feat: Add normalized types and utility functions for handling chat formats
- Introduced `NormalizedContentBlock`, `NormalizedUsage`, and related types for structured data handling. - Implemented utility functions for creating and merging normalized usage statistics. - Added a new module for shared transformers to manage chat formats. feat: Implement app local state management - Created functions to clear application installation state and manage local storage keys. - Added constants for theme and user profile storage keys. test: Add tests for BrandIcon component rendering - Implemented tests to ensure BrandIcon does not force a colored background for recognized brands. test: Enhance ModelAnalysisPanel tests - Added tests for rendering token summaries with compact units in ModelAnalysisPanel. test: Add tests for ModernSelect component - Implemented tests to verify icon rendering for selected options in ModernSelect. test: Add number formatting tests - Created tests for `formatCompactTokenMetric` to validate token formatting logic. feat: Implement account creation intent handling - Added logic to handle session and API key creation intents in the Accounts page. test: Add tests for dashboard performance card - Implemented tests to verify rendering of performance metrics in the dashboard. feat: Implement default connection segment resolution - Added logic to determine initial connection segments based on platform types. test: Add tests for settings factory reset functionality - Implemented tests to verify the factory reset process and local state clearing. test: Add tests for site creation redirect logic - Implemented tests to verify redirection based on site capabilities during creation. test: Add tests for token routes refresh decision action - Implemented tests to verify refresh decision actions in TokenRoutes. test: Add tests for token routes snapshot load - Implemented tests to verify loading of cached snapshot data in TokenRoutes.
This commit is contained in:
cita
@@ -0,0 +1,121 @@
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import { type NormalizedFinalResponse, type NormalizedStreamEvent } from '../../shared/normalized.js';
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import type {
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OpenAiChatNormalizedFinalResponse,
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OpenAiChatNormalizedStreamEvent,
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OpenAiChatUsageDetails,
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} from './model.js';
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function isRecord(value: unknown): value is Record<string, unknown> {
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return !!value && typeof value === 'object';
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}
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function pushUniqueAnnotation(
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annotations: Array<Record<string, unknown>>,
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seenUrls: Set<string>,
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candidate: unknown,
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) {
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if (!isRecord(candidate)) return;
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const citation = isRecord(candidate.url_citation) ? candidate.url_citation : null;
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const url = citation && typeof citation.url === 'string' ? citation.url.trim() : '';
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if (!url || seenUrls.has(url)) return;
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seenUrls.add(url);
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annotations.push(candidate);
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}
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function mergeUsageDetails(
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target: OpenAiChatUsageDetails | undefined,
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next: OpenAiChatUsageDetails | undefined,
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): OpenAiChatUsageDetails | undefined {
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if (!target && !next) return undefined;
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return {
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...(target || {}),
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...(next || {}),
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};
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}
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export type OpenAiChatAggregateState = {
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content: string[];
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reasoning: string[];
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toolCalls: Array<{ id: string; name: string; arguments: string }>;
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finishReason: string | null;
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annotations: Array<Record<string, unknown>>;
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annotationUrls: Set<string>;
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citations: Set<string>;
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usageDetails?: OpenAiChatUsageDetails;
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};
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export function createOpenAiChatAggregateState(): OpenAiChatAggregateState {
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return {
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content: [],
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reasoning: [],
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toolCalls: [],
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finishReason: null,
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annotations: [],
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annotationUrls: new Set<string>(),
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citations: new Set<string>(),
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usageDetails: undefined,
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};
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}
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export function applyOpenAiChatStreamEvent(
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state: OpenAiChatAggregateState,
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event: OpenAiChatNormalizedStreamEvent,
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): OpenAiChatAggregateState {
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if (event.contentDelta) state.content.push(event.contentDelta);
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if (event.reasoningDelta) state.reasoning.push(event.reasoningDelta);
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if (event.finishReason !== undefined) state.finishReason = event.finishReason ?? null;
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if (Array.isArray(event.toolCallDeltas)) {
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for (const delta of event.toolCallDeltas) {
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if (!delta.id && !delta.name && !delta.argumentsDelta) continue;
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const index = Number.isFinite(delta.index) ? Math.max(0, Math.trunc(delta.index)) : state.toolCalls.length;
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while (state.toolCalls.length <= index) {
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state.toolCalls.push({ id: '', name: '', arguments: '' });
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}
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const existing = state.toolCalls[index];
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if (delta.id) existing.id = delta.id;
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if (delta.name) existing.name = delta.name;
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if (delta.argumentsDelta) existing.arguments += delta.argumentsDelta;
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}
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}
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if (Array.isArray(event.annotations)) {
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for (const annotation of event.annotations) {
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pushUniqueAnnotation(state.annotations, state.annotationUrls, annotation);
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}
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}
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if (Array.isArray(event.citations)) {
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for (const citation of event.citations) {
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if (typeof citation === 'string' && citation.trim()) {
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state.citations.add(citation.trim());
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}
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}
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}
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state.usageDetails = mergeUsageDetails(state.usageDetails, event.usageDetails);
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return state;
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}
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export function finalizeOpenAiChatAggregate(
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state: OpenAiChatAggregateState,
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normalized: OpenAiChatNormalizedFinalResponse,
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): OpenAiChatNormalizedFinalResponse {
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const mergedToolCalls = state.toolCalls.filter((item) => item.id || item.name || item.arguments);
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if (
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state.content.length === 0
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&& state.reasoning.length === 0
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&& mergedToolCalls.length === 0
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&& state.annotations.length === 0
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&& state.citations.size === 0
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&& !state.usageDetails
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) {
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return normalized;
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}
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return {
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...normalized,
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content: state.content.join('') || normalized.content,
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reasoningContent: state.reasoning.join('') || normalized.reasoningContent,
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finishReason: state.finishReason ?? normalized.finishReason,
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toolCalls: mergedToolCalls.length > 0 ? mergedToolCalls : normalized.toolCalls,
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annotations: state.annotations.length > 0 ? state.annotations : normalized.annotations,
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citations: state.citations.size > 0 ? Array.from(state.citations).sort() : normalized.citations,
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usageDetails: mergeUsageDetails(normalized.usageDetails, state.usageDetails),
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};
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}
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@@ -0,0 +1,238 @@
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import type {
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OpenAiChatNormalizedFinalResponse,
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OpenAiChatNormalizedStreamEvent,
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OpenAiChatRequestMetadata,
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OpenAiChatUsageDetails,
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} from './model.js';
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function isRecord(value: unknown): value is Record<string, unknown> {
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return !!value && typeof value === 'object' && !Array.isArray(value);
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}
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function asRecord(value: unknown): Record<string, unknown> | null {
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return isRecord(value) ? value : null;
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}
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function toStringArray(value: unknown): string[] {
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if (!Array.isArray(value)) return [];
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return value
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.map((item) => {
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if (typeof item === 'string') return item.trim();
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if (isRecord(item)) {
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const direct = typeof item.url === 'string'
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? item.url
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: typeof item.uri === 'string'
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? item.uri
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: '';
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return direct.trim();
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}
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return '';
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})
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.filter((item) => item.length > 0);
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}
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function toNumericRecord(value: unknown): Record<string, number> | undefined {
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if (!isRecord(value)) return undefined;
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const entries = Object.entries(value)
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.map(([key, raw]) => {
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const numeric = typeof raw === 'number' ? raw : Number(raw);
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if (!Number.isFinite(numeric)) return null;
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return [key, numeric] as const;
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})
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.filter((entry): entry is readonly [string, number] => !!entry);
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if (entries.length <= 0) return undefined;
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return Object.fromEntries(entries);
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}
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function stableKey(value: unknown): string {
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if (Array.isArray(value)) {
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return `[${value.map((item) => stableKey(item)).join(',')}]`;
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}
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if (isRecord(value)) {
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const entries = Object.entries(value)
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.sort(([left], [right]) => left.localeCompare(right))
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.map(([key, nested]) => `${JSON.stringify(key)}:${stableKey(nested)}`);
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return `{${entries.join(',')}}`;
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}
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return JSON.stringify(value);
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}
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function annotationUrl(annotation: Record<string, unknown>): string {
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const urlCitation = asRecord(annotation.url_citation) ?? asRecord(annotation.urlCitation);
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const direct = typeof annotation.url === 'string' ? annotation.url : '';
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const nested = urlCitation && typeof urlCitation.url === 'string' ? urlCitation.url : '';
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return (direct || nested).trim();
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}
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export function dedupeAnnotations(input: unknown): Array<Record<string, unknown>> {
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if (!Array.isArray(input)) return [];
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const seen = new Set<string>();
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const result: Array<Record<string, unknown>> = [];
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for (const item of input) {
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if (!isRecord(item)) continue;
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const key = annotationUrl(item) || stableKey(item);
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if (!key || seen.has(key)) continue;
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seen.add(key);
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result.push(item);
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}
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return result;
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}
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export function dedupeCitations(input: unknown): string[] {
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const citations = new Set<string>();
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for (const citation of toStringArray(input)) {
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citations.add(citation);
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}
