Preserve the active worktree snapshot on dev for follow-on integration
The root checkout already contained a large in-progress proxy, transformer, and settings refactor. This commit captures that exact work on a dedicated local dev branch without disturbing the original dirty worktree, so the changes can be continued or split from dev instead of living only as uncommitted state. Constraint: The source checkout was already dirty on another branch, so the move had to preserve that worktree while excluding local .idea and .omx residue from the commit Rejected: Switch the dirty root checkout directly to dev | risked disrupting the original branch and in-progress state Confidence: medium Scope-risk: broad Reversibility: clean Directive: Treat this as a transfer snapshot; fix the failing oauth/accountTokens/normalized/docs test regressions before merge or publication Tested: git diff --check; npm run typecheck; npm run repo:drift-check Not-tested: Clean test pass; npm test currently fails in oauth routes, accountTokens sync, shared normalized helpers, gemini route expectations, architecture-boundaries, and docs vitepress config
This commit is contained in:
cita-777
@@ -71,6 +71,45 @@ describe('openAiChatTransformer.inbound', () => {
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});
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});
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it('round-trips continuation turnState through chat metadata bridging', () => {
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const built = openAiChatTransformer.buildProtocolRequest({
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operation: 'generate',
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surface: 'openai-chat',
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cliProfile: 'codex',
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requestedModel: 'gpt-5',
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stream: false,
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messages: [{ role: 'user', parts: [{ type: 'text', text: 'hello' }] }],
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continuation: {
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sessionId: 'chat-session-1',
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turnState: 'turn-state-chat-1',
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},
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});
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expect(built).toMatchObject({
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metadata: {
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user_id: 'chat-session-1',
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metapi_turn_state: 'turn-state-chat-1',
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},
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});
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const parsed = openAiChatTransformer.parseRequest({
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model: 'gpt-5',
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messages: [{ role: 'user', content: 'hello' }],
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metadata: {
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user_id: 'chat-session-1',
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metapi_turn_state: 'turn-state-chat-1',
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},
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});
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expect(parsed.error).toBeUndefined();
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expect(parsed.value).toMatchObject({
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continuation: {
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sessionId: 'chat-session-1',
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turnState: 'turn-state-chat-1',
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},
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});
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});
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it('captures chat request metadata fields without changing upstream body', () => {
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const result = openAiChatTransformer.transformRequest({
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model: 'gpt-5',
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@@ -277,6 +316,65 @@ describe('openAiChatTransformer.outbound', () => {
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expect((normalized as any).citations).toEqual(['https://shared.example/citation']);
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});
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it('maps final Responses payload terminal statuses to sub2api-like chat finish reasons', () => {
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const incompleteStop = openAiChatTransformer.transformFinalResponse({
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id: 'resp_final_stop',
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object: 'response',
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model: 'gpt-5',
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status: 'incomplete',
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output: [],
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}, 'gpt-5');
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const incompleteLength = openAiChatTransformer.transformFinalResponse({
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id: 'resp_final_length',
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object: 'response',
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model: 'gpt-5',
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status: 'incomplete',
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incomplete_details: {
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reason: 'max_output_tokens',
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},
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output: [],
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}, 'gpt-5');
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const failedStop = openAiChatTransformer.transformFinalResponse({
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id: 'resp_final_failed',
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object: 'response',
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model: 'gpt-5',
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status: 'failed',
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output: [],
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}, 'gpt-5');
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expect(openAiChatTransformer.serializeFinalResponse(incompleteStop, {
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promptTokens: 1,
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completionTokens: 1,
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totalTokens: 2,
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})).toMatchObject({
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choices: [{
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finish_reason: 'stop',
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}],
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});
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expect(openAiChatTransformer.serializeFinalResponse(incompleteLength, {
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promptTokens: 1,
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completionTokens: 1,
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totalTokens: 2,
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})).toMatchObject({
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choices: [{
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finish_reason: 'length',
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}],
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});
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expect(openAiChatTransformer.serializeFinalResponse(failedStop, {
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promptTokens: 1,
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completionTokens: 1,
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totalTokens: 2,
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})).toMatchObject({
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choices: [{
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finish_reason: 'stop',
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}],
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});
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});
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it('serializes multi-choice chat responses with per-choice annotations, reasoning, tool calls, and shared citations', () => {
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const normalized = openAiChatTransformer.transformFinalResponse({
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id: 'chatcmpl-multi',
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@@ -481,6 +579,67 @@ describe('openAiChatTransformer.stream', () => {
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});
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});
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it('serializes response.incomplete terminal events with sub2api-like finish reasons', () => {
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const context = openAiChatTransformer.createStreamContext('gpt-5');
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const stopEvent = openAiChatTransformer.transformStreamEvent({
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type: 'response.incomplete',
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response: {
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id: 'resp-incomplete-stop',
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model: 'gpt-5',
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status: 'incomplete',
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},
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}, context, 'gpt-5');
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const stopPayloads = parseSsePayloads(
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openAiChatTransformer.serializeStreamEvent(stopEvent, context, createClaudeDownstreamContext()),
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);
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expect((stopPayloads[0] as any).choices[0]).toMatchObject({
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finish_reason: 'stop',
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});
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const lengthEvent = openAiChatTransformer.transformStreamEvent({
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type: 'response.incomplete',
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response: {
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id: 'resp-incomplete-length',
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model: 'gpt-5',
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status: 'incomplete',
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incomplete_details: {
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reason: 'max_output_tokens',
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},
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},
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}, context, 'gpt-5');
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const lengthPayloads = parseSsePayloads(
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openAiChatTransformer.serializeStreamEvent(lengthEvent, context, createClaudeDownstreamContext()),
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);
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expect((lengthPayloads[0] as any).choices[0]).toMatchObject({
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finish_reason: 'length',
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});
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});
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it('serializes response.failed terminal events with stop finish_reason instead of error', () => {
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const context = openAiChatTransformer.createStreamContext('gpt-5');
