mirror of
https://github.com/windmill-labs/windmill.git
synced 2026-08-18 16:02:10 +00:00
fix: address review findings on AI cost tracking
This commit is contained in:
hugocasa
+4
-3
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"db_name": "PostgreSQL",
|
||||
"query": "SELECT\n (CASE $3::text\n WHEN 'day' THEN day::text\n WHEN 'user' THEN email\n WHEN 'session' THEN session_id\n ELSE ''\n END) AS \"key!\",\n provider AS \"provider!\",\n model AS \"model!\",\n SUM(input_tokens)::bigint AS \"input_tokens!\",\n SUM(cache_read_tokens)::bigint AS \"cache_read_tokens!\",\n SUM(cache_write_tokens)::bigint AS \"cache_write_tokens!\",\n SUM(output_tokens)::bigint AS \"output_tokens!\",\n SUM(reported_cost_nano_usd)::bigint AS \"reported_cost_nano_usd\",\n SUM(requests)::bigint AS \"requests!\"\n FROM ai_token_usage\n WHERE workspace_id = $1 AND day >= CURRENT_DATE - $2::int\n GROUP BY 1, provider, model\n ORDER BY 1 DESC, SUM(input_tokens + output_tokens) DESC\n LIMIT 1000",
|
||||
"query": "SELECT\n (CASE $3::text\n WHEN 'day' THEN day::text\n WHEN 'user' THEN email\n WHEN 'session' THEN session_id\n ELSE ''\n END) AS \"key!\",\n provider AS \"provider!\",\n model AS \"model!\",\n SUM(input_tokens)::bigint AS \"input_tokens!\",\n SUM(cache_read_tokens)::bigint AS \"cache_read_tokens!\",\n SUM(cache_write_tokens)::bigint AS \"cache_write_tokens!\",\n SUM(output_tokens)::bigint AS \"output_tokens!\",\n SUM(reported_cost_nano_usd)::bigint AS \"reported_cost_nano_usd\",\n SUM(requests)::bigint AS \"requests!\"\n FROM ai_token_usage\n WHERE workspace_id = $1 AND day > CURRENT_DATE - $2::int\n GROUP BY 1, provider, model\n ORDER BY SUM(input_tokens + cache_read_tokens + cache_write_tokens + output_tokens) DESC\n LIMIT $4",
|
||||
"describe": {
|
||||
"columns": [
|
||||
{
|
||||
@@ -53,7 +53,8 @@
|
||||
"Left": [
|
||||
"Text",
|
||||
"Int4",
|
||||
"Text"
|
||||
"Text",
|
||||
"Int8"
|
||||
]
|
||||
},
|
||||
"nullable": [
|
||||
@@ -68,5 +69,5 @@
|
||||
null
|
||||
]
|
||||
},
|
||||
"hash": "43ec1c4adf6e453848b532bf3fab5de8825d247ea0b337007ee43c9cdffac83d"
|
||||
"hash": "9c4c5ced0473e5c6e4a08995e8ec6b109ee9f016af5d6b9128b7c3a16d560615"
|
||||
}
|
||||
@@ -11886,9 +11886,18 @@ paths:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
type: array
|
||||
items:
|
||||
$ref: "#/components/schemas/AITokenUsageBucket"
|
||||
type: object
|
||||
required:
|
||||
- buckets
|
||||
- truncated
|
||||
properties:
|
||||
buckets:
|
||||
type: array
|
||||
items:
|
||||
$ref: "#/components/schemas/AITokenUsageBucket"
|
||||
truncated:
|
||||
type: boolean
|
||||
description: more buckets matched than were returned, so summing them under-reports
|
||||
|
||||
/w/{workspace}/ai_skills/list:
|
||||
get:
|
||||
@@ -26062,12 +26071,20 @@ components:
|
||||
properties:
|
||||
input:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1000
|
||||
output:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1000
|
||||
cache_read:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1000
|
||||
cache_write:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1000
|
||||
required:
|
||||
- input
|
||||
- output
|
||||
|
||||
@@ -6,7 +6,7 @@ use axum::routing::get;
|
||||
use axum::Json;
|
||||
use axum::{
|
||||
body::Bytes,
|
||||
extract::{Path, Query},
|
||||
extract::{DefaultBodyLimit, Path, Query},
|
||||
response::IntoResponse,
|
||||
routing::post,
|
||||
Extension, Router,
|
||||
@@ -441,7 +441,39 @@ pub struct ModelPriceOverride {
|
||||
pub cache_write: Option<f64>,
|
||||
}
|
||||
|
||||
/// Far above any real per-million-token rate, so a value beyond it is a unit
|
||||
/// mistake rather than a price. The floor matters more: a negative rate would make
|
||||
/// spend subtract, and NaN/infinity would poison every total derived from it.
