mirror of
https://github.com/praveentcom/openproxy.git
synced 2026-07-19 02:17:21 +02:00
feat: update configuration for OpenAI and Anthropic endpoints
- Created a new .env.example file with default environment variables for PORT, OPENAI_UPSTREAM_URL, ANTHROPIC_UPSTREAM_URL, and DATABASE_URL. - Updated .npmignore to exclude all .env files except .env.example. - Revised CONTRIBUTING.md to simplify the contribution process and provide clearer setup instructions. - Enhanced cost.ts with detailed type definitions and improved cost calculation logic. - Updated proxy.ts to include new environment variables and improved logging functionality. - Modified README.md to reflect new configuration instructions and usage examples. - Removed unnecessary dashboard files and streamlined the project structure.
This commit is contained in:
Praveen Thirumurugan
@@ -1,47 +1,232 @@
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// cost.ts
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// Helper for calculating token-based LLM costs
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/**
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* Usage object for logging.
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*
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* @param prompt_tokens: The number of prompt tokens.
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* @param completion_tokens: The number of completion tokens.
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* @param total_tokens: The total number of tokens.
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* @param prompt_tokens_details: The details of the prompt tokens.
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* @returns The usage object.
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*/
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export type Usage = {
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prompt_tokens?: number;
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completion_tokens?: number;
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total_tokens?: number;
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prompt_tokens_details?: {
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cached_tokens?: number;
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}
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};
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};
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/**
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* Cost configuration for a model.
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*
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* @param input: The cost per million prompt tokens (USD).
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* @param cached: The cost per million cached tokens (USD).
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* @param output: The cost per million completion tokens (USD).
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* @returns The cost configuration.
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*/
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export type CostConfig = {
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input: number; // cost per million prompt tokens (USD)
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cached: number; // cost per million cached tokens (USD)
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output: number; // cost per million completion tokens (USD)
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input: number;
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cached: number;
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output: number;
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};
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// Cost table per model (USD per million tokens)
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export const MODEL_COSTS: Record<string, CostConfig> = {
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"glm-4.5-air": { input: 0.2, cached: 0.03, output: 1.1 },
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"glm-4.6": { input: 0.6, cached: 0.11, output: 2.2 },
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"default": { input: 0, cached: 0, output: 0 },
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/**
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* Model pricing table.
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*
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* @param models: Canonical model pricing.
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* @param aliases: Alias to canonical model mapping.
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* @returns The pricing table.
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*/
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export type ModelCostTable = Record<string, CostConfig>;
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/**
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* Helicone API response types
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*/
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interface HeliconeModelCost {
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provider: string;
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model: string;
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operator: "equals" | "startsWith" | "includes";
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input_cost_per_1m: number;
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output_cost_per_1m: number;
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prompt_cache_write_per_1m?: number;
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prompt_cache_read_per_1m?: number;
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show_in_playground?: boolean;
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}
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interface HeliconeApiResponse {
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metadata: {
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total_models: number;
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};
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data: HeliconeModelCost[];
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}
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/**
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* Internal storage for cost data with matching operators
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*/
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interface CostEntry {
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operator: "equals" | "startsWith" | "includes";
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config: CostConfig;
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}
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// Storage for Helicone costs (loaded at runtime)
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let heliconeCosts: Map<string, CostEntry> = new Map();
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let heliconeCostsLoaded = false;
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/**
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* ============================================================================
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* CUSTOM MODEL COSTS
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* ============================================================================
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*
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* Add your custom model costs here. These will take precedence over costs
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* fetched from the Helicone API. This is useful for:
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*
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* - Custom/fine-tuned models (e.g., "zlm-4.6")
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* - Self-hosted models with custom pricing
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* - Overriding Helicone costs for specific models
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* - Models not yet in the Helicone database
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*
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* Format:
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* "model-name": { input: <cost>, cached: <cost>, output: <cost> }
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*
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* All costs are in USD per million tokens.
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*
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* @example
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* ```ts
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* export const CUSTOM_MODEL_COSTS: ModelCostTable = {
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* "zlm-4.6": { input: 2.5, cached: 1.25, output: 10 },
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* "zlm-4.5-air": { input: 0.15, cached: 0.075, output: 0.6 },
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* };
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* ```
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*/
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export const CUSTOM_MODEL_COSTS: ModelCostTable = {
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// Add your custom model costs here
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};
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// Compute total cost (in USD)
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export function calculateCost(model: string, usage?: Usage): number | null {
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/**
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* Fetches and loads cost data from the Helicone API.
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* This should be called once at application startup.