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const event = openAiChatTransformer.transformStreamEvent({
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type: 'response.failed',
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response: {
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id: 'resp-failed-stop',
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model: 'gpt-5',
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status: 'failed',
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},
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}, context, 'gpt-5');
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const payloads = parseSsePayloads(
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openAiChatTransformer.serializeStreamEvent(event, context, createClaudeDownstreamContext()),
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);
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expect((payloads[0] as any).choices[0]).toMatchObject({
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finish_reason: 'stop',
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});
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});
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it('serializes multi-choice stream chunks without collapsing choice-specific deltas', () => {
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const context = openAiChatTransformer.createStreamContext('gpt-5');
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const event = openAiChatTransformer.transformStreamEvent({
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@@ -1,12 +1,20 @@
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import { canonicalRequestFromOpenAiBody, canonicalRequestToOpenAiChatBody } from '../../canonical/request.js';
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import type { CanonicalRequestEnvelope } from '../../canonical/types.js';
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import type { ProtocolBuildContext, ProtocolParseContext } from '../../contracts.js';
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import { type NormalizedFinalResponse, type NormalizedStreamEvent, type StreamTransformContext } from '../../shared/normalized.js';
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import { createChatEndpointStrategy } from '../../shared/chatEndpointStrategy.js';
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import { openAiChatInbound } from './inbound.js';
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import { openAiChatOutbound } from './outbound.js';
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import { createChatProxyStreamSession } from './proxyStream.js';
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import { openAiChatStream } from './stream.js';
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import {
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buildCanonicalRequestToOpenAiChatBody,
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parseOpenAiChatRequestToCanonical,
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} from './requestBridge.js';
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import {
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openAiChatResponseBridge,
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buildNormalizedFinalToOpenAiChatChunks,
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buildNormalizedFinalToOpenAiChatPayload,
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normalizeOpenAiChatFinalToNormalized,
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} from './responseBridge.js';
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import { openAiChatStream } from './streamBridge.js';
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import { openAiChatUsage } from './usage.js';
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import { createOpenAiChatAggregateState, applyOpenAiChatStreamEvent, finalizeOpenAiChatAggregate } from './aggregator.js';
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import type {
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@@ -17,7 +25,7 @@ import type {
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export const openAiChatTransformer = {
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protocol: 'openai/chat' as const,
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inbound: openAiChatInbound,
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outbound: openAiChatOutbound,
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outbound: openAiChatResponseBridge,
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stream: openAiChatStream,
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usage: openAiChatUsage,
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compatibility: {
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@@ -35,41 +43,13 @@ export const openAiChatTransformer = {
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body: unknown,
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ctx?: ProtocolParseContext,
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): { value?: CanonicalRequestEnvelope; error?: { statusCode: number; payload: unknown } } {
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const parsed = openAiChatInbound.parse(body);
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if (parsed.error) {
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return { error: parsed.error };
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}
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if (!parsed.value) {
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return {
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error: {
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statusCode: 400,
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payload: {
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error: {
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message: 'invalid chat request',
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type: 'invalid_request_error',
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},
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},
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},
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};
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}
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return {
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value: canonicalRequestFromOpenAiBody({
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body: parsed.value.parsed.upstreamBody,
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surface: 'openai-chat',
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cliProfile: ctx?.cliProfile,
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operation: ctx?.operation,
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metadata: ctx?.metadata,
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passthrough: ctx?.passthrough,
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continuation: ctx?.continuation,
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}),
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};
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return parseOpenAiChatRequestToCanonical(body, ctx);
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},
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buildProtocolRequest(
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request: CanonicalRequestEnvelope,
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_ctx?: ProtocolBuildContext,
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): Record<string, unknown> {
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return canonicalRequestToOpenAiChatBody(request);
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return buildCanonicalRequestToOpenAiChatBody(request);
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},
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transformRequest(body: unknown): ReturnType<typeof openAiChatInbound.parse> {
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return openAiChatInbound.parse(body);
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@@ -78,7 +58,7 @@ export const openAiChatTransformer = {
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return openAiChatStream.createContext(modelName);
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},
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transformFinalResponse(payload: unknown, modelName: string, fallbackText = ''): NormalizedFinalResponse {
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return openAiChatOutbound.normalizeFinal(payload, modelName, fallbackText);
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return normalizeOpenAiChatFinalToNormalized(payload, modelName, fallbackText);
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},
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transformStreamEvent(payload: unknown, context: StreamTransformContext, modelName: string): NormalizedStreamEvent {
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return openAiChatStream.normalizeEvent(payload, context, modelName);
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@@ -98,9 +78,9 @@ export const openAiChatTransformer = {
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},
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serializeFinalResponse(
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normalized: NormalizedFinalResponse,
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usage: Parameters<typeof openAiChatOutbound.serializeFinal>[1],
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usage: Parameters<typeof buildNormalizedFinalToOpenAiChatPayload>[1],
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) {
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return openAiChatOutbound.serializeFinal(normalized, usage);
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return buildNormalizedFinalToOpenAiChatPayload(normalized, usage);
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},
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serializeUpstreamFinalAsStream(
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payload: unknown,
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@@ -108,16 +88,15 @@ export const openAiChatTransformer = {
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fallbackText: string,
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streamContext: StreamTransformContext,
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) {
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const normalizedFinal = openAiChatOutbound.normalizeFinal(payload, modelName, fallbackText);
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const normalizedFinal = normalizeOpenAiChatFinalToNormalized(payload, modelName, fallbackText);
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streamContext.id = normalizedFinal.id;
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streamContext.model = normalizedFinal.model;
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streamContext.created = normalizedFinal.created;
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return openAiChatOutbound
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.buildSyntheticChunks(normalizedFinal)
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return buildNormalizedFinalToOpenAiChatChunks(normalizedFinal)
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.map((chunk) => `data: ${JSON.stringify(chunk)}\n\n`);
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},
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buildSyntheticChunks(normalized: NormalizedFinalResponse) {
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return openAiChatOutbound.buildSyntheticChunks(normalized);
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return buildNormalizedFinalToOpenAiChatChunks(normalized);
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},
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pullSseEvents(buffer: string) {
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return openAiChatStream.pullSseEvents(buffer);
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@@ -1,189 +1,7 @@
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import {
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buildSyntheticOpenAiChunks,
