can1357
ce4ff34205
- Added optional `phase` field to assistant messages to distinguish between `commentary` and `final_answer` stages. - Updated `openai-responses-server` to parse and propagate phase metadata via text signatures during streaming and batch processing. - Modified `encodeStream` and `buildOutputItems` to trigger new message items when an assistant message phase transition is detected. - Extended schema definitions and test suites to validate phase-aware message handling and history replay.
2614 lines
98 KiB
TypeScript
2614 lines
98 KiB
TypeScript
import type { Effort } from "@oh-my-pi/pi-catalog/effort";
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import { toFirepassWireModelId, toFireworksWireModelId } from "@oh-my-pi/pi-catalog/fireworks-model-id";
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import { isGlm52ReasoningEffortModelId } from "@oh-my-pi/pi-catalog/identity";
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import { getSupportedEfforts } from "@oh-my-pi/pi-catalog/model-thinking";
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import { calculateCost } from "@oh-my-pi/pi-catalog/models";
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import type {
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OpenAICompat,
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OpenAIReasoningDisableMode,
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OpenAIStreamMarkupHealingPattern,
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OpenRouterRouting,
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ResolvedOpenAICompat,
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ResolvedOpenAIResponsesCompat,
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ResolvedOpenAISharedCompat,
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VercelGatewayRouting,
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} from "@oh-my-pi/pi-catalog/types";
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import {
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COREWEAVE_PROJECT_HEADER,
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coreWeaveProjectHeaders,
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hasCoreWeaveProjectHeader,
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} from "@oh-my-pi/pi-catalog/wire/coreweave";
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import { parseGitHubCopilotApiKey } from "@oh-my-pi/pi-catalog/wire/github-copilot";
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import {
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$env,
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extractHttpStatusFromError,
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logger,
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parseStreamingJson,
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parseStreamingJsonThrottled,
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structuredCloneJSON,
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} from "@oh-my-pi/pi-utils";
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import * as AIError from "../error";
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import {
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type Api,
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type AssistantMessage,
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type CacheRetention,
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type Context,
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type ImageContent,
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type Message,
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type MessageAttribution,
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type Model,
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OPENAI_MAX_OUTPUT_TOKENS,
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type Provider,
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type ResolvedServiceTier,
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resolveServiceTier,
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type ServiceTier,
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type StopReason,
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type StreamOptions,
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shouldSendServiceTier,
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type TextContent,
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type TextSignatureV1,
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type ThinkingContent,
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type Tool,
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type ToolCall,
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type ToolResultMessage,
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} from "../types";
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import {
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getOpenAIResponsesHistoryItems,
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getOpenAIResponsesHistoryPayload,
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normalizeResponsesToolCallId,
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normalizeSystemPrompts,
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resolveCacheRetention,
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sanitizeOpenAIResponsesHistoryItemsForReplay,
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} from "../utils";
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import {
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clearStreamingPartialJson,
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kStreamingArgumentsDone,
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kStreamingLastParseLen,
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kStreamingPartialJson,
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} from "../utils/block-symbols";
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import type { AssistantMessageEventStream } from "../utils/event-stream";
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import type { CapturedHttpErrorResponse } from "../utils/http-inspector";
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import { getOpenRouterHeaders } from "../utils/openrouter-headers";
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import { isForcedToolChoice } from "../utils/tool-choice";
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import {
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buildCopilotDynamicHeaders,
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hasCopilotVisionInput,
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resolveGitHubCopilotBaseUrl,
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} from "./github-copilot-headers";
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import type { ChatCompletionCreateParamsStreaming } from "./openai-chat-wire";
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import type { InputItem } from "./openai-codex/request-transformer";
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import type {
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ResponseContentPartAddedEvent,
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ResponseCreateParamsStreaming,
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ResponseCustomToolCall,
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ResponseFunctionToolCall,
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ResponseInput,
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ResponseInputContent,
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ResponseInputImage,
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ResponseInputItem,
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ResponseInputText,
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ResponseOutputItem,
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ResponseOutputMessage,
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ResponseReasoningItem,
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ResponseStatus,
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ResponseStreamEvent,
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} from "./openai-responses-wire";
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import { transformMessages } from "./transform-messages";
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import { joinTextWithImagePlaceholder, NON_VISION_IMAGE_PLACEHOLDER, partitionVisionContent } from "./vision-guard";
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export interface OpenAIModelIdentity {
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provider: string;
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id: string;
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baseUrl?: string;
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}
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|
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export interface OpenAIStrictToolsScope {
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provider: string;
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baseUrl: string | undefined;
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modelId: string;
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}
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|
export interface OpenAIStrictToolsState {
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strictTools: {
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disabledModelScopes: Set<string>;
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};
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}
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|
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export interface OpenAIRequestSetupModel extends OpenAIModelIdentity {
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headers?: Record<string, string>;
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premiumMultiplier?: number;
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compat?: Pick<ResolvedOpenAISharedCompat, "promptCacheSessionHeader">;
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}
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export interface OpenAIResponsesCacheOptions {
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cacheRetention?: CacheRetention;
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sessionId?: string;
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promptCacheKey?: string;
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}
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export interface OpenAIRequestSetupOptions {
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apiKey?: string;
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extraHeaders?: Record<string, string>;
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initiatorOverride?: MessageAttribution;
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messages: Message[];
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defaultBaseUrl?: string;
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|
prependHeaders?: () => Record<string, string>;
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alibabaCodingPlanAuth?: boolean;
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|
azureChatCompletions?: {
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apiVersion: string;
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deploymentName: string;
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};
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openAISessionId?: string;
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promptCacheSessionId?: string;
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}
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|
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export interface OpenAIRequestSetup {
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copilotPremiumRequests: number | undefined;
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baseUrl: string | undefined;
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headers: Record<string, string>;
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query: Record<string, string> | undefined;
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requestHeaders: Record<string, string>;
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}
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function normalizeSakanaRequestBaseUrl(baseUrl: string | undefined): string | undefined {
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const value = baseUrl?.trim();
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if (!value) return undefined;
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const normalized = value.replace(/\/+$/, "");
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return normalized.endsWith("/v1") ? normalized : `${normalized}/v1`;
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}
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function resolveSakanaRequestBaseUrl(): string | undefined {
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return normalizeSakanaRequestBaseUrl($env.SAKANA_BASE_URL) ?? normalizeSakanaRequestBaseUrl($env.FUGU_BASE_URL);
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}
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function applyCoreWeaveProjectHeader(headers: Record<string, string>): void {
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|
if (hasCoreWeaveProjectHeader(headers)) {
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return;
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|
}
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const projectHeaders = coreWeaveProjectHeaders($env);
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|
if (projectHeaders) {
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headers[COREWEAVE_PROJECT_HEADER] = projectHeaders[COREWEAVE_PROJECT_HEADER];
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}
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}
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export function resolveOpenAIRequestSetup(
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model: OpenAIRequestSetupModel,
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options: OpenAIRequestSetupOptions,
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): OpenAIRequestSetup {
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let apiKey = options.apiKey;
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if (!apiKey) {
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if (!$env.OPENAI_API_KEY) {
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throw new AIError.MissingApiKeyError(
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undefined,
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"OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass it as an argument.",
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);
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}
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apiKey = $env.OPENAI_API_KEY;
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}
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const rawApiKey = apiKey;
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let headers = { ...(model.headers ?? {}) };
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if (model.provider === "openrouter") {
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Object.assign(headers, getOpenRouterHeaders());
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}
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Object.assign(headers, options.extraHeaders);
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if (model.provider === "coreweave") {
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applyCoreWeaveProjectHeader(headers);
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}
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if (options.prependHeaders) {
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headers = { ...options.prependHeaders(), ...headers };
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}
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let copilotPremiumRequests: number | undefined;
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let baseUrl = model.baseUrl;
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if (model.provider === "moonshot") {
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// Bundled `moonshot` catalog models hardcode the international endpoint
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// (`api.moonshot.ai`). MOONSHOT_BASE_URL lets users redirect the provider
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// at the China platform (`api.moonshot.cn`), which only accepts China keys
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// and rejects the international host. (#2883)
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const moonshotBaseUrl = $env.MOONSHOT_BASE_URL?.trim();
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if (moonshotBaseUrl) {
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baseUrl = moonshotBaseUrl;
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}
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}
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if (model.provider === "sakana") {
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const sakanaBaseUrl = resolveSakanaRequestBaseUrl();
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if (sakanaBaseUrl) {
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baseUrl = sakanaBaseUrl;
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}
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}
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if (model.provider === "github-copilot") {
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apiKey = parseGitHubCopilotApiKey(rawApiKey).accessToken;
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const copilot = buildCopilotDynamicHeaders({
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messages: options.messages,
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hasImages: hasCopilotVisionInput(options.messages),
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premiumMultiplier: model.premiumMultiplier,
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headers,
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initiatorOverride: options.initiatorOverride,
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|
});
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Object.assign(headers, copilot.headers);
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copilotPremiumRequests = copilot.premiumRequests;
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baseUrl = resolveGitHubCopilotBaseUrl(model.baseUrl, rawApiKey) ?? model.baseUrl;
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}
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if (options.alibabaCodingPlanAuth && model.provider === "alibaba-coding-plan") {
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try {
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const parsed = JSON.parse(rawApiKey);
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if (typeof parsed?.token === "string") {
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apiKey = parsed.token;
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}
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if (typeof parsed?.enterpriseUrl === "string") {
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baseUrl = parsed.enterpriseUrl;
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}
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} catch {
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// Not JSON — use raw apiKey and catalog baseUrl.
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}
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}
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let query: Record<string, string> | undefined;
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if (options.azureChatCompletions && baseUrl?.includes(".openai.azure.com")) {
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if (!baseUrl.includes("/deployments/")) {
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baseUrl = `${baseUrl}/deployments/${options.azureChatCompletions.deploymentName}`;
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}
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query = { "api-version": options.azureChatCompletions.apiVersion };
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}
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if (options.openAISessionId && model.provider === "openai") {
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headers.session_id ??= options.openAISessionId;
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headers["x-client-request-id"] ??= options.openAISessionId;
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}
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if (options.promptCacheSessionId && model.compat?.promptCacheSessionHeader) {
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headers[model.compat.promptCacheSessionHeader] ??= options.promptCacheSessionId;
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}
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if (options.defaultBaseUrl !== undefined) {
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baseUrl = baseUrl ?? ($env.OPENAI_BASE_URL?.trim() || options.defaultBaseUrl);
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}
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const requestHeaders = { ...headers };
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headers.Authorization ??= `Bearer ${apiKey}`;
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return { copilotPremiumRequests, baseUrl, headers, query, requestHeaders };
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}
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|
export function applyOpenAIServiceTier(
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params: { service_tier?: ResolvedServiceTier | "auto" | "default" | null | undefined },
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serviceTier: ServiceTier | null | undefined,
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provider: Provider | undefined,
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): void {
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if (!shouldSendServiceTier(serviceTier, provider)) return;
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const resolved = resolveServiceTier(serviceTier, provider);
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if (resolved === "flex" || resolved === "scale" || resolved === "priority") {
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params.service_tier = resolved;
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}
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}
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/**
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* Standard OpenAI Responses service-tier cost multipliers. The non-Codex
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* Responses path bills the tier it was served (or requested): Flex processing is
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* half price; Priority is a 2x premium. Codex bills the same tiers with its own
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* table (Priority is 2.5x on gpt-5.5) and applies that separately.
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*/
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function getOpenAIResponsesServiceTierCostMultiplier(tier: string | null | undefined): number {
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switch (tier) {
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case "flex":
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return 0.5;
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|
case "priority":
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|
return 2;
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default:
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|
return 1;
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}
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}
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|
|
/**
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|
* Adjust resolved cost by the service tier OpenAI actually billed — parity with
|
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* Codex (`applyCodexServiceTierPricing`), but with the standard (non-Codex)
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* multipliers. The served tier comes from the response echo, falling back to the
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* resolved request tier. Scoped to `provider: "openai"` (the only standard
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* Responses biller) so an echoed `service_tier` from an Azure/OpenRouter/Copilot
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* proxy can never skew those costs.
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*/
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export function applyOpenAIResponsesServiceTierCost(
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model: Pick<Model, "provider">,
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usage: AssistantMessage["usage"],
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|
responseServiceTier: unknown,
|
|
requestServiceTier: ServiceTier | null | undefined,
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|
): void {
|
|
if (model.provider !== "openai") return;
|
|
// The response echo is authoritative when present (OpenAI may downgrade a
|
|
// requested priority/flex turn to default under load); only fall back to the
|
|
// requested tier when the response omits the echo entirely.