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if (Array.isArray(input)) {
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for (const item of input) {
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if (!isRecord(item)) continue;
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const nestedUrl = annotationUrl(item);
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if (nestedUrl) citations.add(nestedUrl);
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}
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}
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return Array.from(citations);
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}
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function extractAnnotationsFromPayload(payload: Record<string, unknown>): Array<Record<string, unknown>> {
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const collected: Array<Record<string, unknown>> = [];
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const append = (value: unknown) => {
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for (const item of dedupeAnnotations(value)) {
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collected.push(item);
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}
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};
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append(payload.annotations);
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const choices = Array.isArray(payload.choices) ? payload.choices : [];
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for (const choice of choices) {
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const choiceRecord = asRecord(choice);
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if (!choiceRecord) continue;
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append(asRecord(choiceRecord.message)?.annotations);
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append(asRecord(choiceRecord.delta)?.annotations);
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}
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return dedupeAnnotations(collected);
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}
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function extractCitationsFromPayload(
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payload: Record<string, unknown>,
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annotations: Array<Record<string, unknown>>,
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): string[] {
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const citations = new Set<string>();
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for (const citation of dedupeCitations(payload.citations)) {
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citations.add(citation);
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}
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const transformerMetadata = asRecord(payload.transformer_metadata) ?? asRecord(payload.transformerMetadata);
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if (transformerMetadata) {
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for (const citation of dedupeCitations(transformerMetadata.citations)) {
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citations.add(citation);
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}
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}
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for (const annotation of annotations) {
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const url = annotationUrl(annotation);
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if (url) citations.add(url);
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}
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return Array.from(citations);
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}
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export function extractChatUsageDetails(payload: Record<string, unknown>): OpenAiChatUsageDetails | undefined {
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const usage = asRecord(payload.usage);
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if (!usage) return undefined;
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const promptDetails = toNumericRecord(usage.prompt_tokens_details ?? usage.promptTokensDetails);
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const completionDetails = toNumericRecord(usage.completion_tokens_details ?? usage.completionTokensDetails);
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if (!promptDetails && !completionDetails) return undefined;
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return {
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...(promptDetails ? { prompt_tokens_details: promptDetails } : {}),
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...(completionDetails ? { completion_tokens_details: completionDetails } : {}),
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};
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}
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export function mergeChatUsageDetails(
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base: OpenAiChatUsageDetails | undefined,
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next: OpenAiChatUsageDetails | undefined,
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): OpenAiChatUsageDetails | undefined {
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if (!base && !next) return undefined;
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const mergeNumericMaps = (
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left: Record<string, number> | undefined,
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right: Record<string, number> | undefined,
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): Record<string, number> | undefined => {
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if (!left && !right) return undefined;
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return Object.fromEntries(
|
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Array.from(new Set([...Object.keys(left || {}), ...Object.keys(right || {})]))
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.map((key) => [key, Math.max(left?.[key] || 0, right?.[key] || 0)]),
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);
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};
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return {
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...(mergeNumericMaps(base?.prompt_tokens_details, next?.prompt_tokens_details)
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? { prompt_tokens_details: mergeNumericMaps(base?.prompt_tokens_details, next?.prompt_tokens_details)! }
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: {}),
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...(mergeNumericMaps(base?.completion_tokens_details, next?.completion_tokens_details)
|
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? { completion_tokens_details: mergeNumericMaps(base?.completion_tokens_details, next?.completion_tokens_details)! }
|
||||
: {}),
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};
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}
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export function extractUsagePayload(payload: Record<string, unknown>): Record<string, unknown> | undefined {
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const usage = asRecord(payload.usage);
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if (!usage) return undefined;
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return usage;
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}
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export function extractChatResponseExtras(payload: unknown): Pick<
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OpenAiChatNormalizedFinalResponse,
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'annotations' | 'citations' | 'usageDetails' | 'usagePayload'
|
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> & Pick<OpenAiChatNormalizedStreamEvent, 'annotations' | 'citations' | 'usageDetails' | 'usagePayload'> {
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const record = asRecord(payload);
|
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if (!record) return {};
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const annotations = extractAnnotationsFromPayload(record);
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const citations = extractCitationsFromPayload(record, annotations);
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const usageDetails = extractChatUsageDetails(record);
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const usagePayload = extractUsagePayload(record);
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|
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return {
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...(annotations.length > 0 ? { annotations } : {}),
|
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...(citations.length > 0 ? { citations } : {}),
|
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...(usageDetails ? { usageDetails } : {}),
|
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...(usagePayload ? { usagePayload } : {}),
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};
|
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}
|
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|
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export function extractChatRequestMetadata(body: unknown): OpenAiChatRequestMetadata | undefined {
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const raw = asRecord(body);
|
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if (!raw) return undefined;
|
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|
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const streamOptions = asRecord(raw.stream_options) ?? asRecord(raw.streamOptions);
|
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const metadata: OpenAiChatRequestMetadata = {
|
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...(raw.modalities !== undefined ? { modalities: raw.modalities } : {}),
|
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...(raw.audio !== undefined ? { audio: raw.audio } : {}),
|
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...(raw.reasoning_effort !== undefined ? { reasoningEffort: raw.reasoning_effort } : {}),
|
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...(raw.reasoning_budget !== undefined ? { reasoningBudget: raw.reasoning_budget } : {}),
|
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...(raw.reasoning_summary !== undefined ? { reasoningSummary: raw.reasoning_summary } : {}),
|
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...(raw.service_tier !== undefined ? { serviceTier: raw.service_tier } : {}),
|
||||
...(raw.top_logprobs !== undefined ? { topLogprobs: raw.top_logprobs } : {}),
|
||||
...(raw.logit_bias !== undefined ? { logitBias: raw.logit_bias } : {}),
|
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...(raw.prompt_cache_key !== undefined ? { promptCacheKey: raw.prompt_cache_key } : {}),
|
||||
...(raw.safety_identifier !== undefined ? { safetyIdentifier: raw.safety_identifier } : {}),
|
||||
...(raw.verbosity !== undefined ? { verbosity: raw.verbosity } : {}),
|
||||
...(raw.response_format !== undefined ? { responseFormat: raw.response_format } : {}),
|
||||
...(streamOptions && streamOptions.include_usage !== undefined
|
||||
? { streamOptionsIncludeUsage: Boolean(streamOptions.include_usage) }
|
||||
: {}),
|
||||
};
|
||||
|
||||
return Object.keys(metadata).length > 0 ? metadata : undefined;
|
||||
}
|
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@@ -0,0 +1,19 @@
|
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import { parseDownstreamChatRequest } from '../../shared/normalized.js';
|
||||
import { extractChatRequestMetadata } from './helpers.js';
|
||||
import type { OpenAiChatParsedRequest } from './model.js';
|
||||
|
||||
export const openAiChatInbound = {
|
||||
parse(body: unknown): { value?: OpenAiChatParsedRequest; error?: { statusCode: number; payload: unknown } } {
|
||||
const parsed = parseDownstreamChatRequest(body, 'openai') as {
|
||||
value?: OpenAiChatParsedRequest;
|
||||
error?: { statusCode: number; payload: unknown };
|
||||
};
|
||||
if (!parsed.value) return parsed;
|
||||
|
||||
parsed.value = {
|
||||
...parsed.value,
|
||||
requestMetadata: extractChatRequestMetadata(body),
|
||||
};
|
||||
return parsed;
|
||||
},
|
||||
};
|
||||
@@ -0,0 +1,234 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import { createClaudeDownstreamContext } from '../../shared/normalized.js';
|
||||
import { openAiChatTransformer } from './index.js';
|
||||
|
||||
function parseSsePayloads(lines: string[]): Array<Record<string, unknown>> {
|
||||
return lines
|
||||
.filter((line) => line.startsWith('data: ') && line.trim() !== 'data: [DONE]')
|
||||
.map((line) => JSON.parse(line.slice(6)) as Record<string, unknown>);
|
||||
}
|
||||
|
||||
describe('openAiChatTransformer.inbound', () => {
|
||||
it('captures chat request metadata fields without changing upstream body', () => {
|
||||
const result = openAiChatTransformer.transformRequest({
|
||||
model: 'gpt-5',
|
||||
messages: [{ role: 'user', content: 'hello' }],
|
||||
modalities: ['text', 'audio'],
|
||||
audio: { voice: 'alloy', format: 'mp3' },
|
||||
reasoning_effort: 'high',
|
||||
reasoning_budget: 1024,
|
||||
reasoning_summary: 'detailed',
|
||||
service_tier: 'priority',
|
||||
top_logprobs: 3,
|
||||
logit_bias: { '42': 5 },
|
||||
prompt_cache_key: 'cache-key',
|
||||
safety_identifier: 'safety-id',
|
||||
verbosity: 'low',
|
||||
response_format: { type: 'json_object' },
|
||||
stream_options: { include_usage: true },
|
||||
});
|
||||
|
||||
expect(result.error).toBeUndefined();
|
||||
expect(result.value?.upstreamBody).toMatchObject({
|
||||
modalities: ['text', 'audio'],
|
||||
audio: { voice: 'alloy', format: 'mp3' },
|
||||
reasoning_effort: 'high',
|
||||
reasoning_budget: 1024,
|
||||
reasoning_summary: 'detailed',
|
||||
service_tier: 'priority',
|
||||
top_logprobs: 3,
|
||||
logit_bias: { '42': 5 },
|
||||
prompt_cache_key: 'cache-key',
|
||||
safety_identifier: 'safety-id',
|
||||
verbosity: 'low',
|
||||
response_format: { type: 'json_object' },
|
||||
stream_options: { include_usage: true },
|
||||
});
|
||||
expect((result.value as any)?.requestMetadata).toEqual({
|
||||
modalities: ['text', 'audio'],
|
||||
audio: { voice: 'alloy', format: 'mp3' },
|
||||
reasoningEffort: 'high',
|
||||
reasoningBudget: 1024,
|
||||
reasoningSummary: 'detailed',
|
||||
serviceTier: 'priority',
|
||||
topLogprobs: 3,
|
||||
logitBias: { '42': 5 },
|
||||
promptCacheKey: 'cache-key',
|
||||
safetyIdentifier: 'safety-id',
|
||||
verbosity: 'low',
|
||||
responseFormat: { type: 'json_object' },
|
||||
streamOptionsIncludeUsage: true,
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('openAiChatTransformer.outbound', () => {
|
||||
it('carries annotations, citations, and detailed usage through final serialization', () => {
|
||||
const normalized = openAiChatTransformer.transformFinalResponse({
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
created: 123,
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: 'stop',
|
||||
message: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
reasoning_content: 'think',
|
||||
annotations: [
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
],
|
||||
},
|
||||
}],
|
||||
citations: ['https://c.example', 'https://c.example'],
|
||||
usage: {
|
||||
prompt_tokens: 11,
|
||||
completion_tokens: 7,
|
||||
total_tokens: 18,
|
||||
prompt_tokens_details: { cached_tokens: 3 },
|
||||
completion_tokens_details: { reasoning_tokens: 2 },
|
||||
},
|
||||
}, 'gpt-5');
|
||||
|
||||
const payload = openAiChatTransformer.serializeFinalResponse(normalized, {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
});
|
||||
|
||||
const choice = (payload as any).choices[0];
|
||||
expect(choice.message.annotations).toEqual([
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
]);
|
||||
expect((payload as any).citations).toEqual(['https://c.example', 'https://a.example']);
|
||||
expect((payload as any).usage).toMatchObject({
|
||||
prompt_tokens: 11,
|
||||
completion_tokens: 7,
|
||||
total_tokens: 18,
|
||||
prompt_tokens_details: { cached_tokens: 3 },
|
||||
completion_tokens_details: { reasoning_tokens: 2 },
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('openAiChatTransformer.stream', () => {
|
||||
it('preserves annotations, citations, and usage payload on serialized stream chunks', () => {
|
||||
const context = openAiChatTransformer.createStreamContext('gpt-5');
|
||||
const event = openAiChatTransformer.transformStreamEvent({
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: null,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
reasoning_content: 'why',
|
||||
annotations: [
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
],
|
||||
},
|
||||
}],
|
||||
citations: ['https://c.example', 'https://a.example'],
|
||||
usage: {
|
||||
prompt_tokens: 11,
|
||||
completion_tokens: 7,
|
||||
total_tokens: 18,
|
||||
prompt_tokens_details: { cached_tokens: 3 },
|
||||
},
|
||||
}, context, 'gpt-5');
|
||||
|
||||
const payloads = parseSsePayloads(
|
||||
openAiChatTransformer.serializeStreamEvent(event, context, createClaudeDownstreamContext()),
|
||||
);
|
||||
|
||||
expect(payloads[0]).toMatchObject({
|
||||
citations: ['https://c.example', 'https://a.example'],
|
||||
usage: {
|
||||
prompt_tokens: 11,
|
||||
completion_tokens: 7,
|
||||
total_tokens: 18,
|
||||
prompt_tokens_details: { cached_tokens: 3 },
|
||||
},
|
||||
});
|
||||
expect(((payloads[0] as any).choices[0] as any).delta.annotations).toEqual([
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('openAiChatTransformer.aggregator', () => {
|
||||
it('deduplicates annotations/citations while aggregating reasoning, tool calls, and usage details', () => {
|
||||
const state = openAiChatTransformer.aggregator.createState();
|
||||
|
||||
openAiChatTransformer.aggregator.applyEvent(state, {
|
||||
contentDelta: 'hel',
|
||||
reasoningDelta: 'why',
|
||||
toolCallDeltas: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
name: 'search',
|
||||
argumentsDelta: '{"q":"cat"',
|
||||
}],
|
||||
annotations: [
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
],
|
||||
citations: ['https://c.example'],
|
||||
usageDetails: {
|
||||
prompt_tokens_details: { cached_tokens: 2 },