|
||||
pub const MAX_MODEL_RATE: f64 = 1000.0;
|
||||
|
||||
impl ModelPriceOverride {
|
||||
pub fn validate(&self, key: &str) -> Result<()> {
|
||||
for (field, rate) in [
|
||||
("input", Some(self.input)),
|
||||
("output", Some(self.output)),
|
||||
("cache_read", self.cache_read),
|
||||
("cache_write", self.cache_write),
|
||||
] {
|
||||
let Some(rate) = rate else { continue };
|
||||
if !rate.is_finite() || rate < 0.0 || rate > MAX_MODEL_RATE {
|
||||
return Err(Error::BadRequest(format!(
|
||||
"Price override for {}: {} must be between 0 and {}",
|
||||
key, field, MAX_MODEL_RATE
|
||||
)));
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl AIConfig {
|
||||
pub fn validate_model_pricing(&self) -> Result<()> {
|
||||
for (key, price) in self.model_pricing.iter().flatten() {
|
||||
price.validate(key)?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn has_providers(&self) -> bool {
|
||||
self.providers
|
||||
.as_ref()
|
||||
@@ -456,7 +488,16 @@ pub fn global_service() -> Router {
|
||||
pub fn workspaced_service() -> Router {
|
||||
let router = Router::new()
|
||||
.route("/proxy/{*ai}", post(proxy).get(proxy))
|
||||
.route("/usage", post(record_ai_usage).get(list_ai_usage));
|
||||
.route(
|
||||
"/usage",
|
||||
post(record_ai_usage)
|
||||
.get(list_ai_usage)
|
||||
// The handler caps how many events it *stores*, but Json deserializes
|
||||
// the whole array first — without a body limit an authenticated member
|
||||
// could make the server allocate and parse an arbitrarily large one.
|
||||
// Sized well above a full batch of the shape below.
|
||||
.layer(DefaultBodyLimit::max(AI_USAGE_BODY_LIMIT)),
|
||||
);
|
||||
|
||||
#[cfg(feature = "bedrock")]
|
||||
let router = router.route("/check_bedrock_credentials", get(check_bedrock_credentials));
|
||||
@@ -492,6 +533,8 @@ struct RecordAIUsagePayload {
|
||||
}
|
||||
|
||||
const MAX_AI_USAGE_EVENTS: usize = 50;
|
||||
/// 64 KiB — a 50-event batch is a few kB even with the longest model ids.
|
||||
const AI_USAGE_BODY_LIMIT: usize = 64 * 1024;
|
||||
/// Well above any single conversation and far below an i64 overflow, so a client
|
||||
/// bug caps out at one absurd row instead of poisoning the running total.
|
||||
const MAX_TOKENS_PER_EVENT: i64 = 100_000_000;
|
||||
@@ -636,12 +679,24 @@ struct AITokenUsageBucket {
|
||||
requests: i64,
|
||||
}
|
||||
|
||||
/// Grouping by session (or by day over a long range) can produce more buckets than
|
||||
/// a table is worth rendering, so the listing is capped. `truncated` says so
|
||||
/// explicitly — a caller that sums the rows into a total must be able to tell that
|
||||
/// the total is partial rather than silently under-reporting spend.