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normalizeUpstreamFinalResponse,
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serializeFinalResponse,
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type NormalizedFinalResponse,
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} from '../../shared/normalized.js';
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import { extractChatChoices, extractChatResponseExtras } from './helpers.js';
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import type { OpenAiChatNormalizedFinalResponse } 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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|
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function mergeUsagePayload(
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usage: unknown,
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normalized: OpenAiChatNormalizedFinalResponse,
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): Record<string, unknown> | undefined {
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const merged = isRecord(usage) ? { ...usage } : {};
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const usagePayload = normalized.usagePayload && isRecord(normalized.usagePayload)
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? normalized.usagePayload
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: null;
|
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|
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if (usagePayload) {
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for (const [key, value] of Object.entries(usagePayload)) {
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if (key === 'prompt_tokens' || key === 'completion_tokens' || key === 'total_tokens') continue;
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if (merged[key] === undefined) merged[key] = value;
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}
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}
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|
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if (normalized.usageDetails?.prompt_tokens_details) {
|
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merged.prompt_tokens_details = normalized.usageDetails.prompt_tokens_details;
|
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}
|
||||
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 {
|
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const choices = extractChatChoices(payload);
|
||||
const primaryChoice = choices[0];
|
||||
return {
|
||||
...normalizeUpstreamFinalResponse(payload, modelName, fallbackText),
|
||||
...(choices.length > 0 ? { choices } : {}),
|
||||
...(primaryChoice?.annotations?.length ? { annotations: primaryChoice.annotations } : {}),
|
||||
...(primaryChoice?.citations?.length ? { citations: primaryChoice.citations } : {}),
|
||||
...extractChatResponseExtras(payload),
|
||||
};
|
||||
},
|
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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>;
|
||||
|
||||
const choices = Array.isArray(chatNormalized.choices) && chatNormalized.choices.length > 0
|
||||
? chatNormalized.choices
|
||||
: [{
|
||||
index: 0,
|
||||
content: normalized.content,
|
||||
reasoningContent: normalized.reasoningContent,
|
||||
toolCalls: normalized.toolCalls,
|
||||
finishReason: normalized.toolCalls.length > 0 ? 'tool_calls' : normalized.finishReason,
|
||||
annotations: chatNormalized.annotations,
|
||||
citations: chatNormalized.citations,
|
||||
}];
|
||||
|
||||
payload.choices = choices.map((choice) => {
|
||||
const message: Record<string, unknown> = {
|
||||
role: choice.role || 'assistant',
|
||||
content: choice.content,
|
||||
};
|
||||
if (choice.reasoningContent) {
|
||||
message.reasoning_content = choice.reasoningContent;
|
||||
}
|
||||
if (Array.isArray(choice.toolCalls) && choice.toolCalls.length > 0) {
|
||||
message.tool_calls = choice.toolCalls.map((toolCall) => ({
|
||||
id: toolCall.id,
|
||||
type: 'function',
|
||||
function: {
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
},
|
||||
}));
|
||||
if (!choice.content) {
|
||||
message.content = '';
|
||||
}
|
||||
}
|
||||
if (Array.isArray(choice.annotations) && choice.annotations.length > 0) {
|
||||
message.annotations = choice.annotations;
|
||||
}
|
||||
|
||||
return {
|
||||
index: choice.index,
|
||||
message,
|
||||
finish_reason: choice.finishReason || (choice.toolCalls?.length ? 'tool_calls' : 'stop'),
|
||||
};
|
||||
}).sort((left, right) => left.index - right.index);
|
||||
|
||||
if (Array.isArray(chatNormalized.citations) && chatNormalized.citations.length > 0) {
|
||||
payload.citations = chatNormalized.citations;
|
||||
}
|
||||
|
||||
const mergedUsage = mergeUsagePayload(payload.usage, chatNormalized);
|
||||
if (mergedUsage) {
|
||||
payload.usage = {
|
||||
...(isRecord(payload.usage) ? payload.usage : {}),
|
||||
...mergedUsage,
|
||||
};
|
||||
}
|
||||
|
||||
return payload;
|
||||
},
|
||||
buildSyntheticChunks(normalized: NormalizedFinalResponse) {
|
||||
const chatNormalized = normalized as OpenAiChatNormalizedFinalResponse;
|
||||
const choices = Array.isArray(chatNormalized.choices) && chatNormalized.choices.length > 0
|
||||
? chatNormalized.choices
|
||||
: undefined;
|
||||
const chunks = choices && choices.length > 1
|
||||
? [
|
||||
{
|
||||
id: normalized.id,
|
||||
object: 'chat.completion.chunk',
|
||||
created: normalized.created,
|
||||
model: normalized.model,
|
||||
choices: choices.map((choice) => {
|
||||
const delta: Record<string, unknown> = {
|
||||
role: choice.role || 'assistant',
|
||||
content: choice.content || '',
|
||||
};
|
||||
if (choice.reasoningContent) delta.reasoning_content = choice.reasoningContent;
|
||||
if (choice.toolCalls.length > 0) {
|
||||
delta.tool_calls = choice.toolCalls.map((toolCall, toolIndex) => ({
|
||||
index: toolIndex,
|
||||
id: toolCall.id,
|
||||
type: 'function',
|
||||
function: {
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
},
|
||||
}));
|
||||
}
|
||||
if (Array.isArray(choice.annotations) && choice.annotations.length > 0) {
|
||||
delta.annotations = choice.annotations;
|
||||
}
|
||||
return {
|
||||
index: choice.index,
|
||||
delta,
|
||||
finish_reason: null,
|
||||
};
|
||||
}).sort((left, right) => left.index - right.index),
|
||||
},
|
||||
{
|
||||
id: normalized.id,
|
||||
object: 'chat.completion.chunk',
|
||||
created: normalized.created,
|
||||
model: normalized.model,
|
||||
choices: choices.map((choice) => ({
|
||||
index: choice.index,
|
||||
delta: {},
|
||||
finish_reason: choice.finishReason || (choice.toolCalls.length > 0 ? 'tool_calls' : 'stop'),
|
||||
})).sort((left, right) => left.index - right.index),
|
||||
},
|
||||
]
|
||||
: buildSyntheticOpenAiChunks(normalized);
|
||||
if (chunks.length <= 0) return chunks;
|
||||
|
||||
if (Array.isArray(chatNormalized.citations) && chatNormalized.citations.length > 0) {
|
||||
chunks[0] = {
|
||||
...chunks[0],
|
||||
citations: chatNormalized.citations,
|
||||
};
|
||||
}
|
||||
|
||||
if (!choices && 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;
|
||||
},
|
||||
};
|
||||
export {
|
||||
openAiChatOutbound,
|
||||
openAiChatResponseBridge,
|
||||
buildNormalizedFinalToOpenAiChatChunks,
|
||||
buildNormalizedFinalToOpenAiChatPayload,
|
||||
normalizeOpenAiChatFinalToNormalized,
|
||||
} from './responseBridge.js';
|
||||
|
||||
@@ -2,8 +2,11 @@ import { anthropicMessagesTransformer } from '../../anthropic/messages/index.js'
|
||||
import { createProxyStreamLifecycle } from '../../shared/protocolLifecycle.js';
|
||||
import { type DownstreamFormat, type ParsedSseEvent } from '../../shared/normalized.js';
|
||||
import { createOpenAiChatAggregateState, applyOpenAiChatStreamEvent, finalizeOpenAiChatAggregate } from './aggregator.js';
|
||||
import { openAiChatOutbound } from './outbound.js';
|
||||
import { openAiChatStream } from './stream.js';
|
||||
import {
|
||||
buildNormalizedFinalToOpenAiChatChunks,
|
||||
normalizeOpenAiChatFinalToNormalized,
|
||||
} from './responseBridge.js';
|
||||
import { openAiChatStream } from './streamBridge.js';
|
||||
import { config } from '../../../config.js';
|
||||
|
||||
type StreamReader = {
|
||||
@@ -50,7 +53,7 @@ export function createChatProxyStreamSession(input: ChatProxyStreamSessionInput)
|
||||
status: 'completed',
|
||||
errorMessage: null,
|
||||
};
|
||||
let terminalNormalizedFinal: ReturnType<typeof openAiChatOutbound.normalizeFinal> | null = null;
|
||||
let terminalNormalizedFinal: ReturnType<typeof normalizeOpenAiChatFinalToNormalized> | null = null;
|
||||
let forwardedDownstreamOutput = false;
|
||||
const pendingWrites: string[] = [];
|
||||
|
||||
@@ -206,7 +209,7 @@ export function createChatProxyStreamSession(input: ChatProxyStreamSessionInput)
|
||||
const needsTerminalFinishChunk = Array.from(chatAggregateState.choices.values())
|
||||
.some((choice) => !choice.finishReason);
|
||||
if (needsTerminalFinishChunk) {
|
||||
const terminalChunk = openAiChatOutbound.buildSyntheticChunks(
|
||||
const terminalChunk = buildNormalizedFinalToOpenAiChatChunks(
|
||||
finalizeOpenAiChatAggregate(chatAggregateState, {
|
||||
id: streamContext.id,
|
||||
model: streamContext.model,
|
||||
@@ -306,14 +309,13 @@ export function createChatProxyStreamSession(input: ChatProxyStreamSessionInput)
|
||||
}
|
||||
}
|
||||
if (input.downstreamFormat === 'openai') {
|
||||
const normalizedFinal = openAiChatOutbound.normalizeFinal(payload, input.modelName, fallbackText);
|
||||
const normalizedFinal = normalizeOpenAiChatFinalToNormalized(payload, input.modelName, fallbackText);
|
||||
terminalNormalizedFinal = normalizedFinal;
|
||||
streamContext.id = normalizedFinal.id;
|
||||
streamContext.model = normalizedFinal.model;
|
||||
streamContext.created = normalizedFinal.created;
|
||||
emitLines(
|
||||
openAiChatOutbound
|
||||
.buildSyntheticChunks(normalizedFinal)
|
||||
buildNormalizedFinalToOpenAiChatChunks(normalizedFinal)
|
||||
.map((chunk) => `data: ${JSON.stringify(chunk)}\n\n`),
|
||||
{ meaningful: true },
|
||||
);
|
||||
|
||||
@@ -0,0 +1,115 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
buildCanonicalRequestToOpenAiChatBody,
|
||||
parseOpenAiChatRequestToCanonical,
|
||||
} from './requestBridge.js';
|
||||
|
||||
describe('openai chat request bridge', () => {
|
||||
it('parses OpenAI Chat bodies into canonical envelopes', () => {
|
||||
const result = parseOpenAiChatRequestToCanonical({
|
||||
model: 'gpt-5',
|
||||
stream: true,
|
||||
messages: [{ role: 'user', content: 'hello' }],
|
||||
prompt_cache_key: 'cache-key',
|
||||
reasoning_effort: 'high',
|
||||
reasoning_budget: 1024,
|
||||
});
|
||||
|
||||
expect(result.error).toBeUndefined();
|
||||
expect(result.value).toMatchObject({
|
||||
operation: 'generate',
|
||||
surface: 'openai-chat',
|
||||
requestedModel: 'gpt-5',
|
||||
continuation: {
|
||||
promptCacheKey: 'cache-key',
|
||||
},
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
budgetTokens: 1024,
|
||||
},
|
||||
});
|
||||
});
|
||||
|
||||
it('builds OpenAI Chat bodies from canonical envelopes', () => {
|
||||
const body = buildCanonicalRequestToOpenAiChatBody({
|
||||
operation: 'generate',
|
||||
surface: 'openai-chat',
|
||||
cliProfile: 'codex',
|
||||
requestedModel: 'gpt-5',
|
||||
stream: true,
|
||||
messages: [{ role: 'user', parts: [{ type: 'text', text: 'hello' }] }],
|
||||
reasoning: {
|
||||
effort: 'medium',
|
||||
budgetTokens: 512,
|
||||
},
|
||||
continuation: {
|
||||
promptCacheKey: 'cache-key',
|
||||
},
|
||||
});
|
||||
|
||||
expect(body).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
stream: true,
|
||||
messages: [{ role: 'user', content: 'hello' }],
|
||||
prompt_cache_key: 'cache-key',
|
||||
reasoning_effort: 'medium',
|
||||
reasoning_budget: 512,
|
||||
});
|
||||
});
|
||||
|
||||
it('preserves assistant reasoning history alongside tool calls when parsing chat requests', () => {
|
||||
const result = parseOpenAiChatRequestToCanonical({
|
||||
model: 'gpt-5',
|
||||
stream: false,
|
||||
messages: [
|
||||
{
|
||||
role: 'assistant',
|
||||
content: '',
|
||||
reasoning_content: 'plan quietly',
|
||||
tool_calls: [{
|
||||
id: 'call_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'Glob',
|
||||
arguments: '{"pattern":"README*"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
{
|
||||
role: 'tool',
|
||||
tool_call_id: 'call_1',
|
||||
content: 'README.md',
