feat: add len variable for tier conditions and LLM prompt helper
This commit is contained in:
CaIon
@@ -1000,11 +1000,82 @@ func TestImageAudioZero(t *testing.T) {
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}
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}
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// ---------------------------------------------------------------------------
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// len variable tests — tier conditions based on context length
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// ---------------------------------------------------------------------------
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const lenTieredExpr = `len <= 200000 ? tier("standard", p * 3 + c * 15 + cr * 0.3) : tier("long_context", p * 6 + c * 22.5 + cr * 0.6)`
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func TestLen_StandardTier(t *testing.T) {
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params := billingexpr.TokenParams{P: 80000, C: 5000, Len: 100000, CR: 20000}
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cost, trace, err := billingexpr.RunExpr(lenTieredExpr, params)
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if err != nil {
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t.Fatal(err)
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}
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want := 80000*3 + 5000*15 + 20000*0.3
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if math.Abs(cost-want) > 1e-6 {
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t.Errorf("cost = %f, want %f", cost, want)
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}
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if trace.MatchedTier != "standard" {
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t.Errorf("tier = %q, want standard", trace.MatchedTier)
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}
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}
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func TestLen_LongContextTier(t *testing.T) {
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// p is low (cache subtracted), but len is high (full context)
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params := billingexpr.TokenParams{P: 50000, C: 5000, Len: 300000, CR: 250000}
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cost, trace, err := billingexpr.RunExpr(lenTieredExpr, params)
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if err != nil {
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t.Fatal(err)
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}
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want := 50000*6 + 5000*22.5 + 250000*0.6
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if math.Abs(cost-want) > 1e-6 {
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t.Errorf("cost = %f, want %f", cost, want)
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}
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if trace.MatchedTier != "long_context" {
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t.Errorf("tier = %q, want long_context (len=300000 > 200000)", trace.MatchedTier)
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}
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}
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func TestLen_BoundaryExact(t *testing.T) {
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params := billingexpr.TokenParams{P: 100000, C: 1000, Len: 200000, CR: 100000}
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_, trace, err := billingexpr.RunExpr(lenTieredExpr, params)
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if err != nil {
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t.Fatal(err)
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}
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if trace.MatchedTier != "standard" {
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t.Errorf("tier = %q, want standard (len=200000 <= 200000)", trace.MatchedTier)
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}
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}
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func TestLen_BoundaryPlusOne(t *testing.T) {
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params := billingexpr.TokenParams{P: 100000, C: 1000, Len: 200001, CR: 100001}
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_, trace, err := billingexpr.RunExpr(lenTieredExpr, params)
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if err != nil {
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t.Fatal(err)
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}
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if trace.MatchedTier != "long_context" {
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t.Errorf("tier = %q, want long_context (len=200001 > 200000)", trace.MatchedTier)
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}
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}
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func TestLen_ZeroDefaultsToZero(t *testing.T) {
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// len defaults to 0 when not set
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params := billingexpr.TokenParams{P: 1000, C: 500}
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_, trace, err := billingexpr.RunExpr(lenTieredExpr, params)
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if err != nil {
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t.Fatal(err)
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}
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if trace.MatchedTier != "standard" {
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t.Errorf("tier = %q, want standard (len=0 <= 200000)", trace.MatchedTier)
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}
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}
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// ---------------------------------------------------------------------------
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// Benchmarks: compile vs cached execution
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// ---------------------------------------------------------------------------
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const benchComplexExpr = `p <= 200000 ? tier("standard", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6 + img * 3 + img_o * 30 + ai * 10 + ao * 40) : tier("long_context", p * 6 + c * 22.5 + cr * 0.6 + cc * 7.5 + cc1h * 12 + img * 6 + img_o * 60 + ai * 20 + ao * 80)`
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const benchComplexExpr = `len <= 200000 ? tier("standard", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6 + img * 3 + img_o * 30 + ai * 10 + ao * 40) : tier("long_context", p * 6 + c * 22.5 + cr * 0.6 + cc * 7.5 + cc1h * 12 + img * 6 + img_o * 60 + ai * 20 + ao * 80)`
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func BenchmarkExprCompile(b *testing.B) {
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for i := 0; i < b.N; i++ {
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@@ -1015,7 +1086,7 @@ func BenchmarkExprCompile(b *testing.B) {
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func BenchmarkExprRunCached(b *testing.B) {
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billingexpr.CompileFromCache(benchComplexExpr)
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params := billingexpr.TokenParams{P: 150000, C: 10000, CR: 30000, CC: 5000, Img: 2000, AI: 1000, AO: 500}
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params := billingexpr.TokenParams{P: 150000, C: 10000, Len: 188000, CR: 30000, CC: 5000, Img: 2000, AI: 1000, AO: 500}
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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billingexpr.RunExpr(benchComplexExpr, params)
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@@ -41,6 +41,7 @@ var (
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var compileEnvPrototypeV1 = map[string]interface{}{
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"p": float64(0),
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"c": float64(0),
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"len": float64(0),
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"cr": float64(0),
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"cc": float64(0),
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"cc1h": float64(0),
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+16
-3
@@ -30,7 +30,8 @@ Powered by [expr-lang/expr](https://github.com/expr-lang/expr). Expressions are
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| 变量 | 含义 |
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|------|------|
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| `p` | 输入 token 数。**自动排除**表达式中单独计价的子类别（见下方说明） |
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| `p` | 输入 token 数（**计价用**）。**自动排除**表达式中单独计价的子类别（见下方说明） |