|
||||
#[derive(Serialize)]
|
||||
struct AITokenUsageListing {
|
||||
buckets: Vec<AITokenUsageBucket>,
|
||||
truncated: bool,
|
||||
}
|
||||
|
||||
const AI_USAGE_MAX_BUCKETS: i64 = 1000;
|
||||
|
||||
async fn list_ai_usage(
|
||||
authed: ApiAuthed,
|
||||
Extension(db): Extension<DB>,
|
||||
Path(w_id): Path<String>,
|
||||
Query(query): Query<ListAIUsageQuery>,
|
||||
) -> Result<Json<Vec<AITokenUsageBucket>>> {
|
||||
) -> Result<Json<AITokenUsageListing>> {
|
||||
require_admin(authed.is_admin, &authed.username)?;
|
||||
|
||||
let days = query.days.unwrap_or(30).clamp(1, 365);
|
||||
@@ -653,7 +708,9 @@ async fn list_ai_usage(
|
||||
)));
|
||||
}
|
||||
|
||||
let rows = sqlx::query_as!(
|
||||
// Fetch one past the cap to detect truncation, and order by spend so a capped
|
||||
// listing keeps the buckets worth looking at rather than an arbitrary slice.
|
||||
let mut rows = sqlx::query_as!(
|
||||
AITokenUsageBucket,
|
||||
r#"SELECT
|
||||
(CASE $3::text
|
||||
@@ -671,18 +728,22 @@ async fn list_ai_usage(
|
||||
SUM(reported_cost_nano_usd)::bigint AS "reported_cost_nano_usd",
|
||||
SUM(requests)::bigint AS "requests!"
|
||||
FROM ai_token_usage
|
||||
WHERE workspace_id = $1 AND day >= CURRENT_DATE - $2::int
|
||||
WHERE workspace_id = $1 AND day > CURRENT_DATE - $2::int
|
||||
GROUP BY 1, provider, model
|
||||
ORDER BY 1 DESC, SUM(input_tokens + output_tokens) DESC
|
||||
LIMIT 1000"#,
|
||||
ORDER BY SUM(input_tokens + cache_read_tokens + cache_write_tokens + output_tokens) DESC
|
||||
LIMIT $4"#,
|
||||
&w_id,
|
||||
days,
|
||||
group_by
|
||||
group_by,
|
||||
AI_USAGE_MAX_BUCKETS + 1
|
||||
)
|
||||
.fetch_all(&db)
|
||||
.await?;
|
||||
|
||||
Ok(Json(rows))
|
||||
let truncated = rows.len() as i64 > AI_USAGE_MAX_BUCKETS;
|
||||
rows.truncate(AI_USAGE_MAX_BUCKETS as usize);
|
||||
|
||||
Ok(Json(AITokenUsageListing { buckets: rows, truncated }))
|
||||
}
|
||||
|
||||
/// Check if AWS Bedrock credentials are available from environment variables.
|
||||
|
||||
@@ -108,6 +108,8 @@ async fn edit_copilot_config(
|
||||
}
|
||||
}
|
||||
|
||||
ai_config.validate_model_pricing()?;
|
||||
|
||||
let mut tx = db.begin().await?;
|
||||
|
||||
sqlx::query!(
|
||||
|
||||
@@ -56,7 +56,7 @@ import {
|
||||
import { dfs } from '$lib/components/flows/previousResults'
|
||||
import { SvelteMap, SvelteSet } from 'svelte/reactivity'
|
||||
import { createLongHash } from '$lib/editorLangUtils'
|
||||
import type { UserDraftItemKind } from '$lib/gen'
|
||||
import type { AIProvider, UserDraftItemKind } from '$lib/gen'
|
||||
import { maskKey } from '$lib/components/sessions/modifiedItemsMask'
|
||||
import { getStringError } from './utils'
|
||||
import { type PasteAttachment } from './pasteTokens'
|
||||
@@ -103,6 +103,7 @@ import {
|
||||
addModelTokenUsage,
|
||||
billedTokens,
|
||||
normalizeContextUsage,
|
||||
type ChatTokenUsage,
|
||||
type ModelTokenUsageTotals
|
||||
} from './tokenUsage'
|
||||
import { logAiUsage } from '$lib/utils/aiUsageReporter'
|
||||
@@ -663,33 +664,27 @@ export class AIChatManager {
|
||||
return Object.keys(snapshot).length > 0 ? snapshot : undefined
|
||||
}
|
||||
|
||||
/** Fold a completed turn's usage into the conversation's running spend and
|
||||
* report it for the workspace usage view. Only token counts leave the browser:
|
||||
* rates are applied when the usage is read, so a corrected price also corrects
|
||||
* everything already recorded. */
|
||||
private recordUsage(byModel: ModelTokenUsageTotals | undefined) {
|
||||
// Accounting must never take a turn down with it: a path that reports no
|
||||
// per-model breakdown simply records nothing.