|
|
const served =
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typeof responseServiceTier === "string"
|
|
? responseServiceTier
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: resolveServiceTier(requestServiceTier, model.provider);
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|
const multiplier = getOpenAIResponsesServiceTierCostMultiplier(served);
|
|
if (multiplier === 1) return;
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usage.cost.input *= multiplier;
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usage.cost.output *= multiplier;
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usage.cost.cacheRead *= multiplier;
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usage.cost.cacheWrite *= multiplier;
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usage.cost.total = usage.cost.input + usage.cost.output + usage.cost.cacheRead + usage.cost.cacheWrite;
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}
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|
|
export interface OpenAIUsageAccountingInput {
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|
promptTokens: number;
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|
outputTokens: number;
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|
cachedTokens: number;
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|
reasoningTokens: number;
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|
cacheWriteOpenRouter: number | undefined;
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|
cacheWriteDeepSeek: number | undefined;
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hasDeepSeekCacheHitAndMiss: boolean;
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|
}
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|
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export interface OpenAIUsageAccounting {
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input: number;
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output: number;
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cacheRead: number;
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cacheWrite: number;
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totalTokens: number;
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reasoningTokens?: number;
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}
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|
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export function calculateOpenAIUsageAccounting(accounting: OpenAIUsageAccountingInput): OpenAIUsageAccounting {
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|
const cacheWriteTokens = accounting.cacheWriteOpenRouter ?? accounting.cacheWriteDeepSeek ?? 0;
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const isDeepSeekUsage =
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accounting.hasDeepSeekCacheHitAndMiss &&
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accounting.cacheWriteOpenRouter === undefined &&
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(accounting.cacheWriteDeepSeek ?? 0) > 0;
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const input = isDeepSeekUsage
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? Math.max(0, accounting.promptTokens - accounting.cachedTokens)
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|
: Math.max(0, accounting.promptTokens - accounting.cachedTokens - cacheWriteTokens);
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const cacheWrite = isDeepSeekUsage ? 0 : cacheWriteTokens;
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return {
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input,
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output: accounting.outputTokens,
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cacheRead: accounting.cachedTokens,
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cacheWrite,
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totalTokens: input + accounting.outputTokens + accounting.cachedTokens + cacheWrite,
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|
...(accounting.reasoningTokens > 0 ? { reasoningTokens: accounting.reasoningTokens } : {}),
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};
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}
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|
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export function normalizeOpenAIResponsesPromptCacheKey(sessionId: string | undefined): string | undefined {
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|
return normalizeOpenAIStableId(sessionId, 64, "pc_");
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|
}
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|
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export function normalizeOpenRouterResponsesSessionId(sessionId: string | undefined): string | undefined {
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|
return normalizeOpenAIStableId(sessionId, 256, "session_");
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|
}
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|
|
export function getOpenAIResponsesPromptCacheKey(options: OpenAIResponsesCacheOptions | undefined): string | undefined {
|
|
if (resolveCacheRetention(options?.cacheRetention) === "none") return undefined;
|
|
return normalizeOpenAIResponsesPromptCacheKey(options?.promptCacheKey ?? options?.sessionId);
|
|
}
|
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|
|
export function getOpenAIResponsesRoutingSessionId(
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|
options: Pick<OpenAIResponsesCacheOptions, "cacheRetention" | "sessionId"> | undefined,
|
|
): string | undefined {
|
|
if (resolveCacheRetention(options?.cacheRetention) === "none") return undefined;
|
|
return normalizeOpenAIResponsesPromptCacheKey(options?.sessionId);
|
|
}
|
|
|
|
export function getOpenRouterResponsesSessionId(
|
|
options: Pick<OpenAIResponsesCacheOptions, "cacheRetention" | "sessionId"> | undefined,
|
|
): string | undefined {
|
|
if (resolveCacheRetention(options?.cacheRetention) === "none") return undefined;
|
|
return normalizeOpenRouterResponsesSessionId(options?.sessionId);
|
|
}
|
|
|
|
export function parseAzureDeploymentNameMap(value: string | undefined): Map<string, string> {
|
|
const map = new Map<string, string>();
|
|
if (!value) return map;
|
|
for (const entry of value.split(",")) {
|
|
const trimmed = entry.trim();
|
|
if (!trimmed) continue;
|
|
const [modelId, deploymentName] = trimmed.split("=", 2);
|
|
if (!modelId || !deploymentName) continue;
|
|
map.set(modelId.trim(), deploymentName.trim());
|
|
}
|
|
return map;
|
|
}
|
|
|
|
export function createOpenAIStrictToolsState(): OpenAIStrictToolsState {
|
|
return {
|
|
strictTools: {
|
|
disabledModelScopes: new Set<string>(),
|
|
},
|
|
};
|
|
}
|
|
|
|
export function clearOpenAIStrictToolsState(state: OpenAIStrictToolsState): void {
|
|
state.strictTools.disabledModelScopes.clear();
|
|
}
|
|
|
|
export function getOpenAIStrictToolsScope(
|
|
model: OpenAIModelIdentity,
|
|
resolvedBaseUrl: string | undefined,
|
|
): OpenAIStrictToolsScope {
|
|
return {
|
|
provider: model.provider,
|
|
baseUrl: resolvedBaseUrl ?? model.baseUrl,
|
|
modelId: model.id,
|
|
};
|
|
}
|
|
|
|
export function isStrictToolsDisabledForScope(
|
|
state: OpenAIStrictToolsState | undefined,
|
|
scope: OpenAIStrictToolsScope | undefined,
|
|
): boolean {
|
|
if (!scope) return false;
|
|
return (
|
|
state?.strictTools.disabledModelScopes.has(`${scope.provider}:${scope.baseUrl ?? ""}:${scope.modelId}`) ?? false
|
|
);
|
|
}
|
|
|
|
export function disableStrictToolsForScope(
|
|
state: OpenAIStrictToolsState | undefined,
|
|
scope: OpenAIStrictToolsScope | undefined,
|
|
): void {
|
|
if (!scope) return;
|
|
state?.strictTools.disabledModelScopes.add(`${scope.provider}:${scope.baseUrl ?? ""}:${scope.modelId}`);
|
|
}
|
|
|
|
export function isOpenRouterAnthropicModel(model: OpenAIModelIdentity): boolean {
|
|
return model.provider === "openrouter" && model.id.toLowerCase().startsWith("anthropic/");
|
|
}
|
|
|
|
/**
|
|
* Append an OpenRouter routing-variant suffix (e.g. `:nitro`, `:floor`, `:online`, `:exacto`)
|
|
* to a model id when no explicit variant is already present. A variant is considered
|
|
* "already present" when `modelId` contains a colon after the last `/` separator —
|
|
* which covers both user-typed selectors (`anthropic/claude-haiku:nitro`) and catalog
|
|
* entries that bake the variant in (`deepseek/deepseek-v3.1-terminus:exacto`).
|
|
*/
|
|
export function applyOpenRouterRoutingVariant(modelId: string, variant: string | undefined): string {
|
|
if (!variant) return modelId;
|
|
const lastSlash = modelId.lastIndexOf("/");
|
|
const lastColon = modelId.lastIndexOf(":");
|
|
if (lastColon > lastSlash) return modelId;
|
|
return `${modelId}:${variant}`;
|
|
}
|
|
|
|
export function applyWireModelIdTransform(
|
|
baseId: string,
|
|
mode: ResolvedOpenAISharedCompat["wireModelIdMode"],
|
|
openrouterVariant?: string,
|
|
): string {
|
|
switch (mode) {
|
|
case "firepass":
|
|
return toFirepassWireModelId(baseId);
|
|
case "fireworks":
|
|
return toFireworksWireModelId(baseId);
|
|
case "openrouter":
|
|
return applyOpenRouterRoutingVariant(baseId, openrouterVariant);
|
|
default:
|
|
return baseId;
|
|
}
|
|
}
|
|
|
|
export interface OpenAIOutputTokenParam {
|
|
field: "max_tokens" | "max_completion_tokens" | "max_output_tokens";
|
|
value: number;
|
|
}
|
|
|
|
export interface ResolveOpenAIOutputTokenInput {
|
|
/** Wire field the endpoint expects for the output cap. */
|
|
field: OpenAIOutputTokenParam["field"];
|
|
/** Caller-supplied output cap (model-defaulted by `stream.ts`, or null/undefined on direct provider calls). */
|
|
maxTokens: number | null | undefined;
|
|
/** Whether the caller explicitly set `maxTokens` (routing omission only applies when false). */
|
|
maxTokensExplicit: boolean;
|
|
/** Model output cap (`model.maxTokens`). */
|
|
modelMaxTokens: number | null | undefined;
|
|
/** Drop the field entirely — proxies with unknown upstream caps (Ollama via `model.omitMaxOutputTokens`). */
|
|
omitMaxOutputTokens: boolean;
|
|
/** The model sits behind OpenRouter (catalog default caps are omitted so each upstream self-caps). */
|
|
isOpenRouterHost: boolean;
|
|
/** Endpoint always needs a cap (Kimi-family TPM math); supplies the model default when the caller did not. */
|
|
alwaysSendMaxTokens: boolean;
|
|
/** Hard provider clamp; defaults to {@link OPENAI_MAX_OUTPUT_TOKENS}. */
|
|
providerOutputClamp?: number;
|
|
}
|
|
|
|
/**
|
|
* Resolve the single output-token wire parameter shared by Chat Completions
|
|
* (`max_tokens`/`max_completion_tokens`) and the Responses family
|
|
* (`max_output_tokens`). Centralizes the provider exceptions that previously
|
|
* lived inline in both `buildParams`:
|
|
* - `alwaysSendMaxTokens`: Kimi-family endpoints derive TPM limits from the
|
|
* cap and require one on every call, so default from the model cap (or
|
|
* {@link OPENAI_MAX_OUTPUT_TOKENS}) when the caller omitted it.
|
|
* - OpenRouter routing omission: OpenRouter fans out to upstreams whose output
|
|
* caps differ from the catalog value, so a catalog default above the routed
|
|
* upstream's cap makes OpenRouter skip that upstream. Omit catalog defaults
|
|
* (explicit caller caps still win) so `provider.order`/`only` is honored.
|
|
* - model/provider clamp: never exceed `model.maxTokens` or the provider clamp
|
|
* (`OPENAI_MAX_OUTPUT_TOKENS`, raised for GLM-5.2 reasoning by the caller).
|
|
* - `omitMaxOutputTokens`: proxies (Ollama) with unknown upstream caps drop it.
|
|
*/
|
|
export function resolveOpenAIOutputTokenParam(
|
|
input: ResolveOpenAIOutputTokenInput,
|
|
): OpenAIOutputTokenParam | undefined {
|
|
if (input.omitMaxOutputTokens) return undefined;
|
|
const requested =
|
|
input.maxTokens ?? (input.alwaysSendMaxTokens ? (input.modelMaxTokens ?? OPENAI_MAX_OUTPUT_TOKENS) : undefined);
|
|
if (requested === undefined) return undefined;
|
|
if (input.isOpenRouterHost && !input.alwaysSendMaxTokens && !input.maxTokensExplicit) return undefined;
|
|
const value = Math.min(
|
|
requested,
|
|
input.modelMaxTokens ?? Number.POSITIVE_INFINITY,
|
|
input.providerOutputClamp ?? OPENAI_MAX_OUTPUT_TOKENS,
|
|
);
|
|
if (!(value > 0)) return undefined;
|
|
return { field: input.field, value };
|
|
}
|
|
|
|
export interface OpenAIGatewayRoutingParams {
|
|
provider?: OpenRouterRouting;
|
|
providerOptions?: { gateway?: { only?: string[]; order?: string[] } };
|
|
}
|
|
|
|
export interface OpenAIGatewayRoutingCompat {
|
|
isOpenRouterHost: boolean;
|
|
openRouterRouting?: OpenRouterRouting;
|
|
isVercelGatewayHost?: boolean;
|
|
vercelGatewayRouting?: VercelGatewayRouting;
|
|
}
|
|
|
|
/**
|
|
* Apply gateway routing preferences to the request body. OpenRouter routes via
|
|
* the top-level `provider` field; the Vercel AI Gateway routes via
|
|
* `providerOptions.gateway`. Both Chat Completions and Responses call this; the
|
|
* Vercel branch is inert for Responses, whose resolved compat never sets
|
|
* `isVercelGatewayHost`.
|
|
*/
|
|
export function applyOpenAIGatewayRouting(
|
|
params: OpenAIGatewayRoutingParams,
|
|
compat: OpenAIGatewayRoutingCompat,
|
|
): void {
|
|
if (compat.isOpenRouterHost && compat.openRouterRouting) {
|
|
params.provider = compat.openRouterRouting;
|
|
}
|
|
if (compat.isVercelGatewayHost && compat.vercelGatewayRouting) {
|
|
const routing = compat.vercelGatewayRouting;
|
|
if (routing.only || routing.order) {
|
|
const gatewayOptions: { only?: string[]; order?: string[] } = {};
|
|
if (routing.only) gatewayOptions.only = routing.only;
|
|
if (routing.order) gatewayOptions.order = routing.order;
|
|
params.providerOptions = { gateway: gatewayOptions };
|
|
}
|
|
}
|
|
}
|
|
|
|
export interface OpenAIExtraBodyOptions {
|
|
/**
|
|
* Fireworks rejects DeepSeek-style `thinking` toggles alongside OpenAI-style
|
|
* `reasoning_effort`; drop `thinking` when the effort field carries the level.
|
|
*/
|
|
dropThinkingWhenReasoningEffort?: boolean;
|
|
}
|
|
|
|
/**
|
|
* Merge a compat/options `extraBody` blob into the request params. When
|
|
* `dropThinkingWhenReasoningEffort` is set and `reasoning_effort` is present,
|
|
* delete the conflicting `thinking` toggle (Fireworks rejects both together).
|
|
*/
|
|
export function applyOpenAIExtraBody<P extends object>(
|
|
params: P,
|
|
extraBody: Record<string, unknown> | undefined,
|
|
options?: OpenAIExtraBodyOptions,
|
|
): void {
|
|
if (!extraBody) return;
|
|
Object.assign(params, extraBody);
|
|
if (options?.dropThinkingWhenReasoningEffort) {
|
|
const shaped = params as { reasoning_effort?: unknown; thinking?: unknown };
|
|
if (shaped.reasoning_effort !== undefined) {
|
|
delete shaped.thinking;
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Chat Completions streaming request body shaped by the OpenAI-family providers.
|
|
* Extends the vendored SDK params with the compat dialect fields pi-ai emits
|
|
* (binary `thinking`, Qwen `enable_thinking`/`chat_template_kwargs`, nested
|
|
* `reasoning`, gateway `provider`/`providerOptions`, sampling extras). Lives in
|
|
* the shared module beside the request-shaping helpers that mutate it.