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| `len` | 输入上下文总长度（**条件判断用**）。不受自动排除影响，始终反映完整输入长度。非 Claude：等于原始 `prompt_tokens`；Claude：等于文本输入 + 缓存读取 + 缓存创建 |
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| `cr` | 缓存命中（读取）token 数 |
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| `cc` | 缓存创建 token 数（Claude 5分钟 TTL / 通用） |
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| `cc1h` | 缓存创建 token 数 — 1小时 TTL（Claude 专用） |
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@@ -51,6 +52,8 @@ Powered by [expr-lang/expr](https://github.com/expr-lang/expr). Expressions are
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**规则：如果表达式使用了某个子类别变量，对应的 token 就从 `p` 或 `c` 中扣除；如果没使用，那些 token 就留在 `p` 或 `c` 里按基础价格计费。**
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> **重要：`len` 不受自动排除影响。** `len` 始终代表完整的输入上下文长度，不管表达式是否单独对缓存/图片/音频定价。因此**阶梯条件应使用 `len` 而非 `p`**，以避免缓存命中导致 `p` 降低而误判档位。
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举例说明（假设上游返回的原始数据：prompt_tokens=1000，其中包含 200 cache read、100 image）：
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| 表达式 | `p` 的值 | 说明 |
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@@ -93,8 +96,8 @@ Powered by [expr-lang/expr](https://github.com/expr-lang/expr). Expressions are
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# Simple flat pricing
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tier("base", p * 2.5 + c * 15 + cr * 0.25)
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# Multi-tier (Claude Sonnet style)
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p <= 200000
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# Multi-tier (Claude Sonnet style) — use len for tier conditions
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len <= 200000
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? tier("standard", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6)
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: tier("long_context", p * 6 + c * 22.5 + cr * 0.6 + cc * 7.5 + cc1h * 12)
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@@ -199,6 +202,16 @@ Example: `p * 2.5 + c * 15 + cr * 0.25`
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- Expression uses `cr` → cache read tokens subtracted from `p`
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- Expression doesn't use `img` → image tokens stay in `p`, priced at $2.50
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### `len` — Context Length Variable
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`len` represents the total input context length, designed for **tier condition evaluation** (e.g. `len <= 200000 ? ...`). Unlike `p`, `len` is never reduced by sub-category exclusion.
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**Computation rules:**
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- **Non-Claude (GPT/OpenAI format)**: `len = prompt_tokens` (the raw total from the upstream response)
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- **Claude format**: `len = input_tokens + cache_read_tokens + cache_creation_tokens` (since Claude's `input_tokens` is text-only, cache must be added back to reflect full context length)
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This ensures that heavy cache usage doesn't cause the tier condition to incorrectly evaluate to a lower tier. For example, if a request has 300K total context but 250K is cached, `p` with cache subtracted would be only 50K (standard tier), while `len` correctly reports 300K (long-context tier).
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### Quota Conversion
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Expression coefficients are $/1M tokens. Conversion to internal quota:
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@@ -13,7 +13,8 @@ import (
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// RunExpr compiles (with cache) and executes an expression string.
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// The environment exposes:
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// - p, c — prompt / completion tokens
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// - p, c — prompt / completion tokens (auto-excluding separately-priced sub-categories)
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// - len — total input context length for tier conditions (never reduced by sub-category exclusion)
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// - cr, cc, cc1h — cache read / creation / creation-1h tokens
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// - tier(name, value) — trace callback that records which tier matched
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// - max, min, abs, ceil, floor — standard math helpers
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@@ -54,6 +55,7 @@ func runProgram(prog *vm.Program, params TokenParams, request RequestInput) (flo
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env := map[string]interface{}{
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"p": params.P,
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"c": params.C,
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"len": params.Len,
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"cr": params.CR,
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"cc": params.CC,
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"cc1h": params.CC1h,
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@@ -14,8 +14,9 @@ type RequestInput struct {
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// Fields beyond P and C are optional — when absent they default to 0,
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// which means cache-unaware expressions keep working unchanged.
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type TokenParams struct {
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P float64 // prompt tokens (text)
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C float64 // completion tokens (text)
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P float64 // prompt tokens (text) — auto-excludes sub-categories priced separately
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C float64 // completion tokens (text) — auto-excludes sub-categories priced separately
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Len float64 // total input context length for tier conditions (non-Claude: raw prompt_tokens; Claude: text + cache read + cache creation)
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CR float64 // cache read (hit) tokens
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CC float64 // cache creation tokens (5-min TTL for Claude, generic for others)
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CC1h float64 // cache creation tokens — 1-hour TTL (Claude only)
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@@ -257,6 +257,7 @@ func modelPriceHelperTiered(c *gin.Context, info *relaycommon.RelayInfo, promptT
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rawCost, trace, err := billingexpr.RunExprWithRequest(exprStr, billingexpr.TokenParams{
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P: float64(promptTokens),
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C: float64(estimatedCompletionTokens),
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Len: float64(promptTokens),
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}, requestInput)
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if err != nil {
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return types.PriceData{}, fmt.Errorf("model %s tiered expr run failed: %w", info.OriginModelName, err)
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@@ -162,6 +162,7 @@ func PostWssConsumeQuota(ctx *gin.Context, relayInfo *relaycommon.RelayInfo, mod
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tieredOk, tieredQuota, tieredRes := TryTieredSettle(relayInfo, billingexpr.TokenParams{
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P: float64(usage.InputTokens),
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C: float64(usage.OutputTokens),
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Len: float64(usage.InputTokens),
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})
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if tieredOk {
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tieredResult = tieredRes
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@@ -35,6 +35,14 @@ func BuildTieredTokenParams(usage *dto.Usage, isClaudeUsageSemantic bool, usedVa
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imgO := float64(usage.CompletionTokenDetails.ImageTokens)
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ao := float64(usage.CompletionTokenDetails.AudioTokens)
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// len = total input context length for tier condition evaluation.
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// Non-Claude: prompt_tokens already includes everything.