|
||||
},
|
||||
} as any);
|
||||
|
||||
openAiChatTransformer.aggregator.applyEvent(state, {
|
||||
contentDelta: 'lo',
|
||||
reasoningDelta: ' now',
|
||||
toolCallDeltas: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
name: 'search',
|
||||
argumentsDelta: ',"k":1}',
|
||||
}],
|
||||
annotations: [
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
{ type: 'url_citation', url_citation: { url: 'https://b.example' } },
|
||||
],
|
||||
citations: ['https://c.example', 'https://d.example'],
|
||||
usageDetails: {
|
||||
completion_tokens_details: { reasoning_tokens: 4 },
|
||||
},
|
||||
finishReason: 'tool_calls',
|
||||
} as any);
|
||||
|
||||
const normalized = openAiChatTransformer.aggregator.finalize(state, {
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
created: 123,
|
||||
content: '',
|
||||
reasoningContent: '',
|
||||
finishReason: 'stop',
|
||||
toolCalls: [],
|
||||
});
|
||||
|
||||
expect(normalized).toMatchObject({
|
||||
content: 'hello',
|
||||
reasoningContent: 'why now',
|
||||
finishReason: 'tool_calls',
|
||||
toolCalls: [{
|
||||
id: 'call_1',
|
||||
name: 'search',
|
||||
arguments: '{"q":"cat","k":1}',
|
||||
}],
|
||||
citations: ['https://c.example', 'https://d.example'],
|
||||
usageDetails: {
|
||||
prompt_tokens_details: { cached_tokens: 2 },
|
||||
completion_tokens_details: { reasoning_tokens: 4 },
|
||||
},
|
||||
});
|
||||
expect((normalized as any).annotations).toEqual([
|
||||
{ type: 'url_citation', url_citation: { url: 'https://a.example' } },
|
||||
{ type: 'url_citation', url_citation: { url: 'https://b.example' } },
|
||||
]);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,59 @@
|
||||
import { type NormalizedFinalResponse, type NormalizedStreamEvent, type ParsedDownstreamChatRequest, type StreamTransformContext } from '../../shared/normalized.js';
|
||||
import { openAiChatInbound } from './inbound.js';
|
||||
import { openAiChatOutbound } from './outbound.js';
|
||||
import { openAiChatStream } from './stream.js';
|
||||
import { openAiChatUsage } from './usage.js';
|
||||
import { createOpenAiChatAggregateState, applyOpenAiChatStreamEvent, finalizeOpenAiChatAggregate } from './aggregator.js';
|
||||
|
||||
export const openAiChatTransformer = {
|
||||
protocol: 'openai/chat' as const,
|
||||
inbound: openAiChatInbound,
|
||||
outbound: openAiChatOutbound,
|
||||
stream: openAiChatStream,
|
||||
usage: openAiChatUsage,
|
||||
aggregator: {
|
||||
createState: createOpenAiChatAggregateState,
|
||||
applyEvent: applyOpenAiChatStreamEvent,
|
||||
finalize: finalizeOpenAiChatAggregate,
|
||||
},
|
||||
transformRequest(body: unknown): ReturnType<typeof openAiChatInbound.parse> {
|
||||
return openAiChatInbound.parse(body);
|
||||
},
|
||||
createStreamContext(modelName: string): StreamTransformContext {
|
||||
return openAiChatStream.createContext(modelName);
|
||||
},
|
||||
transformFinalResponse(payload: unknown, modelName: string, fallbackText = ''): NormalizedFinalResponse {
|
||||
return openAiChatOutbound.normalizeFinal(payload, modelName, fallbackText);
|
||||
},
|
||||
transformStreamEvent(payload: unknown, context: StreamTransformContext, modelName: string): NormalizedStreamEvent {
|
||||
return openAiChatStream.normalizeEvent(payload, context, modelName);
|
||||
},
|
||||
serializeStreamEvent(
|
||||
event: NormalizedStreamEvent,
|
||||
context: StreamTransformContext,
|
||||
claudeContext: Parameters<typeof openAiChatStream.serializeEvent>[2],
|
||||
): string[] {
|
||||
return openAiChatStream.serializeEvent(event, context, claudeContext);
|
||||
},
|
||||
serializeDone(
|
||||
context: StreamTransformContext,
|
||||
claudeContext: Parameters<typeof openAiChatStream.serializeDone>[1],
|
||||
): string[] {
|
||||
return openAiChatStream.serializeDone(context, claudeContext);
|
||||
},
|
||||
serializeFinalResponse(
|
||||
normalized: NormalizedFinalResponse,
|
||||
usage: Parameters<typeof openAiChatOutbound.serializeFinal>[1],
|
||||
) {
|
||||
return openAiChatOutbound.serializeFinal(normalized, usage);
|
||||
},
|
||||
buildSyntheticChunks(normalized: NormalizedFinalResponse) {
|
||||
return openAiChatOutbound.buildSyntheticChunks(normalized);
|
||||
},
|
||||
pullSseEvents(buffer: string) {
|
||||
return openAiChatStream.pullSseEvents(buffer);
|
||||
},
|
||||
};
|
||||
|
||||
export type OpenAiChatTransformer = typeof openAiChatTransformer;
|
||||
export type OpenAiChatParsedRequest = ParsedDownstreamChatRequest;
|
||||
@@ -0,0 +1,44 @@
|
||||
import type {
|
||||
NormalizedFinalResponse,
|
||||
NormalizedStreamEvent,
|
||||
ParsedDownstreamChatRequest,
|
||||
} from '../../shared/normalized.js';
|
||||
|
||||
export type OpenAiChatRequestMetadata = {
|
||||
modalities?: unknown;
|
||||
audio?: unknown;
|
||||
reasoningEffort?: unknown;
|
||||
reasoningBudget?: unknown;
|
||||
reasoningSummary?: unknown;
|
||||
serviceTier?: unknown;
|
||||
topLogprobs?: unknown;
|
||||
logitBias?: unknown;
|
||||
promptCacheKey?: unknown;
|
||||
safetyIdentifier?: unknown;
|
||||
verbosity?: unknown;
|
||||
responseFormat?: unknown;
|
||||
streamOptionsIncludeUsage?: boolean | null;
|
||||
};
|
||||
|
||||
export type OpenAiChatUsageDetails = {
|
||||
prompt_tokens_details?: Record<string, number>;
|
||||
completion_tokens_details?: Record<string, number>;
|
||||
};
|
||||
|
||||
export type OpenAiChatParsedRequest = ParsedDownstreamChatRequest & {
|
||||
requestMetadata?: OpenAiChatRequestMetadata;
|
||||
};
|
||||
|
||||
export type OpenAiChatNormalizedFinalResponse = NormalizedFinalResponse & {
|
||||
annotations?: Array<Record<string, unknown>>;
|
||||
citations?: string[];
|
||||
usageDetails?: OpenAiChatUsageDetails;
|
||||
usagePayload?: Record<string, unknown>;
|
||||
};
|
||||
|
||||
export type OpenAiChatNormalizedStreamEvent = NormalizedStreamEvent & {
|
||||
annotations?: Array<Record<string, unknown>>;
|
||||
citations?: string[];
|
||||
usageDetails?: OpenAiChatUsageDetails;
|
||||
usagePayload?: Record<string, unknown>;
|
||||
};
|
||||
@@ -0,0 +1,99 @@
|
||||
import {
|
||||
buildSyntheticOpenAiChunks,
|
||||
normalizeUpstreamFinalResponse,
|
||||
serializeFinalResponse,
|
||||
type NormalizedFinalResponse,
|
||||
} from '../../shared/normalized.js';
|
||||
import { extractChatResponseExtras } from './helpers.js';
|
||||
import type { OpenAiChatNormalizedFinalResponse } from './model.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object' && !Array.isArray(value);
|
||||
}
|
||||
|
||||
function mergeUsagePayload(
|
||||
usage: unknown,
|
||||
normalized: OpenAiChatNormalizedFinalResponse,
|
||||
): Record<string, unknown> | undefined {
|
||||
const merged = isRecord(usage) ? { ...usage } : {};
|
||||
const usagePayload = normalized.usagePayload && isRecord(normalized.usagePayload)
|
||||
? normalized.usagePayload
|
||||
: null;
|
||||
|
||||
if (usagePayload) {
|
||||
for (const [key, value] of Object.entries(usagePayload)) {
|
||||
if (key === 'prompt_tokens' || key === 'completion_tokens' || key === 'total_tokens') continue;
|
||||
if (merged[key] === undefined) merged[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
if (normalized.usageDetails?.prompt_tokens_details) {
|
||||
merged.prompt_tokens_details = normalized.usageDetails.prompt_tokens_details;
|
||||
}
|
||||
if (normalized.usageDetails?.completion_tokens_details) {
|
||||
merged.completion_tokens_details = normalized.usageDetails.completion_tokens_details;
|
||||
}
|
||||
|
||||
return Object.keys(merged).length > 0 ? merged : undefined;
|
||||
}
|
||||
|
||||
export const openAiChatOutbound = {
|
||||
normalizeFinal(payload: unknown, modelName: string, fallbackText = ''): OpenAiChatNormalizedFinalResponse {
|
||||
return {
|
||||
...normalizeUpstreamFinalResponse(payload, modelName, fallbackText),
|
||||
...extractChatResponseExtras(payload),
|
||||
};
|
||||
},
|
||||
serializeFinal(normalized: NormalizedFinalResponse, usage?: unknown) {
|
||||
const chatNormalized = normalized as OpenAiChatNormalizedFinalResponse;
|
||||
const payload = serializeFinalResponse(
|
||||
'openai',
|
||||
normalized,
|
||||
usage as { promptTokens: number; completionTokens: number; totalTokens: number },
|
||||
) as Record<string, unknown>;
|
||||
|
||||
if (Array.isArray(chatNormalized.citations) && chatNormalized.citations.length > 0) {
|
||||
payload.citations = chatNormalized.citations;
|
||||
}
|
||||
|
||||
if (Array.isArray(chatNormalized.annotations) && chatNormalized.annotations.length > 0) {
|
||||
const choices = Array.isArray(payload.choices) ? payload.choices : [];
|
||||
const firstChoice = choices[0];
|
||||
if (isRecord(firstChoice) && isRecord(firstChoice.message)) {
|
||||
firstChoice.message.annotations = chatNormalized.annotations;
|
||||
}
|
||||
}
|
||||
|
||||
const mergedUsage = mergeUsagePayload(payload.usage, chatNormalized);
|
||||
if (mergedUsage) {
|
||||
payload.usage = {
|
||||
...(isRecord(payload.usage) ? payload.usage : {}),
|
||||
...mergedUsage,
|
||||
};
|
||||
}
|
||||
|
||||
return payload;
|
||||
},
|
||||
buildSyntheticChunks(normalized: NormalizedFinalResponse) {
|
||||
const chunks = buildSyntheticOpenAiChunks(normalized);
|
||||
const chatNormalized = normalized as OpenAiChatNormalizedFinalResponse;
|
||||
if (chunks.length <= 0) return chunks;
|
||||
|
||||
if (Array.isArray(chatNormalized.citations) && chatNormalized.citations.length > 0) {
|
||||
chunks[0] = {
|
||||
...chunks[0],
|
||||
citations: chatNormalized.citations,
|
||||
};
|
||||
}
|
||||
|
||||
if (Array.isArray(chatNormalized.annotations) && chatNormalized.annotations.length > 0) {
|
||||
const firstChunk = isRecord(chunks[0]) ? chunks[0] : null;
|
||||
const firstChoice = firstChunk && Array.isArray(firstChunk.choices) ? firstChunk.choices[0] : null;
|
||||
if (isRecord(firstChoice) && isRecord(firstChoice.delta)) {
|
||||
firstChoice.delta.annotations = chatNormalized.annotations;
|
||||
}
|
||||
}
|
||||
|
||||
return chunks;
|
||||
},
|
||||
};
|
||||
@@ -0,0 +1,109 @@
|
||||
import {
|
||||
createClaudeDownstreamContext,
|
||||
createStreamTransformContext,
|
||||
normalizeUpstreamStreamEvent,
|
||||
pullSseEventsWithDone,
|
||||
serializeNormalizedStreamEvent,
|
||||
serializeStreamDone,
|
||||
type ClaudeDownstreamContext,
|
||||
type StreamTransformContext,
|
||||
} from '../../shared/normalized.js';
|
||||
import { extractChatResponseExtras } from './helpers.js';
|
||||
import type { OpenAiChatNormalizedStreamEvent } from './model.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object' && !Array.isArray(value);
|
||||
}
|
||||
|
||||
function parseSerializedSse(lines: string[]): Array<{ index: number; payload: Record<string, unknown> }> {
|
||||
return lines
|
||||
.map((line, index) => {
|
||||
if (!line.startsWith('data: ') || line.trim() === 'data: [DONE]') return null;
|
||||
try {
|
||||
return {
|
||||
index,
|
||||
payload: JSON.parse(line.slice(6)) as Record<string, unknown>,
|
||||
};
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
})
|
||||
.filter((item): item is { index: number; payload: Record<string, unknown> } => !!item);
|
||||
}
|
||||
|
||||
export const openAiChatStream = {
|
||||
createContext(modelName: string): StreamTransformContext {
|
||||
return createStreamTransformContext(modelName);
|
||||
},
|
||||
normalizeEvent(payload: unknown, context: StreamTransformContext, modelName: string): OpenAiChatNormalizedStreamEvent {
|
||||
return {
|
||||
...normalizeUpstreamStreamEvent(payload, context, modelName),
|
||||
...extractChatResponseExtras(payload),
|
||||
};
|
||||
},
|
||||
serializeEvent(
|
||||
event: OpenAiChatNormalizedStreamEvent,
|
||||
context: StreamTransformContext,
|
||||
downstreamContext?: ClaudeDownstreamContext,
|
||||
): string[] {
|
||||
const lines = serializeNormalizedStreamEvent(
|
||||
'openai',
|
||||
event,
|
||||
context,
|
||||
downstreamContext ?? createClaudeDownstreamContext(),
|
||||
);
|
||||
|
||||
if (
|
||||
(!Array.isArray(event.annotations) || event.annotations.length <= 0)
|
||||
&& (!Array.isArray(event.citations) || event.citations.length <= 0)
|
||||
&& !event.usagePayload
|
||||
&& !event.usageDetails
|
||||
) {
|
||||
return lines;
|
||||
}
|
||||
|
||||
const parsedEvents = parseSerializedSse(lines);
|
||||
if (parsedEvents.length <= 0) return lines;
|
||||
|
||||
for (const parsed of parsedEvents) {
|
||||
const payload = parsed.payload;
|
||||
if (Array.isArray(event.citations) && event.citations.length > 0) {
|
||||
payload.citations = event.citations;
|
||||
}
|
||||
|
||||
const firstChoice = Array.isArray(payload.choices) ? payload.choices[0] : null;
|
||||
if (Array.isArray(event.annotations) && event.annotations.length > 0 && isRecord(firstChoice) && isRecord(firstChoice.delta)) {
|
||||
firstChoice.delta.annotations = event.annotations;
|
||||
}
|
||||
|
||||
if (event.usagePayload || event.usageDetails) {
|
||||
payload.usage = {
|
||||
...(isRecord(event.usagePayload) ? event.usagePayload : {}),
|
||||
...(event.usageDetails?.prompt_tokens_details
|
||||
? { prompt_tokens_details: event.usageDetails.prompt_tokens_details }
|
||||
: {}),
|
||||
...(event.usageDetails?.completion_tokens_details
|
||||
? { completion_tokens_details: event.usageDetails.completion_tokens_details }
|
||||
: {}),
|
||||
};
|
||||
}
|
||||
|
||||
lines[parsed.index] = `data: ${JSON.stringify(payload)}\n\n`;
|
||||
}
|
||||
|
||||
return lines;
|
||||
},
|
||||
serializeDone(
|
||||
context: StreamTransformContext,
|
||||
downstreamContext?: ClaudeDownstreamContext,
|
||||
): string[] {
|
||||
return serializeStreamDone(
|
||||
'openai',
|
||||
context,
|
||||
downstreamContext ?? createClaudeDownstreamContext(),
|
||||
);
|
||||
},
|
||||
pullSseEvents(buffer: string) {
|
||||
return pullSseEventsWithDone(buffer);
|
||||
},
|
||||
};
|
||||
@@ -0,0 +1,6 @@
|
||||
import { createEmptyNormalizedUsage, mergeNormalizedUsage } from '../../shared/normalized.js';
|
||||
|
||||
export const openAiChatUsage = {
|
||||
empty: createEmptyNormalizedUsage,
|
||||
merge: mergeNormalizedUsage,
|
||||
};
|
||||
@@ -0,0 +1,336 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
createOpenAiResponsesAggregateState,
|
||||
serializeConvertedResponsesEvents,
|
||||
type OpenAiResponsesAggregateState,
|
||||
completeResponsesStream,
|
||||
} from './aggregator.js';
|
||||
import { openAiResponsesStream } from './stream.js';
|
||||
|
||||
function collectSerializedEvents(input: string[]): Array<{ event: string; data: any }> {
|
||||
const events: Array<{ event: string; data: any }> = [];
|
||||
for (const chunk of input) {
|
||||
const blocks = chunk.split('\n\n').filter((item) => item.trim().length > 0);
|
||||
for (const block of blocks) {
|
||||
const lines = block.split('\n');
|
||||
const eventLine = lines.find((line) => line.startsWith('event: '));
|
||||
const dataLine = lines.find((line) => line.startsWith('data: '));
|
||||
if (!dataLine) continue;
|
||||
const rawData = dataLine.slice('data: '.length);
|
||||
if (rawData === '[DONE]') {
|
||||
events.push({ event: eventLine?.slice('event: '.length) || 'done', data: '[DONE]' });
|
||||
continue;
|
||||
}
|
||||
events.push({
|
||||
event: eventLine?.slice('event: '.length) || '',
|
||||
data: JSON.parse(rawData),
|
||||
});
|
||||
}
|
||||
}
|
||||
return events;
|
||||
}
|
||||
|
||||
function feedResponsesEvent(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
payload: Record<string, unknown>,
|
||||
): Array<{ event: string; data: any }> {
|
||||
const context = openAiResponsesStream.createContext('gpt-5');
|
||||
const normalizedEvent = openAiResponsesStream.normalizeEvent(payload, context, 'gpt-5');
|
||||
return collectSerializedEvents(
|
||||
serializeConvertedResponsesEvents({
|
||||
state,
|
||||
streamContext: context,
|
||||
event: normalizedEvent,
|
||||
usage: {
|
||||
promptTokens: 0,
|
||||
completionTokens: 0,
|
||||
totalTokens: 0,
|
||||
},
|
||||
}),
|
||||
);
|
||||
}
|
||||
|
||||
describe('openAiResponsesStream / aggregator', () => {
|
||||
it('keeps reasoning separate from assistant text for normalized events and merges usage details', () => {
|
||||
const state = createOpenAiResponsesAggregateState('gpt-5');
|
||||
const context = openAiResponsesStream.createContext('gpt-5');
|
||||
|
||||
const contentEvent = {
|
||||
role: 'assistant',
|
||||
contentDelta: 'hello',
|
||||
};
|
||||
const reasoningEvent = {
|
||||
reasoningDelta: 'plan first',
|
||||
responsesPayload: {
|
||||
usage: {
|
||||
input_tokens: 5,
|
||||
output_tokens: 3,
|
||||
total_tokens: 8,
|
||||
output_tokens_details: { reasoning_tokens: 2 },
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
collectSerializedEvents(
|
||||
serializeConvertedResponsesEvents({
|
||||
state,
|
||||
streamContext: context,
|
||||
event: contentEvent as any,
|
||||
usage: { promptTokens: 5, completionTokens: 3, totalTokens: 8 },
|
||||
}),
|
||||
);
|
||||
|
||||
const reasoningEmitted = collectSerializedEvents(
|
||||
serializeConvertedResponsesEvents({
|
||||
state,
|
||||
streamContext: context,
|
||||
event: reasoningEvent as any,
|
||||
usage: { promptTokens: 5, completionTokens: 3, totalTokens: 8 },
|
||||
}),
|
||||
);
|
||||
|
||||
expect(reasoningEmitted.some((item) => item.event === 'response.reasoning_summary_part.added')).toBe(true);
|
||||
expect(reasoningEmitted.some((item) => item.event === 'response.reasoning_summary_text.delta')).toBe(true);
|
||||
|
||||
const completed = collectSerializedEvents(
|
||||
completeResponsesStream(state, context, { promptTokens: 5, completionTokens: 3, totalTokens: 8 }),
|
||||
).find((item) => item.event === 'response.completed');
|
||||
|
||||
expect(completed?.data.response.output).toEqual([
|
||||
{
|
||||
id: expect.any(String),
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: 'hello',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
id: expect.any(String),
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [
|
||||
{
|
||||
type: 'summary_text',
|
||||
text: 'plan first',
|
||||
},
|
||||
],
|
||||
},
|
||||
]);
|
||||
expect(completed?.data.response.usage).toMatchObject({
|
||||
input_tokens: 5,
|
||||
output_tokens: 3,
|
||||
total_tokens: 8,
|
||||
output_tokens_details: { reasoning_tokens: 2 },
|
||||
});
|
||||
});
|
||||
|
||||
it('tracks reasoning summary events into completed response output', () => {
|
||||
const state = createOpenAiResponsesAggregateState('gpt-5');
|
||||
|
||||
const emitted = [
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.output_item.added',
|
||||
output_index: 0,
|
||||
item: {
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
status: 'in_progress',
|
||||
summary: [],
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.reasoning_summary_part.added',
