_cost_per_million) VALUES ('kimi-k2-novel', 'Kimi K2 Novel', '0.6', '2.5')", [], )?; } // 按 pricing_model 也能定位到该行（model/request_model 都不是 kimi-k2-novel） assert_eq!( db.backfill_missing_usage_costs_for_model("kimi-k2-novel")?, 1 ); let conn = lock_conn!(db.conn); let total_cost: String = conn.query_row( "SELECT total_cost_usd FROM proxy_request_logs WHERE request_id = 'persisted-pricing-model'", [], |row| row.get(0), )?; assert_eq!(total_cost, "0.600000"); Ok(()) } #[test] fn test_scoped_backfill_matches_raw_alias_rows() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); // 代理日志按上游原文落库：带路由前缀和 :free 后缀的别名形式。 // 精准回填的筛选必须归一化后匹配，否则这类行要等全量回填才更新。 insert_usage_log( &conn, "openrouter-alias-zero-cost", "claude", "provider-1", "openrouter/moonshot/kimi-k2-novel:free", "proxy", 1000, 1_000_000, 0, 0, 0, 200, "0", )?; } // 定价缺失时不应回填 assert_eq!(db.backfill_missing_usage_costs()?, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO model_pricing (model_id, display_name, input_cost_per_million, output_cost_per_million) VALUES ('kimi-k2-novel', 'Kimi K2 Novel', '0.6', '2.5')", [], )?; } // 按归一化 ID 精准回填，应命中以原始别名落库的行 assert_eq!( db.backfill_missing_usage_costs_for_model("kimi-k2-novel")?, 1 ); let conn = lock_conn!(db.conn); let total_cost: String = conn.query_row( "SELECT total_cost_usd FROM proxy_request_logs WHERE request_id = 'openrouter-alias-zero-cost'", [], |row| row.get(0), )?; assert_eq!(total_cost, "0.600000"); Ok(()) } #[test] fn test_backfill_missing_usage_costs_keeps_claude_fresh_input() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); insert_usage_log( &conn, "claude-cache-fresh-input", "claude", "_session", "claude-haiku-4-5", "session_log", 1000, 100, 0, 200, 0, 200, "0", )?; } assert_eq!(db.backfill_missing_usage_costs()?, 1); let conn = lock_conn!(db.conn); let (input_cost, cache_read_cost, total_cost): (String, String, String) = conn.query_row( "SELECT input_cost_usd, cache_read_cost_usd, total_cost_usd FROM proxy_request_logs WHERE request_id = 'claude-cache-fresh-input'", [], |row| Ok((row.get(0)?, row.get(1)?, row.get(2)?)), )?; assert_eq!(input_cost, "0.000100"); assert_eq!(cache_read_cost, "0.000020"); assert_eq!(total_cost, "0.000120"); Ok(()) } #[test] fn test_get_usage_summary() -> Result<(), AppError> { let db = Database::memory()?; // 插入测试数据 { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params!["req1", "p1", "claude", "claude-3", 100, 50, "0.01", 100, 200, 1000], )?; conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params!["req2", "p1", "claude", "claude-3", 200, 100, "0.02", 150, 200, 2000], )?; } let summary = db.get_usage_summary(None, None, None, None, None)?; assert_eq!(summary.total_requests, 2); assert_eq!(summary.success_rate, 100.0); Ok(()) } #[test] fn test_get_usage_summary_excludes_partial_rollup_boundary_days() -> Result<(), AppError> { let db = Database::memory()?; let start = local_ts(2024, 1, 1, 12, 0, 0); let end = local_ts(2024, 1, 3, 12, 0, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-01-01", "claude", "p1", "claude-3", 10, 10, 1000, 500, 0, 0, "1.00", 100 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-01-02", "claude", "p1", "claude-3", 20, 19, 2000, 1000, 0, 0, "2.00", 120 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-01-03", "claude", "p1", "claude-3", 30, 29, 3000, 1500, 0, 0, "3.00", 140 ], )?; } let summary = db.get_usage_summary(Some(start), Some(end), Some("claude"), None, None)?; assert_eq!