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// Claude: input_tokens is text-only, so add cache read + cache creation.
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inputLen := p
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if isClaudeUsageSemantic {
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inputLen = p + cr + cc5m + cc1h
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}
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if !isClaudeUsageSemantic {
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if usedVars["cr"] {
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p -= cr
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@@ -69,6 +77,7 @@ func BuildTieredTokenParams(usage *dto.Usage, isClaudeUsageSemantic bool, usedVa
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return billingexpr.TokenParams{
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P: p,
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C: c,
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Len: inputLen,
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CR: cr,
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CC: cc5m,
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CC1h: cc1h,
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@@ -604,6 +604,97 @@ func TestBuildTieredTokenParams_ParityWithRatio_Image(t *testing.T) {
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}
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}
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// ---------------------------------------------------------------------------
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// BuildTieredTokenParams: Len computation tests
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// ---------------------------------------------------------------------------
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func TestBuildTieredTokenParams_Len_GPT(t *testing.T) {
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usage := &dto.Usage{
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PromptTokens: 10000,
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CompletionTokens: 2000,
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PromptTokensDetails: dto.InputTokenDetails{
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CachedTokens: 3000,
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TextTokens: 7000,
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},
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}
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expr := `tier("base", p * 2.5 + c * 15 + cr * 0.25)`
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usedVars := billingexpr.UsedVars(expr)
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params := BuildTieredTokenParams(usage, false, usedVars)
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// Non-Claude: Len = raw PromptTokens
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if params.Len != 10000 {
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t.Fatalf("Len = %f, want 10000 (raw PromptTokens)", params.Len)
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}
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// P should be reduced by cache
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if params.P != 7000 {
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t.Fatalf("P = %f, want 7000 (PromptTokens - CachedTokens)", params.P)
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}
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}
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func TestBuildTieredTokenParams_Len_Claude(t *testing.T) {
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usage := &dto.Usage{
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PromptTokens: 5000,
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CompletionTokens: 2000,
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UsageSemantic: "anthropic",
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PromptTokensDetails: dto.InputTokenDetails{
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CachedTokens: 3000,
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TextTokens: 5000,
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},
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ClaudeCacheCreation5mTokens: 1000,
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ClaudeCacheCreation1hTokens: 500,
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}
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expr := `tier("base", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6)`
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usedVars := billingexpr.UsedVars(expr)
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params := BuildTieredTokenParams(usage, true, usedVars)
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// Claude: Len = PromptTokens + CachedTokens + CacheCreation5m + CacheCreation1h
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wantLen := float64(5000 + 3000 + 1000 + 500)
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if params.Len != wantLen {