|
|
*/
|
|
export type OpenAICompletionsParams = Omit<ChatCompletionCreateParamsStreaming, "reasoning_effort" | "service_tier"> & {
|
|
top_k?: number;
|
|
min_p?: number;
|
|
repetition_penalty?: number;
|
|
thinking?: { type: "enabled" | "disabled"; keep?: "all" };
|
|
enable_thinking?: boolean;
|
|
preserve_thinking?: boolean;
|
|
chat_template_kwargs?: { enable_thinking?: boolean; preserve_thinking?: boolean };
|
|
reasoning?: { effort?: string } | { enabled: false };
|
|
reasoning_effort?: string | null;
|
|
service_tier?: ResolvedServiceTier;
|
|
tool_stream?: boolean;
|
|
provider?: OpenAICompat["openRouterRouting"];
|
|
providerOptions?: { gateway?: { only?: string[]; order?: string[] } };
|
|
};
|
|
|
|
/** Reasoning-relevant slice of caller options the Chat Completions dialect dispatch reads. */
|
|
export interface ChatCompletionsReasoningOptions {
|
|
reasoning?: "minimal" | "low" | "medium" | "high" | "xhigh";
|
|
disableReasoning?: boolean;
|
|
}
|
|
|
|
export type OpenAICompatEndpoint = "chat-completions" | "responses";
|
|
|
|
export type OpenAIReasoningDisableReason = "caller" | "forced-tool-choice" | "tool-choice" | "not-requested";
|
|
|
|
export type OpenAICompatPolicyCompat = ResolvedOpenAISharedCompat &
|
|
Partial<ResolvedOpenAICompat> &
|
|
Partial<ResolvedOpenAIResponsesCompat>;
|
|
|
|
export interface ResolveOpenAICompatPolicyOptions {
|
|
endpoint: OpenAICompatEndpoint;
|
|
compat?: OpenAICompatPolicyCompat;
|
|
reasoning?: string;
|
|
disableReasoning?: boolean;
|
|
toolChoice?: unknown;
|
|
strictResponsesPairing?: boolean;
|
|
includeEncryptedReasoning?: boolean;
|
|
filterReasoningHistory?: boolean;
|
|
omitReasoningEffort?: boolean;
|
|
}
|
|
|
|
export interface OpenAICompatPolicy {
|
|
endpoint: OpenAICompatEndpoint;
|
|
compat: OpenAICompatPolicyCompat;
|
|
reasoning: {
|
|
modelSupported: boolean;
|
|
supportsParams: boolean;
|
|
requestedEffort?: string;
|
|
wireEffort?: string;
|
|
enabled: boolean;
|
|
disabled: boolean;
|
|
disableReason?: OpenAIReasoningDisableReason;
|
|
dialect: ResolvedOpenAISharedCompat["thinkingFormat"];
|
|
disableMode: OpenAIReasoningDisableMode;
|
|
omitReasoningEffort: boolean;
|
|
includeEncryptedReasoning: boolean;
|
|
filterReasoningHistory: boolean;
|
|
requiresReasoningContentForToolCalls: boolean;
|
|
requiresReasoningContentForAllAssistantTurns: boolean;
|
|
allowsSyntheticReasoningContentForToolCalls: boolean;
|
|
reasoningContentField?: OpenAICompat["reasoningContentField"];
|
|
requiresThinkingAsText: boolean;
|
|
};
|
|
tools: {
|
|
strictResponsesPairing: boolean;
|
|
toolCallIdKind: "default" | "openai-40" | "mistral-9-alnum";
|
|
};
|
|
messages: {
|
|
systemRole: "system" | "developer";
|
|
supportsDeveloperRole: boolean;
|
|
supportsMultipleSystemMessages: boolean;
|
|
};
|
|
stream: {
|
|
stripSpecialTokens: "deepseek" | false;
|
|
markupHealingPattern?: OpenAIStreamMarkupHealingPattern;
|
|
reasoningDeltasMayBeCumulative: boolean;
|
|
emptyLengthFinishIsContextError: boolean;
|
|
};
|
|
}
|
|
|
|
function mapOpenAIReasoningEffort(
|
|
model: Pick<Model, "thinking">,
|
|
compat: OpenAICompatPolicyCompat,
|
|
effort: string,
|
|
): string {
|
|
const level = effort as Effort;
|
|
return compat.reasoningEffortMap?.[level] ?? model.thinking?.effortMap?.[level] ?? effort;
|
|
}
|
|
|
|
function isImplicitDisableWhenNotRequested(disableMode: OpenAIReasoningDisableMode): boolean {
|
|
return (
|
|
disableMode === "zai-thinking-disabled" ||
|
|
disableMode === "qwen-enable-thinking-false" ||
|
|
disableMode === "qwen-template-false"
|
|
);
|
|
}
|
|
|
|
export function resolveOpenAICompatPolicy<TApi extends Api>(
|
|
model: Model<TApi>,
|
|
options: ResolveOpenAICompatPolicyOptions,
|
|
): OpenAICompatPolicy {
|
|
const baseCompat = (options.compat ?? model.compat) as OpenAICompatPolicyCompat;
|
|
const requestedEffort = options.reasoning;
|
|
const modelSupported = Boolean(model.reasoning);
|
|
const forcedToolChoiceSuppressesReasoning =
|
|
baseCompat.disableReasoningOnForcedToolChoice &&
|
|
baseCompat.supportsForcedToolChoice &&
|
|
isForcedToolChoice(options.toolChoice);
|
|
const anyToolChoiceSuppressesReasoning =
|
|
!forcedToolChoiceSuppressesReasoning &&
|
|
baseCompat.disableReasoningOnToolChoice &&
|
|
options.toolChoice !== undefined;
|
|
const requestedAndAllowed = requestedEffort !== undefined && !options.disableReasoning && modelSupported;
|
|
const conflictDisableReason: OpenAIReasoningDisableReason | undefined = forcedToolChoiceSuppressesReasoning
|
|
? "forced-tool-choice"
|
|
: anyToolChoiceSuppressesReasoning
|
|
? "tool-choice"
|
|
: undefined;
|
|
const disableReason: OpenAIReasoningDisableReason | undefined = options.disableReasoning
|
|
? "caller"
|
|
: conflictDisableReason;
|
|
const enabledBeforeThinkingVariant = requestedAndAllowed && disableReason === undefined;
|
|
const baseWireEffort =
|
|
enabledBeforeThinkingVariant && requestedEffort !== undefined
|
|
? mapOpenAIReasoningEffort(model, baseCompat, requestedEffort)
|
|
: undefined;
|
|
const disabledByNoneEffort =
|
|
enabledBeforeThinkingVariant &&
|
|
baseCompat.reasoningDisableMode === "zai-thinking-disabled" &&
|
|
baseWireEffort === "none";
|
|
const enabled = enabledBeforeThinkingVariant && !disabledByNoneEffort;
|
|
const compat =
|
|
enabled && baseCompat.whenThinking ? (baseCompat.whenThinking as OpenAICompatPolicyCompat) : baseCompat;
|
|
const omitReasoningEffort =
|
|
options.omitReasoningEffort ?? (compat.omitReasoningEffort || !compat.supportsReasoningEffort);
|
|
const disableMode = compat.reasoningDisableMode;
|
|
let wireEffort =
|
|
enabled && requestedEffort !== undefined ? mapOpenAIReasoningEffort(model, compat, requestedEffort) : undefined;
|
|
const disabledWithoutRequest =
|
|
modelSupported &&
|
|
requestedEffort === undefined &&
|
|
!options.disableReasoning &&
|
|
isImplicitDisableWhenNotRequested(disableMode);
|
|
const disabled =
|
|
(modelSupported && disableReason === "caller") ||
|
|
conflictDisableReason !== undefined ||
|
|
(modelSupported && disabledWithoutRequest) ||
|
|
disabledByNoneEffort;
|
|
if (
|
|
disabled &&
|
|
disableReason === "caller" &&
|
|
requestedEffort === undefined &&
|
|
disableMode === "lowest-effort" &&
|
|
compat.supportsReasoningEffort &&
|
|
!omitReasoningEffort
|
|
) {
|
|
const minEffort = getSupportedEfforts(model)[0];
|
|
if (minEffort === undefined) {
|
|
throw new AIError.ConfigurationError(`Model ${model.provider}/${model.id} has no supported reasoning efforts`);
|
|
}
|
|
wireEffort = mapOpenAIReasoningEffort(model, compat, minEffort);
|
|
}
|
|
|
|
return {
|
|
endpoint: options.endpoint,
|
|
compat,
|
|
reasoning: {
|
|
modelSupported,
|
|
supportsParams: compat.supportsReasoningParams,
|
|
requestedEffort,
|
|
wireEffort,
|
|
enabled,
|
|
disabled,
|
|
disableReason: disableReason ?? (disabledWithoutRequest || disabledByNoneEffort ? "not-requested" : undefined),
|
|
dialect: compat.thinkingFormat,
|
|
requiresReasoningContentForToolCalls: compat.requiresReasoningContentForToolCalls,
|
|
requiresReasoningContentForAllAssistantTurns: compat.requiresReasoningContentForAllAssistantTurns,
|
|
allowsSyntheticReasoningContentForToolCalls: compat.allowsSyntheticReasoningContentForToolCalls,
|
|
reasoningContentField: compat.reasoningContentField,
|
|
requiresThinkingAsText: compat.requiresThinkingAsText,
|
|
disableMode,
|
|
omitReasoningEffort,
|
|
includeEncryptedReasoning: options.includeEncryptedReasoning ?? compat.includeEncryptedReasoning,
|
|
filterReasoningHistory: options.filterReasoningHistory ?? compat.filterReasoningHistory,
|
|
},
|
|
tools: {
|
|
strictResponsesPairing: options.strictResponsesPairing ?? compat.strictResponsesPairing ?? false,
|
|
toolCallIdKind: compat.requiresMistralToolIds
|
|
? "mistral-9-alnum"
|
|
: compat.usesOpenAIToolCallIdLimit
|
|
? "openai-40"
|
|
: "default",
|
|
},
|
|
messages: {
|
|
systemRole: modelSupported && compat.supportsDeveloperRole ? "developer" : "system",
|
|
supportsDeveloperRole: compat.supportsDeveloperRole,
|
|
supportsMultipleSystemMessages: compat.supportsMultipleSystemMessages ?? true,
|
|
},
|
|
stream: {
|
|
stripSpecialTokens: compat.stripDeepseekSpecialTokens ? "deepseek" : false,
|
|
markupHealingPattern: compat.streamMarkupHealingPattern,
|
|
reasoningDeltasMayBeCumulative: compat.reasoningDeltasMayBeCumulative,
|
|
emptyLengthFinishIsContextError: compat.emptyLengthFinishIsContextError,
|
|
},
|
|
};
|
|
}
|
|
|
|
function encodeChatCompletionsDisabledReasoning(
|
|
params: OpenAICompletionsParams,
|
|
disableMode: OpenAIReasoningDisableMode,
|
|
): void {
|
|
delete params.reasoning_effort;
|
|
switch (disableMode) {
|
|
case "zai-thinking-disabled":
|
|
params.thinking = { type: "disabled" };
|
|
break;
|
|
case "qwen-enable-thinking-false":
|
|
params.enable_thinking = false;
|
|
break;
|
|
case "qwen-template-false":
|
|
params.chat_template_kwargs = { ...params.chat_template_kwargs, enable_thinking: false };
|
|
break;
|
|
case "openrouter-enabled-false":
|
|
(params as typeof params & { reasoning?: { effort?: string } | { enabled: false } }).reasoning = {
|
|
enabled: false,
|
|
};
|
|
break;
|
|
default:
|
|
delete params.reasoning;
|
|
break;
|
|
}
|
|
}
|
|
|
|
export function applyChatCompletionsCompatPolicy(params: OpenAICompletionsParams, policy: OpenAICompatPolicy): void {
|
|
// `preserve_thinking` is a chat-template HISTORY knob, not a per-turn
|
|
// thinking switch — it controls whether OLDER assistant turns render
|
|
// with `<think>...</think>` on Qwen3.6+. Emit it BEFORE the reasoning
|
|
// state branches and EVERY early-return below, because the wire shape
|
|
// must carry the kwarg in three cases the auto-detected
|
|
// `qwenPreserveThinking` flag covers but `reasoning.enabled` does not:
|
|
//
|
|
// 1. Discovered local Qwen models. `discoverOpenAICompatibleModels`
|
|
// stamps `reasoning: false` on every spec built from a generic
|
|
// `/v1/models` endpoint (the upstream doesn't advertise the
|
|
// capability), so `model.reasoning === false` → `reasoning.enabled
|
|
// === false`, the body wouldn't otherwise see the kwarg, and the
|
|
// encoder's `replayReasoningContent` branch would keep shipping
|
|
// `reasoning_content` only for the template to strip `<think>` from
|
|
// older turns anyway. Exactly the #3528 / #3541 symptom on every
|
|
// discovered Qwen build.
|
|
// 2. Caller-disabled reasoning. The slot's KV cache still holds prior
|
|
// `<think>...</think>` tokens from earlier thinking turns; the
|
|
// template must keep rendering them or cache invalidates at the
|
|
// first historic `<think>`.
|
|
// 3. Forced-tool-choice / DeepSeek-style auto-disable. Same reasoning
|
|
// as (2) — historic thinking blocks have to survive history replay
|
|
// even when the current turn cannot think.
|
|
//
|
|
// Non-Qwen templates ignore the parameter (jinja `is defined` check
|
|
// silently no-ops), so emitting it unconditionally for the Qwen-family
|
|
// + local-cache compat flag is safe.
|
|
if (policy.compat.qwenPreserveThinking) {
|
|
// Mirror the dialect split that gates `enable_thinking`. The
|
|
// `qwen` dialect rides the top-level field (the only place
|
|
// llama.cpp's `--jinja` hook AND Alibaba Cloud Model Studio's
|
|
// compatible-mode look) while the `qwen-chat-template` dialect
|
|
// (NVIDIA NIM, vLLM/SGLang's chat-template-kwargs path) MUST
|
|
// ride only the kwargs copy — NIM's request schema is
|
|
// `additionalProperties: false` and rejects every unknown
|
|
// top-level field, the very reason `enable_thinking` is
|
|
// route-split this way (#2299, see `catalog/src/compat/openai.ts`
|
|
// thinkingFormat comment).
|
|
if (policy.compat.thinkingFormat === "qwen") {
|
|
params.preserve_thinking = true;
|
|
}
|
|
params.chat_template_kwargs = { ...params.chat_template_kwargs, preserve_thinking: true };
|
|
}
|
|
|
|
const reasoning = policy.reasoning;
|
|
if ((!reasoning.modelSupported && !reasoning.disabled) || !reasoning.supportsParams) return;
|
|
if (reasoning.enabled) {
|
|
switch (reasoning.disableMode) {
|
|
case "zai-thinking-disabled":
|
|
if (reasoning.wireEffort === "none") {
|
|
encodeChatCompletionsDisabledReasoning(params, reasoning.disableMode);
|
|
return;
|
|
}
|
|
params.thinking = { type: "enabled" };
|
|
if (policy.compat.thinkingKeep) params.thinking.keep = policy.compat.thinkingKeep;
|
|
if (policy.compat.supportsReasoningEffort && reasoning.wireEffort !== undefined) {
|
|
params.reasoning_effort = reasoning.wireEffort as Effort;
|
|
}
|
|
break;
|
|
case "qwen-enable-thinking-false":
|
|
params.enable_thinking = true;
|
|
break;
|
|
case "qwen-template-false":
|
|
// Spread so the `preserve_thinking` kwarg hoisted above
|
|
// survives the merge — a bare `{ enable_thinking: true }`
|
|
// would clobber it.
|
|
params.chat_template_kwargs = { ...params.chat_template_kwargs, enable_thinking: true };
|
|
break;
|
|
case "openrouter-enabled-false":
|
|
if (reasoning.wireEffort !== undefined) {
|
|
(params as typeof params & { reasoning?: { effort?: string } }).reasoning = {
|
|
effort: reasoning.wireEffort,
|
|
};
|
|
}
|
|
break;
|
|
default:
|
|
if (!reasoning.omitReasoningEffort && reasoning.wireEffort !== undefined) {
|
|
params.reasoning_effort = reasoning.wireEffort as Effort;
|
|
}
|
|
break;
|
|
}
|
|
return;
|
|
}
|
|
if (!reasoning.disabled) return;
|
|
if (
|
|
reasoning.disableReason === "caller" &&
|
|
reasoning.requestedEffort === undefined &&
|
|
reasoning.disableMode === "lowest-effort" &&
|
|
reasoning.wireEffort !== undefined
|
|
) {
|
|
params.reasoning_effort = reasoning.wireEffort as Effort;
|
|
return;
|
|
}
|
|
encodeChatCompletionsDisabledReasoning(params, reasoning.disableMode);
|
|
}
|
|
|
|
export function applyChatCompletionsReasoningParams(
|
|
params: OpenAICompletionsParams,
|
|
model: Model<"openai-completions">,
|
|
compat: ResolvedOpenAICompat,
|
|
options: (ChatCompletionsReasoningOptions & { toolChoice?: unknown }) | undefined,
|
|
): void {
|
|
applyChatCompletionsCompatPolicy(
|
|
params,
|
|
resolveOpenAICompatPolicy(model, {
|
|
endpoint: "chat-completions",
|
|
compat,
|
|
reasoning: options?.reasoning,
|
|
disableReasoning: options?.disableReasoning,
|
|
toolChoice: options?.toolChoice,
|
|
}),
|
|
);
|
|
}
|
|
|
|
export function disableChatCompletionsReasoningForDialect(
|
|
params: OpenAICompletionsParams,
|
|
compat: ResolvedOpenAICompat,
|
|
): void {
|
|
encodeChatCompletionsDisabledReasoning(params, compat.reasoningDisableMode);
|
|
}
|
|
|
|
/**
|
|
* Z.AI/GLM-5.2 reasoning-effort dialect predicate. GLM-5.2 models served on a
|
|
* Z.AI-format host (thinkingFormat "zai") accept `reasoning_effort`, stream tool
|
|
* calls via `tool_stream`, and clamp output to the model cap. Moonshot Kimi and
|
|
* Xiaomi MiMo also resolve to thinkingFormat "zai" with supportsReasoningEffort
|
|
* true but are NOT GLM-5.2, so the model-id check is load-bearing — never swap it
|
|
* for `compat.supportsReasoningEffort`.