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
expect(result.error).toBeUndefined();
|
||||
expect(result.value?.messages).toEqual([
|
||||
{
|
||||
role: 'assistant',
|
||||
parts: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'plan quietly',
|
||||
thought: true,
|
||||
},
|
||||
{
|
||||
type: 'tool_call',
|
||||
id: 'call_1',
|
||||
name: 'Glob',
|
||||
argumentsJson: '{"pattern":"README*"}',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
role: 'tool',
|
||||
parts: [{
|
||||
type: 'tool_result',
|
||||
toolCallId: 'call_1',
|
||||
resultText: 'README.md',
|
||||
}],
|
||||
},
|
||||
]);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,45 @@
|
||||
import { canonicalRequestFromOpenAiBody, canonicalRequestToOpenAiChatBody } from '../../canonical/openAiRequestBridge.js';
|
||||
import type { CanonicalRequestEnvelope } from '../../canonical/types.js';
|
||||
import type { ProtocolParseContext } from '../../contracts.js';
|
||||
import { openAiChatInbound } from './inbound.js';
|
||||
|
||||
export function parseOpenAiChatRequestToCanonical(
|
||||
body: unknown,
|
||||
ctx?: ProtocolParseContext,
|
||||
): { value?: CanonicalRequestEnvelope; error?: { statusCode: number; payload: unknown } } {
|
||||
const parsed = openAiChatInbound.parse(body);
|
||||
if (parsed.error) {
|
||||
return { error: parsed.error };
|
||||
}
|
||||
if (!parsed.value) {
|
||||
return {
|
||||
error: {
|
||||
statusCode: 400,
|
||||
payload: {
|
||||
error: {
|
||||
message: 'invalid chat request',
|
||||
type: 'invalid_request_error',
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
value: canonicalRequestFromOpenAiBody({
|
||||
body: parsed.value.parsed.upstreamBody,
|
||||
surface: 'openai-chat',
|
||||
cliProfile: ctx?.cliProfile,
|
||||
operation: ctx?.operation,
|
||||
metadata: ctx?.metadata,
|
||||
passthrough: ctx?.passthrough,
|
||||
continuation: ctx?.continuation,
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
export function buildCanonicalRequestToOpenAiChatBody(
|
||||
request: CanonicalRequestEnvelope,
|
||||
): Record<string, unknown> {
|
||||
return canonicalRequestToOpenAiChatBody(request);
|
||||
}
|
||||
@@ -0,0 +1,161 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
buildNormalizedFinalToOpenAiChatChunks,
|
||||
buildNormalizedFinalToOpenAiChatPayload,
|
||||
normalizeOpenAiChatFinalToNormalized,
|
||||
openAiChatResponseBridge,
|
||||
} from './responseBridge.js';
|
||||
import { openAiChatOutbound } from './outbound.js';
|
||||
|
||||
describe('openai chat response bridge', () => {
|
||||
it('normalizes final chat payloads into normalized responses', () => {
|
||||
const normalized = normalizeOpenAiChatFinalToNormalized({
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
created: 123,
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: 'stop',
|
||||
message: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
reasoning_content: 'think',
|
||||
},
|
||||
}],
|
||||
}, 'gpt-5');
|
||||
|
||||
expect(normalized).toMatchObject({
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
content: 'hello',
|
||||
reasoningContent: 'think',
|
||||
finishReason: 'stop',
|
||||
});
|
||||
});
|
||||
|
||||
it('builds final chat payloads and synthetic chunks from normalized responses', () => {
|
||||
const normalized = {
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
created: 123,
|
||||
content: 'hello',
|
||||
reasoningContent: 'think',
|
||||
finishReason: 'stop',
|
||||
toolCalls: [],
|
||||
};
|
||||
|
||||
const payload = buildNormalizedFinalToOpenAiChatPayload(normalized as any, {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
});
|
||||
const chunks = buildNormalizedFinalToOpenAiChatChunks(normalized as any);
|
||||
|
||||
expect(payload).toMatchObject({
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
usage: {
|
||||
prompt_tokens: 11,
|
||||
completion_tokens: 7,
|
||||
total_tokens: 18,
|
||||
},
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: 'stop',
|
||||
message: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
reasoning_content: 'think',
|
||||
},
|
||||
}],
|
||||
});
|
||||
expect(chunks[0]).toMatchObject({
|
||||
id: 'chatcmpl-1',
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
},
|
||||
}],
|
||||
});
|
||||
});
|
||||
|
||||
it('preserves tool-call semantics when bridging normalized tool-turn finals back to chat payloads', () => {
|
||||
const normalized = {
|
||||
id: 'chatcmpl-tool-1',
|
||||
model: 'gpt-5',
|
||||
created: 123,
|
||||
content: '',
|
||||
reasoningContent: 'plan first',
|
||||
finishReason: 'tool_calls',
|
||||
toolCalls: [{
|
||||
id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
}],
|
||||
};
|
||||
|
||||
const payload = buildNormalizedFinalToOpenAiChatPayload(normalized as any, {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
});
|
||||
const chunks = buildNormalizedFinalToOpenAiChatChunks(normalized as any);
|
||||
|
||||
expect(payload).toMatchObject({
|
||||
id: 'chatcmpl-tool-1',
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: 'tool_calls',
|
||||
message: {
|
||||
role: 'assistant',
|
||||
content: '',
|
||||
reasoning_content: 'plan first',
|
||||
tool_calls: [{
|
||||
id: 'call_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
}],
|
||||
});
|
||||
expect(chunks[0]).toMatchObject({
|
||||
id: 'chatcmpl-tool-1',
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: '',
|
||||
reasoning_content: 'plan first',
|
||||
tool_calls: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
}],
|
||||
});
|
||||
expect(chunks[1]).toMatchObject({
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: 'tool_calls',
|
||||
}],
|
||||
});
|
||||
});
|
||||
|
||||
it('keeps the outbound facade pointed at the response bridge object', () => {
|
||||
expect(openAiChatOutbound).toBe(openAiChatResponseBridge);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,210 @@
|
||||
import {
|
||||
buildSyntheticOpenAiChunks,
|
||||
normalizeUpstreamFinalResponse,
|
||||
serializeFinalResponse,
|
||||
type NormalizedFinalResponse,
|
||||
} from '../../shared/normalized.js';
|
||||
import { extractChatChoices, 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 function normalizeOpenAiChatFinalToNormalized(
|
||||
payload: unknown,
|
||||
modelName: string,
|
||||
fallbackText = '',
|
||||
): OpenAiChatNormalizedFinalResponse {
|
||||
const choices = extractChatChoices(payload);
|
||||
const primaryChoice = choices[0];
|
||||
return {
|
||||
...normalizeUpstreamFinalResponse(payload, modelName, fallbackText),
|
||||
...(choices.length > 0 ? { choices } : {}),
|
||||
...(primaryChoice?.annotations?.length ? { annotations: primaryChoice.annotations } : {}),
|
||||
...(primaryChoice?.citations?.length ? { citations: primaryChoice.citations } : {}),
|
||||
...extractChatResponseExtras(payload),
|
||||
};
|
||||
}
|
||||
|
||||
export function buildNormalizedFinalToOpenAiChatPayload(
|
||||
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>;
|
||||
|
||||
const choices = Array.isArray(chatNormalized.choices) && chatNormalized.choices.length > 0
|
||||
? chatNormalized.choices
|
||||
: [{
|
||||
index: 0,
|
||||
content: normalized.content,
|
||||
reasoningContent: normalized.reasoningContent,
|
||||
toolCalls: normalized.toolCalls,
|
||||
finishReason: normalized.toolCalls.length > 0 ? 'tool_calls' : normalized.finishReason,
|
||||
annotations: chatNormalized.annotations,
|
||||
citations: chatNormalized.citations,
|
||||
}];
|
||||
|
||||
payload.choices = choices.map((choice) => {
|
||||
const message: Record<string, unknown> = {
|
||||
role: choice.role || 'assistant',
|
||||
content: choice.content,
|
||||
};
|
||||
if (choice.reasoningContent) {
|
||||
message.reasoning_content = choice.reasoningContent;
|
||||
}
|
||||
if (Array.isArray(choice.toolCalls) && choice.toolCalls.length > 0) {
|
||||
message.tool_calls = choice.toolCalls.map((toolCall) => ({
|
||||
id: toolCall.id,
|
||||
type: 'function',
|
||||
function: {
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
},
|
||||
}));
|
||||
if (!choice.content) {
|
||||
message.content = '';
|
||||
}
|
||||
}
|
||||
if (Array.isArray(choice.annotations) && choice.annotations.length > 0) {
|
||||
message.annotations = choice.annotations;
|
||||
}
|
||||
|
||||
return {
|
||||
index: choice.index,
|
||||
message,
|
||||
finish_reason: choice.finishReason || (choice.toolCalls?.length ? 'tool_calls' : 'stop'),
|
||||
};
|
||||
}).sort((left, right) => left.index - right.index);
|
||||
|
||||
if (Array.isArray(chatNormalized.citations) && chatNormalized.citations.length > 0) {
|
||||
payload.citations = chatNormalized.citations;
|
||||
}
|
||||
|
||||
const mergedUsage = mergeUsagePayload(payload.usage, chatNormalized);
|
||||
if (mergedUsage) {
|
||||
payload.usage = {
|
||||
...(isRecord(payload.usage) ? payload.usage : {}),
|
||||
...mergedUsage,
|
||||
};
|
||||
}
|
||||
|
||||
return payload;
|
||||
}
|
||||
|
||||
export function buildNormalizedFinalToOpenAiChatChunks(normalized: NormalizedFinalResponse) {
|
||||
const chatNormalized = normalized as OpenAiChatNormalizedFinalResponse;
|
||||
const choices = Array.isArray(chatNormalized.choices) && chatNormalized.choices.length > 0
|
||||
? chatNormalized.choices
|
||||
: undefined;
|
||||
const chunks = choices && choices.length > 1
|
||||
? [
|
||||
{
|
||||
id: normalized.id,
|
||||
object: 'chat.completion.chunk',
|
||||
created: normalized.created,
|
||||
model: normalized.model,
|
||||
choices: choices.map((choice) => {
|
||||
const delta: Record<string, unknown> = {
|
||||
role: choice.role || 'assistant',
|
||||
content: choice.content || '',
|
||||
};
|
||||
if (choice.reasoningContent) delta.reasoning_content = choice.reasoningContent;
|
||||
if (choice.toolCalls.length > 0) {
|
||||
delta.tool_calls = choice.toolCalls.map((toolCall, toolIndex) => ({
|
||||
index: toolIndex,
|
||||
id: toolCall.id,
|
||||
type: 'function',
|
||||
function: {
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
},
|
||||
}));
|
||||
}
|
||||
if (Array.isArray(choice.annotations) && choice.annotations.length > 0) {
|
||||
delta.annotations = choice.annotations;
|
||||
}
|
||||
return {
|
||||
index: choice.index,
|
||||
delta,
|
||||
finish_reason: null,
|
||||
};
|
||||
}).sort((left, right) => left.index - right.index),
|
||||
},
|
||||
{
|
||||
id: normalized.id,
|
||||
object: 'chat.completion.chunk',
|
||||
created: normalized.created,
|
||||
model: normalized.model,
|
||||
choices: choices.map((choice) => ({
|
||||
index: choice.index,
|
||||
delta: {},
|
||||
finish_reason: choice.finishReason || (choice.toolCalls.length > 0 ? 'tool_calls' : 'stop'),
|
||||
})).sort((left, right) => left.index - right.index),
|
||||
},
|
||||
]
|
||||
: buildSyntheticOpenAiChunks(normalized);
|
||||
if (chunks.length <= 0) return chunks;
|
||||
|
||||
if (Array.isArray(chatNormalized.citations) && chatNormalized.citations.length > 0) {
|
||||
chunks[0] = {
|
||||
...chunks[0],
|
||||
citations: chatNormalized.citations,
|
||||
};
|
||||
}
|
||||
|
||||
if (!choices && 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;
|
||||
}
|
||||
|
||||
export const openAiChatResponseBridge = {
|
||||
normalizeFinal: normalizeOpenAiChatFinalToNormalized,
|
||||
serializeFinal: buildNormalizedFinalToOpenAiChatPayload,
|
||||
buildSyntheticChunks: buildNormalizedFinalToOpenAiChatChunks,
|
||||
};
|
||||
|
||||
export const openAiChatOutbound = openAiChatResponseBridge;
|
||||
|
||||
export {
|
||||
buildNormalizedFinalToOpenAiChatChunks as buildSyntheticOpenAiChatChunksFromNormalized,
|
||||
buildNormalizedFinalToOpenAiChatPayload as serializeOpenAiChatFinalPayload,
|
||||
normalizeOpenAiChatFinalToNormalized as normalizeOpenAiChatFinalPayload,
|
||||
};
|
||||
@@ -1,190 +1 @@
|
||||
import {
|
||||
createClaudeDownstreamContext,
|
||||
createStreamTransformContext,
|
||||
normalizeUpstreamStreamEvent,
|
||||
pullSseEventsWithDone,
|
||||
serializeNormalizedStreamEvent,
|
||||
serializeStreamDone,
|
||||
type ClaudeDownstreamContext,
|
||||
type StreamTransformContext,
|
||||
} from '../../shared/normalized.js';
|
||||
import { extractChatChoiceEvents, 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 {
|
||||
const normalized = normalizeUpstreamStreamEvent(payload, context, modelName);
|
||||
const choiceEvents = extractChatChoiceEvents(payload);
|
||||
const primaryChoice = choiceEvents[0];
|
||||
const normalizedChoiceEvents = choiceEvents.map((choiceEvent, index) => (
|
||||