|
||||
item_id: 'rs_1',
|
||||
output_index: 0,
|
||||
summary_index: 0,
|
||||
part: {
|
||||
type: 'summary_text',
|
||||
text: '',
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.reasoning_summary_text.delta',
|
||||
item_id: 'rs_1',
|
||||
output_index: 0,
|
||||
summary_index: 0,
|
||||
delta: 'Plan',
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.reasoning_summary_text.done',
|
||||
item_id: 'rs_1',
|
||||
output_index: 0,
|
||||
summary_index: 0,
|
||||
text: 'Plan it out',
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.reasoning_summary_part.done',
|
||||
item_id: 'rs_1',
|
||||
output_index: 0,
|
||||
summary_index: 0,
|
||||
part: {
|
||||
type: 'summary_text',
|
||||
text: 'Plan it out',
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.output_item.done',
|
||||
output_index: 0,
|
||||
item: {
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [],
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.completed',
|
||||
response: {
|
||||
id: 'resp_reasoning',
|
||||
object: 'response',
|
||||
created_at: 1700000000,
|
||||
status: 'completed',
|
||||
model: 'gpt-5',
|
||||
output: [],
|
||||
},
|
||||
}),
|
||||
];
|
||||
|
||||
expect(emitted.some((item) => item.event === 'response.reasoning_summary_part.added')).toBe(true);
|
||||
expect(emitted.some((item) => item.event === 'response.reasoning_summary_text.done')).toBe(true);
|
||||
|
||||
const completed = emitted.find((item) => item.event === 'response.completed');
|
||||
expect(completed?.data.response.output).toEqual([
|
||||
{
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [
|
||||
{
|
||||
type: 'summary_text',
|
||||
text: 'Plan it out',
|
||||
},
|
||||
],
|
||||
},
|
||||
]);
|
||||
});
|
||||
|
||||
it('tracks custom tool input and image generation results', () => {
|
||||
const state = createOpenAiResponsesAggregateState('gpt-5');
|
||||
|
||||
const emitted = [
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.output_item.added',
|
||||
output_index: 0,
|
||||
item: {
|
||||
id: 'ct_1',
|
||||
type: 'custom_tool_call',
|
||||
status: 'in_progress',
|
||||
call_id: 'ct_1',
|
||||
name: 'browser',
|
||||
input: '',
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.custom_tool_call_input.delta',
|
||||
item_id: 'ct_1',
|
||||
output_index: 0,
|
||||
delta: 'open ',
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.custom_tool_call_input.done',
|
||||
item_id: 'ct_1',
|
||||
output_index: 0,
|
||||
input: 'open example.com',
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.output_item.done',
|
||||
output_index: 0,
|
||||
item: {
|
||||
id: 'ct_1',
|
||||
type: 'custom_tool_call',
|
||||
status: 'completed',
|
||||
call_id: 'ct_1',
|
||||
name: 'browser',
|
||||
input: 'open example.com',
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.output_item.added',
|
||||
output_index: 1,
|
||||
item: {
|
||||
id: 'img_1',
|
||||
type: 'image_generation_call',
|
||||
status: 'in_progress',
|
||||
result: null,
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.image_generation_call.partial_image',
|
||||
item_id: 'img_1',
|
||||
output_index: 1,
|
||||
partial_image_index: 0,
|
||||
partial_image_b64: 'abc123',
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.image_generation_call.completed',
|
||||
item_id: 'img_1',
|
||||
output_index: 1,
|
||||
result: 'data:image/png;base64,final',
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.output_item.done',
|
||||
output_index: 1,
|
||||
item: {
|
||||
id: 'img_1',
|
||||
type: 'image_generation_call',
|
||||
status: 'completed',
|
||||
result: 'data:image/png;base64,final',
|
||||
},
|
||||
}),
|
||||
...feedResponsesEvent(state, {
|
||||
type: 'response.completed',
|
||||
response: {
|
||||
id: 'resp_tools',
|
||||
object: 'response',
|
||||
created_at: 1700000001,
|
||||
status: 'completed',
|
||||
model: 'gpt-5',
|
||||
output: [],
|
||||
},
|
||||
}),
|
||||
];
|
||||
|
||||
expect(emitted.some((item) => item.event === 'response.custom_tool_call_input.done')).toBe(true);
|
||||
expect(emitted.some((item) => item.event === 'response.image_generation_call.completed')).toBe(true);
|
||||
|
||||
const completed = emitted.find((item) => item.event === 'response.completed');
|
||||
expect(completed?.data.response.output).toEqual([
|
||||
{
|
||||
id: 'ct_1',
|
||||
type: 'custom_tool_call',
|
||||
status: 'completed',
|
||||
call_id: 'ct_1',
|
||||
name: 'browser',
|
||||
input: 'open example.com',
|
||||
},
|
||||
{
|
||||
id: 'img_1',
|
||||
type: 'image_generation_call',
|
||||
status: 'completed',
|
||||
result: 'data:image/png;base64,final',
|
||||
partial_images: [
|
||||
{
|
||||
partial_image_index: 0,
|
||||
partial_image_b64: 'abc123',
|
||||
},
|
||||
],
|
||||
},
|
||||
]);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,762 @@
|
||||
import { type StreamTransformContext } from '../../shared/normalized.js';
|
||||
import type { OpenAiResponsesStreamEvent } from './stream.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object' && !Array.isArray(value);
|
||||
}
|
||||
|
||||
function asTrimmedString(value: unknown): string {
|
||||
return typeof value === 'string' ? value.trim() : '';
|
||||
}
|
||||
|
||||
function cloneRecord(value: unknown): Record<string, unknown> | null {
|
||||
if (!isRecord(value)) return null;
|
||||
return { ...value };
|
||||
}
|
||||
|
||||
function cloneJson<T>(value: T): T {
|
||||
if (Array.isArray(value)) {
|
||||
return value.map((item) => cloneJson(item)) as T;
|
||||
}
|
||||
if (isRecord(value)) {
|
||||
return Object.fromEntries(
|
||||
Object.entries(value).map(([key, item]) => [key, cloneJson(item)]),
|
||||
) as T;
|
||||
}
|
||||
return value;
|
||||
}
|
||||
|
||||
function ensureResponseId(rawId: string): string {
|
||||
const trimmed = rawId.trim() || `resp_${Date.now()}`;
|
||||
return trimmed.startsWith('resp_') ? trimmed : `resp_${trimmed}`;
|
||||
}
|
||||
|
||||
function ensureOutputItemId(rawId: string, prefix: string, index: number): string {
|
||||
const trimmed = rawId.trim();
|
||||
if (trimmed) return trimmed;
|
||||
return `${prefix}_${index}`;
|
||||
}
|
||||
|
||||
function serializeSse(event: string, payload: unknown): string {
|
||||
return `event: ${event}\ndata: ${JSON.stringify(payload)}\n\n`;
|
||||
}
|
||||
|
||||
function serializeDone(): string {
|
||||
return 'data: [DONE]\n\n';
|
||||
}
|
||||
|
||||
type ResponsesUsageSummary = {
|
||||
promptTokens: number;
|
||||
completionTokens: number;
|
||||
totalTokens: number;
|
||||
cacheReadTokens?: number;
|
||||
cacheCreationTokens?: number;
|
||||
promptTokensIncludeCache?: boolean | null;
|
||||
};
|
||||
|
||||
type AggregateOutputItem = Record<string, unknown>;
|
||||
|
||||
export type OpenAiResponsesAggregateState = {
|
||||
modelName: string;
|
||||
responseId: string | null;
|
||||
createdAt: number | null;
|
||||
outputItems: Array<AggregateOutputItem | undefined>;
|
||||
outputIndexById: Record<string, number>;
|
||||
messageIndex: number | null;
|
||||
reasoningIndexById: Record<string, number>;
|
||||
functionIndexById: Record<string, number>;
|
||||
customToolIndexById: Record<string, number>;
|
||||
imageGenerationIndexById: Record<string, number>;
|
||||
usageExtras: Record<string, unknown>;
|
||||
completed: boolean;
|
||||
failed: boolean;
|
||||
};
|
||||
|
||||
export function createOpenAiResponsesAggregateState(modelName: string): OpenAiResponsesAggregateState {
|
||||
return {
|
||||
modelName,
|
||||
responseId: null,
|
||||
createdAt: null,
|
||||
outputItems: [],
|
||||
outputIndexById: {},
|
||||
messageIndex: null,
|
||||
reasoningIndexById: {},
|
||||
functionIndexById: {},
|
||||
customToolIndexById: {},
|
||||
imageGenerationIndexById: {},
|
||||
usageExtras: {},
|
||||
completed: false,
|
||||
failed: false,
|
||||
};
|
||||
}
|
||||
|
||||
function mergeUsageExtras(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
usagePayload: unknown,
|
||||
): void {
|
||||
if (!isRecord(usagePayload)) return;
|
||||
for (const [key, value] of Object.entries(usagePayload)) {
|
||||
if (key === 'input_tokens' || key === 'output_tokens' || key === 'total_tokens') continue;
|
||||
state.usageExtras[key] = cloneJson(value);
|
||||
}
|
||||
}
|
||||
|
||||
function rememberOutputId(state: OpenAiResponsesAggregateState, index: number, item: AggregateOutputItem) {
|
||||
const itemId = asTrimmedString(item.id);
|
||||
if (itemId) {
|
||||
state.outputIndexById[itemId] = index;
|
||||
}
|
||||
const itemType = asTrimmedString(item.type).toLowerCase();
|
||||
const callId = asTrimmedString(item.call_id);
|
||||
if (itemType === 'function_call') {
|
||||
if (callId) state.functionIndexById[callId] = index;
|
||||
if (itemId) state.functionIndexById[itemId] = index;
|
||||
}
|
||||
if (itemType === 'custom_tool_call') {
|
||||
if (callId) state.customToolIndexById[callId] = index;
|
||||
if (itemId) state.customToolIndexById[itemId] = index;
|
||||
}
|
||||
if (itemType === 'image_generation_call' && itemId) {
|
||||
state.imageGenerationIndexById[itemId] = index;
|
||||
}
|
||||
if (itemType === 'reasoning' && itemId) {
|
||||
state.reasoningIndexById[itemId] = index;
|
||||
}
|
||||
if (itemType === 'message' && state.messageIndex === null) {
|
||||
state.messageIndex = index;
|
||||
}
|
||||
}
|
||||
|
||||
function setOutputItem(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
index: number,
|
||||
item: AggregateOutputItem,
|
||||
): AggregateOutputItem {
|
||||
const current = cloneRecord(state.outputItems[index]) || {};
|
||||
const incoming = cloneJson(item);
|
||||
const next = {
|
||||
...current,
|
||||
...incoming,
|
||||
};
|
||||
if (Array.isArray(current.summary) && (!Array.isArray(incoming.summary) || incoming.summary.length <= 0)) {
|
||||
next.summary = current.summary;
|
||||
}
|
||||
if (Array.isArray(current.content) && (!Array.isArray(incoming.content) || incoming.content.length <= 0)) {
|
||||
next.content = current.content;
|
||||
}
|
||||
if (
|
||||
Array.isArray(current.partial_images)
|
||||
&& (!Array.isArray(incoming.partial_images) || incoming.partial_images.length <= 0)
|
||||
) {
|
||||
next.partial_images = current.partial_images;
|
||||
}
|
||||
state.outputItems[index] = next;
|
||||
rememberOutputId(state, index, next);
|
||||
return next;
|
||||
}
|
||||
|
||||
function ensureOutputItem(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
index: number,
|
||||
factory: () => AggregateOutputItem,
|
||||
): AggregateOutputItem {
|
||||
const existing = state.outputItems[index];
|
||||
if (existing) return existing;
|
||||
return setOutputItem(state, index, factory());
|
||||
}
|
||||
|
||||
function resolveOutputIndex(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
rawIndex: unknown,
|
||||
...candidateIds: Array<unknown>
|
||||
): number {
|
||||
if (typeof rawIndex === 'number' && Number.isFinite(rawIndex)) {
|
||||
return Math.max(0, Math.trunc(rawIndex));
|
||||
}
|
||||
for (const rawId of candidateIds) {
|
||||
const itemId = asTrimmedString(rawId);
|
||||
if (itemId && state.outputIndexById[itemId] !== undefined) {
|
||||
return state.outputIndexById[itemId];
|
||||
}
|
||||
}
|
||||
return state.outputItems.length;
|
||||
}
|
||||
|
||||
function ensureMessageItem(state: OpenAiResponsesAggregateState, indexHint?: number): { index: number; item: AggregateOutputItem } {
|
||||
const index = state.messageIndex ?? indexHint ?? 0;
|
||||
const item = ensureOutputItem(state, index, () => ({
|
||||
id: ensureOutputItemId('', 'msg', index),
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'in_progress',
|
||||
content: [],
|
||||
}));
|
||||
if (!Array.isArray(item.content)) item.content = [];
|
||||
if (state.messageIndex === null) state.messageIndex = index;
|
||||
return { index, item };
|
||||
}
|
||||
|
||||
function ensureMessageOutputTextPart(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
indexHint?: number,
|
||||
): { index: number; item: AggregateOutputItem; part: AggregateOutputItem; created: boolean } {
|
||||
const { index, item } = ensureMessageItem(state, indexHint);
|
||||
const content = Array.isArray(item.content) ? item.content as AggregateOutputItem[] : [];
|
||||
if (!Array.isArray(item.content)) item.content = content;
|
||||
let part = content[0];
|
||||
const created = !isRecord(part) || asTrimmedString(part.type).toLowerCase() !== 'output_text';
|
||||
if (created) {
|
||||
part = { type: 'output_text', text: '' };
|
||||
content[0] = part;
|
||||
}
|
||||
return { index, item, part, created };
|
||||
}
|
||||
|
||||
function ensureReasoningItem(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
itemIdRaw: unknown,
|
||||
indexHint?: unknown,
|
||||
): { index: number; item: AggregateOutputItem; created: boolean } {
|
||||
const itemId = asTrimmedString(itemIdRaw);
|
||||
const existingIndex = itemId ? state.reasoningIndexById[itemId] : undefined;
|
||||
const index = existingIndex ?? resolveOutputIndex(state, indexHint, itemId);
|
||||
const created = !state.outputItems[index];
|
||||
const item = ensureOutputItem(state, index, () => ({
|
||||
id: ensureOutputItemId(itemId, 'rs', index),
|
||||
type: 'reasoning',
|
||||
status: 'in_progress',
|
||||
summary: [],
|
||||
}));
|
||||
if (!Array.isArray(item.summary)) item.summary = [];
|
||||
return { index, item, created };
|
||||
}
|
||||
|
||||
function ensureReasoningSummaryPart(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
itemIdRaw: unknown,
|
||||
summaryIndexRaw: unknown,
|
||||
indexHint?: unknown,
|
||||
): { item: AggregateOutputItem; summary: AggregateOutputItem; index: number; summaryIndex: number; created: boolean } {
|
||||
const reasoningState = ensureReasoningItem(state, itemIdRaw, indexHint);
|
||||
const summaryIndex = typeof summaryIndexRaw === 'number' && Number.isFinite(summaryIndexRaw)
|
||||
? Math.max(0, Math.trunc(summaryIndexRaw))
|
||||
: 0;
|
||||
const summary = Array.isArray(reasoningState.item.summary) ? reasoningState.item.summary as AggregateOutputItem[] : [];
|
||||
if (!Array.isArray(reasoningState.item.summary)) reasoningState.item.summary = summary;
|
||||
let part = summary[summaryIndex];
|
||||
const created = !isRecord(part);
|
||||
if (!isRecord(part)) {
|
||||
part = { type: 'summary_text', text: '' };
|
||||
summary[summaryIndex] = part;
|
||||
}
|
||||
return { item: reasoningState.item, summary: part, index: reasoningState.index, summaryIndex, created: reasoningState.created || created };
|
||||
}
|
||||
|
||||
function ensureFunctionCallItem(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
callIdRaw: unknown,
|
||||
nameRaw: unknown,
|
||||
indexHint?: number,
|
||||
): { index: number; item: AggregateOutputItem; created: boolean } {
|
||||
const callId = asTrimmedString(callIdRaw);
|
||||
const name = asTrimmedString(nameRaw);
|
||||
const existingIndex = callId ? state.functionIndexById[callId] : undefined;
|
||||
const index = existingIndex ?? resolveOutputIndex(state, indexHint, callId);
|
||||
const created = !state.outputItems[index];
|
||||
const item = ensureOutputItem(state, index, () => ({
|
||||
id: ensureOutputItemId(callId, 'fc', index),
|
||||
type: 'function_call',
|
||||
status: 'in_progress',
|
||||
call_id: ensureOutputItemId(callId, 'call', index),
|
||||
name,
|
||||
arguments: '',
|
||||
}));
|
||||
if (callId) item.call_id = ensureOutputItemId(callId, 'call', index);
|
||||
if (name) item.name = name;
|
||||
if (typeof item.arguments !== 'string') item.arguments = '';
|
||||
return { index, item, created };
|
||||
}
|
||||
|
||||
function ensureCustomToolItem(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
itemIdRaw: unknown,
|
||||
callIdRaw: unknown,
|
||||
nameRaw: unknown,
|
||||
indexHint?: number,
|
||||
): { index: number; item: AggregateOutputItem; created: boolean } {
|
||||
const itemId = asTrimmedString(itemIdRaw);
|
||||
const callId = asTrimmedString(callIdRaw);
|
||||
const name = asTrimmedString(nameRaw);
|
||||
const existingIndex = (callId && state.customToolIndexById[callId] !== undefined)
|
||||
? state.customToolIndexById[callId]
|
||||
: (itemId && state.customToolIndexById[itemId] !== undefined ? state.customToolIndexById[itemId] : undefined);
|
||||
const index = existingIndex ?? resolveOutputIndex(state, indexHint, itemId, callId);
|
||||
const created = !state.outputItems[index];
|
||||
const item = ensureOutputItem(state, index, () => ({
|
||||
id: ensureOutputItemId(itemId, 'ct', index),
|
||||
type: 'custom_tool_call',
|
||||
status: 'in_progress',
|
||||
call_id: ensureOutputItemId(callId || itemId, 'call', index),
|
||||
name,
|
||||
input: '',
|
||||
}));
|
||||
if (callId || itemId) item.call_id = ensureOutputItemId(callId || itemId, 'call', index);
|
||||
if (name) item.name = name;
|
||||
if (typeof item.input !== 'string') item.input = '';
|
||||
return { index, item, created };
|
||||
}
|
||||
|
||||
function ensureImageGenerationItem(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
itemIdRaw: unknown,
|
||||
indexHint?: number,
|
||||
): { index: number; item: AggregateOutputItem; created: boolean } {
|
||||
const itemId = asTrimmedString(itemIdRaw);
|
||||
const existingIndex = itemId ? state.imageGenerationIndexById[itemId] : undefined;
|
||||
const index = existingIndex ?? resolveOutputIndex(state, indexHint, itemId);
|
||||
const created = !state.outputItems[index];
|
||||
const item = ensureOutputItem(state, index, () => ({
|
||||
id: ensureOutputItemId(itemId, 'img', index),
|
||||
type: 'image_generation_call',
|
||||
status: 'in_progress',
|
||||
result: null,
|
||||
partial_images: [],
|
||||
}));
|
||||
if (!Array.isArray(item.partial_images)) item.partial_images = [];
|
||||
return { index, item, created };
|
||||
}
|
||||
|
||||
function buildUsagePayload(usage: ResponsesUsageSummary): Record<string, unknown> {
|
||||
const payload: Record<string, unknown> = {
|
||||
input_tokens: usage.promptTokens,
|
||||
output_tokens: usage.completionTokens,
|
||||
total_tokens: usage.totalTokens,
|
||||
};
|
||||
const inputDetails: Record<string, unknown> = {};
|
||||
if ((usage.cacheReadTokens || 0) > 0) inputDetails.cached_tokens = usage.cacheReadTokens;
|
||||
if ((usage.cacheCreationTokens || 0) > 0) inputDetails.cache_creation_tokens = usage.cacheCreationTokens;
|
||||
if (Object.keys(inputDetails).length > 0) payload.input_tokens_details = inputDetails;
|
||||
return {
|
||||
...payload,
|
||||
};
|
||||
}
|
||||
|
||||
function collectOutputText(state: OpenAiResponsesAggregateState): string {
|
||||
const parts: string[] = [];
|
||||
for (const item of state.outputItems) {
|
||||
if (!isRecord(item)) continue;
|
||||
if (asTrimmedString(item.type).toLowerCase() !== 'message') continue;