(summary.total_requests, 20); assert_eq!(summary.total_input_tokens, 2000); assert_eq!(summary.total_output_tokens, 1000); Ok(()) } #[test] fn test_provider_and_model_filters_cover_detail_and_rollup() -> Result<(), AppError> { let db = Database::memory()?; let detail_ts = local_ts(2026, 6, 10, 12, 0, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO providers (id, app_type, name, settings_config) VALUES ('prov-a', 'claude', 'Packy', '{}'), ('prov-b', 'claude', 'DeepSeek', '{}')", [], )?; insert_usage_log( &conn, "a-1", "claude", "prov-a", "claude-sonnet-4-6", "proxy", detail_ts, 100, 10, 0, 0, 200, "1.0", )?; insert_usage_log( &conn, "b-1", "claude", "prov-b", "deepseek-v3", "proxy", detail_ts, 200, 20, 0, 0, 200, "2.0", )?; // 会话占位行：providers 表无此 id，展示名走 CASE 映射。 insert_usage_log( &conn, "s-1", "claude", "_session", "claude-sonnet-4-6", "session_log", detail_ts, 999, 99, 0, 0, 200, "0.5", )?; // 计价模型与请求模型不同的行：模型筛选必须按有效计价模型命中。 insert_usage_log( &conn, "a-2", "claude", "prov-a", "alias-model", "proxy", detail_ts, 50, 5, 0, 0, 200, "0.3", )?; conn.execute( "UPDATE proxy_request_logs SET pricing_model = 'real-model' WHERE request_id = 'a-2'", [], )?; // rollup 历史日行：无范围过滤时全部计入。 conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES ('2026-06-08', 'claude', 'prov-a', 'claude-sonnet-4-6', 5, 5, 500, 50, 0, 0, '5.0', 100), ('2026-06-08', 'claude', 'prov-b', 'deepseek-v3', 7, 7, 700, 70, 0, 0, '7.0', 100)", [], )?; } // ① 汇总按 Provider 展示名过滤：明细 + rollup 都命中。 let packy = db.get_usage_summary(None, None, None, Some("Packy"), None)?; assert_eq!(packy.total_requests, 7, "a-1 + a-2 + rollup 5"); // ② 汇总按模型过滤（有效计价模型口径）。 let deepseek = db.get_usage_summary(None, None, None, None, Some("deepseek-v3"))?; assert_eq!(deepseek.total_requests, 8, "b-1 + rollup 7"); // ③ pricing_model 优先于 model：alias-model 查不到，real-model 查得到。 let by_alias = db.get_usage_summary(None, None, None, None, Some("alias-model"))?; assert_eq!(by_alias.total_requests, 0); let by_real = db.get_usage_summary(None, None, None, None, Some("real-model"))?; assert_eq!(by_real.total_requests, 1); // ④ 会话占位行可按可读名选中。 let session = db.get_usage_summary(None, None, None, Some("Claude (Session)"), None)?; assert_eq!(session.total_requests, 1); // ⑤ Provider 统计 + 模型过滤：只剩 DeepSeek 一行。 let provider_stats = db.get_provider_stats(None, None, None, None, Some("deepseek-v3"))?; assert_eq!(provider_stats.len(), 1); assert_eq!(provider_stats[0].provider_name, "DeepSeek"); assert_eq!(provider_stats[0].request_count, 8); // ⑥ 模型统计 + Provider 过滤：只剩 Packy 名下的模型。 let model_stats = db.get_model_stats(None, None, None, Some("Packy"), None)?; let models: Vec<&str> = model_stats.iter().map(|m| m.model.as_str()).collect(); assert!(models.contains(&"claude-sonnet-4-6")); assert!(models.contains(&"real-model")); assert!