|
||||
for (const entry of Object.values(byModel ?? {})) {
|
||||
this.usageByModel = addModelTokenUsage(
|
||||
this.usageByModel,
|
||||
entry.provider,
|
||||
entry.model,
|
||||
entry.usage
|
||||
)
|
||||
const tokens = billedTokens(entry.usage)
|
||||
logAiUsage({
|
||||
provider: entry.provider,
|
||||
model: entry.model,
|
||||
sessionId: this.sessionId,
|
||||
inputTokens: tokens.input,
|
||||
cacheReadTokens: tokens.cacheRead,
|
||||
cacheWriteTokens: tokens.cacheWrite,
|
||||
outputTokens: tokens.output,
|
||||
costUsd: entry.usage.cost,
|
||||
workspace: this.operatingWorkspace
|
||||
})
|
||||
}
|
||||
/** Fold one completed provider response into the conversation's running spend
|
||||
* and report it for the workspace usage view. Called per response rather than
|
||||
* per turn: a tool loop makes several, each separately billed, and a turn that
|
||||
* fails partway through has still spent everything up to that point.
|
||||
*
|
||||
* Only token counts leave the browser — rates are applied when the usage is
|
||||
* read, so a corrected price also corrects everything already recorded. */
|
||||
private recordUsage(usage: ChatTokenUsage, provider: AIProvider, model: string) {
|
||||
this.usageByModel = addModelTokenUsage(this.usageByModel, provider, model, usage)
|
||||
const tokens = billedTokens(usage)
|
||||
logAiUsage({
|
||||
provider,
|
||||
model,
|
||||
sessionId: this.sessionId,
|
||||
inputTokens: tokens.input,
|
||||
cacheReadTokens: tokens.cacheRead,
|
||||
cacheWriteTokens: tokens.cacheWrite,
|
||||
outputTokens: tokens.output,
|
||||
costUsd: usage.cost,
|
||||
workspace: this.operatingWorkspace
|
||||
})
|
||||
}
|
||||
|
||||
// Serialized, snapshot-at-write-time persistence: two rapid dock actions
|
||||
@@ -2384,6 +2379,14 @@ export class AIChatManager {
|
||||
}
|
||||
return undefined
|
||||
},
|
||||
onUsage: (usage, modelProvider) => {
|
||||
// Accounting must never take a turn down with it.
|
||||
try {
|
||||
this.recordUsage(usage, modelProvider.provider, modelProvider.model)
|
||||
} catch (e) {
|
||||
console.error('Failed to record AI usage', e)
|
||||
}
|
||||
},
|
||||
onBeforeIteration: async (tools, _helpers, modelProvider) => {
|
||||
this.lastIterationModel = modelProvider
|
||||
for (const tool of tools) {
|
||||
@@ -2393,9 +2396,6 @@ export class AIChatManager {
|
||||
}
|
||||
}
|
||||
})
|
||||
if (result.tokenUsage.total > 0) {
|
||||
this.recordUsage(result.tokenUsageByModel)
|
||||
}
|
||||
if (this.isSessionChat && this.sessionId && result.tokenUsage.total > 0) {
|
||||
logFeatureUsage('ai_session', 'tokens', {
|
||||
entityId: this.sessionId,
|
||||
|
||||
@@ -21,13 +21,7 @@ import {
|
||||
} from './openai-responses'
|
||||
import type { Tool, ToolCallbacks } from './shared'
|
||||
import { sanitizeToolCallArguments } from './toolCallArguments'
|
||||
import {
|
||||
addChatTokenUsage,
|
||||
addModelTokenUsage,
|
||||
emptyChatTokenUsage,
|
||||
type ChatTokenUsage,
|
||||
type ModelTokenUsageTotals
|
||||
} from './tokenUsage'
|
||||
import { addChatTokenUsage, emptyChatTokenUsage, type ChatTokenUsage } from './tokenUsage'
|
||||
|
||||
export interface ChatClients {
|
||||
openai: OpenAI
|
||||
@@ -83,14 +77,17 @@ export interface ChatLoopConfig {
|
||||
helpers: any,
|
||||
modelProvider: ReasoningProviderModel
|
||||
) => Promise<void>
|
||||
/** Fired for each completed provider response, before the loop continues. The
|
||||
* loop can fail or be aborted at any iteration, so spend has to be handed over