|
|
*/
|
|
function isZaiReasoningEffortDialect(model: Model<"openai-completions">, compat: ResolvedOpenAICompat): boolean {
|
|
return compat.thinkingFormat === "zai" && isGlm52ReasoningEffortModelId(model.id);
|
|
}
|
|
|
|
/**
|
|
* Output-token clamp for the Z.AI/GLM-5.2 reasoning dialect: these hosts accept
|
|
* the full model window on reasoning turns, so clamp to the model cap. Returns
|
|
* `undefined` for every other model, leaving {@link resolveOpenAIOutputTokenParam}
|
|
* on its default `OPENAI_MAX_OUTPUT_TOKENS` clamp.
|
|
*/
|
|
export function resolveZaiReasoningOutputClamp(
|
|
model: Model<"openai-completions">,
|
|
compat: ResolvedOpenAICompat,
|
|
): number | undefined {
|
|
return isZaiReasoningEffortDialect(model, compat) ? (model.maxTokens ?? OPENAI_MAX_OUTPUT_TOKENS) : undefined;
|
|
}
|
|
|
|
/**
|
|
* Enable `tool_stream` for Z.AI/GLM-5.2 reasoning models when tools are present
|
|
* (GLM-5.2 streams tool-call arguments incrementally and needs the flag to do so).
|
|
*/
|
|
export function applyChatCompletionsToolStream(
|
|
params: OpenAICompletionsParams,
|
|
model: Model<"openai-completions">,
|
|
compat: ResolvedOpenAICompat,
|
|
): void {
|
|
if (
|
|
isZaiReasoningEffortDialect(model, compat) &&
|
|
compat.supportsReasoningEffort &&
|
|
Array.isArray(params.tools) &&
|
|
params.tools.length > 0
|
|
) {
|
|
params.tool_stream = true;
|
|
}
|
|
}
|
|
|
|
export function isCompiledGrammarTooLargeStrictError(
|
|
error: unknown,
|
|
capturedErrorResponse: CapturedHttpErrorResponse | undefined,
|
|
): boolean {
|
|
const status = extractHttpStatusFromError(error) ?? capturedErrorResponse?.status;
|
|
if (status !== 400) return false;
|
|
const messageParts = [error instanceof Error ? error.message : undefined, capturedErrorResponse?.bodyText]
|
|
.filter((value): value is string => typeof value === "string" && value.trim().length > 0)
|
|
.join("\n");
|
|
return (
|
|
/invalid_request_error/i.test(messageParts) &&
|
|
/compiled grammar/i.test(messageParts) &&
|
|
/too large/i.test(messageParts)
|
|
);
|
|
}
|
|
|
|
export function shouldRetryWithoutStrictTools(
|
|
error: unknown,
|
|
capturedErrorResponse: CapturedHttpErrorResponse | undefined,
|
|
strictToolsApplied: boolean,
|
|
tools: Tool[] | undefined,
|
|
): boolean {
|
|
if (!tools || tools.length === 0 || !strictToolsApplied) return false;
|
|
const status = extractHttpStatusFromError(error) ?? capturedErrorResponse?.status;
|
|
if (status !== 400 && status !== 422) return false;
|
|
const messageParts = [error instanceof Error ? error.message : undefined, capturedErrorResponse?.bodyText]
|
|
.filter((value): value is string => typeof value === "string" && value.trim().length > 0)
|
|
.join("\n");
|
|
return /wrong_api_format|mixed values for 'strict'|tool[s]?\b.*strict|\bstrict\b.*tool|tool parameters? schema|invalid schema for function/i.test(
|
|
messageParts,
|
|
);
|
|
}
|
|
|
|
function normalizeOpenAIStableId(value: string | undefined, maxLength: number, hashPrefix: string): string | undefined {
|
|
if (!value || value.length === 0) return undefined;
|
|
const wellFormed = value.toWellFormed();
|
|
if (wellFormed.length <= maxLength) return wellFormed;
|
|
return `${hashPrefix}${Bun.hash(wellFormed).toString(36)}`;
|
|
}
|
|
|
|
export const OPENAI_RESPONSES_PROGRESS_EVENT_TYPES: ReadonlySet<string> = new Set([
|
|
"response.created",
|
|
"response.output_item.added",
|
|
"response.reasoning_summary_part.added",
|
|
"response.reasoning_summary_text.delta",
|
|
"response.reasoning_summary_part.done",
|
|
"response.reasoning_text.delta",
|
|
"response.content_part.added",
|
|
"response.output_text.delta",
|
|
"response.refusal.delta",
|
|
"response.function_call_arguments.delta",
|
|
"response.function_call_arguments.done",
|
|
"response.custom_tool_call_input.delta",
|
|
"response.custom_tool_call_input.done",
|
|
"response.output_item.done",
|
|
"response.completed",
|
|
"response.incomplete",
|
|
"response.failed",
|
|
"error",
|
|
]);
|
|
|
|
export function isOpenAIResponsesProgressEvent(event: unknown): boolean {
|
|
if (!event || typeof event !== "object") return false;
|
|
const type = (event as { type?: unknown }).type;
|
|
return typeof type === "string" && OPENAI_RESPONSES_PROGRESS_EVENT_TYPES.has(type);
|
|
}
|
|
|
|
export function encodeTextSignatureV1(id: string, phase?: TextSignatureV1["phase"]): string {
|
|
const payload: TextSignatureV1 = { v: 1, id };
|
|
if (phase) payload.phase = phase;
|
|
return JSON.stringify(payload);
|
|
}
|
|
|
|
export function parseTextSignature(
|
|
signature: string | undefined,
|
|
): { id: string; phase?: TextSignatureV1["phase"] } | undefined {
|
|
if (!signature) return undefined;
|
|
if (signature.startsWith("{")) {
|
|
try {
|
|
const parsed = JSON.parse(signature) as Partial<TextSignatureV1>;
|
|
if (parsed.v === 1 && typeof parsed.id === "string") {
|
|
if (parsed.phase === "commentary" || parsed.phase === "final_answer") {
|
|
return { id: parsed.id, phase: parsed.phase };
|
|
}
|
|
return { id: parsed.id };
|
|
}
|
|
} catch {
|
|
// Fall through to legacy plain-string handling.
|
|
}
|
|
}
|
|
return { id: signature };
|
|
}
|
|
|
|
export function encodeResponsesToolCallId(callId: string, itemId: string | null | undefined): string {
|
|
const stableItemId = itemId && itemId.length > 0 ? itemId : `fc_${Bun.hash(callId).toString(36)}`;
|
|
return `${callId}|${stableItemId}`;
|
|
}
|
|
|
|
export function normalizeResponsesToolCallIdForTransform(
|
|
id: string,
|
|
model?: Model<Api>,
|
|
source?: AssistantMessage,
|
|
): string {
|
|
if (!id.includes("|")) return id;
|
|
const isForeignToolCall =
|
|
source != null && model != null && (source.provider !== model.provider || source.api !== model.api);
|
|
if (isForeignToolCall) {
|
|
const [callId, itemId] = id.split("|");
|
|
const normalizeIdPart = (part: string): string => {
|
|
const sanitized = part.replace(/[^a-zA-Z0-9_-]/g, "_");
|
|
const truncated = sanitized.length > 64 ? sanitized.slice(0, 64) : sanitized;
|
|
return truncated.replace(/_+$/, "");
|
|
};
|
|
const normalizedCallId = normalizeIdPart(callId);
|
|
let normalizedItemId = `fc_${Bun.hash(itemId).toString(36)}`;
|
|
if (normalizedItemId.length > 64) normalizedItemId = normalizedItemId.slice(0, 64);
|
|
return `${normalizedCallId}|${normalizedItemId}`;
|
|
}
|
|
const normalized = normalizeResponsesToolCallId(id);
|
|
return `${normalized.callId}|${normalized.itemId}`;
|
|
}
|
|
|
|
export function collectKnownCallIds(messages: ResponseInput): Set<string> {
|
|
const knownCallIds = new Set<string>();
|
|
for (const item of messages) {
|
|
if (item.type === "function_call" && typeof item.call_id === "string") {
|
|
knownCallIds.add(item.call_id);
|
|
} else if (
|
|
(item as { type?: string }).type === "custom_tool_call" &&
|
|
typeof (item as { call_id?: string }).call_id === "string"
|
|
) {
|
|
knownCallIds.add((item as { call_id: string }).call_id);
|
|
}
|
|
}
|
|
return knownCallIds;
|
|
}
|
|
|
|
/** Scan replay items for call_ids that were originally custom tool calls. */
|
|
export function collectCustomCallIds(messages: ResponseInput): Set<string> {
|
|
const customCallIds = new Set<string>();
|
|
for (const item of messages) {
|
|
if (
|
|
(item as { type?: string }).type === "custom_tool_call" &&
|
|
typeof (item as { call_id?: string }).call_id === "string"
|
|
) {
|
|
customCallIds.add((item as { call_id: string }).call_id);
|
|
}
|
|
}
|
|
return customCallIds;
|
|
}
|
|
|
|
/**
|
|
* Convert orphan `function_call_output` / `custom_tool_call_output` items —
|
|
* those whose `call_id` has no matching preceding `function_call` /
|
|
* `custom_tool_call` in the same input — into assistant text notes.
|
|
*
|
|
* The Responses API rejects unpaired outputs with
|
|
* `400 No tool call found for function call output with call_id …`. Orphans
|
|
* sneak in through two paths today:
|
|
*
|
|
* - A previous turn's `providerPayload` snapshot replaces the input array via
|
|
* the `dt: false` splice (see {@link convertConversationMessages}), wiping
|
|
* the matching `function_call` while leaving the matching
|
|
* `function_call_output` queued in a later `toolResult`.
|
|
* - A locally-rejected tool call (argument-validation failure, hook reject,
|
|
* aborted turn before the call streamed) produces a tool result without a
|
|
* `function_call` ever landing in any persisted provider payload.
|
|
*
|
|
* Dropping the result loses information the model needs to recover; sending
|
|
* it as-is 400s the request. Folding it into an assistant `message` preserves
|
|
* the payload (call_id + truncated output) while staying within the Responses
|
|
* input grammar. Matches the behavior of {@link transformRequestBody} in the
|
|
* codex provider — issue #1351 / regression of #472.
|
|
*/
|
|
export function repairOrphanResponsesToolOutputs(input: ResponseInput): ResponseInput {
|
|
const knownCallIds = new Set<string>();
|
|
for (const item of input) {
|
|
const t = (item as { type?: string }).type;
|
|
const callId = (item as { call_id?: unknown }).call_id;
|
|
if (typeof callId !== "string") continue;
|
|
if (t === "function_call" || t === "custom_tool_call") knownCallIds.add(callId);
|
|
}
|
|
let hasOrphan = false;
|
|
for (const item of input) {
|
|
const t = (item as { type?: string }).type;
|
|
if (t !== "function_call_output" && t !== "custom_tool_call_output") continue;
|
|
const callId = (item as { call_id?: unknown }).call_id;
|
|
if (typeof callId === "string" && !knownCallIds.has(callId)) {
|
|
hasOrphan = true;
|
|
break;
|
|
}
|
|
}
|
|
if (!hasOrphan) return input;
|
|
return input.map(item => {
|
|
const t = (item as { type?: string }).type;
|
|
if (t !== "function_call_output" && t !== "custom_tool_call_output") return item;
|
|
const record = item as { call_id?: unknown; output?: unknown; name?: unknown };
|
|
const callId = record.call_id;
|
|
if (typeof callId !== "string" || knownCallIds.has(callId)) return item;
|
|
const toolName = typeof record.name === "string" && record.name.length > 0 ? record.name : "tool";
|
|
const rawOutput = record.output;
|
|
let text: string;
|
|
if (typeof rawOutput === "string") text = rawOutput;
|
|
else if (rawOutput == null) text = "";
|
|
else {
|
|
try {
|
|
text = JSON.stringify(rawOutput);
|
|
} catch {
|
|
text = String(rawOutput);
|
|
}
|
|
}
|
|
const ORPHAN_OUTPUT_LIMIT = 16_000;
|
|
if (text.length > ORPHAN_OUTPUT_LIMIT) text = `${text.slice(0, ORPHAN_OUTPUT_LIMIT)}\n...[truncated]`;
|
|
return {
|
|
type: "message",
|
|
role: "assistant",
|
|
content: `[Orphan ${toolName} result; call_id=${callId}]: ${text}`,
|
|
} as ResponseInput[number];
|
|
});
|
|
}
|
|
|
|
/** Placeholder output for a tool call whose result is absent from the input. */
|
|
const ORPHAN_TOOL_CALL_PLACEHOLDER =
|
|
"[No tool output recorded: the tool call was interrupted before it produced a result.]";
|
|
|
|
/**
|
|
* Synthesize a placeholder `function_call_output` / `custom_tool_call_output`
|
|
* for every `function_call` / `custom_tool_call` whose `call_id` has no matching
|
|
* output later in the same input. The Responses API rejects an unpaired call
|
|
* with `400 No tool output found for function call …`.
|
|
*
|
|
* Orphan calls surface when the user branches/navigates the session tree to a
|
|
* node that ends on a tool call (the tool-result child is excluded from the
|
|
* reconstructed history) or when a turn is aborted/crashes after the call
|
|
* streamed but before its result persisted. Dropping the call would erase the
|
|
* assistant's action; a placeholder output keeps the call visible so the model
|
|
* can recover (e.g. re-issue the call). Symmetric to
|
|
* {@link repairOrphanResponsesToolOutputs}.
|
|
*/
|
|
export function repairOrphanResponsesToolCalls(input: ResponseInput): ResponseInput {
|
|
const outputCallIds = new Set<string>();
|
|
for (const item of input) {
|
|
const t = (item as { type?: string }).type;
|
|
if (t !== "function_call_output" && t !== "custom_tool_call_output") continue;
|
|
const callId = (item as { call_id?: unknown }).call_id;
|
|
if (typeof callId === "string") outputCallIds.add(callId);
|
|
}
|
|
let hasOrphan = false;
|
|
for (const item of input) {
|
|
const t = (item as { type?: string }).type;
|
|
if (t !== "function_call" && t !== "custom_tool_call") continue;
|
|
const callId = (item as { call_id?: unknown }).call_id;
|
|
if (typeof callId === "string" && !outputCallIds.has(callId)) {
|
|
hasOrphan = true;
|
|
break;
|
|
}
|
|
}
|
|
if (!hasOrphan) return input;
|
|
const repaired: ResponseInput = [];
|
|
for (const item of input) {
|
|
repaired.push(item);
|
|
const t = (item as { type?: string }).type;
|
|
if (t !== "function_call" && t !== "custom_tool_call") continue;
|
|
const callId = (item as { call_id?: unknown }).call_id;
|
|
if (typeof callId !== "string" || outputCallIds.has(callId)) continue;
|
|
repaired.push({
|
|
type: t === "custom_tool_call" ? "custom_tool_call_output" : "function_call_output",
|
|
call_id: callId,
|
|
output: ORPHAN_TOOL_CALL_PLACEHOLDER,
|
|
} as ResponseInput[number]);
|
|
}
|
|
return repaired;
|
|
}
|
|
|
|
/**
|
|
* Some Responses backends (notably GitHub Copilot) reject the OpenAI image
|
|
* `detail: "original"` value with a 400. When the model does not advertise
|
|
* support for it, degrade `"original"` to `"auto"` so the request still goes
|
|
* through with the closest valid fidelity instead of failing outright. See #2822.