(!models.contains(&"deepseek-v3")); // ⑦ 分应用汇总（Hero 卡片数据源）同样受过滤影响。 let by_app = db.get_usage_summary_by_app(None, None, Some("Packy"), None)?; assert_eq!(by_app.len(), 1); assert_eq!(by_app[0].app_type, "claude"); assert_eq!(by_app[0].summary.total_requests, 7); // ⑧ 趋势（>24h 走天分桶 + rollup 分支）。 let t_start = local_ts(2026, 6, 8, 0, 0, 0); let t_end = local_ts(2026, 6, 10, 23, 59, 0); let trends = db.get_daily_trends(Some(t_start), Some(t_end), None, Some("Packy"), None)?; let total_req: u64 = trends.iter().map(|d| d.request_count).sum(); assert_eq!(total_req, 7, "明细 2 + rollup 5"); // ⑨ 趋势 ≤24h 走小时分桶分支（?1/?2/?3 编号参数与追加过滤混用的路径）， // 同时验证 Provider + 模型组合过滤。 let h_start = local_ts(2026, 6, 10, 0, 0, 0); let h_end = local_ts(2026, 6, 10, 20, 0, 0); let hourly = db.get_daily_trends( Some(h_start), Some(h_end), None, Some("Packy"), Some("claude-sonnet-4-6"), )?; let hourly_req: u64 = hourly.iter().map(|d| d.request_count).sum(); assert_eq!(hourly_req, 1, "仅 a-1 命中（a-2 计价模型不同）"); // ⑩ 请求日志列表与下拉同口径：精确名 + 有效计价模型。 let logs = db.get_request_logs( &LogFilters { provider_name: Some("Packy".to_string()), model: Some("real-model".to_string()), ..Default::default() }, 0, 10, )?; assert_eq!(logs.total, 1); assert_eq!(logs.data[0].request_id, "a-2"); Ok(()) } #[test] fn test_get_usage_summary_includes_end_day_rollup_for_minute_precision_end_time( ) -> Result<(), AppError> { let db = Database::memory()?; let start = local_ts(2024, 1, 1, 0, 0, 0); let end = local_ts(2024, 1, 2, 23, 59, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-01-01", "claude", "p1", "claude-3", 10, 10, 1000, 500, 0, 0, "1.00", 100 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-01-02", "claude", "p1", "claude-3", 20, 19, 2000, 1000, 0, 0, "2.00", 120 ], )?; } let summary = db.get_usage_summary(Some(start), Some(end), Some("claude"), None, None)?; assert_eq!(summary.total_requests, 30); assert_eq!(summary.total_input_tokens, 3000); assert_eq!(summary.total_output_tokens, 1500); Ok(()) } #[test] fn test_effective_usage_dedup_prefers_proxy_for_session_sources() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); insert_usage_log( &conn, "codex-proxy", "codex", "openai", "GPT-5.4", "proxy", 10_000, 100, 20, 10, 7, 200, "0.10", )?; insert_usage_log( &conn, "codex-session-dup", "codex", "_codex_session", "gpt-5.4", "codex_session", 10_060, 100, 20, 10, 0, 200, "0.10", )?; insert_usage_log( &conn, "claude-proxy", "claude", "openai-compatible", "claude-sonnet-4-5", "proxy", 25_000, 300, 60, 20, 5, 200, "0.30", )?; insert_usage_log( &conn, "claude-session-dup", "claude", "_session", "claude-sonnet-4-5", "session_log", 25_060, 300, 60, 20, 5, 200, "0.30", )?; insert_usage_log( &conn, "gemini-proxy", "gemini", "google", "gemini-2.5-pro", "proxy", 20_000, 200, 40, 30, 0, 200, "0.20", )?; insert_usage_log( &conn, "gemini-session-dup", "gemini", "_gemini_session", "gemini-2.5-pro", "gemini_session", 20_060, 200, 40, 30, 0, 200, "0.20", )?; insert_usage_log( &conn, "codex-session-only", "codex", "_codex_session", "gpt-5.4", "codex_session", 30_000, 50, 5, 0, 0, 200, "0.02", )?; } let summary = db.get_usage_summary(None, None, None, None, None)?; assert_eq!