Files
CC-Switch/src-tauri/src/commands/usage.rs
T
Dex Miller 5376ea042b Feat/usage improvements (#508)
* i18n: update cache terminology across all languages

- Change 'Cache Read' to 'Cache Hit' in all languages
- Change 'Cache Write' to 'Cache Creation' in all languages
- Update zh: 缓存读取 → 缓存命中, 缓存写入 → 缓存创建
- Update en: Cache Read → Cache Hit, Cache Write → Cache Creation
- Update ja: キャッシュ読取 → キャッシュヒット, キャッシュ書込 → キャッシュ作成

Affected keys: cacheReadTokens, cacheCreationTokens, cacheReadCost,
cacheWriteCost, cacheRead, cacheWrite

* feat(usage): add cache metrics to trend chart

- Add cache creation tokens visualization (orange line)
- Add cache hit tokens visualization (purple line)
- Add gradient definitions for new cache metrics
- Include cache data in hourly aggregation
- Display cache metrics alongside input/output tokens

This provides better visibility into cache usage patterns over time.

* fix(usage): fix timezone handling in datetime picker

- Add timestampToLocalDatetime() to convert Unix timestamp to local datetime
- Add localDatetimeToTimestamp() with validation for incomplete input
- Fix issue where typing hours/minutes would jump to previous day
- Validate datetime format completeness before conversion
- Use local timezone instead of UTC for datetime-local input

This resolves the issue where users couldn't fine-tune time selection
and the input would jump unexpectedly when editing hours or minutes.

* feat(usage): add auto-refresh for usage statistics

- Add 30-second auto-refresh interval for all usage queries
- Disable background refresh to save resources
- Apply to: summary, trends, provider stats, model stats, request logs
- Queries automatically update when tab is active
- Pause refresh when user switches to another tab

This keeps usage data fresh without manual refresh.

* fix(proxy): improve usage logging and cache token parsing

- Log requests even when usage parsing fails (with default values)
- Add detailed debug logging for usage metrics
- Support cache_read_input_tokens field in Codex responses
- Fallback to input_tokens_details.cached_tokens if needed
- Add test case for cached_tokens in input_tokens_details
- Ensure all requests are tracked in database for analytics

This fixes missing request logs when API responses lack usage data
and improves cache token detection across different response formats.

* style(rust): use inline format args in format! macros

- Replace format!("...", var) with format!("...{var}")
- Update universal provider ID formatting
- Update error message formatting
- Update config.toml generation in Codex provider

Fixes clippy::uninlined_format_args warnings.

* feat(proxy): enhance provider router logging

- Add debug logs for failover queue provider count
- Log circuit breaker state for each provider check
- Add logs for missing current provider scenarios
- Log when no current provider is configured
- Use inline format args for better readability

This improves debugging of provider selection and failover behavior.

* feat(database): update model pricing data

- Update Claude models to full version format (e.g. claude-opus-4-5-20251101)
- Add GPT-5.2 series model pricing (10 models)
- Add GPT-5.1 series model pricing (10 models)
- Add GPT-5 series model pricing (12 models)
- Add Gemini 3 series model pricing (2 models)
- Update Gemini 2.5 series model ID format (use dot separator)
- Unify display names by removing thinking level suffixes

* fix(usage): correct Gemini output token calculation

Fix Gemini API output token parsing to use totalTokenCount - promptTokenCount
instead of candidatesTokenCount alone. This ensures thoughtsTokenCount is
included in output statistics.

- Update from_gemini_response to calculate output from total - input
- Update from_gemini_stream_chunks with same logic for consistency
- Fix from_codex_stream_events to use adjusted token calculation
- Add test case for responses with thoughtsTokenCount
- Update existing tests to match new calculation logic

* fix(usage): correct cache token billing and add Codex format auto-detection

- Avoid double-billing cache tokens by subtracting from input before calculation
- Add smart Codex parser that auto-detects OpenAI vs Codex API format
- Extract model name from Codex responses for accurate tracking

* fix(proxy): improve takeover detection with live config check

- Add live config takeover detection for hot-switch decision
- Rebuild takeover when backup is missing or placeholder remains
- Make detect_takeover_in_live_config_for_app public
- Fix is_takeover_active to use actual takeover status

* refactor(usage): simplify model pricing lookup by removing suffix fallback

Replace complex suffix-stripping fallback with direct prefix/suffix cleanup.
Model IDs are now cleaned by removing vendor prefix (before /) and colon
suffix (after :), then matched exactly against pricing table.

* feat(database): add Chinese AI model pricing data

Add pricing for domestic AI models (CNY/1M tokens):
- Doubao-Seed-Code (ByteDance)
- DeepSeek V3/V3.1/V3.2
- Kimi K2/K2-Thinking/K2-Turbo (Moonshot)
- MiniMax M2/M2.1/M2.1-Lightning
- GLM-4.6/4.7 (Zhipu)
- Mimo V2 Flash (Xiaomi)

Also fix test case to use correct model ID and remove invalid currency column.

* refactor(proxy): improve header forwarding with blacklist approach

Change from whitelist to blacklist mode for request header forwarding.
Only skip headers that will be overridden (auth, host, content-length).
This preserves client's original headers and improves compatibility.

* fix(proxy): bypass timeout and retry configs when failover is disabled

When auto_failover_enabled is false, timeout and retry configurations
should not affect normal request flow. This change ensures:

- create_forwarder: passes 0 for all timeout/retry params when failover
  is disabled, effectively bypassing these checks
- streaming_timeout_config: returns 0 for both first_byte_timeout and
  idle_timeout when failover is disabled

This prevents unnecessary timeout errors and retry attempts when users
have explicitly disabled the failover feature.