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*
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* @returns Promise that resolves when costs are loaded
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*/
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export async function loadHeliconeCosts(): Promise<void> {
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try {
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const response = await fetch("https://www.helicone.ai/api/llm-costs");
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if (!response.ok) {
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throw new Error(`Helicone API returned ${response.status}: ${response.statusText}`);
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}
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const data: HeliconeApiResponse = await response.json();
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heliconeCosts.clear();
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for (const model of data.data) {
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const config: CostConfig = {
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input: model.input_cost_per_1m ?? 0,
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output: model.output_cost_per_1m ?? 0,
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cached: model.prompt_cache_read_per_1m ?? model.input_cost_per_1m ?? 0,
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};
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heliconeCosts.set(model.model.toLowerCase(), {
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operator: model.operator,
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config,
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});
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}
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heliconeCostsLoaded = true;
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console.log(`\x1b[36m 🌎 Loaded ${data.metadata.total_models} model costs from Helicone\x1b[0m`);
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} catch (error) {
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console.warn(`\x1b[33m ⚠️ Failed to load Helicone costs: ${error instanceof Error ? error.message : error}\x1b[0m`);
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}
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}
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/**
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* Gets the cost configuration for a model.
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*
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* Priority order:
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* 1. Custom model costs (CUSTOM_MODEL_COSTS)
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* 2. Helicone API costs (with operator matching)
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* 3. Fallback cost
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*
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* @param model: The model name to look up
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* @returns The cost configuration for the model
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*/
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export function getCostConfig(model: string): CostConfig {
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const normalizedModel = model.toLowerCase();
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/**
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* Check custom costs first (highest priority)
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*/
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if (CUSTOM_MODEL_COSTS[normalizedModel]) {
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return CUSTOM_MODEL_COSTS[normalizedModel];
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} else if (CUSTOM_MODEL_COSTS[model]) {
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return CUSTOM_MODEL_COSTS[model];
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}
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/**
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* Check Helicone costs with operator matching
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*/
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const exactMatch = heliconeCosts.get(normalizedModel);
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if (exactMatch && exactMatch.operator === "equals") {
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return exactMatch.config;
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}
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for (const [pattern, entry] of heliconeCosts) {
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if (entry.operator === "startsWith" && normalizedModel.startsWith(pattern)) {
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return entry.config;
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}
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}
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for (const [pattern, entry] of heliconeCosts) {
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if (entry.operator === "includes" && normalizedModel.includes(pattern)) {
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return entry.config;
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}
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}
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if (exactMatch) {
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return exactMatch.config;
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}
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/**
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* Return fallback since no matching cost was found
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*/
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return { input: 0, cached: 0, output: 0 };
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}
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/**
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* Computes the total cost (in USD) for a given model and usage.
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*
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* @param model: The model to compute the cost for.
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* @param usage: The usage object.
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* @returns The total cost (in USD), or null if no usage data.
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*/
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export function calculateCost(
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model: string,
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usage?: Usage
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): number | null {
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if (!usage) return null;
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const { prompt_tokens = 0, completion_tokens = 0, prompt_tokens_details = { cached_tokens: 0 } } = usage;
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const cost = MODEL_COSTS[model.toLowerCase()] || MODEL_COSTS["default"];
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const {
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prompt_tokens = 0,
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completion_tokens = 0,
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prompt_tokens_details = { cached_tokens: 0 },
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} = usage;
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let inputCost = 0;
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let cachedCost = 0;
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let outputCost = 0;
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const cost = getCostConfig(model);
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if (prompt_tokens_details?.cached_tokens) {
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cachedCost = ((prompt_tokens_details.cached_tokens) / 1_000_000) * cost.cached;
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inputCost = ((prompt_tokens - prompt_tokens_details.cached_tokens) / 1_000_000) * cost.input;
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let inputCost = 0, cachedCost = 0;
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if (prompt_tokens_details.cached_tokens && cost.cached > 0) {
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cachedCost =
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(prompt_tokens_details.cached_tokens / 1_000_000) * cost.cached;
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inputCost =
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((prompt_tokens - prompt_tokens_details.cached_tokens) / 1_000_000) *
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cost.input;
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} else {
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inputCost = ((prompt_tokens) / 1_000_000) * cost.input;
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inputCost = (prompt_tokens / 1_000_000) * cost.input;
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}
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outputCost = (completion_tokens / 1_000_000) * cost.output;
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const outputCost =
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(completion_tokens / 1_000_000) * cost.output;
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const total = inputCost + cachedCost + outputCost;
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return total > 0 ? Number(total.toFixed(6)) : null;
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