index === 0
|
||||
? {
|
||||
...choiceEvent,
|
||||
role: normalized.role !== undefined ? normalized.role : choiceEvent.role,
|
||||
contentDelta: normalized.contentDelta,
|
||||
reasoningDelta: normalized.reasoningDelta,
|
||||
toolCallDeltas: normalized.toolCallDeltas !== undefined
|
||||
? normalized.toolCallDeltas
|
||||
: choiceEvent.toolCallDeltas,
|
||||
finishReason: normalized.finishReason !== undefined
|
||||
? normalized.finishReason
|
||||
: choiceEvent.finishReason,
|
||||
}
|
||||
: choiceEvent
|
||||
));
|
||||
return {
|
||||
...normalized,
|
||||
...(primaryChoice
|
||||
? {
|
||||
choiceIndex: primaryChoice.index,
|
||||
annotations: primaryChoice.annotations,
|
||||
citations: primaryChoice.citations,
|
||||
}
|
||||
: {}),
|
||||
...(normalizedChoiceEvents.length > 0 ? { choiceEvents: normalizedChoiceEvents } : {}),
|
||||
...extractChatResponseExtras(payload),
|
||||
};
|
||||
},
|
||||
serializeEvent(
|
||||
event: OpenAiChatNormalizedStreamEvent,
|
||||
context: StreamTransformContext,
|
||||
downstreamContext?: ClaudeDownstreamContext,
|
||||
): string[] {
|
||||
if (
|
||||
Array.isArray(event.choiceEvents)
|
||||
&& event.choiceEvents.length > 0
|
||||
&& (event.choiceEvents.length > 1 || event.choiceEvents[0]?.index !== 0)
|
||||
) {
|
||||
const usagePayload = event.usagePayload
|
||||
? {
|
||||
...(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 }
|
||||
: {}),
|
||||
}
|
||||
: undefined;
|
||||
return [`data: ${JSON.stringify({
|
||||
id: context.id || `chatcmpl-${Date.now()}`,
|
||||
object: 'chat.completion.chunk',
|
||||
created: context.created,
|
||||
model: context.model,
|
||||
choices: event.choiceEvents
|
||||
.map((choiceEvent) => {
|
||||
const delta: Record<string, unknown> = {};
|
||||
if (choiceEvent.role) delta.role = choiceEvent.role;
|
||||
if (choiceEvent.contentDelta !== undefined) delta.content = choiceEvent.contentDelta;
|
||||
if (choiceEvent.reasoningDelta) delta.reasoning_content = choiceEvent.reasoningDelta;
|
||||
if (Array.isArray(choiceEvent.toolCallDeltas) && choiceEvent.toolCallDeltas.length > 0) {
|
||||
delta.tool_calls = choiceEvent.toolCallDeltas.map((toolCall) => ({
|
||||
index: toolCall.index,
|
||||
...(toolCall.id ? { id: toolCall.id } : {}),
|
||||
type: 'function',
|
||||
function: {
|
||||
...(toolCall.name ? { name: toolCall.name } : {}),
|
||||
...(toolCall.argumentsDelta !== undefined ? { arguments: toolCall.argumentsDelta } : {}),
|
||||
},
|
||||
}));
|
||||
}
|
||||
if (Array.isArray(choiceEvent.annotations) && choiceEvent.annotations.length > 0) {
|
||||
delta.annotations = choiceEvent.annotations;
|
||||
}
|
||||
|
||||
return {
|
||||
index: choiceEvent.index,
|
||||
delta,
|
||||
finish_reason: choiceEvent.finishReason ?? null,
|
||||
};
|
||||
})
|
||||
.sort((left, right) => left.index - right.index),
|
||||
...(Array.isArray(event.citations) && event.citations.length > 0 ? { citations: event.citations } : {}),
|
||||
...(usagePayload && Object.keys(usagePayload).length > 0 ? { usage: usagePayload } : {}),
|
||||
})}\n\n`];
|
||||
}
|
||||
|
||||
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);
|
||||
},
|
||||
};
|
||||
export { openAiChatStream } from './streamBridge.js';
|
||||
|
||||
@@ -0,0 +1,196 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import { createClaudeDownstreamContext } from '../../shared/normalized.js';
|
||||
import { openAiChatStream } from './streamBridge.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('openai chat stream bridge', () => {
|
||||
it('normalizes stream payloads with choice metadata', () => {
|
||||
const context = openAiChatStream.createContext('gpt-5');
|
||||
const normalized = openAiChatStream.normalizeEvent({
|
||||
id: 'chatcmpl-stream-1',
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: null,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
reasoning_content: 'think',
|
||||
},
|
||||
}],
|
||||
}, context, 'gpt-5');
|
||||
|
||||
expect(normalized).toMatchObject({
|
||||
choiceIndex: 0,
|
||||
role: 'assistant',
|
||||
contentDelta: 'hello',
|
||||
reasoningDelta: 'think',
|
||||
});
|
||||
});
|
||||
|
||||
it('serializes stream events and done markers as openai chat chunks', () => {
|
||||
const context = openAiChatStream.createContext('gpt-5');
|
||||
const lines = openAiChatStream.serializeEvent({
|
||||
role: 'assistant',
|
||||
contentDelta: 'hello',
|
||||
} as any, context, createClaudeDownstreamContext());
|
||||
const doneLines = openAiChatStream.serializeDone(context, createClaudeDownstreamContext());
|
||||
|
||||
const payloads = parseSsePayloads(lines);
|
||||
expect(payloads[0]).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: 'hello',
|
||||
},
|
||||
}],
|
||||
});
|
||||
expect(doneLines.join('')).toContain('[DONE]');
|
||||
});
|
||||
|
||||
it('round-trips tool-call deltas without flattening them into plain text', () => {
|
||||
const context = openAiChatStream.createContext('gpt-5');
|
||||
const normalized = openAiChatStream.normalizeEvent({
|
||||
id: 'chatcmpl-stream-tool-1',
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
finish_reason: null,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
tool_calls: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
}],
|
||||
}, context, 'gpt-5');
|
||||
|
||||
expect(normalized).toMatchObject({
|
||||
choiceIndex: 0,
|
||||
choiceEvents: [{
|
||||
index: 0,
|
||||
role: 'assistant',
|
||||
toolCallDeltas: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
argumentsDelta: '{"city":"Shanghai"}',
|
||||
}],
|
||||
}],
|
||||
});
|
||||
|
||||
const payloads = parseSsePayloads(
|
||||
openAiChatStream.serializeEvent(normalized, context, createClaudeDownstreamContext()),
|
||||
);
|
||||
|
||||
expect(payloads[0]).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
choices: [{
|
||||
index: 0,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
tool_calls: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
finish_reason: null,
|
||||
}],
|
||||
});
|
||||
});
|
||||
|
||||
it('does not replay historical tool identity on later multi-choice argument deltas', () => {
|
||||
const context = openAiChatStream.createContext('gpt-5');
|
||||
|
||||
const started = openAiChatStream.normalizeEvent({
|
||||
id: 'chatcmpl-stream-multi-tool-1',
|
||||
model: 'gpt-5',
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
finish_reason: null,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: 'choice-0',
|
||||
},
|
||||
},
|
||||
{
|
||||
index: 1,
|
||||
finish_reason: null,
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
tool_calls: [{
|
||||
index: 0,
|
||||
id: 'call_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'lookup_weather',
|
||||
},
|
||||
}],
|
||||
},
|
||||
},
|
||||
],
|
||||
}, context, 'gpt-5');
|
||||
|
||||
openAiChatStream.serializeEvent(started, context, createClaudeDownstreamContext());
|
||||
|
||||
const continued = openAiChatStream.normalizeEvent({
|
||||
id: 'chatcmpl-stream-multi-tool-1',
|
||||
model: 'gpt-5',
|
||||
choices: [
|
||||
{
|
||||
index: 1,
|
||||
finish_reason: null,
|
||||
delta: {
|
||||
tool_calls: [{
|
||||
index: 0,
|
||||
function: {
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
},
|
||||
],
|
||||
}, context, 'gpt-5');
|
||||
|
||||
const payloads = parseSsePayloads(
|
||||
openAiChatStream.serializeEvent(continued, context, createClaudeDownstreamContext()),
|
||||
);
|
||||
const toolCall = ((((payloads[0] as any).choices[0] as any).delta.tool_calls[0]) as Record<string, unknown>);
|
||||
|
||||
expect((payloads[0] as any).choices[0]).toMatchObject({
|
||||
index: 1,
|
||||
delta: {
|
||||
tool_calls: [{
|
||||
index: 0,
|
||||
function: {
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
}],
|
||||
},
|
||||
finish_reason: null,
|
||||
});
|
||||
expect(toolCall.id).toBeUndefined();
|
||||
expect(toolCall.type).toBeUndefined();
|
||||
expect((toolCall.function as Record<string, unknown>).name).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,218 @@
|
||||
import {
|
||||
createClaudeDownstreamContext,
|
||||
createStreamTransformContext,
|
||||
normalizeUpstreamStreamEvent,
|
||||
pullSseEventsWithDone,
|
||||
serializeNormalizedStreamEvent,
|
||||
serializeStreamDone,
|
||||
type ClaudeDownstreamContext,
|
||||
type StreamTransformContext,
|
||||
} from '../../shared/normalized.js';
|
||||
import { extractChatChoiceEvents, extractChatResponseExtras } from './helpers.js';
|
||||
import type { OpenAiChatNormalizedStreamEvent } from './model.js';
|
||||
|
||||
const multiChoiceToolCallState = new WeakMap<
|
||||
StreamTransformContext,
|
||||
Record<string, { id?: string; name?: string; arguments?: string }>
|
||||
>();
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object' && !Array.isArray(value);
|
||||
}
|
||||
|
||||
function getMultiChoiceToolState(
|
||||
context: StreamTransformContext,
|
||||
): Record<string, { id?: string; name?: string; arguments?: string }> {
|
||||
const existing = multiChoiceToolCallState.get(context);
|
||||
if (existing) return existing;
|
||||
const created: Record<string, { id?: string; name?: string; arguments?: string }> = {};
|
||||
multiChoiceToolCallState.set(context, created);
|
||||
return created;
|
||||
}
|
||||
|
||||
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 {
|
||||
const normalized = normalizeUpstreamStreamEvent(payload, context, modelName);
|
||||
const choiceEvents = extractChatChoiceEvents(payload);
|
||||
const primaryChoice = choiceEvents[0];
|
||||
const normalizedChoiceEvents = choiceEvents.map((choiceEvent, index) => (
|
||||
index === 0
|
||||
? {
|
||||
...choiceEvent,
|
||||
role: normalized.role !== undefined ? normalized.role : choiceEvent.role,
|
||||
contentDelta: normalized.contentDelta,
|
||||
reasoningDelta: normalized.reasoningDelta,
|
||||
toolCallDeltas: normalized.toolCallDeltas !== undefined
|
||||
? normalized.toolCallDeltas
|
||||
: choiceEvent.toolCallDeltas,
|
||||
finishReason: normalized.finishReason !== undefined
|
||||
? normalized.finishReason
|
||||
: choiceEvent.finishReason,
|
||||
}
|
||||
: choiceEvent
|
||||
));
|
||||
return {
|
||||
...normalized,
|
||||
...(primaryChoice
|
||||
? {
|
||||
choiceIndex: primaryChoice.index,
|
||||
annotations: primaryChoice.annotations,
|
||||
citations: primaryChoice.citations,
|
||||
}
|
||||
: {}),
|
||||
...(normalizedChoiceEvents.length > 0 ? { choiceEvents: normalizedChoiceEvents } : {}),
|
||||
...extractChatResponseExtras(payload),
|
||||
};
|
||||
},
|
||||
serializeEvent(
|
||||
event: OpenAiChatNormalizedStreamEvent,
|
||||
context: StreamTransformContext,
|
||||
downstreamContext?: ClaudeDownstreamContext,
|
||||
): string[] {
|
||||
if (
|
||||
Array.isArray(event.choiceEvents)
|
||||
&& event.choiceEvents.length > 0
|
||||
&& (event.choiceEvents.length > 1 || event.choiceEvents[0]?.index !== 0)
|
||||
) {
|
||||
const usagePayload = event.usagePayload
|
||||
? {
|
||||
...(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 }
|
||||
: {}),
|
||||
}
|
||||
: undefined;
|
||||
return [`data: ${JSON.stringify({
|
||||
id: context.id || `chatcmpl-${Date.now()}`,
|
||||
object: 'chat.completion.chunk',
|
||||
created: context.created,
|
||||
model: context.model,
|
||||
choices: event.choiceEvents
|
||||
.map((choiceEvent) => {
|
||||
const delta: Record<string, unknown> = {};
|
||||
if (choiceEvent.role) delta.role = choiceEvent.role;
|
||||
if (choiceEvent.contentDelta !== undefined) delta.content = choiceEvent.contentDelta;
|
||||
if (choiceEvent.reasoningDelta) delta.reasoning_content = choiceEvent.reasoningDelta;
|
||||
if (Array.isArray(choiceEvent.toolCallDeltas) && choiceEvent.toolCallDeltas.length > 0) {
|
||||
const toolState = getMultiChoiceToolState(context);
|
||||
delta.tool_calls = choiceEvent.toolCallDeltas.map((toolCall) => {
|
||||
const toolIndex = Number.isFinite(toolCall.index) ? Math.max(0, Math.trunc(toolCall.index)) : 0;
|
||||
const stateKey = `${choiceEvent.index}:${toolIndex}`;
|
||||
const existing = toolState[stateKey] || {};
|
||||
toolState[stateKey] = {
|
||||
...(toolCall.id || existing.id ? { id: toolCall.id || existing.id } : {}),
|
||||
...(toolCall.name || existing.name ? { name: toolCall.name || existing.name } : {}),
|
||||
arguments: `${existing.arguments || ''}${toolCall.argumentsDelta ?? ''}`,
|
||||
};
|
||||
|
||||
const functionPayload: Record<string, unknown> = {};
|
||||
if (toolCall.name) functionPayload.name = toolCall.name;
|
||||
if (toolCall.argumentsDelta !== undefined) functionPayload.arguments = toolCall.argumentsDelta;