|
||||
* as it happens — a callback only at the end would discard everything the
|
||||
* earlier iterations were already billed for. */
|
||||
onUsage?: (usage: ChatTokenUsage, modelProvider: ReasoningProviderModel) => void
|
||||
}
|
||||
|
||||
export interface ChatLoopResult {
|
||||
addedMessages: ChatCompletionMessageParam[]
|
||||
/** Sum of usage across all loop iterations (suitable for cost accounting). */
|
||||
/** Sum of usage across all loop iterations. */
|
||||
tokenUsage: ChatTokenUsage
|
||||
/** The same usage split per model, so a turn that switched model prices correctly. */
|
||||
tokenUsageByModel: ModelTokenUsageTotals
|
||||
lastIterationUsage: ChatTokenUsage | null
|
||||
hitMaxIterations: boolean
|
||||
}
|
||||
@@ -333,7 +330,6 @@ export async function runChatLoop(config: ChatLoopConfig): Promise<ChatLoopResul
|
||||
|
||||
const addedMessages: ChatCompletionMessageParam[] = config.addedMessages ?? []
|
||||
let tokenUsage = emptyChatTokenUsage()
|
||||
let tokenUsageByModel: ModelTokenUsageTotals = {}
|
||||
let lastIterationUsage: ChatTokenUsage | null = null
|
||||
let iterations = 0
|
||||
let hitMaxIterations = false
|
||||
@@ -344,13 +340,8 @@ export async function runChatLoop(config: ChatLoopConfig): Promise<ChatLoopResul
|
||||
|
||||
const trackUsage = (usage: ChatTokenUsage | null | undefined) => {
|
||||
tokenUsage = addChatTokenUsage(tokenUsage, usage)
|
||||
if (iterationModel) {
|
||||
tokenUsageByModel = addModelTokenUsage(
|
||||
tokenUsageByModel,
|
||||
iterationModel.provider,
|
||||
iterationModel.model,
|
||||
usage
|
||||
)
|
||||
if (usage && iterationModel) {
|
||||
config.onUsage?.(usage, iterationModel)
|
||||
}
|
||||
// Some providers/paths report no usage (prompt 0); keep the last real one.
|
||||
if (usage && usage.prompt > 0) {
|
||||
@@ -594,5 +585,5 @@ export async function runChatLoop(config: ChatLoopConfig): Promise<ChatLoopResul
|
||||
}
|
||||
}
|
||||
|
||||
return { addedMessages, tokenUsage, tokenUsageByModel, lastIterationUsage, hitMaxIterations }
|
||||
return { addedMessages, tokenUsage, lastIterationUsage, hitMaxIterations }
|
||||
}
|
||||
|
||||
@@ -140,6 +140,12 @@ export function buildModelMatchers<T>(entries: [name: string, value: T][]): [Reg
|
||||
* The `provider:model` key the workspace AI settings use for their per-model maps
|
||||
* (`max_tokens_per_model`, `model_pricing`). A bare model id is not enough: the
|
||||
* same id can be served by more than one provider at different rates.
|
||||
*
|
||||
* Matched exactly, unlike the fuzzy tables above. Those tables generalize across
|
||||
* every route to one model on purpose; a per-model *setting* must not, or an
|
||||
* admin could not give two variants of a family different values — and the key is
|
||||
* built from the exact id the provider config lists, which is the same string the
|
||||
* chat sends.
|
||||
*/
|
||||
export function modelKey(provider: AIProvider | string, model: string): string {
|
||||
return `${provider}:${model}`
|
||||
|
||||
@@ -24,10 +24,12 @@ export type PricedTokens = {
|
||||
output: number
|
||||
}
|
||||
|
||||
// Rates that fall out of the input rate unless a provider prices them separately.
|
||||
// Anthropic reads a cached prefix at a tenth of the input rate and writes one at
|
||||
// 1.25x (5-minute TTL, the default the chat uses). Providers whose caching is
|
||||
// automatic never report a cache write, so their write rate is unused.