|
||||
const content = Array.isArray(item.content) ? item.content : [];
|
||||
for (const part of content) {
|
||||
if (!isRecord(part)) continue;
|
||||
const type = asTrimmedString(part.type).toLowerCase();
|
||||
if ((type === 'output_text' || type === 'text') && typeof part.text === 'string' && part.text) {
|
||||
parts.push(part.text);
|
||||
}
|
||||
}
|
||||
}
|
||||
return parts.join('');
|
||||
}
|
||||
|
||||
function materializeResponse(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
streamContext: StreamTransformContext,
|
||||
usage: ResponsesUsageSummary,
|
||||
responseTemplate?: Record<string, unknown> | null,
|
||||
statusOverride?: 'completed' | 'failed',
|
||||
): Record<string, unknown> {
|
||||
const base = cloneRecord(responseTemplate) || {};
|
||||
const responseId = ensureResponseId(
|
||||
asTrimmedString(base.id)
|
||||
|| state.responseId
|
||||
|| streamContext.id
|
||||
|| state.modelName,
|
||||
);
|
||||
const createdAt = (
|
||||
typeof base.created_at === 'number' && Number.isFinite(base.created_at)
|
||||
? base.created_at
|
||||
: (typeof base.created === 'number' && Number.isFinite(base.created) ? base.created : null)
|
||||
) ?? state.createdAt ?? Math.floor(Date.now() / 1000);
|
||||
const output = state.outputItems
|
||||
.filter((item): item is AggregateOutputItem => isRecord(item))
|
||||
.map((item) => {
|
||||
const status = asTrimmedString(item.status).toLowerCase();
|
||||
return {
|
||||
...item,
|
||||
status: status && status !== 'in_progress'
|
||||
? status
|
||||
: (state.failed ? 'failed' : 'completed'),
|
||||
};
|
||||
});
|
||||
|
||||
return {
|
||||
...base,
|
||||
id: responseId,
|
||||
object: 'response',
|
||||
created_at: createdAt,
|
||||
status: statusOverride ?? (state.failed ? 'failed' : 'completed'),
|
||||
model: asTrimmedString(base.model) || streamContext.model || state.modelName,
|
||||
output,
|
||||
output_text: collectOutputText(state),
|
||||
usage: buildUsagePayload(usage),
|
||||
...Object.keys(state.usageExtras).length > 0 ? { usage: { ...buildUsagePayload(usage), ...state.usageExtras } } : {},
|
||||
};
|
||||
}
|
||||
|
||||
function serializeOriginalResponsesEvent(eventType: string, payload: Record<string, unknown>): string[] {
|
||||
return [serializeSse(eventType, payload)];
|
||||
}
|
||||
|
||||
function computeNovelDelta(existingText: string, incomingDelta: string): string {
|
||||
if (!incomingDelta) return '';
|
||||
if (!existingText) return incomingDelta;
|
||||
if (existingText.endsWith(incomingDelta)) return '';
|
||||
if (incomingDelta.startsWith(existingText)) {
|
||||
return incomingDelta.slice(existingText.length);
|
||||
}
|
||||
if (existingText.includes(incomingDelta)) return '';
|
||||
|
||||
const maxOverlap = Math.min(existingText.length, incomingDelta.length);
|
||||
for (let overlap = maxOverlap; overlap > 0; overlap -= 1) {
|
||||
if (existingText.slice(-overlap) === incomingDelta.slice(0, overlap)) {
|
||||
return incomingDelta.slice(overlap);
|
||||
}
|
||||
}
|
||||
return incomingDelta;
|
||||
}
|
||||
|
||||
function applyOriginalResponsesPayload(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
eventType: string,
|
||||
payload: Record<string, unknown>,
|
||||
streamContext: StreamTransformContext,
|
||||
usage: ResponsesUsageSummary,
|
||||
): string[] {
|
||||
switch (eventType) {
|
||||
case 'response.output_item.added':
|
||||
case 'response.output_item.done': {
|
||||
const outputIndex = resolveOutputIndex(state, payload.output_index, (payload.item as Record<string, unknown> | undefined)?.id);
|
||||
const item = cloneRecord(payload.item) || {};
|
||||
if (Object.keys(item).length > 0) {
|
||||
const existing = isRecord(state.outputItems[outputIndex]) ? cloneRecord(state.outputItems[outputIndex]) || {} : {};
|
||||
const next = {
|
||||
...existing,
|
||||
...item,
|
||||
};
|
||||
if ((!Array.isArray(item.content) || item.content.length === 0) && Array.isArray(existing.content)) {
|
||||
next.content = existing.content;
|
||||
}
|
||||
if ((!Array.isArray(item.summary) || item.summary.length === 0) && Array.isArray(existing.summary)) {
|
||||
next.summary = existing.summary;
|
||||
}
|
||||
if ((!Array.isArray(item.partial_images) || item.partial_images.length === 0) && Array.isArray(existing.partial_images)) {
|
||||
next.partial_images = existing.partial_images;
|
||||
}
|
||||
setOutputItem(state, outputIndex, next);
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, {
|
||||
...payload,
|
||||
item: state.outputItems[outputIndex] ?? payload.item,
|
||||
});
|
||||
}
|
||||
case 'response.content_part.added':
|
||||
case 'response.content_part.done': {
|
||||
const outputIndex = resolveOutputIndex(state, payload.output_index, payload.item_id);
|
||||
const contentIndex = typeof payload.content_index === 'number' && Number.isFinite(payload.content_index)
|
||||
? Math.max(0, Math.trunc(payload.content_index))
|
||||
: 0;
|
||||
const message = ensureMessageItem(state, outputIndex).item;
|
||||
const content = Array.isArray(message.content) ? message.content as AggregateOutputItem[] : [];
|
||||
if (!Array.isArray(message.content)) message.content = content;
|
||||
const part = cloneRecord(payload.part);
|
||||
if (part) content[contentIndex] = part;
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.output_text.delta':
|
||||
case 'response.output_text.done': {
|
||||
const outputIndex = resolveOutputIndex(state, payload.output_index, payload.item_id);
|
||||
const textPart = ensureMessageOutputTextPart(state, outputIndex);
|
||||
if (eventType === 'response.output_text.done') {
|
||||
textPart.part.text = typeof payload.text === 'string' ? payload.text : String(payload.text ?? '');
|
||||
} else {
|
||||
textPart.part.text = `${typeof textPart.part.text === 'string' ? textPart.part.text : ''}${typeof payload.delta === 'string' ? payload.delta : ''}`;
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.function_call_arguments.delta':
|
||||
case 'response.function_call_arguments.done': {
|
||||
const entry = ensureFunctionCallItem(
|
||||
state,
|
||||
payload.call_id ?? payload.item_id,
|
||||
payload.name,
|
||||
resolveOutputIndex(state, payload.output_index, payload.item_id, payload.call_id),
|
||||
);
|
||||
if (eventType === 'response.function_call_arguments.done') {
|
||||
entry.item.arguments = typeof payload.arguments === 'string' ? payload.arguments : String(payload.arguments ?? '');
|
||||
} else {
|
||||
entry.item.arguments = `${typeof entry.item.arguments === 'string' ? entry.item.arguments : ''}${typeof payload.delta === 'string' ? payload.delta : ''}`;
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.custom_tool_call_input.delta':
|
||||
case 'response.custom_tool_call_input.done': {
|
||||
const entry = ensureCustomToolItem(
|
||||
state,
|
||||
payload.item_id,
|
||||
payload.call_id,
|
||||
payload.name,
|
||||
resolveOutputIndex(state, payload.output_index, payload.item_id, payload.call_id),
|
||||
);
|
||||
if (eventType === 'response.custom_tool_call_input.done') {
|
||||
entry.item.input = typeof payload.input === 'string' ? payload.input : String(payload.input ?? '');
|
||||
} else {
|
||||
entry.item.input = `${typeof entry.item.input === 'string' ? entry.item.input : ''}${typeof payload.delta === 'string' ? payload.delta : ''}`;
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.reasoning_summary_part.added':
|
||||
case 'response.reasoning_summary_part.done': {
|
||||
const summaryState = ensureReasoningSummaryPart(state, payload.item_id, payload.summary_index, payload.output_index);
|
||||
const part = cloneRecord(payload.part);
|
||||
if (part) {
|
||||
const summary = Array.isArray(summaryState.item.summary) ? summaryState.item.summary as AggregateOutputItem[] : [];
|
||||
summary[summaryState.summaryIndex] = {
|
||||
...summaryState.summary,
|
||||
...part,
|
||||
};
|
||||
summaryState.item.summary = summary;
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.reasoning_summary_text.delta':
|
||||
case 'response.reasoning_summary_text.done': {
|
||||
const summaryState = ensureReasoningSummaryPart(state, payload.item_id, payload.summary_index, payload.output_index);
|
||||
if (eventType === 'response.reasoning_summary_text.done') {
|
||||
summaryState.summary.text = typeof payload.text === 'string' ? payload.text : String(payload.text ?? '');
|
||||
} else {
|
||||
summaryState.summary.text = `${typeof summaryState.summary.text === 'string' ? summaryState.summary.text : ''}${typeof payload.delta === 'string' ? payload.delta : ''}`;
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.image_generation_call.generating':
|
||||
case 'response.image_generation_call.in_progress':
|
||||
case 'response.image_generation_call.partial_image':
|
||||
case 'response.image_generation_call.completed': {
|
||||
const entry = ensureImageGenerationItem(
|
||||
state,
|
||||
payload.item_id,
|
||||
resolveOutputIndex(state, payload.output_index, payload.item_id),
|
||||
);
|
||||
if (eventType === 'response.image_generation_call.partial_image') {
|
||||
const partialImages = Array.isArray(entry.item.partial_images) ? entry.item.partial_images as AggregateOutputItem[] : [];
|
||||
partialImages.push({
|
||||
partial_image_index: payload.partial_image_index,
|
||||
partial_image_b64: payload.partial_image_b64,
|
||||
});
|
||||
entry.item.partial_images = partialImages;
|
||||
}
|
||||
if (payload.result !== undefined) {
|
||||
entry.item.result = payload.result;
|
||||
}
|
||||
if (eventType === 'response.image_generation_call.completed') {
|
||||
entry.item.status = 'completed';
|
||||
}
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
case 'response.completed': {
|
||||
mergeUsageExtras(state, payload.response && isRecord(payload.response) ? payload.response.usage : payload.usage);
|
||||
state.completed = true;
|
||||
const responsePayload = cloneRecord(payload.response);
|
||||
const materialized = materializeResponse(state, streamContext, usage, responsePayload, 'completed');
|
||||
return [serializeSse('response.completed', { ...payload, response: materialized })];
|
||||
}
|
||||
case 'response.failed': {
|
||||
mergeUsageExtras(state, payload.response && isRecord(payload.response) ? payload.response.usage : payload.usage);
|
||||
state.failed = true;
|
||||
const responsePayload = cloneRecord(payload.response);
|
||||
const materialized = materializeResponse(state, streamContext, usage, responsePayload, 'failed');
|
||||
return [serializeSse('response.failed', { ...payload, response: materialized })];
|
||||
}
|
||||
default:
|
||||
mergeUsageExtras(state, payload.usage);
|
||||
return serializeOriginalResponsesEvent(eventType, payload);
|
||||
}
|
||||
}
|
||||
|
||||
function buildSyntheticMessageEvents(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
delta: string,
|
||||
): string[] {
|
||||
const { index, item } = ensureMessageItem(state);
|
||||
const textPartState = ensureMessageOutputTextPart(state, index);
|
||||
const lines: string[] = [];
|
||||
const currentText = typeof textPartState.part.text === 'string' ? textPartState.part.text : '';
|
||||
const novelDelta = computeNovelDelta(currentText, delta);
|
||||
if (textPartState.created) {
|
||||
lines.push(serializeSse('response.output_item.added', {
|
||||
type: 'response.output_item.added',
|
||||
output_index: index,
|
||||
item,
|
||||
}));
|
||||
lines.push(serializeSse('response.content_part.added', {
|
||||
type: 'response.content_part.added',
|
||||
output_index: index,
|
||||
item_id: item.id,
|
||||
content_index: 0,
|
||||
part: { type: 'output_text', text: '' },
|
||||
}));
|
||||
}
|
||||
if (novelDelta) {
|
||||
textPartState.part.text = `${currentText}${novelDelta}`;
|
||||
lines.push(serializeSse('response.output_text.delta', {
|
||||
type: 'response.output_text.delta',
|
||||
output_index: index,
|
||||
item_id: item.id,
|
||||
delta: novelDelta,
|
||||
}));
|
||||
}
|
||||
return lines;
|
||||
}
|
||||
|
||||
function buildSyntheticReasoningEvents(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
delta: string,
|
||||
): string[] {
|
||||
const summaryState = ensureReasoningSummaryPart(state, '', 0, state.outputItems.length);
|
||||
const itemId = asTrimmedString(summaryState.item.id);
|
||||
const lines: string[] = [];
|
||||
const currentText = typeof summaryState.summary.text === 'string' ? summaryState.summary.text : '';
|
||||
const novelDelta = computeNovelDelta(currentText, delta);
|
||||
if (summaryState.created) {
|
||||
lines.push(serializeSse('response.output_item.added', {
|
||||
type: 'response.output_item.added',
|
||||
output_index: summaryState.index,
|
||||
item: summaryState.item,
|
||||
}));
|
||||
lines.push(serializeSse('response.reasoning_summary_part.added', {
|
||||
type: 'response.reasoning_summary_part.added',
|
||||
item_id: itemId,
|
||||
output_index: summaryState.index,
|
||||
summary_index: 0,
|
||||
part: { type: 'summary_text', text: '' },
|
||||
}));
|
||||
}
|
||||
if (novelDelta) {
|
||||
summaryState.summary.text = `${currentText}${novelDelta}`;
|
||||
lines.push(serializeSse('response.reasoning_summary_text.delta', {
|
||||
type: 'response.reasoning_summary_text.delta',
|
||||
item_id: itemId,
|
||||
output_index: summaryState.index,
|
||||
summary_index: 0,
|
||||
delta: novelDelta,
|
||||
}));
|
||||
}
|
||||
return lines;
|
||||
}
|
||||
|
||||
function buildSyntheticToolEvents(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
event: OpenAiResponsesStreamEvent,
|
||||
): string[] {
|
||||
const lines: string[] = [];
|
||||
if (!Array.isArray(event.toolCallDeltas)) return lines;
|
||||
for (const toolDelta of event.toolCallDeltas) {
|
||||
const entry = ensureFunctionCallItem(state, toolDelta.id, toolDelta.name, toolDelta.index);
|
||||
if (entry.created) {
|
||||
lines.push(serializeSse('response.output_item.added', {
|
||||
type: 'response.output_item.added',
|
||||
output_index: entry.index,
|
||||
item: entry.item,
|
||||
}));
|
||||
}
|
||||
if (toolDelta.argumentsDelta !== undefined && toolDelta.argumentsDelta.length > 0) {
|
||||
entry.item.arguments = `${typeof entry.item.arguments === 'string' ? entry.item.arguments : ''}${toolDelta.argumentsDelta}`;
|
||||
lines.push(serializeSse('response.function_call_arguments.delta', {
|
||||
type: 'response.function_call_arguments.delta',
|
||||
item_id: entry.item.id,
|
||||
call_id: entry.item.call_id,
|
||||
output_index: entry.index,
|
||||
name: entry.item.name,
|
||||
delta: toolDelta.argumentsDelta,
|
||||
}));
|
||||
}
|
||||
}
|
||||
return lines;
|
||||
}
|
||||
|
||||
export function serializeConvertedResponsesEvents(input: {
|
||||
state: OpenAiResponsesAggregateState;
|
||||
streamContext: StreamTransformContext;
|
||||
event: OpenAiResponsesStreamEvent;
|
||||
usage: ResponsesUsageSummary;
|
||||
}): string[] {
|
||||
const { state, streamContext, event, usage } = input;
|
||||
mergeUsageExtras(state, event.responsesPayload && isRecord(event.responsesPayload) ? event.responsesPayload.usage : undefined);
|
||||
|
||||
if (event.responsesEventType && event.responsesPayload) {
|
||||
return applyOriginalResponsesPayload(
|
||||
state,
|
||||
event.responsesEventType,
|
||||
event.responsesPayload,
|
||||
streamContext,
|
||||
usage,
|
||||
);
|
||||
}
|
||||
|
||||
const lines: string[] = [];
|
||||
if (event.contentDelta) {
|
||||
lines.push(...buildSyntheticMessageEvents(state, event.contentDelta));
|
||||
}
|
||||
if (event.reasoningDelta) {
|
||||
lines.push(...buildSyntheticReasoningEvents(state, event.reasoningDelta));
|
||||
}
|
||||
lines.push(...buildSyntheticToolEvents(state, event));
|
||||
return lines;
|
||||
}
|
||||
|
||||
export function completeResponsesStream(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
streamContext: StreamTransformContext,
|
||||
usage: ResponsesUsageSummary,
|
||||
): string[] {
|
||||
if (state.failed || state.completed) {
|
||||
return [serializeDone()];
|
||||
}
|
||||
state.completed = true;
|
||||
return [
|
||||
serializeSse('response.completed', {
|
||||
type: 'response.completed',
|
||||
response: materializeResponse(state, streamContext, usage, null, 'completed'),
|
||||
}),
|
||||
serializeDone(),
|
||||
];
|
||||
}
|
||||
|
||||
export function failResponsesStream(
|
||||
state: OpenAiResponsesAggregateState,
|
||||
streamContext: StreamTransformContext,
|
||||
usage: ResponsesUsageSummary,
|
||||
payload: unknown,
|
||||
): string[] {
|
||||
if (state.failed) {
|
||||
return [serializeDone()];
|
||||
}
|
||||
state.failed = true;
|
||||
const errorPayload = cloneRecord(payload);
|
||||
const message = (
|
||||
isRecord(errorPayload?.error) && typeof errorPayload.error.message === 'string'
|
||||
? errorPayload.error.message
|
||||
: (typeof errorPayload?.message === 'string' ? errorPayload.message : 'upstream stream failed')
|
||||
);
|
||||
return [
|
||||
serializeSse('response.failed', {
|
||||
type: 'response.failed',
|
||||
response: materializeResponse(state, streamContext, usage, cloneRecord(errorPayload?.response), 'failed'),
|
||||
error: {
|
||||
message,
|
||||
type: 'upstream_error',
|
||||
},
|
||||
}),
|
||||
serializeDone(),
|
||||
];
|
||||
}
|
||||
@@ -0,0 +1,409 @@
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object';
|
||||
}
|
||||
|
||||
function asTrimmedString(value: unknown): string {
|
||||
return typeof value === 'string' ? value.trim() : '';
|
||||
}
|
||||
|
||||
function toTextBlockType(role: string): 'input_text' | 'output_text' {
|
||||
return role === 'assistant' ? 'output_text' : 'input_text';
|
||||
}
|
||||
|
||||
function normalizeImageUrlValue(value: unknown): string | Record<string, unknown> | null {
|
||||
if (typeof value === 'string' && value.trim().length > 0) return value;
|
||||
if (!isRecord(value)) return null;
|
||||
const url = asTrimmedString(value.url);
|
||||
if (url) return { ...value, url };
|
||||
const imageUrl = asTrimmedString(value.image_url);
|
||||
if (imageUrl) return imageUrl;
|
||||
return Object.keys(value).length > 0 ? value : null;
|
||||
}
|
||||
|
||||
function normalizeAudioInputValue(value: unknown): Record<string, unknown> | null {
|
||||
if (!isRecord(value)) return null;
|
||||
const data = asTrimmedString(value.data);
|
||||
const format = asTrimmedString(value.format);
|
||||
if (!data && !format) return Object.keys(value).length > 0 ? value : null;
|
||||
return {
|
||||
...value,
|
||||
...(data ? { data } : {}),
|
||||
...(format ? { format } : {}),
|
||||
};
|
||||
}
|
||||
|
||||
function normalizeResponsesContentItem(
|
||||
item: unknown,
|
||||
role: string,
|
||||
): Record<string, unknown> | null {
|
||||
if (typeof item === 'string') {
|
||||
const text = item.trim();