(summary.total_requests, 4); // codex-proxy contributes 100-10=90; gemini-proxy contributes 200-30=170 // (both cache-inclusive providers). claude-proxy=300, codex-session-only=50. // 90 + 170 + 300 + 50 = 610. assert_eq!(summary.total_input_tokens, 610); assert_eq!(summary.total_output_tokens, 125); assert_eq!(summary.total_cache_read_tokens, 60); assert_eq!(summary.total_cache_creation_tokens, 12); // real_total = fresh_input(610) + output(125) + cache_create(12) + cache_read(60) = 807 assert_eq!(summary.real_total_tokens, 807); // hit_rate = 60 / (610 + 12 + 60) = 60 / 682 let expected_hit_rate = 60.0_f64 / 682.0_f64; assert!((summary.cache_hit_rate - expected_hit_rate).abs() < 1e-9); let trends = db.get_daily_trends(Some(0), Some(40_000), None, None, None)?; assert_eq!(trends.iter().map(|stat| stat.request_count).sum::(), 4); let provider_stats = db.get_provider_stats(None, None, None, None, None)?; assert_eq!( provider_stats .iter() .map(|stat| stat.request_count) .sum::(), 4 ); assert!(provider_stats .iter() .any(|stat| stat.provider_id == "_codex_session" && stat.request_count == 1)); assert!(!provider_stats .iter() .any(|stat| stat.provider_id == "_gemini_session")); assert!(!provider_stats .iter() .any(|stat| stat.provider_id == "_session")); let model_stats = db.get_model_stats(None, None, None, None, None)?; assert_eq!( model_stats .iter() .map(|stat| stat.request_count) .sum::(), 4 ); let logs = db.get_request_logs(&LogFilters::default(), 0, 10)?; let request_ids: Vec<&str> = logs .data .iter() .map(|log| log.request_id.as_str()) .collect(); assert_eq!(logs.total, 4); assert!(request_ids.contains(&"codex-proxy")); assert!(request_ids.contains(&"claude-proxy")); assert!(request_ids.contains(&"gemini-proxy")); assert!(request_ids.contains(&"codex-session-only")); assert!(!request_ids.contains(&"codex-session-dup")); assert!(!request_ids.contains(&"claude-session-dup")); assert!(!request_ids.contains(&"gemini-session-dup")); let breakdown = crate::services::session_usage::get_data_source_breakdown(&db)?; let proxy_count = breakdown .iter() .find(|item| item.data_source == "proxy") .map(|item| item.request_count); let codex_session_count = breakdown .iter() .find(|item| item.data_source == "codex_session") .map(|item| item.request_count); let gemini_session_count = breakdown .iter() .find(|item| item.data_source == "gemini_session") .map(|item| item.request_count); let session_log_count = breakdown .iter() .find(|item| item.data_source == "session_log") .map(|item| item.request_count); assert_eq!(proxy_count, Some(3)); assert_eq!(codex_session_count, Some(1)); assert_eq!(gemini_session_count, None); assert_eq!