|
|
*/
|
|
function clampResponsesImageDetail(
|
|
detail: ImageContent["detail"],
|
|
supportsImageDetailOriginal: boolean,
|
|
): ResponseInputImage["detail"] {
|
|
const resolved = detail ?? "auto";
|
|
return resolved === "original" && !supportsImageDetailOriginal ? "auto" : resolved;
|
|
}
|
|
|
|
export function convertResponsesInputContent(
|
|
content: string | Array<TextContent | ImageContent>,
|
|
supportsImages: boolean,
|
|
supportsImageDetailOriginal: boolean,
|
|
): ResponseInputContent[] | undefined {
|
|
if (typeof content === "string") {
|
|
if (content.trim().length === 0) return undefined;
|
|
return [{ type: "input_text", text: content.toWellFormed() } satisfies ResponseInputText];
|
|
}
|
|
|
|
const { textBlocks, imageBlocks, omittedImages } = partitionVisionContent(content, supportsImages);
|
|
const normalizedContent: ResponseInputContent[] = [];
|
|
for (const item of textBlocks) {
|
|
const text = item.text.toWellFormed();
|
|
if (text.trim().length === 0) continue;
|
|
normalizedContent.push({
|
|
type: "input_text",
|
|
text,
|
|
} satisfies ResponseInputText);
|
|
}
|
|
for (const item of imageBlocks) {
|
|
normalizedContent.push({
|
|
type: "input_image",
|
|
detail: clampResponsesImageDetail(item.detail, supportsImageDetailOriginal),
|
|
image_url: `data:${item.mimeType};base64,${item.data}`,
|
|
} satisfies ResponseInputImage);
|
|
}
|
|
if (omittedImages) {
|
|
normalizedContent.push({
|
|
type: "input_text",
|
|
text: NON_VISION_IMAGE_PLACEHOLDER,
|
|
} satisfies ResponseInputText);
|
|
}
|
|
return normalizedContent.length > 0 ? normalizedContent : undefined;
|
|
}
|
|
|
|
export interface BuildResponsesInputOptions<TApi extends Api> {
|
|
model: Model<TApi>;
|
|
context: Context;
|
|
strictResponsesPairing: boolean;
|
|
supportsImageDetailOriginal: boolean;
|
|
systemRole?: "system" | "developer";
|
|
nativeHistory?: {
|
|
replay: boolean;
|
|
filterReasoning: boolean;
|
|
};
|
|
includeThinkingSignatures?: boolean;
|
|
developerStringContent?: boolean;
|
|
repairOrphanOutputs?: boolean;
|
|
}
|
|
|
|
export function buildResponsesInput<TApi extends Api>(options: BuildResponsesInputOptions<TApi>): ResponseInput {
|
|
const messages: ResponseInput = [];
|
|
const systemPrompts = options.systemRole ? normalizeSystemPrompts(options.context.systemPrompt) : [];
|
|
for (const systemPrompt of systemPrompts) {
|
|
messages.push({ role: options.systemRole as "system" | "developer", content: systemPrompt });
|
|
}
|
|
|
|
let knownCallIds = new Set<string>();
|
|
const customCallIds = new Set<string>();
|
|
const transformedMessages = transformMessages(
|
|
options.context.messages,
|
|
options.model,
|
|
normalizeResponsesToolCallIdForTransform,
|
|
);
|
|
const filterReasoning = <T extends { type?: string }>(items: T[]): T[] =>
|
|
options.nativeHistory?.filterReasoning ? items.filter(item => item?.type !== "reasoning") : items;
|
|
const includeThinkingSignatures = options.includeThinkingSignatures ?? options.nativeHistory?.replay ?? true;
|
|
|
|
let msgIndex = 0;
|
|
for (const msg of transformedMessages) {
|
|
if (msg.role === "user" || msg.role === "developer") {
|
|
const providerPayload = (msg as { providerPayload?: AssistantMessage["providerPayload"] }).providerPayload;
|
|
const historyItems = options.nativeHistory
|
|
? getOpenAIResponsesHistoryItems(providerPayload, options.model.provider)
|
|
: undefined;
|
|
const shouldReplayPayloadItems =
|
|
options.nativeHistory?.replay ||
|
|
(historyItems?.some(item => {
|
|
if (!item || typeof item !== "object") return false;
|
|
const candidate = item as { type?: unknown };
|
|
return candidate.type === "compaction" || candidate.type === "compaction_summary";
|
|
}) ??
|
|
false);
|
|
if (historyItems && shouldReplayPayloadItems) {
|
|
messages.push(...sanitizeOpenAIResponsesHistoryItemsForReplay(filterReasoning(historyItems)));
|
|
knownCallIds = collectKnownCallIds(messages);
|
|
for (const id of collectCustomCallIds(messages)) customCallIds.add(id);
|
|
msgIndex++;
|
|
continue;
|
|
}
|
|
const content = convertResponsesInputContent(
|
|
msg.content,
|
|
options.model.input.includes("image"),
|
|
options.supportsImageDetailOriginal,
|
|
);
|
|
if (!content) continue;
|
|
messages.push({
|
|
role: "user",
|
|
content:
|
|
options.developerStringContent && msg.role === "developer" && typeof msg.content === "string"
|
|
? msg.content.toWellFormed()
|
|
: content,
|
|
});
|
|
} else if (msg.role === "assistant") {
|
|
const assistantMsg = msg as AssistantMessage;
|
|
const providerPayload =
|
|
options.nativeHistory?.replay &&
|
|
assistantMsg.api === options.model.api &&
|
|
assistantMsg.model === options.model.id
|
|
? getOpenAIResponsesHistoryPayload(
|
|
assistantMsg.providerPayload,
|
|
options.model.provider,
|
|
assistantMsg.provider,
|
|
)
|
|
: undefined;
|
|
const historyItems = providerPayload?.items;
|
|
if (historyItems) {
|
|
const sanitizedHistoryItems = sanitizeOpenAIResponsesHistoryItemsForReplay(filterReasoning(historyItems));
|
|
if (providerPayload?.dt) {
|
|
messages.push(...sanitizedHistoryItems);
|
|
} else {
|
|
messages.splice(0, messages.length, ...sanitizedHistoryItems);
|
|
}
|
|
knownCallIds = collectKnownCallIds(messages);
|
|
for (const id of collectCustomCallIds(messages)) customCallIds.add(id);
|
|
msgIndex++;
|
|
continue;
|
|
}
|
|
|
|
const outputItems = convertResponsesAssistantMessage(
|
|
assistantMsg,
|
|
options.model,
|
|
msgIndex,
|
|
knownCallIds,
|
|
includeThinkingSignatures,
|
|
customCallIds,
|
|
);
|
|
if (outputItems.length === 0) continue;
|
|
messages.push(...outputItems);
|
|
} else if (msg.role === "toolResult") {
|
|
appendResponsesToolResultMessages(
|
|
messages,
|
|
msg,
|
|
options.model,
|
|
options.strictResponsesPairing,
|
|
options.supportsImageDetailOriginal,
|
|
knownCallIds,
|
|
customCallIds,
|
|
);
|
|
}
|
|
msgIndex++;
|
|
}
|
|
|
|
const withRepairedOutputs = options.repairOrphanOutputs ? repairOrphanResponsesToolOutputs(messages) : messages;
|
|
return repairOrphanResponsesToolCalls(withRepairedOutputs);
|
|
}
|
|
|
|
export function convertResponsesAssistantMessage<TApi extends Api>(
|
|
assistantMsg: AssistantMessage,
|
|
model: Model<TApi>,
|
|
msgIndex: number,
|
|
knownCallIds: Set<string>,
|
|
includeThinkingSignatures = true,
|
|
customCallIds?: Set<string>,
|
|
): ResponseInput {
|
|
const outputItems: ResponseInput = [];
|
|
let unsignedTextBlocks = 0;
|
|
const isDifferentModel =
|
|
assistantMsg.model !== model.id && assistantMsg.provider === model.provider && assistantMsg.api === model.api;
|
|
|
|
for (const block of assistantMsg.content) {
|
|
if (block.type === "thinking" && assistantMsg.stopReason !== "error") {
|
|
if (!includeThinkingSignatures) {
|
|
continue;
|
|
}
|
|
if (block.thinkingSignature) {
|
|
try {
|
|
outputItems.push(JSON.parse(block.thinkingSignature) as ResponseReasoningItem);
|
|
} catch {
|
|
// Legacy/corrupt persisted signature — skip the reasoning item
|
|
// rather than failing the whole request build.
|
|
}
|
|
}
|
|
continue;
|
|
}
|
|
|
|
if (block.type === "text") {
|
|
const parsedSignature = parseTextSignature(block.textSignature);
|
|
let msgId = parsedSignature?.id;
|
|
if (!msgId) {
|
|
// Distinct ids per unsigned block: several text blocks in one message
|
|
// (cross-provider replay downgrades thinking → text) must not share an id.
|
|
msgId = unsignedTextBlocks === 0 ? `msg_${msgIndex}` : `msg_${msgIndex}_${unsignedTextBlocks}`;
|
|
unsignedTextBlocks += 1;
|
|
} else if (msgId.length > 64) {
|
|
msgId = `msg_${Bun.hash(msgId).toString(36)}`;
|
|
}
|
|
outputItems.push({
|
|
type: "message",
|
|
role: "assistant",
|
|
content: [{ type: "output_text", text: block.text.toWellFormed(), annotations: [] }],
|
|
status: "completed",
|
|
id: msgId,
|
|
phase: parsedSignature?.phase,
|
|
} satisfies ResponseOutputMessage);
|
|
continue;
|
|
}
|
|
|
|
if (block.type !== "toolCall") {
|
|
continue;
|
|
}
|
|
|
|
const normalized = normalizeResponsesToolCallId(block.id, block.customWireName ? "ctc" : "fc");
|
|
let itemId: string | undefined = normalized.itemId;
|
|
if (isDifferentModel && (itemId?.startsWith("fc_") || itemId?.startsWith("fcr_") || itemId?.startsWith("ctc_"))) {
|
|
itemId = undefined;
|
|
}
|
|
knownCallIds.add(normalized.callId);
|
|
if (block.customWireName) {
|
|
const rawInput = typeof block.arguments?.input === "string" ? block.arguments.input : "";
|
|
customCallIds?.add(normalized.callId);
|
|
outputItems.push({
|
|
type: "custom_tool_call",
|
|
id: itemId,
|
|
call_id: normalized.callId,
|
|
name: block.customWireName,
|
|
input: rawInput,
|
|
} as ResponseInput[number]);
|
|
continue;
|
|
}
|
|
outputItems.push({
|
|
type: "function_call",
|
|
id: itemId,
|
|
call_id: normalized.callId,
|
|
name: block.name,
|
|
arguments: JSON.stringify(block.arguments),
|
|
});
|
|
}
|
|
|
|
return outputItems;
|
|
}
|
|
|
|
export function appendResponsesToolResultMessages<TApi extends Api>(
|
|
messages: ResponseInput,
|
|
toolResult: ToolResultMessage,
|
|
model: Model<TApi>,
|
|
strictResponsesPairing: boolean,
|
|
supportsImageDetailOriginal: boolean,
|
|
knownCallIds: ReadonlySet<string>,
|
|
customCallIds?: ReadonlySet<string>,
|
|
): void {
|
|
const supportsImages = model.input.includes("image");
|
|
const textResult = toolResult.content
|
|
.filter((block): block is TextContent => block.type === "text")
|
|
.map(block => block.text)
|
|
.join("\n");
|
|
const hasImages = toolResult.content.some((block): block is ImageContent => block.type === "image");
|
|
const omittedImages = hasImages && !supportsImages;
|
|
const normalized = normalizeResponsesToolCallId(toolResult.toolCallId);
|
|
const output = (
|
|
omittedImages
|
|
? joinTextWithImagePlaceholder(textResult, true)
|
|
: textResult.length > 0
|
|
? textResult
|
|
: "(see attached image)"
|
|
).toWellFormed();
|
|
if (strictResponsesPairing && !knownCallIds.has(normalized.callId)) {
|
|
// Strict backends (Azure, Copilot) reject unpaired outputs outright, but
|
|
// silently dropping the result loses information the model needs. Fold it
|
|
// into an assistant note instead (same shape as repairOrphanResponsesToolOutputs).
|
|
const limit = 16_000;
|
|
const noteText = output.length > limit ? `${output.slice(0, limit)}\n...[truncated]` : output;
|
|
messages.push({
|
|
type: "message",
|
|
role: "assistant",
|
|
content: `[Orphan ${toolResult.toolName || "tool"} result; call_id=${normalized.callId}]: ${noteText}`,
|
|
} as ResponseInput[number]);
|
|
return;
|
|
}
|
|
if (customCallIds?.has(normalized.callId)) {
|
|
messages.push({
|
|
type: "custom_tool_call_output",
|
|
call_id: normalized.callId,
|
|
output,
|
|
} as ResponseInput[number]);
|
|
} else {
|
|
messages.push({
|
|
type: "function_call_output",
|
|
call_id: normalized.callId,
|
|
output,
|
|
});
|
|
}
|
|
|
|
if (!hasImages || !supportsImages) {
|
|
return;
|
|
}
|
|
|
|
const contentParts: ResponseInputContent[] = [
|
|
{ type: "input_text", text: "Attached image(s) from tool result:" } satisfies ResponseInputText,
|
|
];
|
|
for (const block of toolResult.content) {
|
|
if (block.type === "image") {
|
|
contentParts.push({
|
|
type: "input_image",
|
|
detail: clampResponsesImageDetail(block.detail, supportsImageDetailOriginal),
|
|
image_url: `data:${block.mimeType};base64,${block.data}`,
|
|
} satisfies ResponseInputImage);
|
|
}
|
|
}
|
|
messages.push({ role: "user", content: contentParts });
|
|
}
|
|
|
|
/**
|
|
* Per-block accumulation helpers shared by the two Responses decode loops —
|
|
* {@link processResponsesStream} (generic Responses) and the Codex stream
|
|
* handler in `openai-codex-responses.ts`. Each endpoint keeps its own
|
|
* item-routing, terminal handling, and transport bookkeeping; these own only
|
|
* the leaf mutations on an already-resolved open block, so the
|
|
* append/parse/finalize logic lives in exactly one place. The caller passes the
|
|
* `contentIndex` its router resolved (generic uses `output.content.indexOf`;
|
|
* Codex uses the open item's recorded index) so the emitted stream events match
|
|
* each decoder's existing behavior byte-for-byte.