|
||||
return text ? { type: toTextBlockType(role), text } : null;
|
||||
}
|
||||
|
||||
if (!isRecord(item)) return null;
|
||||
|
||||
const type = asTrimmedString(item.type).toLowerCase();
|
||||
if (!type) {
|
||||
const text = asTrimmedString(item.text ?? item.content ?? item.output_text);
|
||||
return text ? { type: toTextBlockType(role), text } : null;
|
||||
}
|
||||
|
||||
if (type === 'input_text' || type === 'output_text' || type === 'text') {
|
||||
const text = asTrimmedString(item.text ?? item.content ?? item.output_text);
|
||||
if (!text) return null;
|
||||
return {
|
||||
...item,
|
||||
type: type === 'text' ? toTextBlockType(role) : type,
|
||||
text,
|
||||
};
|
||||
}
|
||||
|
||||
if (type === 'input_image' || type === 'image_url') {
|
||||
const imageUrl = normalizeImageUrlValue(item.image_url ?? item.url);
|
||||
if (!imageUrl) return null;
|
||||
return {
|
||||
...item,
|
||||
type: 'input_image',
|
||||
image_url: imageUrl,
|
||||
};
|
||||
}
|
||||
|
||||
if (type === 'input_audio') {
|
||||
const inputAudio = normalizeAudioInputValue(item.input_audio);
|
||||
if (!inputAudio) return null;
|
||||
return {
|
||||
...item,
|
||||
type: 'input_audio',
|
||||
input_audio: inputAudio,
|
||||
};
|
||||
}
|
||||
|
||||
if (type === 'function_call' || type === 'function_call_output') {
|
||||
return item;
|
||||
}
|
||||
|
||||
return item;
|
||||
}
|
||||
|
||||
export function normalizeResponsesMessageContent(content: unknown, role: string): unknown {
|
||||
if (Array.isArray(content)) {
|
||||
const normalized = content
|
||||
.map((item) => normalizeResponsesContentItem(item, role))
|
||||
.filter((item): item is Record<string, unknown> => !!item);
|
||||
return normalized.length > 0 ? normalized : content;
|
||||
}
|
||||
|
||||
const single = normalizeResponsesContentItem(content, role);
|
||||
if (single) return [single];
|
||||
return content;
|
||||
}
|
||||
|
||||
function toResponsesInputMessageFromText(text: string): Record<string, unknown> {
|
||||
return {
|
||||
type: 'message',
|
||||
role: 'user',
|
||||
content: [{ type: 'input_text', text }],
|
||||
};
|
||||
}
|
||||
|
||||
export function normalizeResponsesMessageItem(item: Record<string, unknown>): Record<string, unknown> {
|
||||
const type = asTrimmedString(item.type).toLowerCase();
|
||||
if (type === 'function_call' || type === 'function_call_output') {
|
||||
return item;
|
||||
}
|
||||
|
||||
const role = asTrimmedString(item.role).toLowerCase() || 'user';
|
||||
const normalizedContent = normalizeResponsesMessageContent(item.content ?? item.text, role);
|
||||
|
||||
if (type === 'message') {
|
||||
return {
|
||||
...item,
|
||||
role,
|
||||
content: normalizedContent,
|
||||
};
|
||||
}
|
||||
|
||||
if (asTrimmedString(item.role)) {
|
||||
return {
|
||||
type: 'message',
|
||||
role,
|
||||
content: normalizedContent,
|
||||
};
|
||||
}
|
||||
|
||||
if (typeof item.content === 'string') {
|
||||
const text = item.content.trim();
|
||||
return text ? toResponsesInputMessageFromText(text) : item;
|
||||
}
|
||||
|
||||
return item;
|
||||
}
|
||||
|
||||
export function normalizeResponsesInputForCompatibility(input: unknown): unknown {
|
||||
if (typeof input === 'string') {
|
||||
const normalized = input.trim();
|
||||
if (!normalized) return input;
|
||||
return [toResponsesInputMessageFromText(normalized)];
|
||||
}
|
||||
|
||||
if (Array.isArray(input)) {
|
||||
return input.map((item) => {
|
||||
if (typeof item === 'string') {
|
||||
const normalized = item.trim();
|
||||
return normalized ? toResponsesInputMessageFromText(normalized) : item;
|
||||
}
|
||||
if (!isRecord(item)) return item;
|
||||
return normalizeResponsesMessageItem(item);
|
||||
});
|
||||
}
|
||||
|
||||
if (isRecord(input)) {
|
||||
return [normalizeResponsesMessageItem(input)];
|
||||
}
|
||||
|
||||
return input;
|
||||
}
|
||||
|
||||
function extractTextFromResponsesContent(content: unknown): string {
|
||||
if (typeof content === 'string') return content.trim();
|
||||
if (!Array.isArray(content)) return '';
|
||||
return content
|
||||
.map((item) => {
|
||||
if (!isRecord(item)) return '';
|
||||
return asTrimmedString(item.text ?? item.content ?? item.output_text);
|
||||
})
|
||||
.filter((item) => item.length > 0)
|
||||
.join('\n');
|
||||
}
|
||||
|
||||
function safeJsonStringify(value: unknown): string {
|
||||
try {
|
||||
return JSON.stringify(value);
|
||||
} catch {
|
||||
return '';
|
||||
}
|
||||
}
|
||||
|
||||
function normalizeOpenAiToolArguments(raw: unknown): string {
|
||||
if (typeof raw === 'string') return raw;
|
||||
if (raw === undefined || raw === null) return '';
|
||||
if (typeof raw === 'number' || typeof raw === 'boolean') return String(raw);
|
||||
if (Array.isArray(raw) || isRecord(raw)) {
|
||||
return safeJsonStringify(raw);
|
||||
}
|
||||
return '';
|
||||
}
|
||||
|
||||
function normalizeToolMessageContent(raw: unknown): string {
|
||||
if (raw === undefined || raw === null) return '';
|
||||
if (typeof raw === 'string') return raw;
|
||||
if (typeof raw === 'number' || typeof raw === 'boolean') return String(raw);
|
||||
if (Array.isArray(raw)) {
|
||||
const normalized = normalizeResponsesMessageContent(raw, 'user');
|
||||
const text = extractTextFromResponsesContent(normalized);
|
||||
return text || safeJsonStringify(raw);
|
||||
}
|
||||
if (isRecord(raw)) {
|
||||
const normalized = normalizeResponsesMessageContent(raw, 'user');
|
||||
const text = extractTextFromResponsesContent(normalized);
|
||||
return text || safeJsonStringify(raw);
|
||||
}
|
||||
return '';
|
||||
}
|
||||
|
||||
function toFiniteNumber(value: unknown): number | null {
|
||||
return typeof value === 'number' && Number.isFinite(value) ? value : null;
|
||||
}
|
||||
|
||||
const ALLOWED_RESPONSES_FIELDS = new Set([
|
||||
'model',
|
||||
'input',
|
||||
'instructions',
|
||||
'max_output_tokens',
|
||||
'max_completion_tokens',
|
||||
'temperature',
|
||||
'top_p',
|
||||
'truncation',
|
||||
'tools',
|
||||
'tool_choice',
|
||||
'parallel_tool_calls',
|
||||
'metadata',
|
||||
'reasoning',
|
||||
'store',
|
||||
'stream',
|
||||
'user',
|
||||
'previous_response_id',
|
||||
'text',
|
||||
'audio',
|
||||
'include',
|
||||
'response_format',
|
||||
'service_tier',
|
||||
'stop',
|
||||
'n',
|
||||
]);
|
||||
|
||||
export function convertOpenAiBodyToResponsesBody(
|
||||
openaiBody: Record<string, unknown>,
|
||||
modelName: string,
|
||||
stream: boolean,
|
||||
): Record<string, unknown> {
|
||||
const rawMessages = Array.isArray(openaiBody.messages) ? openaiBody.messages : [];
|
||||
const systemContents: string[] = [];
|
||||
const inputItems: Array<Record<string, unknown>> = [];
|
||||
|
||||
for (const item of rawMessages) {
|
||||
if (!isRecord(item)) continue;
|
||||
const role = asTrimmedString(item.role).toLowerCase() || 'user';
|
||||
|
||||
if (role === 'system' || role === 'developer') {
|
||||
const normalized = normalizeResponsesMessageContent(item.content, 'user');
|
||||
const content = extractTextFromResponsesContent(normalized).trim();
|
||||
if (content) systemContents.push(content);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (role === 'assistant') {
|
||||
const normalizedAssistantContent = normalizeResponsesMessageContent(item.content, 'assistant');
|
||||
if (Array.isArray(normalizedAssistantContent) && normalizedAssistantContent.length > 0) {
|
||||
inputItems.push({
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
content: normalizedAssistantContent,
|
||||
});
|
||||
}
|
||||
|
||||
const rawToolCalls = Array.isArray(item.tool_calls) ? item.tool_calls : [];
|
||||
for (let index = 0; index < rawToolCalls.length; index += 1) {
|
||||
const toolCall = rawToolCalls[index];
|
||||
if (!isRecord(toolCall)) continue;
|
||||
const functionPart = isRecord(toolCall.function) ? toolCall.function : {};
|
||||
const callId = asTrimmedString(toolCall.id) || `call_${Date.now()}_${index}`;
|
||||
const name = (
|
||||
asTrimmedString(functionPart.name)
|
||||
|| asTrimmedString(toolCall.name)
|
||||
|| `tool_${index}`
|
||||
);
|
||||
const argumentsValue = normalizeOpenAiToolArguments(
|
||||
functionPart.arguments ?? toolCall.arguments,
|
||||
);
|
||||
|
||||
inputItems.push({
|
||||
type: 'function_call',
|
||||
call_id: callId,
|
||||
name,
|
||||
arguments: argumentsValue,
|
||||
});
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
if (role === 'tool') {
|
||||
const callId = asTrimmedString(item.tool_call_id) || asTrimmedString(item.id);
|
||||
if (!callId) continue;
|
||||
|
||||
inputItems.push({
|
||||
type: 'function_call_output',
|
||||
call_id: callId,
|
||||
output: normalizeToolMessageContent(item.content),
|
||||
});
|
||||
continue;
|
||||
}
|
||||
|
||||
const normalizedUserContent = normalizeResponsesMessageContent(item.content, 'user');
|
||||
if (Array.isArray(normalizedUserContent) && normalizedUserContent.length > 0) {
|
||||
inputItems.push({
|
||||
type: 'message',
|
||||
role: 'user',
|
||||
content: normalizedUserContent,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const maxOutputTokens = (
|
||||
toFiniteNumber(openaiBody.max_output_tokens)
|
||||
?? toFiniteNumber(openaiBody.max_completion_tokens)
|
||||
?? toFiniteNumber(openaiBody.max_tokens)
|
||||
?? 4096
|
||||
);
|
||||
|
||||
const body: Record<string, unknown> = {
|
||||
model: modelName,
|
||||
stream,
|
||||
max_output_tokens: maxOutputTokens,
|
||||
input: inputItems,
|
||||
};
|
||||
|
||||
if (systemContents.length > 0) {
|
||||
body.instructions = systemContents.join('\n\n');
|
||||
}
|
||||
|
||||
const temperature = toFiniteNumber(openaiBody.temperature);
|
||||
if (temperature !== null) body.temperature = temperature;
|
||||
|
||||
const topP = toFiniteNumber(openaiBody.top_p);
|
||||
if (topP !== null) body.top_p = topP;
|
||||
|
||||
if (openaiBody.metadata !== undefined) body.metadata = openaiBody.metadata;
|
||||
if (openaiBody.reasoning !== undefined) body.reasoning = openaiBody.reasoning;
|
||||
if (openaiBody.parallel_tool_calls !== undefined) body.parallel_tool_calls = openaiBody.parallel_tool_calls;
|
||||
if (openaiBody.tools !== undefined) body.tools = openaiBody.tools;
|
||||
if (openaiBody.tool_choice !== undefined) body.tool_choice = openaiBody.tool_choice;
|
||||
|
||||
return {
|
||||
...body,
|
||||
input: normalizeResponsesInputForCompatibility(body.input),
|
||||
};
|
||||
}
|
||||
|
||||
export function sanitizeResponsesBodyForProxy(
|
||||
body: Record<string, unknown>,
|
||||
modelName: string,
|
||||
stream: boolean,
|
||||
): Record<string, unknown> {
|
||||
let normalized: Record<string, unknown> = {
|
||||
...body,
|
||||
model: modelName,
|
||||
stream,
|
||||
};
|
||||
|
||||
if (normalized.input === undefined) {
|
||||
if (Array.isArray((normalized as Record<string, unknown>).messages)) {
|
||||
normalized = convertOpenAiBodyToResponsesBody(normalized, modelName, stream);
|
||||
} else {
|
||||
const prompt = asTrimmedString((normalized as Record<string, unknown>).prompt);
|
||||
if (prompt) {
|
||||
normalized = {
|
||||
...normalized,
|
||||
input: [toResponsesInputMessageFromText(prompt)],
|
||||
};
|
||||
}
|
||||
}
|
||||
} else {
|
||||
normalized = {
|
||||
...normalized,
|
||||
input: normalizeResponsesInputForCompatibility(normalized.input),
|
||||
};
|
||||
}
|
||||
|
||||
const sanitized: Record<string, unknown> = {};
|
||||
for (const [key, value] of Object.entries(normalized)) {
|
||||
if (!ALLOWED_RESPONSES_FIELDS.has(key)) continue;
|
||||
if (key === 'max_completion_tokens') continue;
|
||||
sanitized[key] = value;
|
||||
}
|
||||
|
||||
const maxOutputTokens = toFiniteNumber(normalized.max_output_tokens);
|
||||
if (maxOutputTokens !== null && maxOutputTokens > 0) {
|
||||
sanitized.max_output_tokens = Math.trunc(maxOutputTokens);
|
||||
} else {
|
||||
const maxCompletionTokens = toFiniteNumber(normalized.max_completion_tokens);
|
||||
if (maxCompletionTokens !== null && maxCompletionTokens > 0) {
|
||||
sanitized.max_output_tokens = Math.trunc(maxCompletionTokens);
|
||||
}
|
||||
}
|
||||
|
||||
sanitized.model = modelName;
|
||||
sanitized.stream = stream;
|
||||
return sanitized;
|
||||
}
|
||||
@@ -0,0 +1,261 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
convertOpenAiBodyToResponsesBody,
|
||||
convertResponsesBodyToOpenAiBody,
|
||||
sanitizeResponsesBodyForProxy,
|
||||
} from './conversion.js';
|
||||
|
||||
describe('sanitizeResponsesBodyForProxy', () => {
|
||||
it('preserves newer Responses request fields needed by the proxy', () => {
|
||||
const result = sanitizeResponsesBodyForProxy(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
safety_identifier: 'safe-user-1',
|
||||
max_tool_calls: 3,
|
||||
prompt_cache_key: 'cache-key',
|
||||
prompt_cache_retention: { scope: 'session' },
|
||||
stream_options: { include_obfuscation: true },
|
||||
background: true,
|
||||
text: { format: { type: 'text' }, verbosity: 'high' },
|
||||
},
|
||||
'gpt-5',
|
||||
true,
|
||||
);
|
||||
|
||||
expect(result).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
stream: true,
|
||||
safety_identifier: 'safe-user-1',
|
||||
max_tool_calls: 3,
|
||||
prompt_cache_key: 'cache-key',
|
||||
prompt_cache_retention: { scope: 'session' },
|
||||
stream_options: { include_obfuscation: true },
|
||||
background: true,
|
||||
text: { format: { type: 'text' }, verbosity: 'high' },
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('convertOpenAiBodyToResponsesBody', () => {
|
||||
it('maps extra request fields and preserves custom/image_generation tools', () => {
|
||||
const result = convertOpenAiBodyToResponsesBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
messages: [{ role: 'user', content: 'draw a cat' }],
|
||||
safety_identifier: 'safe-user-2',
|
||||
max_tool_calls: 2,
|
||||
prompt_cache_key: 'prompt-key',
|
||||
prompt_cache_retention: { scope: 'workspace' },
|
||||
stream_options: { include_obfuscation: true },
|
||||
background: false,
|
||||
verbosity: 'low',
|
||||
tools: [
|
||||
{
|
||||
type: 'custom',
|
||||
name: 'browser',
|
||||
description: 'browse the web',
|
||||
format: { type: 'text' },
|
||||
},
|
||||
{
|
||||
type: 'image_generation',
|
||||
background: 'transparent',
|
||||
size: '1024x1024',
|
||||
},
|
||||
],
|
||||
},
|
||||
'gpt-5',
|
||||
false,
|
||||
);
|
||||
|
||||
expect(result).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
stream: false,
|
||||
safety_identifier: 'safe-user-2',
|
||||
max_tool_calls: 2,
|
||||
prompt_cache_key: 'prompt-key',
|
||||
prompt_cache_retention: { scope: 'workspace' },
|
||||
stream_options: { include_obfuscation: true },
|
||||
background: false,
|
||||
text: { verbosity: 'low' },
|
||||
tools: [
|
||||
{
|
||||
type: 'custom',
|
||||
name: 'browser',
|
||||
description: 'browse the web',
|
||||
format: { type: 'text' },
|
||||
},
|
||||
{
|
||||
type: 'image_generation',
|
||||
background: 'transparent',
|
||||
size: '1024x1024',
|
||||
},
|
||||
],
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('convertResponsesBodyToOpenAiBody', () => {
|
||||
it('preserves richer Responses request fields back onto the OpenAI-compatible body', () => {
|
||||
const result = convertResponsesBodyToOpenAiBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
input: [{ type: 'message', role: 'user', content: [{ type: 'input_text', text: 'hello' }] }],
|
||||
safety_identifier: 'safe-user-3',
|
||||
max_tool_calls: 4,
|
||||
prompt_cache_key: 'prompt-key-2',
|
||||
prompt_cache_retention: { scope: 'project' },
|
||||
stream_options: { include_obfuscation: true },
|
||||
background: true,
|
||||
text: { format: { type: 'json_object' }, verbosity: 'high' },
|
||||
tools: [
|
||||
{
|
||||
type: 'custom',
|
||||
name: 'browser',
|
||||
format: { type: 'grammar', syntax: 'lark' },
|
||||
},
|
||||
{
|
||||
type: 'image_generation',
|
||||
background: 'transparent',
|
||||
partial_images: 2,
|
||||
output_format: 'png',
|
||||
},
|
||||
],
|
||||
},
|
||||
'gpt-5',
|
||||
true,
|
||||
);
|
||||
|
||||
expect(result).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
stream: true,
|
||||
safety_identifier: 'safe-user-3',
|
||||
max_tool_calls: 4,
|
||||
prompt_cache_key: 'prompt-key-2',
|
||||
prompt_cache_retention: { scope: 'project' },
|
||||
stream_options: { include_obfuscation: true },
|
||||
background: true,
|
||||
verbosity: 'high',
|
||||
tools: [
|
||||
{
|
||||
type: 'custom',
|
||||
name: 'browser',
|
||||
format: { type: 'grammar', syntax: 'lark' },
|
||||
},
|
||||
{
|
||||
type: 'image_generation',
|
||||
background: 'transparent',
|
||||
partial_images: 2,
|
||||
output_format: 'png',
|
||||
},
|
||||
],
|
||||
});
|
||||
});
|
||||
|
||||
it('converts custom tool calls and outputs into OpenAI-compatible tool messages', () => {
|
||||
const result = convertResponsesBodyToOpenAiBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
input: [
|
||||
{
|
||||
type: 'custom_tool_call',
|
||||
id: 'ct_1',
|
||||
call_id: 'ct_1',
|
||||
name: 'browser',
|
||||
input: 'open example.com',
|
||||
},
|
||||
{
|
||||
type: 'custom_tool_call_output',
|
||||
call_id: 'ct_1',
|
||||
output: 'done',
|
||||
},
|
||||
],
|
||||
},
|
||||
'gpt-5',
|
||||
false,
|
||||
);
|
||||
|
||||
expect(result.messages).toEqual([
|
||||
{
|
||||
role: 'assistant',
|
||||
content: '',
|
||||
tool_calls: [
|
||||
{
|
||||
id: 'ct_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'browser',
|
||||
arguments: 'open example.com',
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
role: 'tool',
|
||||
tool_call_id: 'ct_1',
|
||||
content: 'done',
|
||||
},
|
||||
]);
|
||||
});
|
||||
|
||||
it('converts reasoning items back into assistant content instead of dropping them', () => {
|
||||
const result = convertResponsesBodyToOpenAiBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
input: [
|
||||
{
|
||||
type: 'reasoning',
|
||||
id: 'rs_1',
|
||||
status: 'completed',
|
||||
summary: [
|
||||
{ type: 'summary_text', text: 'Think step by step' },
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
'gpt-5',
|
||||
false,
|
||||
);
|
||||
|
||||
expect(result.messages).toEqual([
|
||||
{
|
||||
role: 'assistant',
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'Think step by step',
|
||||
},
|
||||
],
|
||||
},
|
||||
]);
|
||||
});
|
||||
|
||||
it('preserves remaining request fields needed for OpenAI-compatible downstream fallback', () => {
|
||||
const result = convertResponsesBodyToOpenAiBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
user: 'user-123',
|
||||
include: ['reasoning.encrypted_content'],
|
||||
previous_response_id: 'resp_prev',
|
||||
truncation: 'auto',
|
||||
service_tier: 'priority',
|
||||
top_logprobs: 4,