(session_log_count, None); Ok(()) } #[test] fn test_effective_usage_dedup_keeps_non_matching_session_rows() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); insert_usage_log( &conn, "proxy-base", "codex", "openai", "gpt-5.4", "proxy", 10_000, 100, 20, 10, 0, 200, "0.10", )?; insert_usage_log( &conn, "session-outside-window", "codex", "_codex_session", "gpt-5.4", "codex_session", 10_601, 100, 20, 10, 0, 200, "0.10", )?; insert_usage_log( &conn, "session-token-mismatch", "codex", "_codex_session", "gpt-5.4", "codex_session", 10_060, 101, 20, 10, 0, 200, "0.10", )?; insert_usage_log( &conn, "session-app-mismatch", "gemini", "_gemini_session", "gpt-5.4", "gemini_session", 10_060, 100, 20, 10, 0, 200, "0.10", )?; insert_usage_log( &conn, "session-model-mismatch", "codex", "_codex_session", "different-model", "codex_session", 10_060, 100, 20, 10, 0, 200, "0.10", )?; insert_usage_log( &conn, "proxy-error", "codex", "openai", "gpt-5.4", "proxy", 20_000, 300, 60, 0, 0, 500, "0.00", )?; insert_usage_log( &conn, "session-matches-error-proxy", "codex", "_codex_session", "gpt-5.4", "codex_session", 20_060, 300, 60, 0, 0, 200, "0.30", )?; insert_usage_log( &conn, "claude-proxy-cache-creation", "claude", "anthropic", "claude-sonnet-4-5", "proxy", 30_000, 100, 20, 10, 5, 200, "0.10", )?; insert_usage_log( &conn, "claude-session-cache-creation-mismatch", "claude", "_session", "claude-sonnet-4-5", "session_log", 30_060, 100, 20, 10, 0, 200, "0.10", )?; } let summary = db.get_usage_summary(None, None, None, None, None)?; assert_eq!(summary.total_requests, 9); let logs = db.get_request_logs(&LogFilters::default(), 0, 10)?; let request_ids: Vec<&str> = logs .data .iter() .map(|log| log.request_id.as_str()) .collect(); assert_eq!(logs.total, 9); assert!(request_ids.contains(&"session-outside-window")); assert!(request_ids.contains(&"session-token-mismatch")); assert!(request_ids.contains(&"session-app-mismatch")); assert!(request_ids.contains(&"session-model-mismatch")); assert!(request_ids.contains(&"session-matches-error-proxy")); assert!(request_ids.contains(&"claude-session-cache-creation-mismatch")); Ok(()) } #[test] fn test_get_model_stats() -> Result<(), AppError> { let db = Database::memory()?; // 插入测试数据 { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "req1", "p1", "claude", "claude-3-sonnet", 100, 50, "0.01", 100, 200, 1000 ], )?; } let stats = db.get_model_stats(None, None, None, None, None)?; assert_eq!(stats.len(), 1); assert_eq!(stats[0].model, "claude-3-sonnet"); assert_eq!(stats[0].request_count, 1); Ok(()) } #[test] fn test_get_provider_stats_with_time_filter() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params!["old", "p1", "claude", "claude-3", 100, 50, "0.01", 100, 200, 1000], )?; conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params!["new", "p1", "claude", "claude-3", 200, 75, "0.02", 120, 200, 2000], )?; } let stats = db.get_provider_stats(Some(1500), Some(2500), Some("claude"), None, None)?; assert_eq!(stats.len(), 1); assert_eq!(stats[0].provider_id, "p1"); assert_eq!(stats[0].request_count, 1); assert_eq!(stats[0].total_tokens, 275); Ok(()) } #[test] fn test_get_provider_stats_labels_opencode_session_provider() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); insert_usage_log( &conn, "opencode-session", "opencode", "_opencode_session", "opencode-model", "opencode_session", 1000, 100, 50, 0, 0, 200, "0.01", )?; } let stats = db.get_provider_stats(None, None, Some("opencode"), None, None)?; assert_eq!(stats.len(), 1); assert_eq!