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t.Fatalf("Len = %f, want %f (text + cache read + cache creation)", params.Len, wantLen)
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}
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// Claude: P is not reduced (isClaudeUsageSemantic = true)
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if params.P != 5000 {
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t.Fatalf("P = %f, want 5000 (no subtraction for Claude)", params.P)
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}
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}
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func TestBuildTieredTokenParams_Len_TierCondition(t *testing.T) {
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// Test that len-based tier conditions work correctly when p is reduced by cache
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usage := &dto.Usage{
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PromptTokens: 300000,
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CompletionTokens: 5000,
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PromptTokensDetails: dto.InputTokenDetails{
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CachedTokens: 250000,
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TextTokens: 50000,
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},
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}
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expr := `len <= 200000 ? tier("standard", p * 3 + c * 15 + cr * 0.3) : tier("long_context", p * 6 + c * 22.5 + cr * 0.6)`
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usedVars := billingexpr.UsedVars(expr)
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params := BuildTieredTokenParams(usage, false, usedVars)
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// Len = 300000 (raw prompt), P = 50000 (300000 - 250000 cache)
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if params.Len != 300000 {
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t.Fatalf("Len = %f, want 300000", params.Len)
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}
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if params.P != 50000 {
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t.Fatalf("P = %f, want 50000", params.P)
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}
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// Run expression: len=300000 > 200000, so long_context tier
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cost, trace, err := billingexpr.RunExpr(expr, params)
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if err != nil {
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t.Fatal(err)
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}
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if trace.MatchedTier != "long_context" {
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t.Fatalf("tier = %s, want long_context (len=300000 but p=50000)", trace.MatchedTier)
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}
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// long_context: 50000*6 + 5000*22.5 + 250000*0.6
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wantCost := 50000.0*6 + 5000*22.5 + 250000*0.6
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if math.Abs(cost-wantCost) > 1e-6 {
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t.Fatalf("cost = %f, want %f", cost, wantCost)
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}
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}
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// ---------------------------------------------------------------------------
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// Stress test: 1000 concurrent goroutines, complex tiered expr vs ratio,
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// random token counts, verify correctness and measure performance
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@@ -54,10 +54,10 @@ func SmokeTestExpr(exprStr string) error {
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func smokeTestExpr(exprStr string) error {
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vectors := []billingexpr.TokenParams{
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{P: 0, C: 0},
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{P: 1000, C: 1000},
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{P: 100000, C: 100000},
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{P: 1000000, C: 1000000},
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{P: 0, C: 0, Len: 0},
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{P: 1000, C: 1000, Len: 1000},
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{P: 100000, C: 100000, Len: 100000},
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{P: 1000000, C: 1000000, Len: 1000000},
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}
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requests := []billingexpr.RequestInput{
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{},
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@@ -21,7 +21,7 @@ import React from 'react';