|
||||
},
|
||||
'gpt-5',
|
||||
true,
|
||||
);
|
||||
|
||||
expect(result).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
stream: true,
|
||||
user: 'user-123',
|
||||
include: ['reasoning.encrypted_content'],
|
||||
previous_response_id: 'resp_prev',
|
||||
truncation: 'auto',
|
||||
service_tier: 'priority',
|
||||
top_logprobs: 4,
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,698 @@
|
||||
import {
|
||||
normalizeResponsesInputForCompatibility as normalizeResponsesInputForCompatibilityViaCompatibility,
|
||||
normalizeResponsesMessageItem,
|
||||
} from './compatibility.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object';
|
||||
}
|
||||
|
||||
function asTrimmedString(value: unknown): string {
|
||||
return typeof value === 'string' ? value.trim() : '';
|
||||
}
|
||||
|
||||
function toFiniteNumber(value: unknown): number | null {
|
||||
return typeof value === 'number' && Number.isFinite(value) ? value : null;
|
||||
}
|
||||
|
||||
function safeJsonStringify(value: unknown): string {
|
||||
try {
|
||||
return JSON.stringify(value);
|
||||
} catch {
|
||||
return '';
|
||||
}
|
||||
}
|
||||
|
||||
function cloneRecord(value: unknown): Record<string, unknown> | null {
|
||||
if (!isRecord(value)) return null;
|
||||
return { ...value };
|
||||
}
|
||||
|
||||
function cloneJsonValue<T>(value: T): T {
|
||||
if (Array.isArray(value)) {
|
||||
return value.map((item) => cloneJsonValue(item)) as T;
|
||||
}
|
||||
if (isRecord(value)) {
|
||||
return Object.fromEntries(
|
||||
Object.entries(value).map(([key, item]) => [key, cloneJsonValue(item)]),
|
||||
) as T;
|
||||
}
|
||||
return value;
|
||||
}
|
||||
|
||||
function parseJsonString(raw: string): unknown {
|
||||
const trimmed = raw.trim();
|
||||
if (!trimmed) return {};
|
||||
try {
|
||||
return JSON.parse(trimmed);
|
||||
} catch {
|
||||
return { value: raw };
|
||||
}
|
||||
}
|
||||
|
||||
export function extractTextContent(value: unknown): string {
|
||||
if (typeof value === 'string') return value;
|
||||
if (Array.isArray(value)) {
|
||||
return value
|
||||
.map((item) => extractTextContent(item))
|
||||
.filter((item) => item.length > 0)
|
||||
.join('\n');
|
||||
}
|
||||
if (!isRecord(value)) return '';
|
||||
|
||||
const direct = asTrimmedString(
|
||||
value.text
|
||||
?? value.content
|
||||
?? value.input_text
|
||||
?? value.output_text
|
||||
?? value.reasoning
|
||||
?? value.reasoning_content
|
||||
?? value.thinking,
|
||||
);
|
||||
if (direct) return direct;
|
||||
|
||||
if (Array.isArray(value.parts)) return extractTextContent(value.parts);
|
||||
if (Array.isArray(value.content)) return extractTextContent(value.content);
|
||||
if (Array.isArray(value.output)) return extractTextContent(value.output);
|
||||
return '';
|
||||
}
|
||||
|
||||
function normalizeOpenAiToolArguments(raw: unknown): string {
|
||||
if (typeof raw === 'string') return raw;
|
||||
if (raw === undefined || raw === null) return '';
|
||||
if (typeof raw === 'number' || typeof raw === 'boolean') return String(raw);
|
||||
if (Array.isArray(raw) || isRecord(raw)) return safeJsonStringify(raw);
|
||||
return '';
|
||||
}
|
||||
|
||||
function normalizeToolOutput(raw: unknown): string {
|
||||
const text = extractTextContent(raw).trim();
|
||||
if (text) return text;
|
||||
if (raw === undefined || raw === null) return '';
|
||||
if (typeof raw === 'string') return raw;
|
||||
if (typeof raw === 'number' || typeof raw === 'boolean') return String(raw);
|
||||
if (Array.isArray(raw) || isRecord(raw)) return safeJsonStringify(raw);
|
||||
return '';
|
||||
}
|
||||
|
||||
export function normalizeResponsesInputForCompatibility(input: unknown): unknown {
|
||||
return normalizeResponsesInputForCompatibilityViaCompatibility(input);
|
||||
}
|
||||
|
||||
function toResponsesInputMessageFromText(text: string): Record<string, unknown> {
|
||||
return {
|
||||
type: 'message',
|
||||
role: 'user',
|
||||
content: [{ type: 'input_text', text }],
|
||||
};
|
||||
}
|
||||
|
||||
function convertOpenAiToolsToResponses(rawTools: unknown): unknown {
|
||||
if (!Array.isArray(rawTools)) return rawTools;
|
||||
|
||||
const converted = rawTools
|
||||
.map((item) => {
|
||||
if (!isRecord(item)) return null;
|
||||
|
||||
const type = asTrimmedString(item.type).toLowerCase();
|
||||
if (type === 'function' && isRecord(item.function)) {
|
||||
const fn = item.function;
|
||||
const name = asTrimmedString(fn.name);
|
||||
if (!name) return null;
|
||||
|
||||
const mapped: Record<string, unknown> = {
|
||||
type: 'function',
|
||||
name,
|
||||
};
|
||||
const description = asTrimmedString(fn.description);
|
||||
if (description) mapped.description = description;
|
||||
if (fn.parameters !== undefined) mapped.parameters = fn.parameters;
|
||||
if (fn.strict !== undefined) mapped.strict = fn.strict;
|
||||
return mapped;
|
||||
}
|
||||
|
||||
if (type === 'function' && asTrimmedString(item.name)) {
|
||||
return item;
|
||||
}
|
||||
|
||||
if (type === 'image_generation') {
|
||||
return item;
|
||||
}
|
||||
|
||||
if (type === 'custom' && asTrimmedString(item.name)) {
|
||||
return item;
|
||||
}
|
||||
|
||||
return null;
|
||||
})
|
||||
.filter((item): item is Record<string, unknown> => !!item);
|
||||
|
||||
return converted.length > 0 ? converted : rawTools;
|
||||
}
|
||||
|
||||
function convertOpenAiToolChoiceToResponses(rawToolChoice: unknown): unknown {
|
||||
if (rawToolChoice === undefined) return undefined;
|
||||
if (typeof rawToolChoice === 'string') return rawToolChoice;
|
||||
if (!isRecord(rawToolChoice)) return rawToolChoice;
|
||||
|
||||
const type = asTrimmedString(rawToolChoice.type).toLowerCase();
|
||||
if (type === 'function' && isRecord(rawToolChoice.function)) {
|
||||
const name = asTrimmedString(rawToolChoice.function.name);
|
||||
if (!name) return 'required';
|
||||
return { type: 'function', name };
|
||||
}
|
||||
|
||||
return rawToolChoice;
|
||||
}
|
||||
|
||||
function normalizeResponsesBodyForCompatibility(
|
||||
body: Record<string, unknown>,
|
||||
): Record<string, unknown> {
|
||||
const nextInput = normalizeResponsesInputForCompatibility(body.input);
|
||||
if (nextInput === body.input) return body;
|
||||
return {
|
||||
...body,
|
||||
input: nextInput,
|
||||
};
|
||||
}
|
||||
|
||||
export function normalizeResponsesMessageContent(role: string, content: unknown): Array<Record<string, unknown>> {
|
||||
const normalized = normalizeResponsesMessageItem({
|
||||
type: 'message',
|
||||
role,
|
||||
content,
|
||||
});
|
||||
|
||||
if (isRecord(normalized) && Array.isArray(normalized.content)) {
|
||||
return normalized.content.filter((item): item is Record<string, unknown> => isRecord(item));
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
const ALLOWED_RESPONSES_FIELDS = new Set([
|
||||
'model',
|
||||
'input',
|
||||
'instructions',
|
||||
'max_output_tokens',
|
||||
'max_completion_tokens',
|
||||
'temperature',
|
||||
'top_p',
|
||||
'truncation',
|
||||
'tools',
|
||||
'tool_choice',
|
||||
'parallel_tool_calls',
|
||||
'metadata',
|
||||
'reasoning',
|
||||
'store',
|
||||
'safety_identifier',
|
||||
'stream',
|
||||
'stream_options',
|
||||
'user',
|
||||
'max_tool_calls',
|
||||
'prompt_cache_key',
|
||||
'prompt_cache_retention',
|
||||
'background',
|
||||
'previous_response_id',
|
||||
'text',
|
||||
'audio',
|
||||
'include',
|
||||
'response_format',
|
||||
'service_tier',
|
||||
'stop',
|
||||
'n',
|
||||
]);
|
||||
|
||||
export function sanitizeResponsesBodyForProxy(
|
||||
body: Record<string, unknown>,
|
||||
modelName: string,
|
||||
stream: boolean,
|
||||
): Record<string, unknown> {
|
||||
let normalized = normalizeResponsesBodyForCompatibility({
|
||||
...body,
|
||||
model: modelName,
|
||||
stream,
|
||||
});
|
||||
|
||||
if (normalized.input === undefined) {
|
||||
if (Array.isArray((normalized as Record<string, unknown>).messages)) {
|
||||
normalized = normalizeResponsesBodyForCompatibility(
|
||||
convertOpenAiBodyToResponsesBody(normalized, modelName, stream),
|
||||
);
|
||||
} else {
|
||||
const prompt = asTrimmedString((normalized as Record<string, unknown>).prompt);
|
||||
if (prompt) {
|
||||
normalized = {
|
||||
...normalized,
|
||||
input: [toResponsesInputMessageFromText(prompt)],
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const sanitized: Record<string, unknown> = {};
|
||||
for (const [key, value] of Object.entries(normalized)) {
|
||||
if (!ALLOWED_RESPONSES_FIELDS.has(key)) continue;
|
||||
if (key === 'max_completion_tokens') continue;
|
||||
sanitized[key] = value;
|
||||
}
|
||||
|
||||
const maxOutputTokens = toFiniteNumber(normalized.max_output_tokens);
|
||||
if (maxOutputTokens !== null && maxOutputTokens > 0) {
|
||||
sanitized.max_output_tokens = Math.trunc(maxOutputTokens);
|
||||
} else {
|
||||
const maxCompletionTokens = toFiniteNumber(normalized.max_completion_tokens);
|
||||
if (maxCompletionTokens !== null && maxCompletionTokens > 0) {
|
||||
sanitized.max_output_tokens = Math.trunc(maxCompletionTokens);
|
||||
}
|
||||
}
|
||||
|
||||
sanitized.model = modelName;
|
||||
sanitized.stream = stream;
|
||||
return sanitized;
|
||||
}
|
||||
|
||||
export function convertOpenAiBodyToResponsesBody(
|
||||
openaiBody: Record<string, unknown>,
|
||||
modelName: string,
|
||||
stream: boolean,
|
||||
): Record<string, unknown> {
|
||||
const rawMessages = Array.isArray(openaiBody.messages) ? openaiBody.messages : [];
|
||||
const systemContents: string[] = [];
|
||||
const inputItems: Array<Record<string, unknown>> = [];
|
||||
|
||||
for (const item of rawMessages) {
|
||||
if (!isRecord(item)) continue;
|
||||
const role = asTrimmedString(item.role).toLowerCase() || 'user';
|
||||
|
||||
if (role === 'system' || role === 'developer') {
|
||||
const content = extractTextContent(item.content).trim();
|
||||
if (content) systemContents.push(content);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (role === 'assistant') {
|
||||
const normalizedContent = normalizeResponsesMessageContent('assistant', item.content);
|
||||
if (normalizedContent.length > 0) {
|
||||
inputItems.push({
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
content: normalizedContent,
|
||||
});
|
||||
}
|
||||
|
||||
const rawToolCalls = Array.isArray(item.tool_calls) ? item.tool_calls : [];
|
||||
for (let index = 0; index < rawToolCalls.length; index += 1) {
|
||||
const toolCall = rawToolCalls[index];
|
||||
if (!isRecord(toolCall)) continue;
|
||||
const functionPart = isRecord(toolCall.function) ? toolCall.function : {};
|
||||
const callId = asTrimmedString(toolCall.id) || `call_${Date.now()}_${index}`;
|
||||
const name = (
|
||||
asTrimmedString(functionPart.name)
|
||||
|| asTrimmedString(toolCall.name)
|
||||
|| `tool_${index}`
|
||||
);
|
||||
const argumentsValue = normalizeOpenAiToolArguments(
|
||||
functionPart.arguments ?? toolCall.arguments,
|
||||
);
|
||||
|
||||
inputItems.push({
|
||||
type: 'function_call',
|
||||
call_id: callId,
|
||||
name,
|
||||
arguments: argumentsValue,
|
||||
});
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
if (role === 'tool') {
|
||||
const callId = asTrimmedString(item.tool_call_id) || asTrimmedString(item.id);
|
||||
if (!callId) continue;
|
||||
inputItems.push({
|
||||
type: 'function_call_output',
|
||||
call_id: callId,
|
||||
output: normalizeToolOutput(item.content),
|
||||
});
|
||||
continue;
|
||||
}
|
||||
|
||||
const normalizedContent = normalizeResponsesMessageContent('user', item.content);
|
||||
if (normalizedContent.length <= 0) continue;
|
||||
inputItems.push({
|
||||
type: 'message',
|
||||
role: 'user',
|
||||
content: normalizedContent,
|
||||
});
|
||||
}
|
||||
|
||||
const maxOutputTokens = (
|
||||
toFiniteNumber(openaiBody.max_output_tokens)
|
||||
?? toFiniteNumber(openaiBody.max_completion_tokens)
|
||||
?? toFiniteNumber(openaiBody.max_tokens)
|
||||
?? 4096
|
||||
);
|
||||
|
||||
const body: Record<string, unknown> = {
|
||||
model: modelName,
|
||||
stream,
|
||||
max_output_tokens: maxOutputTokens,
|
||||
input: inputItems,
|
||||
};
|
||||
|
||||
if (systemContents.length > 0) {
|
||||
body.instructions = systemContents.join('\n\n');
|
||||
}
|
||||
|
||||
const temperature = toFiniteNumber(openaiBody.temperature);
|
||||
if (temperature !== null) body.temperature = temperature;
|
||||
|
||||
const topP = toFiniteNumber(openaiBody.top_p);
|
||||
if (topP !== null) body.top_p = topP;
|
||||
|
||||
if (openaiBody.metadata !== undefined) body.metadata = openaiBody.metadata;
|
||||
if (openaiBody.reasoning !== undefined) body.reasoning = openaiBody.reasoning;
|
||||
if (openaiBody.parallel_tool_calls !== undefined) body.parallel_tool_calls = openaiBody.parallel_tool_calls;
|
||||
if (openaiBody.tools !== undefined) body.tools = convertOpenAiToolsToResponses(openaiBody.tools);
|
||||
if (openaiBody.safety_identifier !== undefined) body.safety_identifier = openaiBody.safety_identifier;
|
||||
if (openaiBody.max_tool_calls !== undefined) body.max_tool_calls = openaiBody.max_tool_calls;
|
||||
if (openaiBody.prompt_cache_key !== undefined) body.prompt_cache_key = openaiBody.prompt_cache_key;
|
||||
if (openaiBody.prompt_cache_retention !== undefined) {
|
||||
body.prompt_cache_retention = openaiBody.prompt_cache_retention;
|
||||
}
|
||||
if (openaiBody.background !== undefined) body.background = openaiBody.background;
|
||||
if (openaiBody.user !== undefined) body.user = openaiBody.user;
|
||||
if (openaiBody.include !== undefined) body.include = cloneJsonValue(openaiBody.include);
|
||||
if (openaiBody.previous_response_id !== undefined) body.previous_response_id = openaiBody.previous_response_id;
|
||||
if (openaiBody.truncation !== undefined) body.truncation = openaiBody.truncation;
|
||||
if (openaiBody.service_tier !== undefined) body.service_tier = openaiBody.service_tier;
|
||||
if (openaiBody.top_logprobs !== undefined) body.top_logprobs = openaiBody.top_logprobs;
|
||||
if (openaiBody.stream_options !== undefined) body.stream_options = openaiBody.stream_options;
|
||||
|
||||
const textConfig = cloneRecord(openaiBody.text) || {};
|
||||
const verbosity = (
|
||||
asTrimmedString(openaiBody.verbosity)
|
||||
|| asTrimmedString(isRecord(openaiBody.text) ? openaiBody.text.verbosity : undefined)
|
||||
);
|
||||
if (verbosity) {
|
||||
textConfig.verbosity = verbosity;
|
||||
}
|
||||
if (Object.keys(textConfig).length > 0) {
|
||||
body.text = textConfig;
|
||||
}
|
||||
|
||||
const responsesToolChoice = convertOpenAiToolChoiceToResponses(openaiBody.tool_choice);
|
||||
if (responsesToolChoice !== undefined) body.tool_choice = responsesToolChoice;
|
||||
|
||||
return normalizeResponsesBodyForCompatibility(body);
|
||||
}
|
||||
|
||||
type OpenAiToolCall = {
|
||||
id: string;
|
||||
type: 'function';
|
||||
function: {
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
};
|
||||
|
||||
function toOpenAiToolCall(item: Record<string, unknown>, fallbackIndex: number): OpenAiToolCall | null {
|
||||
const callId = (
|
||||
asTrimmedString(item.call_id)
|
||||
|| asTrimmedString(item.id)
|
||||
|| `call_${Date.now()}_${fallbackIndex}`
|
||||
);
|
||||
const name = asTrimmedString(item.name);
|
||||
if (!name) return null;
|
||||
|
||||
return {
|
||||
id: callId,
|
||||
type: 'function',
|
||||
function: {
|
||||
name,
|
||||
arguments: normalizeOpenAiToolArguments(item.arguments ?? item.input),
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function normalizeOpenAiContentBlock(item: Record<string, unknown>): string | Record<string, unknown> | null {
|
||||
const type = asTrimmedString(item.type).toLowerCase();
|
||||
if (!type) {
|
||||
const text = extractTextContent(item).trim();
|
||||
return text ? { type: 'text', text } : null;
|
||||
}
|
||||
|
||||
if (
|
||||
type === 'input_text'
|
||||
|| type === 'output_text'
|
||||
|| type === 'text'
|
||||
|| type === 'summary_text'
|
||||
|| type === 'reasoning_text'
|
||||
) {
|
||||
const text = extractTextContent(item).trim();
|
||||
return text ? { type: 'text', text } : null;
|
||||
}
|
||||
|
||||
if (type === 'input_image') {
|
||||
const imageUrl = item.image_url ?? item.url;
|
||||
if (imageUrl === undefined) return null;
|
||||
return {
|
||||
type: 'image_url',
|
||||
image_url: imageUrl,
|
||||
};
|
||||
}
|
||||
|
||||
if (type === 'input_audio' && item.input_audio !== undefined) {
|
||||
return {
|
||||
type: 'input_audio',
|
||||
input_audio: item.input_audio,
|
||||
};
|
||||
}
|
||||
|
||||
if (type === 'reasoning' || type === 'thinking' || type === 'redacted_reasoning') {
|
||||
const text = extractTextContent(item).trim();
|
||||
return text ? { type: 'text', text } : null;
|
||||
}
|
||||
|
||||
return item;
|
||||
}
|
||||
|
||||
function toOpenAiMessageContent(content: unknown): string | Array<string | Record<string, unknown>> {
|
||||
if (typeof content === 'string') return content;
|
||||
if (!Array.isArray(content)) {
|
||||
if (isRecord(content)) {
|
||||
const single = normalizeOpenAiContentBlock(content);
|
||||
if (!single) return '';
|
||||
return typeof single === 'string' ? single : [single];
|
||||
}
|
||||
return '';
|
||||
}
|
||||
|
||||
const blocks = content
|
||||
.map((item) => {
|
||||
if (typeof item === 'string') return item.trim() ? item : null;
|
||||
if (!isRecord(item)) return null;
|
||||
return normalizeOpenAiContentBlock(item);
|
||||
})
|
||||
.filter((item): item is string | Record<string, unknown> => !!item);
|
||||
|
||||
if (blocks.length === 1 && typeof blocks[0] === 'string') {
|
||||
return blocks[0];
|
||||
}
|
||||
return blocks;
|
||||
}
|
||||
|
||||
function convertResponsesToolsToOpenAi(rawTools: unknown): unknown {
|
||||
if (!Array.isArray(rawTools)) return rawTools;
|
||||
|
||||
return rawTools.map((item) => {
|
||||
if (!isRecord(item)) return item;
|
||||
const type = asTrimmedString(item.type).toLowerCase();
|
||||
|
||||
if (type === 'custom' || type === 'image_generation') return item;
|
||||
if (type !== 'function') return item;
|
||||
if (isRecord(item.function) && asTrimmedString(item.function.name)) return item;
|
||||
|
||||
const name = asTrimmedString(item.name);
|
||||
if (!name) return item;
|
||||
|
||||
const fn: Record<string, unknown> = { name };
|
||||
const description = asTrimmedString(item.description);
|
||||
if (description) fn.description = description;
|
||||
if (item.parameters !== undefined) fn.parameters = item.parameters;
|
||||
if (item.strict !== undefined) fn.strict = item.strict;
|
||||
|
||||
return {
|
||||
type: 'function',
|
||||
function: fn,
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
function convertResponsesToolChoiceToOpenAi(rawToolChoice: unknown): unknown {