(stats[0].provider_id, "_opencode_session"); assert_eq!(stats[0].provider_name, "OpenCode (Session)"); Ok(()) } #[test] fn test_get_provider_stats_excludes_partial_rollup_boundary_days() -> Result<(), AppError> { let db = Database::memory()?; let start = local_ts(2024, 2, 1, 12, 0, 0); let end = local_ts(2024, 2, 3, 12, 0, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-02-01", "claude", "p-rollup", "claude-3", 5, 5, 500, 250, 0, 0, "0.50", 100 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-02-02", "claude", "p-rollup", "claude-3", 8, 7, 800, 400, 0, 0, "0.80", 120 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-02-03", "claude", "p-rollup", "claude-3", 12, 11, 1200, 600, 0, 0, "1.20", 140 ], )?; } let stats = db.get_provider_stats(Some(start), Some(end), Some("claude"), None, None)?; assert_eq!(stats.len(), 1); assert_eq!(stats[0].provider_id, "p-rollup"); assert_eq!(stats[0].request_count, 8); assert_eq!(stats[0].total_tokens, 1200); Ok(()) } #[test] fn test_get_daily_trends_respects_shorter_than_24_hours() -> Result<(), AppError> { let db = Database::memory()?; { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "req-short", "p1", "claude", "claude-3", 100, 50, "0.01", 100, 200, 10_800 ], )?; } let stats = db.get_daily_trends(Some(0), Some(15 * 60 * 60), Some("claude"), None, None)?; assert_eq!(stats.len(), 15); assert_eq!(stats[3].request_count, 1); Ok(()) } #[test] fn test_get_daily_trends_groups_ranges_longer_than_24_hours_by_local_day( ) -> Result<(), AppError> { let db = Database::memory()?; let start = local_ts(2024, 3, 1, 12, 0, 0); let end = local_ts(2024, 3, 3, 12, 0, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "day-1-detail", "p1", "claude", "claude-3", 100, 50, "0.01", 100, 200, local_ts(2024, 3, 1, 13, 0, 0) ], )?; conn.execute( "INSERT INTO proxy_request_logs ( request_id, provider_id, app_type, model, input_tokens, output_tokens, total_cost_usd, latency_ms, status_code, created_at ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "day-3-detail", "p1", "claude", "claude-3", 200, 75, "0.02", 110, 200, local_ts(2024, 3, 3, 10, 0, 0) ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-03-02", "claude", "p1", "claude-3", 4, 4, 400, 200, 0, 0, "0.40", 120 ], )?; } let stats = db.get_daily_trends(Some(start), Some(end), Some("claude"), None, None)?; assert_eq!(stats.len(), 3); assert_eq!(stats[0].request_count, 1); assert_eq!(stats[0].total_tokens, 150); assert_eq!(stats[1].request_count, 4); assert_eq!(stats[1].total_tokens, 600); assert_eq!(stats[2].request_count, 1); assert_eq!(stats[2].total_tokens, 275); Ok(()) } #[test] fn test_get_model_stats_excludes_partial_rollup_boundary_days() -> Result<(), AppError> { let db = Database::memory()?; let start = local_ts(2024, 4, 1, 12, 0, 0); let end = local_ts(2024, 4, 3, 12, 0, 0); { let conn = lock_conn!