|
||||
// Fallbacks for entries that do not price their cache separately: Anthropic reads a
|
||||
// cached prefix at a tenth of the input rate and writes one at 1.25x (5-minute TTL,
|
||||
// the default the chat uses). The read ratio is NOT universal — OpenAI and Google
|
||||
// discount a cached read far less — so every non-Anthropic entry below states its own
|
||||
// `cacheRead` rather than inheriting this. Providers whose caching is automatic never
|
||||
// report a cache write, so their write rate is unused.
|
||||
const CACHE_READ_RATIO = 0.1
|
||||
const CACHE_WRITE_RATIO = 1.25
|
||||
|
||||
@@ -72,25 +74,26 @@ const MODEL_PRICES: [name: string, price: PriceEntry][] = [
|
||||
['claude-opus', { input: 5, output: 25 }],
|
||||
['claude-sonnet', { input: 3, output: 15 }],
|
||||
['claude-haiku', { input: 1, output: 5 }],
|
||||
// OpenAI — cached input is a tenth of input, and there is no separate charge
|
||||
// for writing the cache, so the write rate never applies (the OpenAI usage
|
||||
// parsers report no cache-write tokens). The -mini/-nano entries must precede
|
||||
// OpenAI — the cached-input discount varies by family (a tenth on gpt-5, a
|
||||
// quarter on 4.1 and the o-series, half on 4o), so each entry carries its own
|
||||
// rate. There is no charge for writing the cache and no usage field reporting
|
||||
// one, so the write rate never applies. The -mini/-nano entries must precede
|
||||
// the family entry, which would otherwise claim them.
|
||||
['gpt-5-mini', { input: 0.25, output: 2 }],
|
||||
['gpt-5-nano', { input: 0.05, output: 0.4 }],
|
||||
['gpt-5', { input: 1.25, output: 10 }],
|
||||
['gpt-4.1-mini', { input: 0.4, output: 1.6 }],
|
||||
['gpt-4.1-nano', { input: 0.1, output: 0.4 }],
|
||||
['gpt-4.1', { input: 2, output: 8 }],
|
||||
['gpt-4o-mini', { input: 0.15, output: 0.6 }],
|
||||
['gpt-4o', { input: 2.5, output: 10 }],
|
||||
['o4-mini', { input: 1.1, output: 4.4 }],
|
||||
['o3-mini', { input: 1.1, output: 4.4 }],
|
||||
['o3', { input: 2, output: 8 }],
|
||||
// Google
|
||||
['gemini-2.5-flash-lite', { input: 0.1, output: 0.4 }],
|
||||
['gemini-2.5-flash', { input: 0.3, output: 2.5 }],
|
||||
['gemini-2.5-pro', { input: 1.25, output: 10 }]
|
||||
['gpt-5-mini', { input: 0.25, output: 2, cacheRead: 0.025 }],
|
||||
['gpt-5-nano', { input: 0.05, output: 0.4, cacheRead: 0.005 }],
|
||||
['gpt-5', { input: 1.25, output: 10, cacheRead: 0.125 }],
|
||||
['gpt-4.1-mini', { input: 0.4, output: 1.6, cacheRead: 0.1 }],
|
||||
['gpt-4.1-nano', { input: 0.1, output: 0.4, cacheRead: 0.025 }],
|
||||
['gpt-4.1', { input: 2, output: 8, cacheRead: 0.5 }],
|
||||
['gpt-4o-mini', { input: 0.15, output: 0.6, cacheRead: 0.075 }],
|
||||
['gpt-4o', { input: 2.5, output: 10, cacheRead: 1.25 }],
|
||||
['o4-mini', { input: 1.1, output: 4.4, cacheRead: 0.275 }],
|
||||
['o3-mini', { input: 1.1, output: 4.4, cacheRead: 0.55 }],
|
||||
['o3', { input: 2, output: 8, cacheRead: 0.5 }],
|
||||
// Google — a cached read is a quarter of input across the 2.5 family
|
||||
['gemini-2.5-flash-lite', { input: 0.1, output: 0.4, cacheRead: 0.025 }],
|
||||
['gemini-2.5-flash', { input: 0.3, output: 2.5, cacheRead: 0.075 }],
|
||||
['gemini-2.5-pro', { input: 1.25, output: 10, cacheRead: 0.31 }]
|
||||
]
|
||||
|
||||
const MODEL_PRICE_MATCHERS = buildModelMatchers(
|
||||
@@ -116,12 +119,27 @@ export function getKnownModelPrice(model: string): ModelPrice | undefined {
|
||||
* rate, so an admin who only knows their input/output pricing does not have to
|
||||
* invent the other two.