|
|
*/
|
|
type ResponsesToolCallBlock = ToolCall & { [kStreamingPartialJson]: string; [kStreamingLastParseLen]?: number };
|
|
|
|
export function appendReasoningSummaryPart(
|
|
item: ResponseReasoningItem,
|
|
part: ResponseReasoningItem["summary"][number],
|
|
): void {
|
|
item.summary = item.summary || [];
|
|
item.summary.push(part);
|
|
}
|
|
|
|
export function appendReasoningSummaryTextDelta(
|
|
item: ResponseReasoningItem,
|
|
block: ThinkingContent,
|
|
delta: string,
|
|
stream: AssistantMessageEventStream,
|
|
output: AssistantMessage,
|
|
contentIndex: number,
|
|
): void {
|
|
item.summary = item.summary || [];
|
|
const lastPart = item.summary[item.summary.length - 1];
|
|
if (!lastPart) return;
|
|
block.thinking += delta;
|
|
lastPart.text += delta;
|
|
stream.push({ type: "thinking_delta", contentIndex, delta, partial: output });
|
|
}
|
|
|
|
export function appendReasoningSummaryPartDone(
|
|
item: ResponseReasoningItem,
|
|
block: ThinkingContent,
|
|
stream: AssistantMessageEventStream,
|
|
output: AssistantMessage,
|
|
contentIndex: number,
|
|
): void {
|
|
item.summary = item.summary || [];
|
|
const lastPart = item.summary[item.summary.length - 1];
|
|
if (!lastPart) return;
|
|
block.thinking += "\n\n";
|
|
lastPart.text += "\n\n";
|
|
stream.push({ type: "thinking_delta", contentIndex, delta: "\n\n", partial: output });
|
|
}
|
|
|
|
export function appendMessageContentPart(
|
|
item: ResponseOutputMessage,
|
|
part: ResponseContentPartAddedEvent["part"] | undefined,
|
|
): void {
|
|
item.content = item.content || [];
|
|
if (part && (part.type === "output_text" || part.type === "refusal")) {
|
|
item.content.push(part);
|
|
}
|
|
}
|
|
|
|
export function appendMessageTextDelta(
|
|
item: ResponseOutputMessage,
|
|
block: TextContent,
|
|
delta: string,
|
|
stream: AssistantMessageEventStream,
|
|
output: AssistantMessage,
|
|
contentIndex: number,
|
|
partType: "output_text" | "refusal",
|
|
): void {
|
|
item.content = item.content || [];
|
|
let lastPart = item.content[item.content.length - 1];
|
|
if (lastPart?.type !== partType) {
|
|
// `content_part.added` never arrived (lossy proxy) — synthesize the part
|
|
// so live text still streams instead of freezing until output_item.done.
|
|
lastPart =
|
|
partType === "output_text"
|
|
? { type: "output_text", text: "", annotations: [] }
|
|
: { type: "refusal", refusal: "" };
|
|
item.content.push(lastPart);
|
|
}
|
|
block.text += delta;
|
|
if (lastPart.type === "output_text") {
|
|
lastPart.text += delta;
|
|
} else {
|
|
lastPart.refusal += delta;
|
|
}
|
|
stream.push({ type: "text_delta", contentIndex, delta, partial: output });
|
|
}
|
|
|
|
export function accumulateToolCallArgumentsDelta(
|
|
block: ResponsesToolCallBlock,
|
|
delta: string,
|
|
stream: AssistantMessageEventStream,
|
|
output: AssistantMessage,
|
|
contentIndex: number,
|
|
): void {
|
|
block[kStreamingPartialJson] += delta;
|
|
const throttled = parseStreamingJsonThrottled(block[kStreamingPartialJson], block[kStreamingLastParseLen] ?? 0);
|
|
if (throttled) {
|
|
block.arguments = throttled.value;
|
|
block[kStreamingLastParseLen] = throttled.parsedLen;
|
|
}
|
|
stream.push({ type: "toolcall_delta", contentIndex, delta, partial: output });
|
|
}
|
|
|
|
/**
|
|
* Finalize streamed function-call arguments from the authoritative `.done`
|
|
* payload. The caller owns the `argumentsDone` flag (generic Responses sets it;
|
|
* Codex's block shape has no such field), so this only rewrites `arguments` and
|
|
* drops the transient accumulation fields.
|
|
*/
|
|
export function finalizeToolCallArgumentsDone(block: ResponsesToolCallBlock, args: string): void {
|
|
block[kStreamingPartialJson] = args;
|
|
block.arguments = parseStreamingJson(block[kStreamingPartialJson]);
|
|
clearStreamingPartialJson(block);
|
|
}
|
|
|
|
export function accumulateCustomToolCallInputDelta(
|
|
block: ResponsesToolCallBlock,
|
|
delta: string,
|
|
stream: AssistantMessageEventStream,
|
|
output: AssistantMessage,
|
|
contentIndex: number,
|
|
): void {
|
|
block[kStreamingPartialJson] += delta;
|
|
block.arguments = { input: block[kStreamingPartialJson] };
|
|
stream.push({ type: "toolcall_delta", contentIndex, delta, partial: output });
|
|
}
|
|
|
|
export function finalizeCustomToolCallInputDone(block: ResponsesToolCallBlock, input: string): void {
|
|
block[kStreamingPartialJson] = input;
|
|
block.arguments = { input };
|
|
}
|
|
|
|
export interface ProcessResponsesStreamOptions {
|
|
onFirstToken?: () => void;
|
|
onOutputItemDone?: (item: ResponseOutputItem) => void;
|
|
/**
|
|
* Called when a terminal `response.completed` or `response.incomplete` event
|
|
* is successfully processed. Only invoked on the successful-completion path;
|
|
* thrown failure (`response.failed`) and cancellation paths never call this.
|
|
* Used by callers to detect premature stream closure (i.e. the stream ended
|
|
* without a recognized terminal event).
|
|
*/
|
|
onCompleted?: () => void;
|
|
/**
|
|
* Caller-requested service tier, used to bill the served tier when the
|
|
* response omits the `service_tier` echo. Only applied for `provider: "openai"`.
|
|
*/
|
|
requestServiceTier?: ServiceTier;
|
|
}
|
|
|
|
export async function processResponsesStream<TApi extends Api>(
|
|
openaiStream: AsyncIterable<ResponseStreamEvent>,
|
|
output: AssistantMessage,
|
|
stream: AssistantMessageEventStream,
|
|
model: Model<TApi>,
|
|
options?: ProcessResponsesStreamOptions,
|
|
): Promise<void> {
|
|
type StreamingToolCallBlock = ToolCall & {
|
|
[kStreamingPartialJson]: string;
|
|
[kStreamingLastParseLen]?: number;
|
|
[kStreamingArgumentsDone]?: boolean;
|
|
};
|
|
interface StreamingItem {
|
|
item: ResponseReasoningItem | ResponseOutputMessage | ResponseFunctionToolCall | ResponseCustomToolCall;
|
|
block: ThinkingContent | TextContent | StreamingToolCallBlock;
|
|
}
|
|
|
|
// Multiple items (parallel function_calls in particular) can be open at the same
|
|
// time. OpenAI's spec routes every per-item event by `output_index`/`item_id`;
|
|
// see https://github.com/can1357/oh-my-pi/issues/1880 — llama.cpp emits parallel
|
|
// function_call deltas interleaved, and a singleton `current` reference would
|
|
// fold them into the wrong block and drop arguments on every call but the last.
|
|
//
|
|
// OpenAI-compatible hosts can compound this by omitting `item.id` and
|
|
// `output_index` on `output_item.added` while routing later argument deltas to
|
|
// either the bare `call_id` or a synthesized `fc_<call_id>` item id. Register
|
|
// both keys so each delta reaches its own block instead of falling back to the
|
|
// most recently added parallel call.
|
|
const openItemsByOutputIndex = new Map<number, StreamingItem>();
|
|
const openItemsByItemId = new Map<string, StreamingItem>();
|
|
const openItemsByPrefixedCallId = new Map<string, StreamingItem>();
|
|
let lastOpenItem: StreamingItem | null = null;
|
|
const openItemsInOrder: StreamingItem[] = [];
|
|
|
|
const prefixedFunctionCallItemKey = (callId: string | undefined): string | undefined =>
|
|
callId ? `fc_${callId}` : undefined;
|
|
|
|
const registerOpenItem = (
|
|
outputIndex: number | undefined,
|
|
itemId: string | undefined,
|
|
entry: StreamingItem,
|
|
alternateItemKey?: string,
|
|
prefixedAlternateItemKey?: string,
|
|
): void => {
|
|
if (typeof outputIndex === "number") openItemsByOutputIndex.set(outputIndex, entry);
|
|
if (itemId) openItemsByItemId.set(itemId, entry);
|
|
if (alternateItemKey && alternateItemKey !== itemId) openItemsByItemId.set(alternateItemKey, entry);
|
|
if (
|
|
prefixedAlternateItemKey &&
|
|
prefixedAlternateItemKey !== itemId &&
|
|
prefixedAlternateItemKey !== alternateItemKey
|
|
) {
|
|
openItemsByPrefixedCallId.set(prefixedAlternateItemKey, entry);
|
|
}
|
|
openItemsInOrder.push(entry);
|
|
lastOpenItem = entry;
|
|
};
|
|
const lookupOpenItem = (event: { output_index?: number; item_id?: string }): StreamingItem | undefined => {
|
|
const hasKey = typeof event.output_index === "number" || event.item_id !== undefined;
|
|
if (typeof event.output_index === "number") {
|
|
const found = openItemsByOutputIndex.get(event.output_index);
|
|
if (found) return found;
|
|
}
|
|
if (event.item_id) {
|
|
const found = openItemsByItemId.get(event.item_id);
|
|
if (found) return found;
|
|
}
|
|
// Keyed events whose item already closed are stale; drop them instead of
|
|
// routing to a sibling. Only fully identifierless mock/proxy events use the
|
|
// legacy singleton fallback.
|
|
return hasKey ? undefined : (lastOpenItem ?? undefined);
|
|
};
|
|
const hasOpenItemKey = (event: { output_index?: number; item_id?: string }): boolean =>
|
|
typeof event.output_index === "number" || event.item_id !== undefined;
|
|
const lookupOpenToolCallAlias = (
|
|
event: { output_index?: number; item_id?: string },
|
|
type: "function_call" | "custom_tool_call",
|
|
): StreamingItem | undefined => {
|
|
if (typeof event.output_index === "number") {
|
|
const byOutputIndex = openItemsByOutputIndex.get(event.output_index);
|
|
if (byOutputIndex) return byOutputIndex;
|
|
// A lossy host (llama.cpp/Ollama, issue #2015) can omit `output_index` on
|
|
// `output_item.added` while still stamping the spec-required field on the
|
|
// delta. The index was never registered, so fall through to the prefixed
|
|
// alias / exact item-id maps instead of dropping to `lastOpenItem`.
|
|
}
|
|
if (event.item_id) {
|
|
// Prefixed call-id aliases share the same wire namespace as real call ids.
|
|
// Argument/input events can use the prefixed form, while final
|
|
// output_item.done events below use exact call ids; keep aliases in a
|
|
// separate map so a real `call_id: "fc_x"` cannot overwrite the alias
|
|
// for `call_id: "x"`.
|
|
const alias = openItemsByPrefixedCallId.get(event.item_id);
|
|
if (alias?.item.type === type) return alias;
|
|
const exact = openItemsByItemId.get(event.item_id);
|
|
if (exact) return exact;
|
|
}
|
|
return lookupOpenItem(event);
|
|
};
|
|
const lookupOpenFunctionCallItem = (event: {
|
|
output_index?: number;
|
|
item_id?: string;
|
|
}): StreamingItem | undefined => {
|
|
if (hasOpenItemKey(event)) return lookupOpenToolCallAlias(event, "function_call");
|
|
for (const candidate of openItemsInOrder) {
|
|
if (
|
|
candidate.item.type === "function_call" &&
|
|
candidate.block.type === "toolCall" &&
|
|
!candidate.block[kStreamingArgumentsDone]
|
|
) {
|
|
return candidate;
|
|
}
|
|
}
|
|
return lastOpenItem?.item.type === "function_call" ? lastOpenItem : undefined;
|
|
};
|
|
const closeOpenItem = (
|
|
outputIndex: number | undefined,
|
|
itemId: string | undefined,
|
|
entry: StreamingItem | undefined,
|
|
alternateItemKey?: string,
|
|
prefixedAlternateItemKey?: string,
|
|
): void => {
|
|
if (typeof outputIndex === "number") openItemsByOutputIndex.delete(outputIndex);
|
|
if (itemId) openItemsByItemId.delete(itemId);
|
|
if (alternateItemKey && alternateItemKey !== itemId) openItemsByItemId.delete(alternateItemKey);
|
|
if (
|
|
prefixedAlternateItemKey &&
|
|
prefixedAlternateItemKey !== itemId &&
|
|
prefixedAlternateItemKey !== alternateItemKey &&
|
|
openItemsByPrefixedCallId.get(prefixedAlternateItemKey) === entry
|
|
) {
|
|
openItemsByPrefixedCallId.delete(prefixedAlternateItemKey);
|
|
}
|
|
if (entry) {
|
|
const index = openItemsInOrder.indexOf(entry);
|
|
if (index >= 0) openItemsInOrder.splice(index, 1);
|
|
}
|
|
if (entry && lastOpenItem === entry) lastOpenItem = null;
|
|
};
|
|
const contentIndexOf = (block: ThinkingContent | TextContent | StreamingToolCallBlock): number =>
|
|
output.content.indexOf(block);
|
|
|
|
let sawFirstToken = false;
|
|
|
|
for await (const event of openaiStream) {
|
|
if (event.type === "response.created") {
|
|
output.responseId = event.response.id;
|
|
} else if (event.type === "response.output_item.added") {
|
|
if (!sawFirstToken) {
|
|
sawFirstToken = true;
|
|
options?.onFirstToken?.();
|
|
}
|
|
const item = event.item;
|
|
if (item.type === "reasoning") {
|
|
const block: ThinkingContent = { type: "thinking", thinking: "", itemId: item.id };
|
|
output.content.push(block);
|
|
registerOpenItem(event.output_index, item.id, { item, block });
|
|
stream.push({ type: "thinking_start", contentIndex: contentIndexOf(block), partial: output });
|
|
} else if (item.type === "message") {
|
|
const block: TextContent = {
|
|
type: "text",
|
|
text: "",
|
|
textSignature: encodeTextSignatureV1(item.id, item.phase ?? undefined),
|
|
};
|
|
output.content.push(block);
|
|
registerOpenItem(event.output_index, item.id, { item, block });
|
|
stream.push({ type: "text_start", contentIndex: contentIndexOf(block), partial: output });
|
|
} else if (item.type === "function_call") {
|
|
const block: StreamingToolCallBlock = {
|
|
type: "toolCall",
|
|
id: encodeResponsesToolCallId(item.call_id, item.id),
|
|
name: item.name,
|
|
arguments: {},
|
|
[kStreamingPartialJson]: item.arguments || "",
|
|
};
|
|
output.content.push(block);
|
|
registerOpenItem(
|
|
event.output_index,
|
|
item.id,
|
|
{ item, block },
|
|
item.call_id,
|
|
prefixedFunctionCallItemKey(item.call_id),
|
|
);
|
|
stream.push({ type: "toolcall_start", contentIndex: contentIndexOf(block), partial: output });
|
|
} else if (item.type === "custom_tool_call") {
|
|
const block: StreamingToolCallBlock = {
|
|
type: "toolCall",
|
|
id: encodeResponsesToolCallId(item.call_id, item.id),
|
|
// Preserve the raw wire name (e.g. `apply_patch`). The agent-loop
|
|
// dispatcher matches it against both `Tool.name` and
|
|
// `Tool.customWireName`, so this stays wire-accurate through
|
|
// history replay while still routing to the right handler.
|
|
name: item.name,
|
|
arguments: { input: item.input ?? "" },
|
|
customWireName: item.name,
|
|
// Custom tools stream a raw string, but we reuse `partialJson` as the
|
|
// accumulation buffer so later code that inspects the field still works.
|
|
[kStreamingPartialJson]: item.input ?? "",
|
|
};
|
|
output.content.push(block);
|
|
registerOpenItem(
|
|
event.output_index,
|
|
item.id,
|
|
{ item, block },
|
|
item.call_id,
|
|
prefixedFunctionCallItemKey(item.call_id),
|
|
);
|
|
stream.push({ type: "toolcall_start", contentIndex: contentIndexOf(block), partial: output });
|
|
}
|
|
} else if (event.type === "response.reasoning_summary_part.added") {
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "reasoning") appendReasoningSummaryPart(entry.item, event.part);
|
|
} else if (event.type === "response.reasoning_summary_text.delta") {
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "reasoning" && entry.block.type === "thinking") {
|
|
appendReasoningSummaryTextDelta(
|
|
entry.item,
|
|
entry.block,
|
|
event.delta,
|
|
stream,
|
|
output,
|
|
contentIndexOf(entry.block),
|
|
);
|
|
}
|
|
} else if (event.type === "response.reasoning_summary_part.done") {
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "reasoning" && entry.block.type === "thinking") {
|
|
appendReasoningSummaryPartDone(entry.item, entry.block, stream, output, contentIndexOf(entry.block));
|
|
}
|
|
} else if (event.type === "response.reasoning_text.delta") {
|
|
// Raw reasoning text delta from local providers that stream thinking
|
|
// directly rather than via the OpenAI summary tracking protocol.