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import { Avatar, Tag, Table, Typography } from '@douyinfe/semi-ui';
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import { IconPriceTag } from '@douyinfe/semi-icons';
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import { parseTiersFromExpr, getCurrencyConfig } from '../../../../../helpers';
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import { BILLING_VARS } from '../../../../../constants';
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import { BILLING_PRICING_VARS } from '../../../../../constants';
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import {
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splitBillingExprAndRequestRules,
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tryParseRequestRuleExpr,
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@@ -113,7 +113,7 @@ export default function DynamicPricingBreakdown({ billingExpr, t }) {
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);
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}
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const priceFields = BILLING_VARS.map((v) => [v.field, v.shortLabel]);
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const priceFields = BILLING_PRICING_VARS.map((v) => [v.field, v.shortLabel]);
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const tierColumns = [
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{
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+12
-5
@@ -13,6 +13,7 @@
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export const BILLING_VARS = [
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{ key: 'p', field: 'inputPrice', tierField: 'input_unit_cost', label: '输入价格', shortLabel: '输入', side: 'input', isBase: true },
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{ key: 'c', field: 'outputPrice', tierField: 'output_unit_cost', label: '补全价格', shortLabel: '补全', side: 'output', isBase: true },
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{ key: 'len', field: null, tierField: null, label: '输入长度', shortLabel: '长度', side: 'condition', isConditionOnly: true },
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{ key: 'cr', field: 'cacheReadPrice', tierField: 'cache_read_unit_cost', label: '缓存读取价格', shortLabel: '缓存读', side: 'input', group: 'cache' },
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{ key: 'cc', field: 'cacheCreatePrice', tierField: 'cache_create_unit_cost', label: '缓存创建价格', shortLabel: '缓存创建', side: 'input', group: 'cache' },
|
||||
{ key: 'cc1h', field: 'cacheCreate1hPrice', tierField: 'cache_create_1h_unit_cost', label: '1h缓存创建价格', shortLabel: '1h缓存创建', side: 'input', group: 'cache' },
|
||||
@@ -24,18 +25,20 @@ export const BILLING_VARS = [
|
||||
|
||||
export const BILLING_VAR_KEYS = BILLING_VARS.map((v) => v.key);
|
||||
|
||||
export const BILLING_EXTRA_VARS = BILLING_VARS.filter((v) => !v.isBase);
|
||||
export const BILLING_PRICING_VARS = BILLING_VARS.filter((v) => !v.isConditionOnly);
|
||||
|
||||
export const BILLING_EXTRA_VARS = BILLING_VARS.filter((v) => !v.isBase && !v.isConditionOnly);
|
||||
|
||||
export const BILLING_VAR_KEY_TO_FIELD = Object.fromEntries(
|
||||
BILLING_VARS.map((v) => [v.key, v.field]),
|
||||
BILLING_PRICING_VARS.map((v) => [v.key, v.field]),
|
||||
);
|
||||
|
||||
export const BILLING_VAR_FIELD_TO_LABEL = Object.fromEntries(
|
||||
BILLING_VARS.map((v) => [v.field, v.label]),
|
||||
BILLING_PRICING_VARS.map((v) => [v.field, v.label]),
|
||||
);
|
||||
|
||||
export const BILLING_VAR_FIELD_TO_SHORT_LABEL = Object.fromEntries(
|
||||
BILLING_VARS.map((v) => [v.field, v.shortLabel]),
|
||||
BILLING_PRICING_VARS.map((v) => [v.field, v.shortLabel]),
|
||||
);
|
||||
|
||||
export const BILLING_CACHE_VAR_MAP = BILLING_EXTRA_VARS.map((v) => ({
|
||||
@@ -44,6 +47,10 @@ export const BILLING_CACHE_VAR_MAP = BILLING_EXTRA_VARS.map((v) => ({
|
||||
}));
|
||||
|
||||
export const BILLING_VAR_REGEX = new RegExp(
|
||||
`\\b(${BILLING_VAR_KEYS.join('|')})\\s*\\*\\s*([\\d.eE+-]+)`,
|
||||
`\\b(${BILLING_PRICING_VARS.map((v) => v.key).join('|')})\\s*\\*\\s*([\\d.eE+-]+)`,
|
||||
'g',
|
||||
);
|
||||
|
||||
export const BILLING_CONDITION_VARS = BILLING_VARS.filter(
|
||||
(v) => v.isBase || v.isConditionOnly,
|
||||
).map((v) => v.key);
|
||||
|
||||
Vendored
+5
-5
@@ -22,7 +22,7 @@ import { Modal, Tag, Typography, Avatar } from '@douyinfe/semi-ui';
|
||||
import { copy, showSuccess } from './utils';
|
||||
import { MOBILE_BREAKPOINT } from '../hooks/common/useIsMobile';
|
||||
import {
|
||||
BILLING_VARS,
|
||||
BILLING_PRICING_VARS,
|
||||
BILLING_VAR_KEY_TO_FIELD,
|
||||
BILLING_VAR_REGEX,
|
||||
} from '../constants';
|
||||
@@ -2246,7 +2246,7 @@ export function parseTiersFromExpr(exprStr) {
|
||||
if (!exprStr) return [];
|
||||
try {
|
||||
const { body } = stripExprVersion(exprStr);
|
||||
const condGroup = `((?:(?:p|c)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)(?:\\s*&&\\s*(?:p|c)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)*)`;
|
||||
const condGroup = `((?:(?:p|c|len)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)(?:\\s*&&\\s*(?:p|c|len)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)*)`;
|
||||
const tierRe = new RegExp(`(?:${condGroup}\\s*\\?\\s*)?tier\\("([^"]*)",\\s*([^)]+)\\)`, 'g');