|
||||
|
||||
return {
|
||||
index: toolIndex,
|
||||
...(toolCall.id ? { id: toolCall.id } : {}),
|
||||
...(toolCall.id || toolCall.name ? { type: 'function' } : {}),
|
||||
...(Object.keys(functionPayload).length > 0 ? { function: functionPayload } : {}),
|
||||
};
|
||||
});
|
||||
}
|
||||
if (Array.isArray(choiceEvent.annotations) && choiceEvent.annotations.length > 0) {
|
||||
delta.annotations = choiceEvent.annotations;
|
||||
}
|
||||
|
||||
return {
|
||||
index: choiceEvent.index,
|
||||
delta,
|
||||
finish_reason: choiceEvent.finishReason ?? null,
|
||||
};
|
||||
})
|
||||
.sort((left, right) => left.index - right.index),
|
||||
...(Array.isArray(event.citations) && event.citations.length > 0 ? { citations: event.citations } : {}),
|
||||
...(usagePayload && Object.keys(usagePayload).length > 0 ? { usage: usagePayload } : {}),
|
||||
})}\n\n`];
|
||||
}
|
||||
|
||||
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);
|
||||
},
|
||||
};
|
||||
@@ -1,5 +1,5 @@
|
||||
import { type StreamTransformContext } from '../../shared/normalized.js';
|
||||
import type { OpenAiResponsesStreamEvent } from './stream.js';
|
||||
import type { OpenAiResponsesStreamEvent } from './streamBridge.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object' && !Array.isArray(value);
|
||||
|
||||
@@ -1,8 +1,10 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
import { normalizeCodexResponsesBodyForProxy } from './codexCompatibility.js';
|
||||
|
||||
const CODEX_DEFAULT_INSTRUCTIONS = 'You are a helpful coding assistant.';
|
||||
|
||||
describe('normalizeCodexResponsesBodyForProxy', () => {
|
||||
it('normalizes codex responses bodies before proxying upstream', () => {
|
||||
it('extracts codex system input into top-level instructions before proxying upstream', () => {
|
||||
const body = normalizeCodexResponsesBodyForProxy({
|
||||
input: [
|
||||
{
|
||||
@@ -10,7 +12,13 @@ describe('normalizeCodexResponsesBodyForProxy', () => {
|
||||
role: 'system',
|
||||
content: [{ type: 'input_text', text: 'be precise' }],
|
||||
},
|
||||
{
|
||||
type: 'message',
|
||||
role: 'user',
|
||||
content: [{ type: 'input_text', text: 'hello' }],
|
||||
},
|
||||
],
|
||||
instructions: 'keep edits narrow',
|
||||
max_output_tokens: 512,
|
||||
max_completion_tokens: 256,
|
||||
max_tokens: 128,
|
||||
@@ -22,16 +30,28 @@ describe('normalizeCodexResponsesBodyForProxy', () => {
|
||||
input: [
|
||||
{
|
||||
type: 'message',
|
||||
role: 'developer',
|
||||
content: [{ type: 'input_text', text: 'be precise' }],
|
||||
role: 'user',
|
||||
content: [{ type: 'input_text', text: 'hello' }],
|
||||
},
|
||||
],
|
||||
instructions: '',
|
||||
instructions: 'be precise\n\nkeep edits narrow',
|
||||
store: false,
|
||||
temperature: 0.3,
|
||||
});
|
||||
});
|
||||
|
||||
it('supplies a non-empty default instructions string for codex when missing', () => {
|
||||
const body = normalizeCodexResponsesBodyForProxy({
|
||||
input: 'hello',
|
||||
}, 'codex');
|
||||
|
||||
expect(body).toEqual({
|
||||
input: 'hello',
|
||||
instructions: CODEX_DEFAULT_INSTRUCTIONS,
|
||||
store: false,
|
||||
});
|
||||
});
|
||||
|
||||
it('leaves non-codex bodies untouched', () => {
|
||||
const source = {
|
||||
input: 'hello',
|
||||
|
||||
@@ -6,15 +6,76 @@ function asTrimmedString(value: unknown): string {
|
||||
return typeof value === 'string' ? value.trim() : '';
|
||||
}
|
||||
|
||||
const CODEX_DEFAULT_INSTRUCTIONS = 'You are a helpful coding assistant.';
|
||||
|
||||
function extractTextFromContent(content: unknown): string {
|
||||
if (typeof content === 'string') return content;
|
||||
if (Array.isArray(content)) {
|
||||
return content
|
||||
.map((item) => extractTextFromContent(item))
|
||||
.filter((item) => item.length > 0)
|
||||
.join('');
|
||||
}
|
||||
if (!isRecord(content)) return '';
|
||||
if (typeof content.text === 'string') return content.text;
|
||||
if (typeof content.content === 'string') return content.content;
|
||||
if (typeof content.output_text === 'string') return content.output_text;
|
||||
if (typeof content.input_text === 'string') return content.input_text;
|
||||
return '';
|
||||
}
|
||||
|
||||
function extractSystemMessagesToInstructions(
|
||||
body: Record<string, unknown>,
|
||||
): Record<string, unknown> {
|
||||
if (!Array.isArray(body.input) || body.input.length <= 0) return body;
|
||||
|
||||
const extractedSystemTexts: string[] = [];
|
||||
const remainingInput: unknown[] = [];
|
||||
|
||||
for (const item of body.input) {
|
||||
if (!isRecord(item)) {
|
||||
remainingInput.push(item);
|
||||
continue;
|
||||
}
|
||||
if (asTrimmedString(item.type).toLowerCase() !== 'message') {
|
||||
remainingInput.push(item);
|
||||
continue;
|
||||
}
|
||||
if (asTrimmedString(item.role).toLowerCase() !== 'system') {
|
||||
remainingInput.push(item);
|
||||
continue;
|
||||
}
|
||||
|
||||
const text = extractTextFromContent(item.content);
|
||||
if (text.trim()) {
|
||||
extractedSystemTexts.push(text);
|
||||
}
|
||||
}
|
||||
|
||||
if (extractedSystemTexts.length <= 0) return body;
|
||||
|
||||
const extractedInstructions = extractedSystemTexts.join('\n\n');
|
||||
const existingInstructions = typeof body.instructions === 'string' ? body.instructions : '';
|
||||
const nextInstructions = existingInstructions.trim()
|
||||
? `${extractedInstructions}\n\n${existingInstructions}`
|
||||
: extractedInstructions;
|
||||
|
||||
return {
|
||||
...body,
|
||||
input: remainingInput,
|
||||
instructions: nextInstructions,
|
||||
};
|
||||
}
|
||||
|
||||
function ensureCodexResponsesInstructions(
|
||||
body: Record<string, unknown>,
|
||||
sitePlatform: string,
|
||||
): Record<string, unknown> {
|
||||
if (sitePlatform !== 'codex') return body;
|
||||
if (typeof body.instructions === 'string') return body;
|
||||
if (typeof body.instructions === 'string' && body.instructions.trim()) return body;
|
||||
return {
|
||||
...body,
|
||||
instructions: '',
|
||||
instructions: CODEX_DEFAULT_INSTRUCTIONS,
|
||||
};
|
||||
}
|
||||
|
||||
@@ -41,35 +102,12 @@ function stripCodexUnsupportedResponsesFields(
|
||||
return next;
|
||||
}
|
||||
|
||||
function convertCodexSystemRoleToDeveloper(input: unknown): unknown {
|
||||
if (!Array.isArray(input)) return input;
|
||||
return input.map((item) => {
|
||||
if (!isRecord(item)) return item;
|
||||
if (asTrimmedString(item.type).toLowerCase() !== 'message') return item;
|
||||
if (asTrimmedString(item.role).toLowerCase() !== 'system') return item;
|
||||
return {
|
||||
...item,
|
||||
role: 'developer',
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
function applyCodexResponsesCompatibility(
|
||||
body: Record<string, unknown>,
|
||||
sitePlatform: string,
|
||||
): Record<string, unknown> {
|
||||
if (sitePlatform !== 'codex') return body;
|
||||
|
||||
const next: Record<string, unknown> = {
|
||||
...body,
|
||||
input: convertCodexSystemRoleToDeveloper(body.input),
|
||||
};
|
||||
|
||||
if (typeof next.instructions !== 'string') {
|
||||
next.instructions = '';
|
||||
}
|
||||
|
||||
return next;
|
||||
return extractSystemMessagesToInstructions(body);
|
||||
}
|
||||
|
||||
export function normalizeCodexResponsesBodyForProxy(
|
||||
|
||||
@@ -7,6 +7,10 @@ import {
|
||||
convertOpenAiBodyToResponsesBody,
|
||||
sanitizeResponsesBodyForProxy,
|
||||
} from './conversion.js';
|
||||
import {
|
||||
hasEndpointMismatchHint,
|
||||
inferRequiredEndpointFromProtocolError,
|
||||
} from '../../shared/endpointCompatibility.js';
|
||||
|
||||
function isRecord(value: unknown): value is Record<string, unknown> {
|
||||
return !!value && typeof value === 'object';
|
||||
@@ -18,11 +22,17 @@ function asTrimmedString(value: unknown): string {
|
||||
|
||||
export function buildResponsesCompatibilityBodies(
|
||||
body: Record<string, unknown>,
|
||||
options?: {
|
||||
sitePlatform?: string;
|
||||
},
|
||||
): Record<string, unknown>[] {
|
||||
const candidates: Record<string, unknown>[] = [];
|
||||
const seen = new Set<string>();
|
||||
const useSemanticDedupe = asTrimmedString(options?.sitePlatform).toLowerCase() === 'sub2api';
|
||||
try {
|
||||
const originalKey = JSON.stringify(body);
|
||||
const originalKey = useSemanticDedupe
|
||||
? fingerprintCompatibilityValue(body)
|
||||
: JSON.stringify(body);
|
||||
if (originalKey) seen.add(originalKey);
|
||||
} catch {
|
||||
// ignore non-serializable bodies
|
||||
@@ -32,7 +42,9 @@ export function buildResponsesCompatibilityBodies(
|
||||
if (!next) return;
|
||||
let key = '';
|
||||
try {
|
||||
key = JSON.stringify(next);
|
||||
key = useSemanticDedupe
|
||||
? fingerprintCompatibilityValue(next)
|
||||
: JSON.stringify(next);
|
||||
} catch {
|
||||
return;
|
||||
}
|
||||
@@ -77,19 +89,28 @@ export function buildResponsesCompatibilityBodies(
|
||||
'background',
|
||||
'top_logprobs',
|
||||
] as const;
|
||||
const normalizedSitePlatform = asTrimmedString(options?.sitePlatform).toLowerCase();
|
||||
if (normalizedSitePlatform === 'sub2api' && body.store !== undefined) {
|
||||
richCandidate.store = cloneJsonValue(body.store);
|
||||
}
|
||||
for (const key of passthroughFields) {
|
||||
if (body[key] === undefined) continue;
|
||||
richCandidate[key] = cloneJsonValue(body[key]);
|
||||
}
|
||||
push(richCandidate);
|
||||
}
|
||||
push(buildStrictResponsesBody(body));
|
||||
if (!shouldPreserveResponsesCompatibilitySemantics(options?.sitePlatform, body)) {
|
||||
push(buildStrictResponsesBody(body));
|
||||
}
|
||||
return candidates;
|
||||
}
|
||||
|
||||
export function buildResponsesCompatibilityHeaderCandidates(
|
||||
headers: Record<string, string>,
|
||||
stream: boolean,
|
||||
options?: {
|
||||
sitePlatform?: string;
|
||||
},
|
||||
): Record<string, string>[] {
|
||||
const candidates: Record<string, string>[] = [];
|
||||
const seen = new Set<string>();
|
||||
@@ -106,6 +127,12 @@ export function buildResponsesCompatibilityHeaderCandidates(
|
||||
|
||||
push(headers);
|
||||
|
||||
const normalizedSitePlatform = asTrimmedString(options?.sitePlatform).toLowerCase();
|
||||
if (normalizedSitePlatform === 'sub2api') {
|
||||
push(buildSub2ApiResponsesCompatibilityHeaders(headers, stream));
|
||||
return candidates;
|
||||
}
|
||||
|
||||
const minimal: Record<string, string> = {};
|
||||
for (const [rawKey, rawValue] of Object.entries(headers)) {
|
||||
const key = rawKey.toLowerCase();
|
||||
@@ -129,6 +156,7 @@ export function shouldRetryResponsesCompatibility(input: {
|
||||
endpoint: string;
|
||||
status: number;
|
||||
rawErrText: string;
|
||||
body: Record<string, unknown>;
|
||||
}): boolean {
|
||||
if (input.endpoint !== 'responses') return false;
|
||||
if (input.status !== 400) return false;
|
||||
@@ -138,6 +166,7 @@ export function shouldRetryResponsesCompatibility(input: {
|
||||
const message = parsedError.message.trim().toLowerCase();
|
||||
const compact = `${type} ${code} ${message}`.trim();
|
||||
const rawCompact = (input.rawErrText || '').toLowerCase();
|
||||
const requiredEndpoint = inferRequiredEndpointFromProtocolError(input.rawErrText);
|
||||
|
||||
if (
|
||||
compact.includes('invalid_api_key')
|
||||
@@ -150,11 +179,30 @@ export function shouldRetryResponsesCompatibility(input: {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (type === 'upstream_error' || code === 'upstream_error') return true;
|
||||
if (message === 'upstream_error' || message === 'upstream request failed') return true;
|
||||
if (rawCompact.includes('upstream_error')) return true;
|
||||
const hasCompatibilityHint = (
|
||||
compact.includes('unsupported')
|
||||
|| compact.includes('not supported')
|
||||
|| compact.includes('application/json')
|
||||
|| compact.includes('content-type')
|
||||
|| compact.includes('request validation failed')
|
||||
|| requiredEndpoint !== null
|
||||
|| hasEndpointMismatchHint(input.rawErrText)
|
||||
|| compact.includes('invalid_request_error')
|
||||
|| compact.includes('bad_response_status_code')
|
||||
|| compact.includes('openai_error')
|
||||
|| compact.includes('unsupported legacy protocol')
|
||||
);
|
||||
if (hasCompatibilityHint) return true;
|
||||
|
||||
return true;
|
||||
const compatibilityCandidates = buildResponsesCompatibilityBodies(input.body);
|
||||
if (compatibilityCandidates.length > 0) {
|
||||
if (type === 'upstream_error' || code === 'upstream_error') return true;
|
||||
if (message === 'upstream_error' || message === 'upstream request failed') return true;