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "reasoning" && entry.block.type === "thinking") {
|
|
entry.block.thinking += event.delta;
|
|
stream.push({
|
|
type: "thinking_delta",
|
|
contentIndex: contentIndexOf(entry.block),
|
|
delta: event.delta,
|
|
partial: output,
|
|
});
|
|
}
|
|
} else if (event.type === "response.content_part.added") {
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "message") appendMessageContentPart(entry.item, event.part);
|
|
} else if (event.type === "response.output_text.delta") {
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "message" && entry.block.type === "text") {
|
|
appendMessageTextDelta(
|
|
entry.item,
|
|
entry.block,
|
|
event.delta,
|
|
stream,
|
|
output,
|
|
contentIndexOf(entry.block),
|
|
"output_text",
|
|
);
|
|
}
|
|
} else if (event.type === "response.refusal.delta") {
|
|
const entry = lookupOpenItem(event);
|
|
if (entry?.item.type === "message" && entry.block.type === "text") {
|
|
appendMessageTextDelta(
|
|
entry.item,
|
|
entry.block,
|
|
event.delta,
|
|
stream,
|
|
output,
|
|
contentIndexOf(entry.block),
|
|
"refusal",
|
|
);
|
|
}
|
|
} else if (event.type === "response.function_call_arguments.delta") {
|
|
const entry = lookupOpenFunctionCallItem(event);
|
|
if (entry?.item.type === "function_call" && entry.block.type === "toolCall") {
|
|
accumulateToolCallArgumentsDelta(entry.block, event.delta, stream, output, contentIndexOf(entry.block));
|
|
}
|
|
} else if (event.type === "response.function_call_arguments.done") {
|
|
const entry = lookupOpenFunctionCallItem(event);
|
|
if (entry?.item.type === "function_call" && entry.block.type === "toolCall") {
|
|
finalizeToolCallArgumentsDone(entry.block, event.arguments);
|
|
entry.block[kStreamingArgumentsDone] = true;
|
|
}
|
|
} else if (event.type === "response.custom_tool_call_input.delta") {
|
|
const entry = lookupOpenToolCallAlias(event, "custom_tool_call");
|
|
if (entry?.item.type === "custom_tool_call" && entry.block.type === "toolCall") {
|
|
accumulateCustomToolCallInputDelta(entry.block, event.delta, stream, output, contentIndexOf(entry.block));
|
|
}
|
|
} else if (event.type === "response.custom_tool_call_input.done") {
|
|
const entry = lookupOpenToolCallAlias(event, "custom_tool_call");
|
|
if (entry?.item.type === "custom_tool_call" && entry.block.type === "toolCall") {
|
|
finalizeCustomToolCallInputDone(entry.block, event.input);
|
|
}
|
|
} else if (event.type === "response.output_item.done") {
|
|
const item = structuredCloneJSON(event.item);
|
|
options?.onOutputItemDone?.(item);
|
|
const entry =
|
|
item.type === "function_call" || item.type === "custom_tool_call"
|
|
? lookupOpenItem({ output_index: event.output_index, item_id: item.id ?? item.call_id })
|
|
: lookupOpenItem({ output_index: event.output_index, item_id: item.id });
|
|
if (item.type === "reasoning") {
|
|
const thinking =
|
|
item.summary?.length > 0
|
|
? item.summary.map(part => part.text).join("\n\n")
|
|
: item.content?.[0]?.type === "reasoning_text"
|
|
? (item.content[0].text ?? "")
|
|
: "";
|
|
// Prefer the routed entry; the bare itemId find misroutes when ids are
|
|
// absent (`undefined === undefined` matches the FIRST thinking block) and
|
|
// misses entirely when the done-event id drifts from the added-event id.
|
|
const reasoningBlock =
|
|
entry?.block.type === "thinking"
|
|
? entry.block
|
|
: (output.content.find(b => b.type === "thinking" && (b as ThinkingContent).itemId === item.id) as
|
|
| ThinkingContent
|
|
| undefined);
|
|
if (reasoningBlock) {
|
|
reasoningBlock.thinking = thinking;
|
|
reasoningBlock.thinkingSignature = JSON.stringify(item);
|
|
stream.push({
|
|
type: "thinking_end",
|
|
contentIndex: contentIndexOf(reasoningBlock),
|
|
content: thinking,
|
|
partial: output,
|
|
});
|
|
}
|
|
closeOpenItem(event.output_index, item.id, entry);
|
|
} else if (item.type === "message") {
|
|
const block = entry?.block.type === "text" ? entry.block : undefined;
|
|
const text = item.content
|
|
.map(part => (part.type === "output_text" ? (part.text ?? "") : (part.refusal ?? "")))
|
|
.join("");
|
|
const textSignature = encodeTextSignatureV1(item.id, item.phase ?? undefined);
|
|
let contentIndex: number;
|
|
if (block) {
|
|
block.text = text;
|
|
block.textSignature = textSignature;
|
|
contentIndex = contentIndexOf(block);
|
|
} else {
|
|
// `output_item.added` never arrived (lossy proxy) — synthesize the
|
|
// block so the final message still carries the authoritative text.
|
|
const synthesized: TextContent = { type: "text", text, textSignature };
|
|
output.content.push(synthesized);
|
|
contentIndex = output.content.length - 1;
|
|
}
|
|
stream.push({ type: "text_end", contentIndex, content: text, partial: output });
|
|
closeOpenItem(event.output_index, item.id, entry);
|
|
} else if (item.type === "function_call") {
|
|
const block = entry?.block.type === "toolCall" ? entry.block : undefined;
|
|
const args = block?.[kStreamingArgumentsDone]
|
|
? block.arguments
|
|
: block?.[kStreamingPartialJson]
|
|
? parseStreamingJson(block[kStreamingPartialJson])
|
|
: parseStreamingJson(item.arguments || "{}");
|
|
const toolCall: ToolCall = {
|
|
type: "toolCall",
|
|
id: encodeResponsesToolCallId(item.call_id, item.id),
|
|
name: item.name,
|
|
arguments: args,
|
|
};
|
|
let contentIndex: number;
|
|
if (block) {
|
|
// Persist the authoritative final args on the stored block. The
|
|
// throttled delta parser may have skipped the last partial parse,
|
|
// leaving block.arguments stale (often `{}`); the emitted toolCall
|
|
// and the persisted block must agree.
|
|
block.arguments = args;
|
|
clearStreamingPartialJson(block);
|
|
contentIndex = contentIndexOf(block);
|
|
} else {
|
|
// `output_item.added` never arrived (lossy proxy) — synthesize the
|
|
// block so the final message carries the call the consumer was told
|
|
// completed (the agent loop executes tools from message.content).
|
|
output.content.push(toolCall);
|
|
contentIndex = output.content.length - 1;
|
|
}
|
|
closeOpenItem(event.output_index, item.id, entry, item.call_id, prefixedFunctionCallItemKey(item.call_id));
|
|
stream.push({ type: "toolcall_end", contentIndex, toolCall, partial: output });
|
|
} else if (item.type === "custom_tool_call") {
|
|
const block = entry?.block.type === "toolCall" ? entry.block : undefined;
|
|
const rawInput = block?.[kStreamingPartialJson] ? block[kStreamingPartialJson] : (item.input ?? "");
|
|
const toolCall: ToolCall = {
|
|
type: "toolCall",
|
|
id: encodeResponsesToolCallId(item.call_id, item.id),
|
|
name: item.name,
|
|
arguments: { input: rawInput },
|
|
customWireName: item.name,
|
|
};
|
|
let contentIndex: number;
|
|
if (block) {
|
|
// Persist the final input on the stored block and drop the transient
|
|
// accumulation buffer, mirroring the function_call branch above.
|
|
block.arguments = { input: rawInput };
|
|
clearStreamingPartialJson(block);
|
|
contentIndex = contentIndexOf(block);
|
|
} else {
|
|
output.content.push(toolCall);
|
|
contentIndex = output.content.length - 1;
|
|
}
|
|
closeOpenItem(event.output_index, item.id, entry, item.call_id, prefixedFunctionCallItemKey(item.call_id));
|
|
stream.push({ type: "toolcall_end", contentIndex, toolCall, partial: output });
|
|
}
|
|
} else if (event.type === "response.completed" || event.type === "response.incomplete") {
|
|
const response = event.response;
|
|
finalizePendingResponsesToolCalls(output);
|
|
if (response?.id) {
|
|
output.responseId = response.id;
|
|
}
|
|
populateResponsesUsageFromResponse(output, response?.usage);
|
|
calculateCost(model, output.usage);
|
|
applyOpenAIResponsesServiceTierCost(
|
|
model,
|
|
output.usage,
|
|
(response as { service_tier?: unknown } | undefined)?.service_tier,
|
|
options?.requestServiceTier,
|
|
);
|
|
output.stopReason = mapOpenAIResponsesStopReason(response?.status);
|
|
if (response?.status === "failed" || response?.status === "cancelled") {
|
|
const error = response?.error ?? (response as any)?.status_details?.error;
|
|
const details = response?.incomplete_details;
|
|
const statusDetailsReason = (response as any)?.status_details?.reason;
|
|
const message = error
|
|
? `${error.code || "unknown"}: ${error.message || "no message"}`
|
|
: details?.reason
|
|
? `incomplete: ${details.reason}`
|
|
: typeof statusDetailsReason === "string" && statusDetailsReason.length > 0
|
|
? `status_details: ${statusDetailsReason}`
|
|
: "Unknown error (no error details in response)";
|
|
throw new AIError.ProviderResponseError(message, { provider: model.provider, kind: "output" });
|
|
}
|
|
if (response?.status === "incomplete" && response.incomplete_details?.reason === "content_filter") {
|
|
// A content-filtered turn is a failure, not a token-cap truncation —
|
|
// mapping it to "length" would route the agent loop into "shorten your
|
|
// output" recovery against a filtered prompt.
|
|
throw new AIError.ProviderResponseError("incomplete: content_filter", {
|
|
provider: model.provider,
|
|
kind: "content-blocked",
|
|
});
|
|
}
|
|
promoteResponsesToolUseStopReason(output, (response as { end_turn?: boolean } | undefined)?.end_turn);
|
|
options?.onCompleted?.();
|
|
// `response.completed`/`response.incomplete` is the last event of a
|
|
// Responses stream. Stop pulling instead of waiting for the server to
|
|
// close the connection: misbehaving providers keep the socket open
|
|
// after the terminal event, which would park this loop until the idle
|
|
// watchdog converts an already-successful turn into a timeout error.
|
|
// Breaking unwinds the iterator chain (the consumer's `.return()`
|
|
// reaches the SDK stream), actively releasing the connection.
|
|
break;
|
|
} else if (event.type === "error") {
|
|
const err = (event as any).error ?? event;
|
|
const code = err.code ?? "unknown";
|
|
const message = err.message ?? "no message";
|
|
throw new AIError.ProviderResponseError(`Error Code ${code}: ${message}`, {
|
|
provider: model.provider,
|
|
kind: "output",
|
|
});
|
|
} else if (event.type === "response.failed") {
|
|
populateResponsesUsageFromResponse(output, event.response?.usage);
|
|
const error = event.response?.error ?? (event.response as any)?.status_details?.error;
|
|
const details = event.response?.incomplete_details;
|
|
const message = error
|
|
? `${error.code || "unknown"}: ${error.message || "no message"}`
|
|
: details?.reason
|
|
? `incomplete: ${details.reason}`
|
|
: "Unknown error (no error details in response)";
|
|
throw new AIError.ProviderResponseError(message, { provider: model.provider, kind: "output" });
|
|
}
|
|
}
|
|
}
|
|
|
|
export function mapOpenAIResponsesStopReason(status: ResponseStatus | undefined): StopReason {
|
|
if (!status) return "stop";
|
|
switch (status) {
|
|
case "completed":
|
|
return "stop";
|
|
case "incomplete":
|
|
return "length";
|
|
case "failed":
|
|
case "cancelled":
|
|
return "error";
|
|
case "in_progress":
|
|
case "queued":
|
|
return "stop";
|
|
default: {
|
|
// Compile-time exhaustiveness; at runtime a brand-new status from the
|
|
// server must degrade gracefully instead of failing a fully-streamed
|
|
// response.
|
|
const exhaustive: never = status;
|
|
logger.warn("Unhandled OpenAI Responses stop reason", { status: exhaustive });
|
|
return "stop";
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Finalize any streamed toolCall block whose `output_item.done` never arrived
|
|
* (lossy proxy, or a terminal event that raced the per-item done): parse the
|
|
* accumulated `partialJson` into authoritative arguments and strip the transient
|
|
* streaming fields so they never persist. Shared by the chat-Responses decoder
|
|
* and the Codex decoder. Closed blocks already cleared these fields, so walking
|
|
* the full content list leaves them untouched.