(db.conn); conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-04-01", "claude", "p1", "claude-3-haiku", 6, 6, 600, 300, 0, 0, "0.60", 100 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-04-02", "claude", "p1", "claude-3-haiku", 9, 8, 900, 450, 0, 0, "0.90", 110 ], )?; conn.execute( "INSERT INTO usage_daily_rollups ( date, app_type, provider_id, model, request_count, success_count, input_tokens, output_tokens, cache_read_tokens, cache_creation_tokens, total_cost_usd, avg_latency_ms ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", params![ "2024-04-03", "claude", "p1", "claude-3-haiku", 12, 11, 1200, 600, 0, 0, "1.20", 130 ], )?; } let stats = db.get_model_stats(Some(start), Some(end), Some("claude"), None, None)?; assert_eq!(stats.len(), 1); assert_eq!(stats[0].model, "claude-3-haiku"); assert_eq!(stats[0].request_count, 9); assert_eq!(stats[0].total_tokens, 1350); Ok(()) } #[test] fn test_strip_model_date_suffix_is_utf8_safe() { assert_eq!( strip_model_date_suffix("模型-2026-05-14").as_deref(), Some("模型") ); assert_eq!(strip_model_date_suffix("abc🚀12345678"), None); } #[test] fn test_strip_model_date_suffix_handles_six_digit_yymmdd() { // 火山方舟 6 位 YYMMDD 后缀应被剥离（doubao 全系都用这种格式）。 assert_eq!( strip_model_date_suffix("doubao-seed-2-1-pro-260628").as_deref(), Some("doubao-seed-2-1-pro") ); assert_eq!( strip_model_date_suffix("doubao-seed-1-6-250615").as_deref(), Some("doubao-seed-1-6") ); // 8 位 YYYYMMDD 仍照旧剥离。 assert_eq!( strip_model_date_suffix("claude-3-5-sonnet-20241022").as_deref(), Some("claude-3-5-sonnet") ); // 月/日非法的 6 位尾巴（版本号等）不剥离，避免误伤。 assert_eq!(strip_model_date_suffix("foo-bar-123456"), None); // 月=34 assert_eq!(strip_model_date_suffix("widget-209900"), None); // 月=99 assert_eq!(strip_model_date_suffix("gizmo-251200"), None); // 日=00 } #[test] fn test_pricing_resolves_volcengine_dated_model_to_bare_seed_row() -> Result<(), AppError> { // 回归：火山真实用量带 6 位日期后缀（doubao-seed-2-1-pro-260628）， // 必须能归一化命中定价表里的裸名 seed 行（doubao-seed-2-1-pro），否则成本显示 $0。 let db = Database::memory()?; let conn = lock_conn!(db.conn); conn.execute( "INSERT OR REPLACE INTO model_pricing ( model_id, display_name, input_cost_per_million, output_cost_per_million, cache_read_cost_per_million, cache_creation_cost_per_million ) VALUES ('doubao-seed-2-1-pro', 'Doubao Seed 2.1 Pro', '0.84', '4.2', '0.17', '0')", [], )?; let row = find_model_pricing_row(&conn, "doubao-seed-2-1-pro-260628")?; assert!( row.is_some(), "带日期的火山模型应通过 6 位日期剥离命中裸名定价行" ); let (input, output, ..) = row.unwrap(); assert_eq!(input, "0.84"); assert_eq!(output, "4.2"); Ok(()) } #[test] fn test_prefix_pricing_does_not_match_short_base_model_to_variant() -> Result<(), AppError> { let db = Database::memory()?; let conn = lock_conn!(db.conn); conn.execute("DELETE FROM model_pricing WHERE model_id LIKE 'gpt-5%'", [])?; for (model_id, display_name) in [("gpt-5-mini", "GPT-5 Mini"), ("gpt-5-pro", "GPT-5 Pro")] { conn.execute( "INSERT INTO model_pricing ( model_id, display_name, input_cost_per_million, output_cost_per_million, cache_read_cost_per_million, cache_creation_cost_per_million ) VALUES (?1, ?2, '1', '2', '0', '0')", params![model_id, display_name], )?; } let result = find_model_pricing_row(&conn, "gpt-5")?; assert!( result.is_none(), "缺少 gpt-5 基础定价时，不应前缀误匹配到 gpt-5-mini/gpt-5-pro" ); Ok(()) } #[test] fn test_model_pricing_matching() -> Result<(), AppError> { let db = Database::memory()?; let conn = lock_conn!