|
||||
*/
|
||||
/** A rate that would make spend negative, infinite or NaN is not a price. The API
|
||||
* validates what it stores, but an instance-level config is written as an untyped
|
||||
* settings blob, so the reader refuses bad values rather than rendering nonsense. */
|
||||
function isUsableRate(rate: number | undefined): boolean {
|
||||
return rate === undefined || (Number.isFinite(rate) && rate >= 0)
|
||||
}
|
||||
|
||||
export function resolveModelPrice(
|
||||
provider: AIProvider | string,
|
||||
model: string,
|
||||
overrides: Record<string, ModelPriceOverride> | undefined
|
||||
): ResolvedModelPrice | undefined {
|
||||
const override = overrides?.[modelKey(provider, model)]
|
||||
const candidate = overrides?.[modelKey(provider, model)]
|
||||
const override =
|
||||
candidate &&
|
||||
isUsableRate(candidate.input) &&
|
||||
isUsableRate(candidate.output) &&
|
||||
isUsableRate(candidate.cache_read) &&
|
||||
isUsableRate(candidate.cache_write)
|
||||
? candidate
|
||||
: undefined
|
||||
if (override) {
|
||||
return {
|
||||
source: 'override',
|
||||
|
||||
@@ -588,8 +588,6 @@
|
||||
|
||||
<ModelPricing {aiProviders} bind:modelPricing />
|
||||
|
||||
<AiUsagePanel {modelPricing} />
|
||||
|
||||
<SettingCard label="Custom system prompts" description={promptDescription}>
|
||||
<div class="flex items-center gap-2 pt-1">
|
||||
<Button
|
||||
@@ -624,6 +622,10 @@
|
||||
scope={promptScope}
|
||||
/>
|
||||
|
||||
{#if promptScope === 'workspace'}
|
||||
<AiUsagePanel workspace={effectiveWorkspace} {modelPricing} />
|
||||
{/if}
|
||||
|
||||
{#if showWorkspaceOverrideEditor}
|
||||
<SettingsFooter
|
||||
hasUnsavedChanges={dirty}
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
<script lang="ts">
|
||||
import { AiService, type AITokenUsageBucket, type ModelPriceOverride } from '$lib/gen'
|
||||
import { workspaceStore } from '$lib/stores'
|
||||
import { formatUsd, priceSpend, type ModelSpend } from '../copilot/modelPricing'
|
||||
import { formatTokenCount } from '../copilot/chat/tokenUsage'
|
||||
import SettingCard from '../instanceSettings/SettingCard.svelte'
|
||||
@@ -9,7 +8,13 @@
|
||||
import ToggleButton from '../common/toggleButton-v2/ToggleButton.svelte'
|
||||
import { resource } from 'runed'
|
||||
|
||||
let { modelPricing }: { modelPricing: Record<string, ModelPriceOverride> } = $props()
|
||||
// The workspace is passed in rather than read from the store: this settings
|
||||
// component is also mounted for the instance scope, where the workspace in
|
||||
// focus has nothing to do with the config being edited.