|
||||
if (rawCompact.includes('upstream_error')) return true;
|
||||
if (compact.length > 0) return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
export function shouldDowngradeResponsesChatToMessages(
|
||||
@@ -164,7 +212,7 @@ export function shouldDowngradeResponsesChatToMessages(
|
||||
): boolean {
|
||||
if (!endpointPath.includes('/chat/completions')) return false;
|
||||
if (status < 400 || status >= 500) return false;
|
||||
return /messages\s+is\s+required/i.test(upstreamErrorText);
|
||||
return inferRequiredEndpointFromProtocolError(upstreamErrorText) === 'messages';
|
||||
}
|
||||
|
||||
function toFiniteNumber(value: unknown): number | null {
|
||||
@@ -183,6 +231,20 @@ function cloneJsonValue<T>(value: T): T {
|
||||
return value;
|
||||
}
|
||||
|
||||
function fingerprintCompatibilityValue(value: unknown): string {
|
||||
if (value === undefined) return '__undefined__';
|
||||
if (value === null || typeof value !== 'object') {
|
||||
return JSON.stringify(value);
|
||||
}
|
||||
if (Array.isArray(value)) {
|
||||
return `[${value.map((item) => fingerprintCompatibilityValue(item)).join(',')}]`;
|
||||
}
|
||||
const entries = Object.entries(value)
|
||||
.sort(([a], [b]) => a.localeCompare(b))
|
||||
.map(([key, item]) => `${JSON.stringify(key)}:${fingerprintCompatibilityValue(item)}`);
|
||||
return `{${entries.join(',')}}`;
|
||||
}
|
||||
|
||||
function parseUpstreamErrorShape(rawText: string): {
|
||||
type: string;
|
||||
code: string;
|
||||
@@ -208,6 +270,60 @@ function getExplicitResponsesInstructions(body: Record<string, unknown>): string
|
||||
return typeof body.instructions === 'string' ? body.instructions.trim() : '';
|
||||
}
|
||||
|
||||
function shouldPreserveResponsesCompatibilitySemantics(
|
||||
sitePlatform: string | undefined,
|
||||
body: Record<string, unknown>,
|
||||
): boolean {
|
||||
if (asTrimmedString(sitePlatform).toLowerCase() !== 'sub2api') return false;
|
||||
return [
|
||||
'store',
|
||||
'include',
|
||||
'reasoning',
|
||||
'previous_response_id',
|
||||
'truncation',
|
||||
'text',
|
||||
'service_tier',
|
||||
'safety_identifier',
|
||||
'max_tool_calls',
|
||||
'prompt_cache_key',
|
||||
'prompt_cache_retention',
|
||||
'background',
|
||||
'top_logprobs',
|
||||
].some((key) => body[key] !== undefined);
|
||||
}
|
||||
|
||||
const SUB2API_RESPONSES_COMPATIBILITY_HEADER_ALLOWLIST = new Set([
|
||||
'accept',
|
||||
'accept-language',
|
||||
'authorization',
|
||||
'content-type',
|
||||
'conversation-id',
|
||||
'conversation_id',
|
||||
'openai-beta',
|
||||
'originator',
|
||||
'session-id',
|
||||
'session_id',
|
||||
'user-agent',
|
||||
'x-api-key',
|
||||
'x-codex-turn-metadata',
|
||||
'x-codex-turn-state',
|
||||
]);
|
||||
|
||||
function buildSub2ApiResponsesCompatibilityHeaders(
|
||||
headers: Record<string, string>,
|
||||
stream: boolean,
|
||||
): Record<string, string> {
|
||||
const next: Record<string, string> = {};
|
||||
for (const [rawKey, rawValue] of Object.entries(headers)) {
|
||||
const key = rawKey.toLowerCase();
|
||||
if (!SUB2API_RESPONSES_COMPATIBILITY_HEADER_ALLOWLIST.has(key)) continue;
|
||||
next[key] = rawValue;
|
||||
}
|
||||
if (!next['content-type']) next['content-type'] = 'application/json';
|
||||
if (stream && !next.accept) next.accept = 'text/event-stream';
|
||||
return next;
|
||||
}
|
||||
|
||||
function stripResponsesMetadata(
|
||||
body: Record<string, unknown>,
|
||||
): Record<string, unknown> | null {
|
||||
|
||||
@@ -7,10 +7,12 @@ import {
|
||||
} from './conversion.js';
|
||||
import {
|
||||
buildResponsesCompatibilityBodies,
|
||||
buildResponsesCompatibilityHeaderCandidates,
|
||||
convertOpenAiBodyToResponsesBody as convertOpenAiBodyToResponsesBodyViaCompatibility,
|
||||
normalizeResponsesInputForCompatibility as normalizeResponsesInputForCompatibilityViaCompatibility,
|
||||
normalizeResponsesMessageContent as normalizeResponsesMessageContentViaCompatibility,
|
||||
sanitizeResponsesBodyForProxy as sanitizeResponsesBodyForProxyViaCompatibility,
|
||||
shouldRetryResponsesCompatibility,
|
||||
} from './compatibility.js';
|
||||
import {
|
||||
normalizeResponsesInputForCompatibility,
|
||||
@@ -704,6 +706,77 @@ describe('convertOpenAiBodyToResponsesBody', () => {
|
||||
expect(result.include).toBeUndefined();
|
||||
});
|
||||
|
||||
it('does not synthesize max_output_tokens when chat-style input omits all token-limit fields', () => {
|
||||
const result = convertOpenAiBodyToResponsesBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
messages: [{ role: 'user', content: 'hello' }],
|
||||
},
|
||||
'gpt-5',
|
||||
false,
|
||||
);
|
||||
|
||||
expect(result.max_output_tokens).toBeUndefined();
|
||||
});
|
||||
|
||||
it('clamps explicit chat token limits to the sub2api floor when converting to Responses bodies', () => {
|
||||
const result = convertOpenAiBodyToResponsesBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
messages: [{ role: 'user', content: 'hello' }],
|
||||
max_tokens: 64,
|
||||
},
|
||||
'gpt-5',
|
||||
false,
|
||||
);
|
||||
|
||||
expect(result.max_output_tokens).toBe(128);
|
||||
});
|
||||
|
||||
it('uses sub2api-compatible placeholders for empty tool arguments and empty tool outputs', () => {
|
||||
const result = convertOpenAiBodyToResponsesBody(
|
||||
{
|
||||
model: 'gpt-5',
|
||||
messages: [
|
||||
{
|
||||
role: 'assistant',
|
||||
tool_calls: [
|
||||
{
|
||||
id: 'call_empty_1',
|
||||
type: 'function',
|
||||
function: {
|
||||
name: 'lookup_weather',
|
||||
arguments: '',
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
role: 'tool',
|
||||
tool_call_id: 'call_empty_1',
|
||||
content: '',
|
||||
},
|
||||
],
|
||||
},
|
||||
'gpt-5',
|
||||
false,
|
||||
);
|
||||
|
||||
expect(result.input).toEqual([
|
||||
{
|
||||
type: 'function_call',
|
||||
call_id: 'call_empty_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{}',
|
||||
},
|
||||
{
|
||||
type: 'function_call_output',
|
||||
call_id: 'call_empty_1',
|
||||
output: '(empty)',
|
||||
},
|
||||
]);
|
||||
});
|
||||
|
||||
it('maps OpenAI file-style content blocks into inline-only Responses input_file blocks', () => {
|
||||
const result = convertOpenAiBodyToResponsesBody(
|
||||
{
|
||||
@@ -1568,6 +1641,87 @@ describe('convertResponsesBodyToOpenAiBody', () => {
|
||||
});
|
||||
});
|
||||
|
||||
it('does not generate extra compatibility bodies for sub2api when responses-only semantics are already present', () => {
|
||||
const candidates = buildResponsesCompatibilityBodies({
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
stream: true,
|
||||
store: false,
|
||||
include: ['reasoning.encrypted_content'],
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
},
|
||||
previous_response_id: 'resp_prev_9',
|
||||
prompt_cache_key: 'cache-key-9',
|
||||
service_tier: 'priority',
|
||||
background: true,
|
||||
}, {
|
||||
sitePlatform: 'sub2api',
|
||||
});
|
||||
|
||||
expect(candidates).toEqual([]);
|
||||
});
|
||||
|
||||
it('builds a sub2api-safe compatibility header candidate that preserves session and codex passthrough headers', () => {
|
||||
const candidates = buildResponsesCompatibilityHeaderCandidates({
|
||||
accept: 'text/event-stream',
|
||||
authorization: 'Bearer sk-test',
|
||||
'content-type': 'application/json',
|
||||
'accept-language': 'zh-CN',
|
||||
'openai-beta': 'responses-2025-03-11',
|
||||
originator: 'codex_cli_rs',
|
||||
'user-agent': 'client-ua/1.0',
|
||||
session_id: 'session-123',
|
||||
conversation_id: 'conversation-123',
|
||||
'x-codex-turn-state': 'turn-state',
|
||||
'x-codex-turn-metadata': 'turn-metadata',
|
||||
'x-stainless-lang': 'typescript',
|
||||
version: '0.202.0',
|
||||
}, true, {
|
||||
sitePlatform: 'sub2api',
|
||||
});
|
||||
|
||||
expect(candidates).toContainEqual({
|
||||
accept: 'text/event-stream',
|
||||
authorization: 'Bearer sk-test',
|
||||
'content-type': 'application/json',
|
||||
'accept-language': 'zh-CN',
|
||||
'openai-beta': 'responses-2025-03-11',
|
||||
originator: 'codex_cli_rs',
|
||||
'user-agent': 'client-ua/1.0',
|
||||
session_id: 'session-123',
|
||||
conversation_id: 'conversation-123',
|
||||
'x-codex-turn-state': 'turn-state',
|
||||
'x-codex-turn-metadata': 'turn-metadata',
|
||||
});
|
||||
expect(candidates).not.toContainEqual({
|
||||
accept: 'text/event-stream',
|
||||
authorization: 'Bearer sk-test',
|
||||
'content-type': 'application/json',
|
||||
});
|
||||
});
|
||||
|
||||
it('deduplicates semantically identical sub2api retry bodies even when field order differs', () => {
|
||||
const candidates = buildResponsesCompatibilityBodies({
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
previous_response_id: 'resp_prev_9',
|
||||
include: ['reasoning.encrypted_content'],
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
},
|
||||
prompt_cache_key: 'cache-key-9',
|
||||
service_tier: 'priority',
|
||||
background: true,
|
||||
stream: true,
|
||||
store: false,
|
||||
}, {
|
||||
sitePlatform: 'sub2api',
|
||||
});
|
||||
|
||||
expect(candidates).toEqual([]);
|
||||
});
|
||||
|
||||
it('preserves tools and tool_choice across all compatibility retry bodies', () => {
|
||||
const candidates = buildResponsesCompatibilityBodies({
|
||||
model: 'gpt-5',
|
||||
@@ -1618,6 +1772,39 @@ describe('convertResponsesBodyToOpenAiBody', () => {
|
||||
}
|
||||
});
|
||||
|
||||
it('does not retry auth-shaped failures and still retries generic upstream errors through compatibility variants', () => {
|
||||
expect(shouldRetryResponsesCompatibility({
|
||||
endpoint: 'responses',
|
||||
status: 400,
|
||||
rawErrText: JSON.stringify({
|
||||
error: {
|
||||
message: 'invalid_api_key',
|
||||
type: 'authentication_error',
|
||||
},
|
||||
}),
|
||||
body: {
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
},
|
||||
})).toBe(false);
|
||||
|
||||
expect(shouldRetryResponsesCompatibility({
|
||||
endpoint: 'responses',
|
||||
status: 400,
|
||||
rawErrText: JSON.stringify({
|
||||
error: {
|
||||
message: '',
|
||||
type: 'upstream_error',
|
||||
},
|
||||
}),
|
||||
body: {
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
metadata: { trace_id: 'req-1' },
|
||||
},
|
||||
})).toBe(true);
|
||||
});
|
||||
|
||||
it('maps native tool choices and strict function tools into OpenAI-compatible fallback bodies', () => {
|
||||
const result = convertResponsesBodyToOpenAiBody(
|
||||
{
|
||||
|
||||
@@ -439,6 +439,8 @@ const RESPONSES_COMPATIBILITY_FILTER_FIELDS = new Set([
|
||||
'verbosity',
|
||||
]);
|
||||
|
||||
const MIN_RESPONSES_MAX_OUTPUT_TOKENS = 128;
|
||||
|
||||
export function sanitizeResponsesBodyForProxy(
|
||||
body: Record<string, unknown>,
|
||||
modelName: string,
|
||||
@@ -571,7 +573,7 @@ export function convertOpenAiBodyToResponsesBody(
|
||||
type: 'function_call',
|
||||
call_id: callId,
|
||||
name: getShortToolName(name, toolNameMap),
|
||||
arguments: argumentsValue,
|
||||
arguments: argumentsValue || '{}',
|
||||
});
|
||||
}
|
||||
continue;
|
||||
@@ -580,10 +582,16 @@ export function convertOpenAiBodyToResponsesBody(
|
||||
if (role === 'tool') {
|
||||
const callId = asTrimmedString(item.tool_call_id) || asTrimmedString(item.id);
|
||||
if (!callId) continue;
|
||||
const output = normalizeToolOutput(item.content);
|
||||
inputItems.push({
|
||||
type: 'function_call_output',
|
||||
call_id: callId,
|
||||
output: normalizeToolOutput(item.content),
|
||||
output: (
|
||||
(typeof output === 'string' && output === '')
|
||||
|| (Array.isArray(output) && output.length === 0)
|
||||
)
|
||||
? '(empty)'
|
||||
: output,
|
||||
});
|
||||
continue;
|
||||
}
|
||||
@@ -597,20 +605,25 @@ export function convertOpenAiBodyToResponsesBody(
|
||||
});
|
||||
}
|
||||
|
||||
const maxOutputTokens = (
|
||||
const requestedMaxOutputTokens = (
|
||||
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 (requestedMaxOutputTokens !== null && requestedMaxOutputTokens > 0) {
|
||||
body.max_output_tokens = Math.max(
|
||||
MIN_RESPONSES_MAX_OUTPUT_TOKENS,
|
||||
Math.trunc(requestedMaxOutputTokens),
|
||||
);
|
||||
}
|
||||
|
||||
if (systemContents.length > 0) {
|
||||
body.instructions = systemContents.join('\n\n');
|
||||
}
|
||||
|
||||
@@ -72,6 +72,41 @@ describe('openAiResponsesTransformer.inbound', () => {
|
||||
});
|
||||
});
|
||||
|
||||
it('round-trips continuation turnState through responses metadata bridging', () => {
|
||||
const built = openAiResponsesTransformer.buildProtocolRequest({
|
||||
operation: 'generate',