|
||||
const tiers = [];
|
||||
let m;
|
||||
@@ -2255,7 +2255,7 @@ export function parseTiersFromExpr(exprStr) {
|
||||
const conditions = [];
|
||||
if (condStr) {
|
||||
for (const cp of condStr.split(/\s*&&\s*/)) {
|
||||
const cm = cp.trim().match(/^(p|c)\s*(<|<=|>|>=)\s*([\d.eE+]+)$/);
|
||||
const cm = cp.trim().match(/^(p|c|len)\s*(<|<=|>|>=)\s*([\d.eE+]+)$/);
|
||||
if (cm) conditions.push({ var: cm[1], op: cm[2], value: Number(cm[3]) });
|
||||
}
|
||||
}
|
||||
@@ -2293,7 +2293,7 @@ export function renderTieredModelPrice(opts) {
|
||||
const { symbol, rate } = getCurrencyConfig();
|
||||
const gr = groupRatio || 1;
|
||||
|
||||
const priceLines = BILLING_VARS.map((v) => [v.field, v.label]);
|
||||
const priceLines = BILLING_PRICING_VARS.map((v) => [v.field, v.label]);
|
||||
|
||||
const lines = [
|
||||
buildBillingText('命中档位：{{tier}}', { tier: matchedTier || tier.label }),
|
||||
@@ -2334,7 +2334,7 @@ export function renderTieredModelPriceSimple(opts) {
|
||||
];
|
||||
|
||||
if (tier && isPriceDisplayMode(displayMode)) {
|
||||
const priceSegments = BILLING_VARS.map((v) => [v.field, v.shortLabel]);
|
||||
const priceSegments = BILLING_PRICING_VARS.map((v) => [v.field, v.shortLabel]);
|
||||
for (const [field, label] of priceSegments) {
|
||||
if (tier[field] > 0) {
|
||||
segments.push({
|
||||
|
||||
Vendored
+2
-2
@@ -18,7 +18,7 @@ For commercial licensing, please contact support@quantumnous.com
|
||||
*/
|
||||
|
||||
import { Toast, Pagination } from '@douyinfe/semi-ui';
|
||||
import { toastConstants, BILLING_VARS, BILLING_VAR_REGEX } from '../constants';
|
||||
import { toastConstants, BILLING_PRICING_VARS, BILLING_VAR_REGEX } from '../constants';
|
||||
import React from 'react';
|
||||
import { toast } from 'react-toastify';
|
||||
import {
|
||||
@@ -927,7 +927,7 @@ export const formatDynamicPriceSummary = (billingExpr, t, groupRatio = 1) => {
|
||||
}
|
||||
const hasCoeffs = 'p' in varCoeffs || 'c' in varCoeffs;
|
||||
|
||||
const varLabels = BILLING_VARS.map((v) => [v.key, v.label]);
|
||||
const varLabels = BILLING_PRICING_VARS.map((v) => [v.key, v.label]);
|
||||
|
||||
const hasTimeCondition = /\b(?:hour|minute|weekday|month|day)\(/.test(exprBody);
|
||||
const hasRequestCondition = /\b(?:param|header)\(/.test(exprBody);
|
||||
|
||||
@@ -31,9 +31,10 @@ import {
|
||||
TextArea,
|
||||
Typography,
|
||||
} from '@douyinfe/semi-ui';
|
||||
import { IconDelete, IconPlus } from '@douyinfe/semi-icons';
|
||||
import { IconCopy, IconDelete, IconPlus } from '@douyinfe/semi-icons';
|
||||
import { renderQuota } from '../../../../helpers/render';
|
||||
import { BILLING_EXTRA_VARS, BILLING_CACHE_VAR_MAP } from '../../../../constants';
|
||||
import { copy, showSuccess } from '../../../../helpers';
|
||||
import { BILLING_EXTRA_VARS, BILLING_CACHE_VAR_MAP, BILLING_CONDITION_VARS } from '../../../../constants';
|
||||
import {
|
||||
createEmptyCondition,
|
||||
createEmptyTimeCondition,
|
||||
@@ -70,6 +71,7 @@ function priceToUnitCost(price) {
|
||||
|
||||
const OPS = ['<', '<=', '>', '>='];
|
||||
const VAR_OPTIONS = [
|
||||
{ value: 'len', label: 'len (长度)' },
|
||||
{ value: 'p', label: 'p (输入)' },
|
||||
{ value: 'c', label: 'c (输出)' },
|
||||
];
|
||||
@@ -224,7 +226,7 @@ function tryParseVisualConfig(exprStr) {
|
||||
}
|
||||
|
||||
// Multi-tier: cond1 ? tier(body) : cond2 ? tier(body) : tier(body)
|
||||
const condGroup = `((?:(?:p|c)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)(?:\\s*&&\\s*(?:p|c)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)*)`;
|
||||
const condGroup = `((?:(?:p|c|len)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)(?:\\s*&&\\s*(?:p|c|len)\\s*(?:<|<=|>|>=)\\s*[\\d.eE+]+)*)`;
|
||||
const tierRe = new RegExp(
|
||||
`(?:${condGroup}\\s*\\?\\s*)?tier\\("([^"]*)",\\s*${bodyPat}\\)`,
|
||||
'g',
|
||||
@@ -237,7 +239,7 @@ function tryParseVisualConfig(exprStr) {
|
||||
if (condStr) {
|
||||
const condParts = condStr.split(/\s*&&\s*/);
|
||||
for (const cp of condParts) {
|
||||
const cm = cp.trim().match(/^(p|c)\s*(<|<=|>|>=)\s*([\d.eE+]+)$/);
|
||||
const cm = cp.trim().match(/^(p|c|len)\s*(<|<=|>|>=)\s*([\d.eE+]+)$/);
|
||||
if (cm) {
|
||||
conditions.push({ var: cm[1], op: cm[2], value: Number(cm[3]) });
|
||||
}
|
||||
@@ -283,7 +285,7 @@ function ConditionRow({ cond, onChange, onRemove, t }) {
|
||||
}}>
|
||||
<Select
|
||||
size='small'
|
||||
value={cond.var || 'p'}
|
||||
value={cond.var || 'len'}
|
||||
onChange={(val) => onChange({ ...cond, var: val })}
|
||||
>
|
||||
{VAR_OPTIONS.map((v) => (
|
||||
@@ -500,7 +502,7 @@ function ExtendedPriceBlock({ tier, index, onUpdate, t }) {
|
||||
function VisualTierCard({ tier, index, isLast, isOnly, onUpdate, onRemove, t }) {
|
||||
const conditions = tier.conditions || [];
|
||||
|
||||
const varLabel = { p: t('输入'), c: t('输出') };
|
||||
const varLabel = { len: t('长度'), p: t('输入'), c: t('输出') };
|
||||
const condSummary = useMemo(() => {
|
||||
if (conditions.length === 0) return t('无条件（兜底档）');
|
||||
return conditions
|
||||
@@ -525,7 +527,7 @@ function VisualTierCard({ tier, index, isLast, isOnly, onUpdate, onRemove, t })
|
||||
const addCondition = () => {
|
||||