|
|
*/
|
|
export function finalizePendingResponsesToolCalls(output: AssistantMessage): void {
|
|
for (const block of output.content) {
|
|
if (block.type !== "toolCall") continue;
|
|
const pending = block as ToolCall & {
|
|
[kStreamingPartialJson]?: string;
|
|
[kStreamingLastParseLen]?: number;
|
|
[kStreamingArgumentsDone]?: boolean;
|
|
};
|
|
if (pending[kStreamingPartialJson] && !pending[kStreamingArgumentsDone]) {
|
|
pending.arguments =
|
|
pending.customWireName !== undefined
|
|
? { input: pending[kStreamingPartialJson] }
|
|
: parseStreamingJson(pending[kStreamingPartialJson]);
|
|
}
|
|
clearStreamingPartialJson(pending);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Apply the Responses terminal stop-reason invariants shared by the chat-Responses
|
|
* and Codex decoders: a turn that produced tool calls becomes `toolUse`, and a
|
|
* Codex-lineage `end_turn: false` marker pauses the turn so the agent loop
|
|
* re-samples instead of ending. Callers set `output.stopReason` from the wire
|
|
* status first via {@link mapOpenAIResponsesStopReason}.
|
|
*/
|
|
export function promoteResponsesToolUseStopReason(output: AssistantMessage, endTurn: boolean | undefined): void {
|
|
if (output.content.some(block => block.type === "toolCall") && output.stopReason === "stop") {
|
|
output.stopReason = "toolUse";
|
|
}
|
|
if (endTurn === false && output.stopReason === "stop") {
|
|
output.stopDetails = { type: "pause_turn" };
|
|
}
|
|
}
|
|
|
|
/** Initial empty `AssistantMessage` that streaming providers accumulate into. */
|
|
export function createInitialResponsesAssistantMessage(api: Api, provider: string, modelId: string): AssistantMessage {
|
|
return {
|
|
role: "assistant",
|
|
content: [],
|
|
api,
|
|
provider,
|
|
model: modelId,
|
|
usage: {
|
|
input: 0,
|
|
output: 0,
|
|
cacheRead: 0,
|
|
cacheWrite: 0,
|
|
totalTokens: 0,
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
|
|
},
|
|
stopReason: "stop",
|
|
timestamp: Date.now(),
|
|
};
|
|
}
|
|
|
|
/** Extension fields we add on top of `ResponseCreateParamsStreaming` across the Responses-family providers. */
|
|
export type ResponsesSamplingParamsExtras = {
|
|
top_p?: number;
|
|
top_k?: number;
|
|
min_p?: number;
|
|
presence_penalty?: number;
|
|
repetition_penalty?: number;
|
|
};
|
|
|
|
type CommonResponsesParams = ResponseCreateParamsStreaming & ResponsesSamplingParamsExtras;
|
|
|
|
type CommonSamplingOptions = Pick<
|
|
StreamOptions,
|
|
"temperature" | "topP" | "topK" | "minP" | "presencePenalty" | "repetitionPenalty" | "maxTokens"
|
|
> & { serviceTier?: ServiceTier };
|
|
|
|
/**
|
|
* Apply the common `StreamOptions` → Responses sampling-parameter mapping (max output tokens,
|
|
* temperature, top-p/k, min-p, presence/repetition penalties, service tier). Mutates `params`.
|
|
*
|
|
* `max_output_tokens` is suppressed when {@link Model.omitMaxOutputTokens} is `true`, so
|
|
* proxies (notably Ollama) that forward to upstream APIs with an unknown output-token cap
|
|
* can let the upstream apply its own default instead of 400-ing on `maxTokens` values that
|
|
* reflect the model's context window rather than the upstream output limit.
|
|
*/
|
|
export function applyCommonResponsesSamplingParams<P extends CommonResponsesParams>(
|
|
params: P,
|
|
options: CommonSamplingOptions | undefined,
|
|
model: Pick<Model, "provider" | "omitMaxOutputTokens" | "maxTokens">,
|
|
): void {
|
|
if (options?.maxTokens && !model.omitMaxOutputTokens) {
|
|
params.max_output_tokens = Math.min(
|
|
options.maxTokens,
|
|
model.maxTokens ?? Number.POSITIVE_INFINITY,
|
|
OPENAI_MAX_OUTPUT_TOKENS,
|
|
);
|
|
}
|
|
if (options?.temperature !== undefined) params.temperature = options.temperature;
|
|
if (options?.topP !== undefined) params.top_p = options.topP;
|
|
if (options?.topK !== undefined) params.top_k = options.topK;
|
|
if (options?.minP !== undefined) params.min_p = options.minP;
|
|
if (options?.presencePenalty !== undefined) params.presence_penalty = options.presencePenalty;
|
|
if (options?.repetitionPenalty !== undefined) params.repetition_penalty = options.repetitionPenalty;
|
|
applyOpenAIServiceTier(params, options?.serviceTier, model.provider);
|
|
}
|
|
|
|
type ReasoningOptions = {
|
|
reasoning?: string;
|
|
reasoningSummary?: "auto" | "detailed" | "concise" | null;
|
|
disableReasoning?: boolean;
|
|
toolChoice?: unknown;
|
|
};
|
|
|
|
export interface ApplyResponsesCompatPolicyOptions {
|
|
reasoningSummary?: "auto" | "detailed" | "concise" | null;
|
|
mapEffort?: (effort: string) => string;
|
|
}
|
|
|
|
export function applyResponsesCompatPolicy<P extends ResponseCreateParamsStreaming>(
|
|
params: P,
|
|
messages: ResponseInput,
|
|
policy: OpenAICompatPolicy,
|
|
options: ApplyResponsesCompatPolicyOptions | undefined,
|
|
): number {
|
|
const reasoning = policy.reasoning;
|
|
if (!reasoning.modelSupported) return 0;
|
|
if (reasoning.includeEncryptedReasoning) {
|
|
const include = params.include ?? [];
|
|
if (!include.includes("reasoning.encrypted_content")) include.push("reasoning.encrypted_content");
|
|
params.include = include;
|
|
}
|
|
|
|
if (reasoning.disabled) {
|
|
if (reasoning.disableMode === "openrouter-enabled-false") {
|
|
params.reasoning = { enabled: false } as P["reasoning"];
|
|
return 0;
|
|
}
|
|
if (
|
|
reasoning.disableMode === "lowest-effort" &&
|
|
reasoning.wireEffort !== undefined &&
|
|
!reasoning.omitReasoningEffort
|
|
) {
|
|
type ReasoningParam = NonNullable<ResponseCreateParamsStreaming["reasoning"]>;
|
|
params.reasoning = { effort: reasoning.wireEffort as ReasoningParam["effort"] } as P["reasoning"] &
|
|
ReasoningParam;
|
|
return 0;
|
|
}
|
|
if (policy.compat.requiresJuiceZeroHack && reasoning.requestedEffort === undefined) {
|
|
messages.push({
|
|
role: "developer",
|
|
content: [{ type: "input_text", text: "# Juice: 0 !important" }],
|
|
});
|
|
return 1;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
if (reasoning.requestedEffort !== undefined || options?.reasoningSummary !== undefined) {
|
|
if (reasoning.omitReasoningEffort) {
|
|
if (options?.reasoningSummary !== undefined && options.reasoningSummary !== null) {
|
|
type ReasoningParam = NonNullable<ResponseCreateParamsStreaming["reasoning"]>;
|
|
params.reasoning = { summary: options.reasoningSummary || "auto" } as P["reasoning"] & ReasoningParam;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
const requested = reasoning.requestedEffort ?? "medium";
|
|
const wireEffort = reasoning.wireEffort ?? options?.mapEffort?.(requested) ?? requested;
|
|
type ReasoningParam = NonNullable<ResponseCreateParamsStreaming["reasoning"]>;
|
|
const reasoningParams: ReasoningParam = {
|
|
effort: wireEffort as ReasoningParam["effort"],
|
|
};
|
|
if (options?.reasoningSummary !== null) {
|
|
reasoningParams.summary = options?.reasoningSummary || "auto";
|
|
}
|
|
params.reasoning = reasoningParams as P["reasoning"];
|
|
return 0;
|
|
}
|
|
|
|
if (policy.compat.requiresJuiceZeroHack) {
|
|
messages.push({
|
|
role: "developer",
|
|
content: [{ type: "input_text", text: "# Juice: 0 !important" }],
|
|
});
|
|
return 1;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
/**
|
|
* Apply reasoning-related Responses parameters. Default behavior comes from
|
|
* catalog compat; include/omit arguments are explicit adapter-wrapper overrides.
|
|
*/
|
|
export function applyResponsesReasoningParams<P extends ResponseCreateParamsStreaming>(
|
|
params: P,
|
|
model: Model<"openai-responses" | "azure-openai-responses" | "openai-codex-responses">,
|
|
options: ReasoningOptions | undefined,
|
|
messages: ResponseInput,
|
|
mapEffort?: (effort: string) => string,
|
|
includeEncryptedReasoning?: boolean,
|
|
omitReasoningEffort?: boolean,
|
|
): number {
|
|
return applyResponsesCompatPolicy(
|
|
params,
|
|
messages,
|
|
resolveOpenAICompatPolicy(model, {
|
|
endpoint: "responses",
|
|
reasoning: options?.reasoning,
|
|
disableReasoning: options?.disableReasoning,
|
|
toolChoice: options?.toolChoice,
|
|
includeEncryptedReasoning,
|
|
omitReasoningEffort,
|
|
}),
|
|
{ reasoningSummary: options?.reasoningSummary, mapEffort },
|
|
);
|
|
}
|
|
|
|
/** Populate `output.usage` from a Responses-API `response.usage` payload. Does not invoke `calculateCost`. */
|
|
export function populateResponsesUsageFromResponse(
|
|
output: AssistantMessage,
|
|
usage:
|
|
| {
|
|
input_tokens?: number | null;
|
|
output_tokens?: number | null;
|
|
total_tokens?: number | null;
|
|
prompt_cache_hit_tokens?: number | null;
|
|
prompt_cache_miss_tokens?: number | null;
|
|
input_tokens_details?: {
|
|
cached_tokens?: number | null;
|
|
cache_write_tokens?: number | null;
|
|
orchestration_input_tokens?: number | null;
|
|
orchestration_input_cached_tokens?: number | null;
|
|
} | null;
|
|
output_tokens_details?: {
|
|
reasoning_tokens?: number | null;
|
|
orchestration_output_tokens?: number | null;
|
|
} | null;
|
|
}
|
|
| null
|
|
| undefined,
|
|
): void {
|
|
if (!usage) return;
|
|
const details = usage.input_tokens_details;
|
|
const outputDetails = usage.output_tokens_details;
|
|
const orchestrationInputTokens = details?.orchestration_input_tokens ?? 0;
|
|
const orchestrationInputCachedTokens = details?.orchestration_input_cached_tokens ?? 0;
|
|
const orchestrationOutputTokens = outputDetails?.orchestration_output_tokens ?? 0;
|
|
const accounting = calculateOpenAIUsageAccounting({
|
|
promptTokens: (usage.input_tokens ?? 0) + orchestrationInputTokens,
|
|
outputTokens: (usage.output_tokens ?? 0) + orchestrationOutputTokens,
|
|
cachedTokens: (details?.cached_tokens ?? usage.prompt_cache_hit_tokens ?? 0) + orchestrationInputCachedTokens,
|
|
reasoningTokens: outputDetails?.reasoning_tokens ?? 0,
|
|
cacheWriteOpenRouter: details?.cache_write_tokens ?? undefined,
|
|
cacheWriteDeepSeek: usage.prompt_cache_miss_tokens ?? undefined,
|
|
hasDeepSeekCacheHitAndMiss:
|
|
usage.prompt_cache_hit_tokens !== undefined && usage.prompt_cache_miss_tokens !== undefined,
|
|
});
|
|
|
|
// Wholesale replacement must not drop provider-annotated extras (Copilot
|
|
// premium-request accounting): the failed/cancelled paths throw right after
|
|
// this call with no later chance to re-apply.
|
|
const premiumRequests = output.usage.premiumRequests;
|
|
output.usage = {
|
|
...accounting,
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
|
|
};
|
|
if (premiumRequests !== undefined) {
|
|
output.usage.premiumRequests = premiumRequests;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Structural equality for the chain prefix/option check, equivalent to the
|
|
* default {@link Bun.deepEquals} (own enumerable keys, `absent ≡ own-undefined`)
|
|
* except for two deliberate exclusions:
|
|
* - **symbol-keyed properties are ignored** — `for…in` walks enumerable
|
|
* *string* keys only (never symbols); these are plain wire items whose
|
|
* prototype contributes no enumerable keys, so iteration is effectively
|
|
* own-string-keyed. That is how the transient streaming symbols
|
|
* (`block-symbols.ts`) stamped onto live request items are excluded (the
|
|
* deep-cloned baseline never carries them). Do NOT add an
|
|
* `Object.getOwnPropertySymbols` pass, or those symbols resurface and break
|
|
* chaining.
|
|
* - keys listed in `omitKeys` are skipped (the option compare omits `input`
|
|
* and the per-turn `client_metadata`).
|
|
* A defined value differing across sides IS a difference; a key undefined or
|
|
* absent on both stays equal. Nested values use full {@link Bun.deepEquals}.
|
|
*/
|
|
function deepEqualsWithout(a: unknown, b: unknown, omitKeys?: Record<string, boolean>): boolean {
|
|
if (!a || !b || typeof a !== "object" || typeof b !== "object") return Bun.deepEquals(a, b);
|
|
const ao = a as Record<string, unknown>;
|
|
const bo = b as Record<string, unknown>;
|
|
for (const key in ao) {
|
|
if (omitKeys?.[key]) continue;
|
|
const av = ao[key];
|
|
const bv = bo[key];
|
|
if (av !== bv && !Bun.deepEquals(av, bv)) return false;
|
|
}
|
|
for (const key in bo) {
|
|
if (omitKeys?.[key]) continue;
|
|
if (bo[key] !== undefined && !(key in ao)) return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
const TOP_LEVEL_EXCLUDE_MAP = {
|
|
input: true,
|
|
client_metadata: true,
|
|
};
|
|
|
|
/**
|
|
* Strict-prefix delta for stateful `previous_response_id` chaining (used by the
|
|
* platform Responses provider and the Codex provider on both transports):
|
|
* returns the input items the current request appends beyond the previous
|
|
* request's input plus the previous response's output items, or null when the
|
|
* request options differ or history mutated (the chain must break). Per-turn
|
|
* `client_metadata` (e.g. rotating turn ids) is excluded from the option
|
|
* comparison; codex-rs excludes it from the same check.
|
|
*/
|
|
export function buildResponsesDeltaInput<TItem extends ResponseInputItem | InputItem>(
|
|
previous: { input?: TItem[] } | undefined,
|
|
previousResponseItems: readonly TItem[] | undefined,
|
|
current: { input?: TItem[] },
|
|
): TItem[] | null {
|
|
if (!previous) return null;
|
|
if (!Array.isArray(previous.input) || !Array.isArray(current.input)) return null;
|
|
if (!deepEqualsWithout(previous, current, TOP_LEVEL_EXCLUDE_MAP)) {
|
|
return null;
|
|
}
|
|
|
|
const baselineLen = (previous.input?.length ?? 0) + (previousResponseItems?.length ?? 0);
|
|
if (current.input.length <= baselineLen) return null;
|
|
|
|
let index = 0;
|
|
for (const series of [previous.input, previousResponseItems]) {
|
|
if (!series) continue;
|
|
for (const item of series) {
|
|
if (deepEqualsWithout(item, current.input[index])) {
|
|
index++;
|
|
} else {
|
|
return null;
|
|
}
|
|
}
|
|
}
|
|
return current.input.slice(index) as TItem[];
|
|
}
|