* fix(proxy): handle zero value input in failover config fields

* refactor(proxy): remove retry logic and add enabled check for failover

* refactor(proxy): distinguish circuit-open from no-provider errors

* Align usage stats to sliding windows

* feat(proxy): add body and header filtering for upstream requests

* feat(proxy): enable transparent passthrough for headers

- Passthrough anthropic-beta header as-is from client
- Passthrough anthropic-version header from client
- Passthrough client IP headers (x-forwarded-for, x-real-ip) by default
- Filter private params (underscore-prefixed fields) from request body
- No database changes required

* feat(proxy): extract session ID from client requests for logging

- Add SessionIdExtractor to parse session ID from Claude/Codex requests
- Support extraction from metadata.user_id, headers, previous_response_id
- Pass session_id through RequestContext to usage logger
- Enable request correlation by session in proxy_request_logs
2025-12-31 22:57:00 +08:00

180 lines
5.1 KiB
Rust

//! 使用统计相关命令
use crate::error::AppError;
use crate::services::usage_stats::*;
use crate::store::AppState;
use tauri::State;
/// 获取使用量汇总
#[tauri::command]
pub fn get_usage_summary(
state: State<'_, AppState>,
start_date: Option<i64>,
end_date: Option<i64>,
) -> Result<UsageSummary, AppError> {
state.db.get_usage_summary(start_date, end_date)
}
/// 获取每日趋势
#[tauri::command]
pub fn get_usage_trends(
state: State<'_, AppState>,
start_date: Option<i64>,
end_date: Option<i64>,
) -> Result<Vec<DailyStats>, AppError> {
state.db.get_daily_trends(start_date, end_date)
}
/// 获取 Provider 统计
#[tauri::command]
pub fn get_provider_stats(state: State<'_, AppState>) -> Result<Vec<ProviderStats>, AppError> {
state.db.get_provider_stats()
}
/// 获取模型统计
#[tauri::command]
pub fn get_model_stats(state: State<'_, AppState>) -> Result<Vec<ModelStats>, AppError> {
state.db.get_model_stats()
}
/// 获取请求日志列表
#[tauri::command]
pub fn get_request_logs(
state: State<'_, AppState>,
filters: LogFilters,
page: u32,
page_size: u32,
) -> Result<PaginatedLogs, AppError> {
state.db.get_request_logs(&filters, page, page_size)
}
/// 获取单个请求详情
#[tauri::command]
pub fn get_request_detail(
state: State<'_, AppState>,
request_id: String,
) -> Result<Option<RequestLogDetail>, AppError> {
state.db.get_request_detail(&request_id)
}
/// 获取模型定价列表
#[tauri::command]
pub fn get_model_pricing(state: State<'_, AppState>) -> Result<Vec<ModelPricingInfo>, AppError> {
log::info!("获取模型定价列表");
state.db.ensure_model_pricing_seeded()?;
let db = state.db.clone();
let conn = crate::database::lock_conn!(db.conn);
// 检查表是否存在
let table_exists: bool = conn
.query_row(
"SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name='model_pricing'",
[],
|row| row.get::<_, i64>(0).map(|count| count > 0),
)
.unwrap_or(false);
if !table_exists {
log::error!("model_pricing 表不存在,可能需要重启应用以触发数据库迁移");
return Ok(Vec::new());
}
let mut stmt = conn.prepare(
"SELECT model_id, display_name, input_cost_per_million, output_cost_per_million,
cache_read_cost_per_million, cache_creation_cost_per_million
FROM model_pricing
ORDER BY display_name",
)?;
let rows = stmt.query_map([], |row| {
Ok(ModelPricingInfo {
model_id: row.get(0)?,
display_name: row.get(1)?,
input_cost_per_million: row.get(2)?,
output_cost_per_million: row.get(3)?,
cache_read_cost_per_million: row.get(4)?,
cache_creation_cost_per_million: row.get(5)?,
})
})?;
let mut pricing = Vec::new();
for row in rows {
pricing.push(row?);
}
log::info!("成功获取 {} 条模型定价数据", pricing.len());
Ok(pricing)
}
/// 更新模型定价
#[tauri::command]
pub fn update_model_pricing(
state: State<'_, AppState>,
model_id: String,
display_name: String,
input_cost: String,
output_cost: String,
cache_read_cost: String,
cache_creation_cost: String,
) -> Result<(), AppError> {
let db = state.db.clone();
let conn = crate::database::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 (?1, ?2, ?3, ?4, ?5, ?6)",
rusqlite::params![
model_id,
display_name,
input_cost,
output_cost,
cache_read_cost,
cache_creation_cost
],
)
.map_err(|e| AppError::Database(format!("更新模型定价失败: {e}")))?;
Ok(())
}
/// 检查 Provider 使用限额
#[tauri::command]
pub fn check_provider_limits(
state: State<'_, AppState>,
provider_id: String,
app_type: String,
) -> Result<crate::services::usage_stats::ProviderLimitStatus, AppError> {
state.db.check_provider_limits(&provider_id, &app_type)
}
/// 删除模型定价
#[tauri::command]
pub fn delete_model_pricing(state: State<'_, AppState>, model_id: String) -> Result<(), AppError> {
let db = state.db.clone();
let conn = crate::database::lock_conn!(db.conn);
conn.execute(
"DELETE FROM model_pricing WHERE model_id = ?1",
rusqlite::params![model_id],
)
.map_err(|e| AppError::Database(format!("删除模型定价失败: {e}")))?;
log::info!("已删除模型定价: {model_id}");
Ok(())
}
/// 模型定价信息
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct ModelPricingInfo {
pub model_id: String,
pub display_name: String,
pub input_cost_per_million: String,
pub output_cost_per_million: String,
pub cache_read_cost_per_million: String,
pub cache_creation_cost_per_million: String,
}