(db.conn); // 准备额外定价数据，覆盖前缀/后缀清洗场景 conn.execute( "INSERT OR REPLACE INTO model_pricing ( model_id, display_name, input_cost_per_million, output_cost_per_million, cache_read_cost_per_million, cache_creation_cost_per_million ) VALUES (?, ?, ?, ?, ?, ?)", params![ "claude-haiku-4.5", "Claude Haiku 4.5", "1.0", "2.0", "0.0", "0.0" ], )?; // 测试精确匹配（seed_model_pricing 已预置 claude-sonnet-4-5-20250929） let result = find_model_pricing_row(&conn, "claude-sonnet-4-5-20250929")?; assert!( result.is_some(), "应该能精确匹配 claude-sonnet-4-5-20250929" ); // 清洗：去除前缀和冒号后缀 let result = find_model_pricing_row(&conn, "anthropic/claude-haiku-4.5")?; assert!( result.is_some(), "带前缀的模型 anthropic/claude-haiku-4.5 应能匹配到 claude-haiku-4.5" ); let result = find_model_pricing_row(&conn, "moonshotai/kimi-k2-0905:exa")?; assert!( result.is_some(), "带前缀+冒号后缀的模型应清洗后匹配到 kimi-k2-0905" ); // 清洗：@ 替换为 -（seed_model_pricing 已预置 gpt-5.2-codex-low） let result = find_model_pricing_row(&conn, "gpt-5.2-codex@low")?; assert!( result.is_some(), "带 @ 分隔符的模型 gpt-5.2-codex@low 应能匹配到 gpt-5.2-codex-low" ); let result = find_model_pricing_row(&conn, "OpenAI/GPT-5.5@HIGH")?; assert!( result.is_some(), "大小写混合的 GPT-5.5 模型应能归一化匹配到 gpt-5.5-high" ); let result = find_model_pricing_row(&conn, "OpenAI/GPT-5.5-2026-05-14")?; assert!( result.is_some(), "OpenAI 日期后缀模型应能回退到 gpt-5.5 基础定价" ); let result = find_model_pricing_row(&conn, "google/gemini-3-pro-preview-20260514")?; assert!( result.is_some(), "Gemini 日期后缀模型应能回退到 gemini-3-pro-preview 基础定价" ); // Claude Desktop route 短 ID：应通过前缀匹配到带日期的定价 let result = find_model_pricing_row(&conn, "claude-haiku-4-5")?; assert!( result.is_some(), "Claude Desktop 短路由 claude-haiku-4-5 应能匹配到 claude-haiku-4-5-20251001" ); let result = find_model_pricing_row(&conn, "anthropic/claude-opus-4.8")?; assert!( result.is_some(), "聚合商点号格式 anthropic/claude-opus-4.8 应能匹配到 claude-opus-4-8" ); // Claude Desktop 旧版/异常包装的非 Anthropic route：claude-gpt-5.5 → gpt-5.5 let result = find_model_pricing_row(&conn, "claude-gpt-5.5")?; assert!( result.is_some(), "带 claude- 包装的非 Anthropic 模型应能剥离后匹配到真实模型定价" ); // Bedrock/Vertex 常见形态：provider 前缀 + -vN 后缀 + :0 修饰 let result = find_model_pricing_row(&conn, "global.anthropic.claude-haiku-4-5-20251001-v1:0")?; assert!( result.is_some(), "Bedrock/Vertex 风格 Claude 模型 ID 应能归一化到基础 Claude 模型定价" ); let result = find_model_pricing_row(&conn, "global.anthropic.claude-opus-4-8-v1:0")?; assert!( result.is_some(), "Bedrock 风格 Claude Opus 4.8 模型 ID 应能归一化到基础 Claude 模型定价" ); let result = find_model_pricing_row(&conn, "claude-opus-4-8@20260527")?; assert!( result.is_some(), "Vertex 风格 Claude Opus 4.8 模型 ID 应能归一化到基础 Claude 模型定价" ); // Reasoning effort 后缀：没有专门价格时回退到基础模型 let result = find_model_pricing_row(&conn, "gpt-5.4@low")?; assert!( result.is_some(), "缺少专门 effort 价格时应回退到 gpt-5.4 基础模型定价" ); // Kimi Code 是订阅/额度模型，不应伪装成公开按 token 计费模型 let result = find_model_pricing_row(&conn, "kimi-for-coding")?; assert!(result.is_none(), "kimi-for-coding 没有固定 token 单价"); // 测试不存在的模型 let result = find_model_pricing_row(&conn, "unknown-model-123")?; assert!(result.is_none(), "不应该匹配不存在的模型"); Ok(()) } }