if (conditions.length >= 2) return;
|
||||
const usedVars = conditions.map((c) => c.var);
|
||||
const nextVar = usedVars.includes('p') ? 'c' : 'p';
|
||||
const nextVar = usedVars.includes('len') ? 'c' : 'len';
|
||||
onUpdate(index, 'conditions', [
|
||||
...conditions,
|
||||
{ var: nextVar, op: '<', value: 200000 },
|
||||
@@ -694,7 +696,7 @@ function VisualEditor({ visualConfig, onChange, t }) {
|
||||
) {
|
||||
newTiers[newTiers.length - 1] = {
|
||||
...newTiers[newTiers.length - 1],
|
||||
conditions: [{ var: 'p', op: '<', value: 200000 }],
|
||||
conditions: [{ var: 'len', op: '<', value: 200000 }],
|
||||
};
|
||||
}
|
||||
newTiers.push({
|
||||
@@ -723,7 +725,7 @@ function VisualEditor({ visualConfig, onChange, t }) {
|
||||
<div>
|
||||
<Banner
|
||||
type='info'
|
||||
description={t('每个档位可设置 0~2 个条件（对 p 和 c），最后一档为兜底档无需条件。')}
|
||||
description={t('每个档位可设置 0~2 个条件（对 len、p 和 c），最后一档为兜底档无需条件。len 为输入上下文总长度（含缓存），推荐用于阶梯条件。')}
|
||||
style={{ marginBottom: 12 }}
|
||||
/>
|
||||
|
||||
@@ -762,16 +764,16 @@ const PRESET_GROUPS = [
|
||||
presets: [
|
||||
{ key: 'flat', label: 'Flat', expr: 'tier("base", p * 2 + c * 4)' },
|
||||
{ key: 'claude-opus', label: 'Claude Opus 4.6', expr: 'tier("base", p * 5 + c * 25 + cr * 0.5 + cc * 6.25 + cc1h * 10)' },
|
||||
{ key: 'gpt-5.4', label: 'GPT-5.4', expr: 'p <= 272000 ? tier("standard", p * 2.5 + c * 15 + cr * 0.25) : tier("long_context", p * 5 + c * 22.5 + cr * 0.5)' },
|
||||
{ key: 'gpt-5.4', label: 'GPT-5.4', expr: 'len <= 272000 ? tier("standard", p * 2.5 + c * 15 + cr * 0.25) : tier("long_context", p * 5 + c * 22.5 + cr * 0.5)' },
|
||||
],
|
||||
},
|
||||
{
|
||||
group: '阶梯计费',
|
||||
presets: [
|
||||
{ key: 'claude-sonnet', label: 'Claude Sonnet 4.5', expr: 'p <= 200000 ? tier("standard", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6) : tier("long_context", p * 6 + c * 22.5 + cr * 0.6 + cc * 7.5 + cc1h * 12)' },
|
||||
{ key: 'qwen3-max', label: 'Qwen3 Max', expr: 'p <= 32000 ? tier("short", p * 1.2 + c * 6 + cr * 0.24 + cc * 1.5) : p <= 128000 ? tier("mid", p * 2.4 + c * 12 + cr * 0.48 + cc * 3) : tier("long", p * 3 + c * 15 + cr * 0.6 + cc * 3.75)' },
|
||||
{ key: 'glm-4.5-air', label: 'GLM-4.5 Air', expr: 'p < 32000 && c < 200 ? tier("short_output", p * 0.8 + c * 2 + cr * 0.16) : p < 32000 && c >= 200 ? tier("long_output", p * 0.8 + c * 6 + cr * 0.16) : tier("mid_context", p * 1.2 + c * 8 + cr * 0.24)' },
|
||||
{ key: 'doubao-seed-1.8', label: 'Doubao Seed 1.8', expr: 'p <= 32000 && c <= 200 ? tier("discount", p * 0.8 + c * 2 + cr * 0.16 + cc * 0.17) : p <= 32000 ? tier("short", p * 0.8 + c * 8 + cr * 0.16 + cc * 0.17) : p <= 128000 ? tier("mid", p * 1.2 + c * 16 + cr * 0.16 + cc * 0.17) : tier("long", p * 2.4 + c * 24 + cr * 0.16 + cc * 0.17)' },
|
||||
{ key: 'claude-sonnet', label: 'Claude Sonnet 4.5', expr: 'len <= 200000 ? tier("standard", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6) : tier("long_context", p * 6 + c * 22.5 + cr * 0.6 + cc * 7.5 + cc1h * 12)' },
|
||||
{ key: 'qwen3-max', label: 'Qwen3 Max', expr: 'len <= 32000 ? tier("short", p * 1.2 + c * 6 + cr * 0.24 + cc * 1.5) : len <= 128000 ? tier("mid", p * 2.4 + c * 12 + cr * 0.48 + cc * 3) : tier("long", p * 3 + c * 15 + cr * 0.6 + cc * 3.75)' },
|
||||
{ key: 'glm-4.5-air', label: 'GLM-4.5 Air', expr: 'len < 32000 && c < 200 ? tier("short_output", p * 0.8 + c * 2 + cr * 0.16) : len < 32000 && c >= 200 ? tier("long_output", p * 0.8 + c * 6 + cr * 0.16) : tier("mid_context", p * 1.2 + c * 8 + cr * 0.24)' },
|
||||
{ key: 'doubao-seed-1.8', label: 'Doubao Seed 1.8', expr: 'len <= 32000 && c <= 200 ? tier("discount", p * 0.8 + c * 2 + cr * 0.16 + cc * 0.17) : len <= 32000 ? tier("short", p * 0.8 + c * 8 + cr * 0.16 + cc * 0.17) : len <= 128000 ? tier("mid", p * 1.2 + c * 16 + cr * 0.16 + cc * 0.17) : tier("long", p * 2.4 + c * 24 + cr * 0.16 + cc * 0.17)' },
|
||||
],
|
||||
},
|
||||
{
|
||||
@@ -793,7 +795,7 @@ const PRESET_GROUPS = [
|
||||
},
|
||||
{
|
||||
key: 'gpt-5.4-tiers', label: 'GPT-5.4 Priority/Flex',
|
||||
expr: 'p <= 272000 ? tier("standard", p * 2.5 + c * 15 + cr * 0.25) : tier("long_context", p * 5 + c * 22.5 + cr * 0.5)',
|
||||
expr: 'len <= 272000 ? tier("standard", p * 2.5 + c * 15 + cr * 0.25) : tier("long_context", p * 5 + c * 22.5 + cr * 0.5)',
|
||||
requestRules: [
|
||||
{ conditions: [{ source: SOURCE_PARAM, path: 'service_tier', mode: MATCH_EQ, value: 'priority' }], multiplier: '2' },
|
||||
{ conditions: [{ source: SOURCE_PARAM, path: 'service_tier', mode: MATCH_EQ, value: 'flex' }], multiplier: '0.5' },
|
||||
@@ -880,7 +882,8 @@ function RawExprEditor({ exprString, onChange, t }) {
|
||||
<div>
|
||||
<div>
|
||||
{t('变量')}: <code>p</code> ({t('输入 Token')}), <code>c</code> (
|
||||
{t('输出 Token')}), <code>cr</code> ({t('缓存读取')}),{' '}
|
||||
{t('输出 Token')}), <code>len</code> ({t('输入长度')}),{' '}
|
||||
<code>cr</code> ({t('缓存读取')}),{' '}
|
||||
<code>cc</code> ({t('缓存创建')}),{' '}
|
||||
<code>cc1h</code> ({t('缓存创建-1小时')})
|
||||
</div>
|
||||
@@ -968,7 +971,11 @@ function evalExprLocally(exprStr, p, c, extraTokenValues) {
|
||||
matchedTier = name;
|
||||
return value;
|
||||
};
|
||||
const env = { p, c, tier: tierFn, max: Math.max, min: Math.min, abs: Math.abs, ceil: Math.ceil, floor: Math.floor };