|
||||
if (rawToolChoice === undefined) return undefined;
|
||||
if (typeof rawToolChoice === 'string') return rawToolChoice;
|
||||
if (!isRecord(rawToolChoice)) return rawToolChoice;
|
||||
|
||||
const type = asTrimmedString(rawToolChoice.type).toLowerCase();
|
||||
if (type === 'function') {
|
||||
if (isRecord(rawToolChoice.function) && asTrimmedString(rawToolChoice.function.name)) {
|
||||
return rawToolChoice;
|
||||
}
|
||||
|
||||
const name = asTrimmedString(rawToolChoice.name);
|
||||
if (!name) return 'required';
|
||||
return {
|
||||
type: 'function',
|
||||
function: { name },
|
||||
};
|
||||
}
|
||||
|
||||
if (type === 'auto' || type === 'none' || type === 'required') {
|
||||
return type;
|
||||
}
|
||||
|
||||
return rawToolChoice;
|
||||
}
|
||||
|
||||
export function convertResponsesBodyToOpenAiBody(
|
||||
body: Record<string, unknown>,
|
||||
modelName: string,
|
||||
stream: boolean,
|
||||
): Record<string, unknown> {
|
||||
const messages: Array<Record<string, unknown>> = [];
|
||||
const input = body.input;
|
||||
let functionCallIndex = 0;
|
||||
let pendingToolCalls: OpenAiToolCall[] = [];
|
||||
|
||||
const flushPendingToolCalls = () => {
|
||||
if (pendingToolCalls.length <= 0) return;
|
||||
messages.push({
|
||||
role: 'assistant',
|
||||
content: '',
|
||||
tool_calls: pendingToolCalls,
|
||||
});
|
||||
pendingToolCalls = [];
|
||||
};
|
||||
|
||||
const pushToolOutputMessage = (callIdRaw: unknown, outputRaw: unknown) => {
|
||||
const toolCallId = asTrimmedString(callIdRaw);
|
||||
if (!toolCallId) return;
|
||||
messages.push({
|
||||
role: 'tool',
|
||||
tool_call_id: toolCallId,
|
||||
content: normalizeToolOutput(outputRaw),
|
||||
});
|
||||
};
|
||||
|
||||
const processInputItem = (item: unknown) => {
|
||||
if (typeof item === 'string') {
|
||||
flushPendingToolCalls();
|
||||
const text = item.trim();
|
||||
if (text) messages.push({ role: 'user', content: text });
|
||||
return;
|
||||
}
|
||||
|
||||
if (!isRecord(item)) return;
|
||||
|
||||
const itemType = asTrimmedString(item.type).toLowerCase();
|
||||
if (itemType === 'function_call' || itemType === 'custom_tool_call') {
|
||||
const toolCall = toOpenAiToolCall(item, functionCallIndex);
|
||||
functionCallIndex += 1;
|
||||
if (toolCall) pendingToolCalls.push(toolCall);
|
||||
return;
|
||||
}
|
||||
|
||||
if (itemType === 'function_call_output' || itemType === 'custom_tool_call_output') {
|
||||
flushPendingToolCalls();
|
||||
pushToolOutputMessage(item.call_id ?? item.id, item.output ?? item.content);
|
||||
return;
|
||||
}
|
||||
|
||||
if (itemType === 'reasoning') {
|
||||
flushPendingToolCalls();
|
||||
const reasoningContent = toOpenAiMessageContent(item.summary ?? item.content ?? item);
|
||||
const hasReasoningContent = typeof reasoningContent === 'string'
|
||||
? reasoningContent.trim().length > 0
|
||||
: Array.isArray(reasoningContent) && reasoningContent.length > 0;
|
||||
if (!hasReasoningContent) return;
|
||||
|
||||
messages.push({
|
||||
role: 'assistant',
|
||||
content: reasoningContent,
|
||||
});
|
||||
return;
|
||||
}
|
||||
|
||||
flushPendingToolCalls();
|
||||
const role = asTrimmedString(item.role).toLowerCase() || 'user';
|
||||
const normalizedRole = role === 'developer' ? 'system' : role;
|
||||
const content = toOpenAiMessageContent(item.content ?? item.input ?? item);
|
||||
|
||||
if (normalizedRole === 'tool') {
|
||||
pushToolOutputMessage(item.tool_call_id ?? item.call_id ?? item.id, item.content);
|
||||
return;
|
||||
}
|
||||
|
||||
const hasContent = typeof content === 'string'
|
||||
? content.trim().length > 0
|
||||
: Array.isArray(content) && content.length > 0;
|
||||
if (!hasContent) return;
|
||||
|
||||
messages.push({
|
||||
role: normalizedRole,
|
||||
content,
|
||||
});
|
||||
};
|
||||
|
||||
if (typeof input === 'string') {
|
||||
const text = input.trim();
|
||||
if (text) messages.push({ role: 'user', content: text });
|
||||
} else if (Array.isArray(input)) {
|
||||
for (const item of input) processInputItem(item);
|
||||
} else if (isRecord(input)) {
|
||||
processInputItem(input);
|
||||
}
|
||||
flushPendingToolCalls();
|
||||
|
||||
const instructions = asTrimmedString(body.instructions);
|
||||
if (instructions) {
|
||||
messages.unshift({ role: 'system', content: instructions });
|
||||
}
|
||||
|
||||
const payload: Record<string, unknown> = {
|
||||
model: modelName,
|
||||
stream,
|
||||
messages,
|
||||
};
|
||||
|
||||
if (typeof body.temperature === 'number' && Number.isFinite(body.temperature)) {
|
||||
payload.temperature = body.temperature;
|
||||
}
|
||||
if (typeof body.top_p === 'number' && Number.isFinite(body.top_p)) {
|
||||
payload.top_p = body.top_p;
|
||||
}
|
||||
if (typeof body.max_output_tokens === 'number' && Number.isFinite(body.max_output_tokens)) {
|
||||
payload.max_tokens = body.max_output_tokens;
|
||||
}
|
||||
if (body.parallel_tool_calls !== undefined) payload.parallel_tool_calls = body.parallel_tool_calls;
|
||||
if (body.tools !== undefined) payload.tools = convertResponsesToolsToOpenAi(body.tools);
|
||||
if (body.tool_choice !== undefined) payload.tool_choice = convertResponsesToolChoiceToOpenAi(body.tool_choice);
|
||||
if (body.safety_identifier !== undefined) payload.safety_identifier = body.safety_identifier;
|
||||
if (body.max_tool_calls !== undefined) payload.max_tool_calls = body.max_tool_calls;
|
||||
if (body.prompt_cache_key !== undefined) payload.prompt_cache_key = body.prompt_cache_key;
|
||||
if (body.prompt_cache_retention !== undefined) payload.prompt_cache_retention = body.prompt_cache_retention;
|
||||
if (body.background !== undefined) payload.background = body.background;
|
||||
if (body.user !== undefined) payload.user = body.user;
|
||||
if (body.include !== undefined) payload.include = cloneJsonValue(body.include);
|
||||
if (body.previous_response_id !== undefined) payload.previous_response_id = body.previous_response_id;
|
||||
if (body.truncation !== undefined) payload.truncation = body.truncation;
|
||||
if (body.service_tier !== undefined) payload.service_tier = body.service_tier;
|
||||
if (body.top_logprobs !== undefined) payload.top_logprobs = body.top_logprobs;
|
||||
if (body.stream_options !== undefined) payload.stream_options = body.stream_options;
|
||||
if (isRecord(body.text) && asTrimmedString(body.text.verbosity)) {
|
||||
payload.verbosity = asTrimmedString(body.text.verbosity);
|
||||
}
|
||||
|
||||
return payload;
|
||||
}
|
||||
@@ -0,0 +1,6 @@
|
||||
import { normalizeResponsesInputForCompatibility, normalizeResponsesMessageItem } from './compatibility.js';
|
||||
|
||||
export const openAiResponsesInbound = {
|
||||
normalizeInput: normalizeResponsesInputForCompatibility,
|
||||
normalizeMessage: normalizeResponsesMessageItem,
|
||||
};
|
||||
@@ -0,0 +1,66 @@
|
||||
import { type StreamTransformContext } from '../../shared/normalized.js';
|
||||
import {
|
||||
convertOpenAiBodyToResponsesBody,
|
||||
convertResponsesBodyToOpenAiBody,
|
||||
normalizeResponsesInputForCompatibility,
|
||||
normalizeResponsesMessageContent,
|
||||
sanitizeResponsesBodyForProxy,
|
||||
} from './conversion.js';
|
||||
import { normalizeResponsesMessageItem } from './compatibility.js';
|
||||
import {
|
||||
type OpenAiResponsesAggregateState,
|
||||
completeResponsesStream,
|
||||
createOpenAiResponsesAggregateState,
|
||||
failResponsesStream,
|
||||
serializeConvertedResponsesEvents,
|
||||
} from './aggregator.js';
|
||||
import { openAiResponsesOutbound } from './outbound.js';
|
||||
import { openAiResponsesStream } from './stream.js';
|
||||
import { openAiResponsesUsage } from './usage.js';
|
||||
|
||||
export const openAiResponsesTransformer = {
|
||||
protocol: 'openai/responses' as const,
|
||||
inbound: {
|
||||
normalizeInput: normalizeResponsesInputForCompatibility,
|
||||
normalizeMessage: normalizeResponsesMessageItem,
|
||||
normalizeContent: normalizeResponsesMessageContent,
|
||||
sanitizeProxyBody: sanitizeResponsesBodyForProxy,
|
||||
fromOpenAiBody: convertOpenAiBodyToResponsesBody,
|
||||
toOpenAiBody: convertResponsesBodyToOpenAiBody,
|
||||
},
|
||||
outbound: openAiResponsesOutbound,
|
||||
stream: openAiResponsesStream,
|
||||
usage: openAiResponsesUsage,
|
||||
aggregator: {
|
||||
createState: createOpenAiResponsesAggregateState,
|
||||
serialize: serializeConvertedResponsesEvents,
|
||||
complete: completeResponsesStream,
|
||||
fail: failResponsesStream,
|
||||
},
|
||||
transformRequest(body: unknown) {
|
||||
return body;
|
||||
},
|
||||
createStreamContext(modelName: string): StreamTransformContext {
|
||||
return openAiResponsesStream.createContext(modelName);
|
||||
},
|
||||
transformFinalResponse(payload: unknown, modelName: string, fallbackText = '') {
|
||||
return openAiResponsesOutbound.normalizeFinal(payload, modelName, fallbackText);
|
||||
},
|
||||
transformStreamEvent(payload: unknown, context: StreamTransformContext, modelName: string) {
|
||||
return openAiResponsesStream.normalizeEvent(payload, context, modelName);
|
||||
},
|
||||
pullSseEvents(buffer: string) {
|
||||
return openAiResponsesStream.pullSseEvents(buffer);
|
||||
},
|
||||
};
|
||||
|
||||
export type OpenAiResponsesTransformer = typeof openAiResponsesTransformer;
|
||||
export type OpenAiResponsesAggregate = OpenAiResponsesAggregateState;
|
||||
export {
|
||||
convertOpenAiBodyToResponsesBody,
|
||||
convertResponsesBodyToOpenAiBody,
|
||||
normalizeResponsesInputForCompatibility,
|
||||
normalizeResponsesMessageContent,
|
||||
normalizeResponsesMessageItem,
|
||||
sanitizeResponsesBodyForProxy,
|
||||
};
|
||||
@@ -0,0 +1,182 @@
|
||||
import {
|
||||
normalizeUpstreamFinalResponse,
|
||||
type NormalizedFinalResponse,
|
||||
} from '../../shared/normalized.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object';
|
||||
}
|
||||
|
||||
function asTrimmedString(value: unknown): string {
|
||||
return typeof value === 'string' ? value.trim() : '';
|
||||
}
|
||||
|
||||
function ensureResponseId(rawId: string): string {
|
||||
const trimmed = rawId.trim() || `resp_${Date.now()}`;
|
||||
return trimmed.startsWith('resp_') ? trimmed : `resp_${trimmed}`;
|
||||
}
|
||||
|
||||
function ensureMessageId(rawId: string): string {
|
||||
const trimmed = rawId.trim() || `msg_${Date.now()}`;
|
||||
return trimmed.startsWith('msg_') ? trimmed : `msg_${trimmed}`;
|
||||
}
|
||||
|
||||
function ensureFunctionCallId(rawId: string): string {
|
||||
const trimmed = rawId.trim();
|
||||
if (!trimmed) return `call_${Date.now()}`;
|
||||
return trimmed.startsWith('call_') ? trimmed : `call_${trimmed}`;
|
||||
}
|
||||
|
||||
function stringifyToolInput(input: unknown): string {
|
||||
if (typeof input === 'string') return input;
|
||||
if (!input || typeof input !== 'object') return '';
|
||||
try {
|
||||
return JSON.stringify(input);
|
||||
} catch {
|
||||
return '';
|
||||
}
|
||||
}
|
||||
|
||||
export type ResponsesUsageSummary = {
|
||||
promptTokens: number;
|
||||
completionTokens: number;
|
||||
totalTokens: number;
|
||||
};
|
||||
|
||||
export type ResponsesToolCall = {
|
||||
id: string;
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
|
||||
function extractToolCallsFromUpstream(payload: unknown): ResponsesToolCall[] {
|
||||
if (!isRecord(payload)) return [];
|
||||
|
||||
if (Array.isArray(payload.choices)) {
|
||||
const choice = payload.choices[0];
|
||||
const message = isRecord((choice as any)?.message) ? (choice as any).message : {};
|
||||
const toolCalls = Array.isArray((message as any).tool_calls) ? (message as any).tool_calls : [];
|
||||
return toolCalls
|
||||
.map((item: unknown, index: number) => {
|
||||
if (!isRecord(item)) return null;
|
||||
const fn = isRecord(item.function) ? item.function : {};
|
||||
const id = typeof item.id === 'string' && item.id.trim().length > 0
|
||||
? item.id
|
||||
: `call_${index}`;
|
||||
const name = typeof fn.name === 'string' ? fn.name : '';
|
||||
const args = typeof fn.arguments === 'string' ? fn.arguments : '';
|
||||
return {
|
||||
id: ensureFunctionCallId(id),
|
||||
name,
|
||||
arguments: args,
|
||||
};
|
||||
})
|
||||
.filter((item): item is ResponsesToolCall => !!item);
|
||||
}
|
||||
|
||||
if (payload.type === 'message' && Array.isArray(payload.content)) {
|
||||
return payload.content
|
||||
.map((item: unknown, index: number) => {
|
||||
if (!isRecord(item) || item.type !== 'tool_use') return null;
|
||||
const id = typeof item.id === 'string' && item.id.trim().length > 0
|
||||
? item.id
|
||||
: `call_${index}`;
|
||||
const name = typeof item.name === 'string' ? item.name : '';
|
||||
const args = stringifyToolInput(item.input);
|
||||
return {
|
||||
id: ensureFunctionCallId(id),
|
||||
name,
|
||||
arguments: args,
|
||||
};
|
||||
})
|
||||
.filter((item): item is ResponsesToolCall => !!item);
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
export function normalizeResponsesFinalPayload(
|
||||
payload: unknown,
|
||||
modelName: string,
|
||||
fallbackText = '',
|
||||
): NormalizedFinalResponse {
|
||||
return normalizeUpstreamFinalResponse(payload, modelName, fallbackText);
|
||||
}
|
||||
|
||||
export function serializeResponsesFinalPayload(input: {
|
||||
upstreamPayload: unknown;
|
||||
normalized: NormalizedFinalResponse;
|
||||
usage: ResponsesUsageSummary;
|
||||
}): Record<string, unknown> {
|
||||
const { upstreamPayload, normalized, usage } = input;
|
||||
if (isRecord(upstreamPayload) && upstreamPayload.object === 'response') {
|
||||
return upstreamPayload;
|
||||
}
|
||||
|
||||
const normalizedId = typeof normalized.id === 'string' && normalized.id.trim()
|
||||
? normalized.id.trim()
|
||||
: `resp_${Date.now()}`;
|
||||
const responseId = ensureResponseId(normalizedId);
|
||||
const messageId = ensureMessageId(normalizedId);
|
||||
const toolCalls = extractToolCallsFromUpstream(upstreamPayload);
|
||||
|
||||
const output: Array<Record<string, unknown>> = [];
|
||||
if (normalized.reasoningContent) {
|
||||
output.push({
|
||||
id: ensureMessageId(`${normalizedId}_reasoning`),
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [{
|
||||
type: 'summary_text',
|
||||
text: normalized.reasoningContent,
|
||||
}],
|
||||
});
|
||||
}
|
||||
if (normalized.content || toolCalls.length === 0) {
|
||||
output.push({
|
||||
id: messageId,
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [{
|
||||
type: 'output_text',
|
||||
text: normalized.content || '',
|
||||
}],
|
||||
});
|
||||
}
|
||||
|
||||
for (const toolCall of toolCalls) {
|
||||
output.push({
|
||||
id: toolCall.id,
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: toolCall.id,
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
id: responseId,
|
||||
object: 'response',
|
||||
created: normalized.created,
|
||||
status: 'completed',
|
||||
model: normalized.model,
|
||||
output,
|
||||
output_text: normalized.content || '',
|
||||
usage: {
|
||||
input_tokens: usage.promptTokens,
|
||||
output_tokens: usage.completionTokens,
|
||||
total_tokens: usage.totalTokens,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
export const openAiResponsesOutbound = {
|
||||
normalizeFinal: normalizeResponsesFinalPayload,
|
||||
serializeFinal: serializeResponsesFinalPayload,
|
||||
};
|
||||
|
||||
export {
|
||||
serializeResponsesFinalPayload as toResponsesPayload,
|
||||
};
|
||||
@@ -0,0 +1,80 @@
|
||||
import {
|
||||
createStreamTransformContext,
|
||||
normalizeUpstreamStreamEvent,
|
||||
pullSseEventsWithDone,
|
||||
type NormalizedStreamEvent,
|
||||
type StreamTransformContext,
|
||||
} from '../../shared/normalized.js';
|
||||
import {
|
||||
completeResponsesStream,
|
||||
createOpenAiResponsesAggregateState,
|
||||
failResponsesStream,
|
||||
serializeConvertedResponsesEvents,
|
||||
type OpenAiResponsesAggregateState,
|
||||
} from './aggregator.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object';
|
||||
}
|
||||
|
||||
export type OpenAiResponsesStreamEvent = NormalizedStreamEvent & {
|
||||
responsesEventType?: string;
|
||||
responsesPayload?: Record<string, unknown>;
|
||||
usagePayload?: Record<string, unknown>;
|
||||
};
|
||||
|
||||
export const openAiResponsesStream = {
|
||||
eventNames: [
|
||||
'response.created',
|
||||
'response.in_progress',
|
||||
'response.output_item.added',
|
||||
'response.output_item.done',
|
||||
'response.content_part.added',
|
||||
'response.content_part.done',
|
||||
'response.output_text.delta',
|
||||
'response.output_text.done',
|
||||
'response.function_call_arguments.delta',
|
||||
'response.function_call_arguments.done',
|
||||
'response.custom_tool_call_input.delta',
|
||||
'response.custom_tool_call_input.done',
|
||||
'response.reasoning_summary_part.added',
|
||||
'response.reasoning_summary_part.done',
|
||||
'response.reasoning_summary_text.delta',
|
||||
'response.reasoning_summary_text.done',
|
||||
'response.image_generation_call.generating',
|
||||
'response.image_generation_call.in_progress',
|
||||
'response.image_generation_call.partial_image',
|
||||
'response.image_generation_call.completed',
|
||||
'response.completed',
|
||||
'response.failed',
|
||||
] as const,
|
||||
createContext(modelName: string): StreamTransformContext {
|
||||
return createStreamTransformContext(modelName);
|
||||
},
|
||||
normalizeEvent(payload: unknown, context: StreamTransformContext, modelName: string): OpenAiResponsesStreamEvent {
|
||||
const normalized = normalizeUpstreamStreamEvent(payload, context, modelName) as OpenAiResponsesStreamEvent;
|
||||
if (isRecord(payload) && typeof payload.type === 'string' && payload.type.startsWith('response.')) {
|
||||
normalized.responsesEventType = payload.type;
|
||||
normalized.responsesPayload = payload;
|
||||
}
|
||||
if (isRecord(payload) && isRecord(payload.usage)) {
|
||||
normalized.usagePayload = payload.usage;
|
||||
}
|
||||
return normalized;
|
||||
},
|
||||
pullSseEvents(buffer: string) {
|
||||
return pullSseEventsWithDone(buffer);
|
||||
},
|
||||
};
|
||||
|
||||
export type ResponsesStreamState = OpenAiResponsesAggregateState;
|
||||
|
||||
export function createResponsesStreamState(modelName: string): ResponsesStreamState {
|
||||
return createOpenAiResponsesAggregateState(modelName);
|
||||
}
|
||||
|
||||
export {
|
||||
completeResponsesStream,
|
||||
failResponsesStream,
|
||||
serializeConvertedResponsesEvents,
|
||||
};
|
||||
@@ -0,0 +1,6 @@
|
||||
import { createEmptyNormalizedUsage, mergeNormalizedUsage } from '../../shared/normalized.js';
|
||||
|
||||
export const openAiResponsesUsage = {
|
||||
empty: createEmptyNormalizedUsage,
|
||||
merge: mergeNormalizedUsage,
|
||||
};
|
||||
Reference in New Issue
Block a user