|
||||
let {
|
||||
workspace,
|
||||
modelPricing
|
||||
}: { workspace: string; modelPricing: Record<string, ModelPriceOverride> } = $props()
|
||||
|
||||
type GroupBy = 'day' | 'user' | 'model' | 'session'
|
||||
|
||||
@@ -23,9 +28,9 @@
|
||||
]
|
||||
|
||||
let usage = resource(
|
||||
() => ({ workspace: $workspaceStore, days, groupBy }),
|
||||
() => ({ workspace, days, groupBy }),
|
||||
async ({ workspace, days, groupBy }) =>
|
||||
workspace ? await AiService.listAiUsage({ workspace, days, groupBy }) : []
|
||||
workspace ? await AiService.listAiUsage({ workspace, days, groupBy }) : undefined
|
||||
)
|
||||
|
||||
// The API groups by (dimension, provider, model) so every bucket resolves to a
|
||||
@@ -52,7 +57,7 @@
|
||||
}
|
||||
}
|
||||
|
||||
let priced = $derived(priceSpend((usage.current ?? []).map(toSpend), modelPricing))
|
||||
let priced = $derived(priceSpend((usage.current?.buckets ?? []).map(toSpend), modelPricing))
|
||||
|
||||
let rows = $derived.by(() => {
|
||||
const byKey = new Map<
|
||||
@@ -110,9 +115,17 @@
|
||||
<div class="flex flex-row items-baseline gap-2">
|
||||
<span class="text-lg font-semibold tabular-nums">{formatUsd(priced.total)}</span>
|
||||
<span class="text-xs text-tertiary">
|
||||
total{priced.hasUnpriced ? ', excluding models with no price' : ''}
|
||||
{usage.current?.truncated ? 'across the rows below' : 'total'}{priced.hasUnpriced
|
||||
? ', excluding models with no price'
|
||||
: ''}
|
||||
</span>
|
||||
</div>
|
||||
{#if usage.current?.truncated}
|
||||
<p class="text-xs text-tertiary">
|
||||
More rows matched than are shown; the biggest spenders are listed. Narrow the range or
|
||||
group differently to see the rest.
|
||||
</p>
|
||||
{/if}
|
||||
<div class="overflow-x-auto border rounded-md">
|
||||
<table class="w-full text-xs">
|
||||
<thead class="bg-surface-secondary">
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
<script lang="ts">
|
||||
import type { AIConfig, AIProvider, ModelPriceOverride } from '$lib/gen'
|
||||
import { Badge, Button } from '../common'
|
||||
import TextInput from '../text_input/TextInput.svelte'
|
||||
import { getKnownModelPrice } from '../copilot/modelPricing'
|
||||
import { modelKey } from '../copilot/modelConfig'
|
||||
import { ChevronDown, ChevronUp } from 'lucide-svelte'
|
||||
@@ -133,21 +134,26 @@
|
||||
{#each ['input', 'output'] as const as field}
|
||||
<div class="flex items-center gap-1">
|
||||
<span class="text-xs text-secondary">{field}</span>
|
||||
<input
|
||||
type="number"
|
||||
min="0"
|
||||
max={MAX_RATE}
|
||||
step="0.01"
|
||||
value={rates?.[field] ?? ''}
|
||||
placeholder="—"
|
||||
oninput={(e) => {
|
||||
const value = parseFloat(e.currentTarget.value)
|
||||
if (!isNaN(value)) {
|
||||
updateRate(provider as AIProvider, model, field, value)
|
||||
}
|
||||
}}
|
||||
class="w-20 px-2 py-1 text-xs text-center border border-gray-200 dark:border-gray-700 rounded bg-surface focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent"
|
||||
/>
|
||||
<div class="w-24">
|
||||
<TextInput
|
||||
value={rates?.[field] ?? ''}
|
||||
size="sm"
|
||||
error={!!errors[key]}
|
||||
inputProps={{
|
||||
type: 'number',
|
||||
min: 0,
|
||||
max: MAX_RATE,
|
||||
step: 0.01,
|
||||
placeholder: '—',
|
||||
oninput: (e: Event & { currentTarget: HTMLInputElement }) => {
|
||||
const value = parseFloat(e.currentTarget.value)
|
||||
if (!isNaN(value)) {
|
||||
updateRate(provider as AIProvider, model, field, value)
|
||||
}
|
||||
}
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
{/each}
|
||||
<span class="text-xs text-secondary whitespace-nowrap">$ / 1M</span>
|
||||
@@ -158,6 +164,11 @@
|
||||
set one.
|
||||
</div>
|
||||
{/if}
|
||||
{#if overridden && (rates?.input === 0 || rates?.output === 0)}
|
||||
<div class="text-xs text-red-500">
|
||||
A rate left at 0 prices those tokens as free — set both.
|
||||
</div>
|
||||
{/if}
|
||||
{#if overridden}
|
||||
<div class="text-xs text-primary flex flex-row items-center gap-1">
|
||||
<span>Overriding the built-in price</span>
|
||||
|
||||
Binary file not shown.
Reference in New Issue
Block a user