|
||||
const cacheReadTokens = extraTokenValues.cacheReadTokens || 0;
|
||||
const cacheCreateTokens = extraTokenValues.cacheCreateTokens || 0;
|
||||
const cacheCreate1hTokens = extraTokenValues.cacheCreate1hTokens || 0;
|
||||
const len = p + cacheReadTokens + cacheCreateTokens + cacheCreate1hTokens;
|
||||
const env = { p, c, len, tier: tierFn, max: Math.max, min: Math.min, abs: Math.abs, ceil: Math.ceil, floor: Math.floor };
|
||||
for (const field of EXTRA_ESTIMATOR_FIELDS) {
|
||||
env[field.var] = extraTokenValues[field.stateKey] || 0;
|
||||
}
|
||||
@@ -1220,6 +1227,146 @@ function RuleGroupCard({ group, index, onChange, onRemove, t }) {
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// LLM prompt helper — copyable prompt for LLM-assisted expression design
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
const LLM_PROMPT_TEMPLATE = `你是一个 AI API 计费表达式设计助手。用户需要你帮忙设计一个计费表达式（billing expression），用于 AI API 网关的模型计费。
|
||||
|
||||
## 表达式语言
|
||||
|
||||
表达式基于 expr-lang/expr，支持标准算术运算和三元运算符。
|
||||
|
||||
### Token 变量
|
||||
|
||||
输入侧：
|
||||
- p — 输入 token 数（计价用）。系统会自动排除表达式中单独计价的子类别（如用了 cr，缓存 token 就从 p 中扣除）
|
||||
- len — 输入上下文总长度（条件判断用）。不受自动排除影响，始终反映完整输入长度。用于阶梯条件判断
|
||||
- cr — 缓存命中（读取）token 数
|
||||
- cc — 缓存创建 token 数（5分钟 TTL）
|
||||
- cc1h — 缓存创建 token 数（1小时 TTL，Claude 专用）
|
||||
- img — 图片输入 token 数
|
||||
- ai — 音频输入 token 数
|
||||
|
||||
输出侧：
|
||||
- c — 输出 token 数。同样会自动排除单独计价的子类别
|
||||
- img_o — 图片输出 token 数
|
||||
- ao — 音频输出 token 数
|
||||
|
||||
### p/c 自动排除机制
|
||||
|
||||
p 和 c 是兜底变量，代表所有没有被表达式单独定价的 token。如果表达式使用了某个子类别变量（如 cr），对应 token 就从 p 中扣除，避免重复计费。没用到的子类别 token 则留在 p/c 中按基础价格计费。
|
||||
|
||||
重要：len 不受自动排除影响。阶梯条件应使用 len 而非 p，以避免缓存命中导致 p 降低而误判档位。
|
||||
|
||||
### 内置函数
|
||||
|
||||
- tier(name, value) — 标记计费档位名称，必须包裹费用表达式
|
||||
- max(a, b)、min(a, b) — 取大/小值
|
||||
- ceil(x)、floor(x)、abs(x) — 向上取整、向下取整、绝对值
|
||||
- header(name) — 读取请求头
|
||||
- param(path) — 读取请求体 JSON 路径（gjson 语法）
|
||||
- has(source, substr) — 子字符串检查
|
||||
- hour(tz)、minute(tz)、weekday(tz)、month(tz)、day(tz) — 时间函数，tz 为时区如 "Asia/Shanghai"
|
||||
|
||||
### 价格系数
|
||||
|
||||
表达式中的数字系数是 $/1M tokens 的价格。例如 p * 2.5 表示输入 $2.50/1M tokens。
|
||||
|
||||
## 表达式示例
|
||||
|
||||
简单定价：
|
||||
tier("base", p * 2.5 + c * 15)
|
||||
|
||||
带缓存的定价：
|
||||
tier("base", p * 2.5 + c * 15 + cr * 0.25)
|
||||
|
||||
多档阶梯（用 len 做条件）：
|
||||
len <= 200000
|
||||
? tier("standard", p * 3 + c * 15 + cr * 0.3 + cc * 3.75 + cc1h * 6)
|
||||
: tier("long_context", p * 6 + c * 22.5 + cr * 0.6 + cc * 7.5 + cc1h * 12)
|
||||
|
||||
图片模型：
|
||||
tier("base", p * 2 + c * 8 + img * 2.5)
|
||||
|
||||
多模态含音频：
|
||||
tier("base", p * 0.43 + c * 3.06 + img * 0.78 + ai * 3.81 + ao * 15.11)
|
||||
|
||||
三档阶梯示例：
|
||||
len <= 128000
|
||||
? tier("standard", p * 1.1 + c * 4.4)
|
||||
: (len <= 1000000
|
||||
? tier("medium", p * 2.2 + c * 8.8)
|
||||
: tier("long", p * 4.4 + c * 17.6))
|
||||
|
||||
## 规则
|
||||
|
||||
1. 每个叶子分支必须用 tier("名称", 费用表达式) 包裹
|
||||
2. tier 名称用英文，如 "base"、"standard"、"long_context"
|
||||
3. 阶梯条件用 len（不要用 p），支持 <、<=、>、>=
|
||||
4. 多档用嵌套三元运算符：条件1 ? tier(...) : (条件2 ? tier(...) : tier(...))
|
||||
5. 价格系数直接写供应商官方 $/1M tokens 价格
|
||||
6. 不需要缓存/图片/音频单独定价时可以不写对应变量，它们的 token 会自动包含在 p/c 中
|
||||
|
||||
请根据用户提供的模型信息和定价需求，生成计费表达式。`;
|
||||
|
||||
function LlmPromptHelper({ t, model }) {
|
||||
const [open, setOpen] = useState(false);
|
||||
|
||||
const modelName = model?.name || '';
|
||||
const prompt = useMemo(() => {
|
||||
if (modelName) {
|
||||
return LLM_PROMPT_TEMPLATE + `\n\n当前模型：${modelName}`;
|
||||
}
|
||||
return LLM_PROMPT_TEMPLATE;
|
||||
}, [modelName]);
|
||||
|
||||
const handleCopy = useCallback(async () => {
|
||||
const ok = await copy(prompt);
|
||||
if (ok) showSuccess(t('已复制到剪贴板'));
|
||||
}, [prompt, t]);
|
||||
|
||||
return (
|
||||
<div style={{ marginBottom: 12 }}>
|
||||
<Button
|
||||
theme='borderless'
|
||||
size='small'
|
||||
icon={<IconCopy />}
|
||||
onClick={() => setOpen(!open)}
|
||||
style={{ color: 'var(--semi-color-tertiary)' }}
|
||||
>
|
||||
{t('LLM 辅助设计提示词')}
|
||||
</Button>
|
||||
<Collapsible isOpen={open}>
|
||||
<Card
|
||||
bodyStyle={{ padding: 12 }}
|
||||
style={{ marginTop: 8, background: 'var(--semi-color-fill-0)' }}
|
||||
>
|
||||
<div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: 8 }}>
|
||||
<Text size='small' type='secondary'>
|
||||
{t('复制以下提示词发送给 LLM（如 ChatGPT / Claude），让它帮你设计计费表达式')}
|
||||
</Text>
|
||||
<Button
|
||||
icon={<IconCopy />}
|
||||
size='small'
|
||||
theme='light'
|
||||
onClick={handleCopy}
|
||||
>
|
||||
{t('复制提示词')}
|
||||
</Button>
|
||||
</div>
|
||||
<TextArea
|
||||
value={prompt}
|
||||
readonly
|
||||
autosize={{ minRows: 6, maxRows: 20 }}
|
||||
style={{ fontFamily: 'monospace', fontSize: 12 }}
|
||||
/>
|
||||
</Card>
|
||||
</Collapsible>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Main component
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -1543,6 +1690,8 @@ export default function TieredPricingEditor({ model, onExprChange, requestRuleEx
|
||||
</div>
|
||||
</Card>
|
||||
|
||||
<LlmPromptHelper t={t} model={model} />
|
||||
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user