|
||||
surface: 'openai-responses',
|
||||
cliProfile: 'codex',
|
||||
requestedModel: 'gpt-5',
|
||||
stream: false,
|
||||
messages: [{ role: 'user', parts: [{ type: 'text', text: 'hello' }] }],
|
||||
continuation: {
|
||||
turnState: 'turn-state-responses-1',
|
||||
},
|
||||
});
|
||||
|
||||
expect(built).toMatchObject({
|
||||
metadata: {
|
||||
metapi_turn_state: 'turn-state-responses-1',
|
||||
},
|
||||
});
|
||||
|
||||
const parsed = openAiResponsesTransformer.parseRequest({
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
metadata: {
|
||||
metapi_turn_state: 'turn-state-responses-1',
|
||||
},
|
||||
});
|
||||
|
||||
expect(parsed.error).toBeUndefined();
|
||||
expect(parsed.value).toMatchObject({
|
||||
continuation: {
|
||||
turnState: 'turn-state-responses-1',
|
||||
},
|
||||
});
|
||||
});
|
||||
|
||||
it('returns a protocol request envelope with a normalized responses body', () => {
|
||||
const result = openAiResponsesTransformer.transformRequest({
|
||||
model: 'gpt-5',
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
import { canonicalRequestFromOpenAiBody, canonicalRequestToOpenAiChatBody } from '../../canonical/request.js';
|
||||
import type { CanonicalRequestEnvelope } from '../../canonical/types.js';
|
||||
import type { ProtocolBuildContext, ProtocolParseContext } from '../../contracts.js';
|
||||
import { type StreamTransformContext } from '../../shared/normalized.js';
|
||||
@@ -23,11 +22,19 @@ import {
|
||||
failResponsesStream,
|
||||
serializeConvertedResponsesEvents,
|
||||
} from './aggregator.js';
|
||||
import { openAiResponsesOutbound } from './outbound.js';
|
||||
import { openAiResponsesInbound } from './inbound.js';
|
||||
import { createResponsesProxyStreamSession } from './proxyStream.js';
|
||||
import {
|
||||
buildCanonicalRequestToOpenAiResponsesBody,
|
||||
parseOpenAiResponsesRequestToCanonical,
|
||||
} from './requestBridge.js';
|
||||
import {
|
||||
openAiResponsesResponseBridge,
|
||||
buildNormalizedFinalToOpenAiResponsesPayload,
|
||||
normalizeOpenAiResponsesFinalToNormalized,
|
||||
} from './responseBridge.js';
|
||||
import { createResponsesEndpointStrategy } from './routeCompatibility.js';
|
||||
import { openAiResponsesStream } from './stream.js';
|
||||
import { openAiResponsesStream } from './streamBridge.js';
|
||||
import { openAiResponsesUsage } from './usage.js';
|
||||
import type {
|
||||
OpenAiResponsesParsedRequest as OpenAiResponsesParsedRequestModel,
|
||||
@@ -45,7 +52,7 @@ export const openAiResponsesTransformer = {
|
||||
fromOpenAiBody: convertOpenAiBodyToResponsesBody,
|
||||
toOpenAiBody: convertResponsesBodyToOpenAiBody,
|
||||
},
|
||||
outbound: openAiResponsesOutbound,
|
||||
outbound: openAiResponsesResponseBridge,
|
||||
stream: openAiResponsesStream,
|
||||
usage: openAiResponsesUsage,
|
||||
compatibility: {
|
||||
@@ -68,59 +75,13 @@ export const openAiResponsesTransformer = {
|
||||
body: unknown,
|
||||
ctx?: ProtocolParseContext,
|
||||
): { value?: CanonicalRequestEnvelope; error?: { statusCode: number; payload: unknown } } {
|
||||
const parsed = openAiResponsesInbound.parse(body, {
|
||||
defaultEncryptedReasoningInclude: ctx?.defaultEncryptedReasoningInclude,
|
||||
});
|
||||
if (parsed.error) {
|
||||
return { error: parsed.error };
|
||||
}
|
||||
if (!parsed.value) {
|
||||
return {
|
||||
error: {
|
||||
statusCode: 400,
|
||||
payload: {
|
||||
error: {
|
||||
message: 'invalid responses request',
|
||||
type: 'invalid_request_error',
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
const responsesBody = parsed.value.parsed.normalizedBody;
|
||||
const openAiBody = convertResponsesBodyToOpenAiBody(
|
||||
responsesBody,
|
||||
typeof responsesBody.model === 'string' ? responsesBody.model : parsed.value.model,
|
||||
responsesBody.stream === true,
|
||||
{ defaultEncryptedReasoningInclude: ctx?.defaultEncryptedReasoningInclude },
|
||||
);
|
||||
|
||||
return {
|
||||
value: canonicalRequestFromOpenAiBody({
|
||||
body: openAiBody,
|
||||
surface: 'openai-responses',
|
||||
cliProfile: ctx?.cliProfile,
|
||||
operation: ctx?.operation,
|
||||
metadata: ctx?.metadata,
|
||||
passthrough: ctx?.passthrough,
|
||||
continuation: ctx?.continuation,
|
||||
}),
|
||||
};
|
||||
return parseOpenAiResponsesRequestToCanonical(body, ctx);
|
||||
},
|
||||
buildProtocolRequest(
|
||||
request: CanonicalRequestEnvelope,
|
||||
_ctx?: ProtocolBuildContext,
|
||||
): Record<string, unknown> {
|
||||
const openAiBody = canonicalRequestToOpenAiChatBody(request);
|
||||
if (request.reasoning) {
|
||||
openAiBody.reasoning = {
|
||||
...(request.reasoning.effort ? { effort: request.reasoning.effort } : {}),
|
||||
...(request.reasoning.budgetTokens !== undefined ? { budget_tokens: request.reasoning.budgetTokens } : {}),
|
||||
...(request.reasoning.summary ? { summary: request.reasoning.summary } : {}),
|
||||
};
|
||||
}
|
||||
return convertOpenAiBodyToResponsesBody(openAiBody, request.requestedModel, request.stream);
|
||||
return buildCanonicalRequestToOpenAiResponsesBody(request);
|
||||
},
|
||||
transformRequest(
|
||||
body: unknown,
|
||||
@@ -132,7 +93,7 @@ export const openAiResponsesTransformer = {
|
||||
return openAiResponsesStream.createContext(modelName);
|
||||
},
|
||||
transformFinalResponse(payload: unknown, modelName: string, fallbackText = '') {
|
||||
return openAiResponsesOutbound.normalizeFinal(payload, modelName, fallbackText);
|
||||
return normalizeOpenAiResponsesFinalToNormalized(payload, modelName, fallbackText);
|
||||
},
|
||||
transformStreamEvent(payload: unknown, context: StreamTransformContext, modelName: string) {
|
||||
return openAiResponsesStream.normalizeEvent(payload, context, modelName);
|
||||
@@ -140,6 +101,15 @@ export const openAiResponsesTransformer = {
|
||||
pullSseEvents(buffer: string) {
|
||||
return openAiResponsesStream.pullSseEvents(buffer);
|
||||
},
|
||||
serializeFinalResponse(
|
||||
normalized: Parameters<typeof buildNormalizedFinalToOpenAiResponsesPayload>[0]['normalized'],
|
||||
input: Omit<Parameters<typeof buildNormalizedFinalToOpenAiResponsesPayload>[0], 'normalized'>,
|
||||
) {
|
||||
return buildNormalizedFinalToOpenAiResponsesPayload({
|
||||
...input,
|
||||
normalized,
|
||||
});
|
||||
},
|
||||
};
|
||||
|
||||
export type OpenAiResponsesTransformer = typeof openAiResponsesTransformer;
|
||||
|
||||
@@ -1,449 +1,12 @@
|
||||
import {
|
||||
normalizeUpstreamFinalResponse,
|
||||
type NormalizedFinalResponse,
|
||||
} from '../../shared/normalized.js';
|
||||
import { decodeOpenAiEncryptedReasoning } from '../../shared/reasoningTransport.js';
|
||||
import { decodeResponsesMcpCompatToolCall } from './mcpCompatibility.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 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_${trimmed}`;
|
||||
}
|
||||
|
||||
function ensureMessageId(rawId: string): string {
|
||||
const trimmed = rawId.trim() || `msg_${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_${trimmed}`;
|
||||
}
|
||||
|
||||
function stringifyToolInput(input: unknown): string {
|
||||
if (typeof input === 'string') return input;
|
||||
if (!input || typeof input !== 'object') return '';
|
||||
try {
|
||||
return JSON.stringify(input);
|
||||
} catch {
|
||||
return '';
|
||||
}
|
||||
}
|
||||
|
||||
export type ResponsesUsageSummary = {
|
||||
promptTokens: number;
|
||||
completionTokens: number;
|
||||
totalTokens: number;
|
||||
inputTokensDetails?: Record<string, unknown>;
|
||||
outputTokensDetails?: Record<string, unknown>;
|
||||
};
|
||||
|
||||
export type ResponsesToolCall = {
|
||||
id: string;
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
|
||||
export type ResponsesFinalSerializationMode = 'response' | 'compact';
|
||||
|
||||
type ResponsesOutputItem = Record<string, unknown>;
|
||||
|
||||
function extractToolCallsFromUpstream(payload: unknown): ResponsesToolCall[] {
|
||||
if (!isRecord(payload)) return [];
|
||||
|
||||
if (Array.isArray(payload.choices)) {
|
||||
const choice = payload.choices[0];
|
||||
const message = isRecord((choice as any)?.message) ? (choice as any).message : {};
|
||||
const toolCalls = Array.isArray((message as any).tool_calls) ? (message as any).tool_calls : [];
|
||||
return toolCalls
|
||||
.map((item: unknown, index: number) => {
|
||||
if (!isRecord(item)) return null;
|
||||
const fn = isRecord(item.function) ? item.function : {};
|
||||
const id = typeof item.id === 'string' && item.id.trim().length > 0
|
||||
? item.id
|
||||
: `call_${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 [];
|
||||
}
|
||||
|
||||
function extractToolCallsFromNormalized(normalized: NormalizedFinalResponse): ResponsesToolCall[] {
|
||||
return Array.isArray(normalized.toolCalls)
|
||||
? normalized.toolCalls
|
||||
.map((item) => {
|
||||
const id = ensureFunctionCallId(asTrimmedString(item.id));
|
||||
const name = asTrimmedString(item.name);
|
||||
if (!name) return null;
|
||||
return {
|
||||
id,
|
||||
name,
|
||||
arguments: typeof item.arguments === 'string' ? item.arguments : '',
|
||||
};
|
||||
})
|
||||
.filter((item): item is ResponsesToolCall => !!item)
|
||||
: [];
|
||||
}
|
||||
|
||||
function extractAnnotationsFromUpstream(payload: unknown): unknown[] {
|
||||
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 annotations = Array.isArray((message as any).annotations) ? (message as any).annotations : [];
|
||||
return annotations.map((item) => cloneJson(item));
|
||||
}
|
||||
|
||||
if (payload.type === 'message' && Array.isArray(payload.content)) {
|
||||
const annotations = payload.content
|
||||
.filter((item): item is Record<string, unknown> => isRecord(item))
|
||||
.flatMap((item) => (Array.isArray(item.annotations) ? item.annotations : []));
|
||||
return annotations.map((item) => cloneJson(item));
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
function extractSyntheticOutputItemsFromUpstream(payload: unknown): ResponsesOutputItem[] {
|
||||
if (!isRecord(payload) || !Array.isArray(payload.output)) return [];
|
||||
|
||||
return payload.output
|
||||
.map((rawItem): ResponsesOutputItem | null => {
|
||||
if (!isRecord(rawItem)) return null;
|
||||
const itemType = asTrimmedString(rawItem.type).toLowerCase();
|
||||
if (!itemType) return null;
|
||||
|
||||
if (itemType === 'message') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: 'message',
|
||||
role: asTrimmedString(rawItem.role) || 'assistant',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
content: Array.isArray(rawItem.content) ? cloneJson(rawItem.content) : [],
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'reasoning') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: 'reasoning',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
encrypted_content: rawItem.encrypted_content,
|
||||
summary: Array.isArray(rawItem.summary) ? cloneJson(rawItem.summary) : [],
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'function_call') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureFunctionCallId(''),
|
||||
type: 'function_call',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
call_id: asTrimmedString(rawItem.call_id) || ensureFunctionCallId(asTrimmedString(rawItem.id)),
|
||||
name: asTrimmedString(rawItem.name),
|
||||
arguments: typeof rawItem.arguments === 'string' ? rawItem.arguments : '',
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'custom_tool_call') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureFunctionCallId(''),
|
||||
type: 'custom_tool_call',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
call_id: asTrimmedString(rawItem.call_id) || ensureFunctionCallId(asTrimmedString(rawItem.id)),
|
||||
name: asTrimmedString(rawItem.name),
|
||||
input: typeof rawItem.input === 'string' ? rawItem.input : '',
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'function_call_output' || itemType === 'custom_tool_call_output') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: itemType,
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
call_id: asTrimmedString(rawItem.call_id),
|
||||
output: cloneJson(rawItem.output),
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'image_generation_call') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: 'image_generation_call',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
result: cloneJson(rawItem.result),
|
||||
partial_images: Array.isArray(rawItem.partial_images) ? cloneJson(rawItem.partial_images) : [],
|
||||
...(rawItem.background !== undefined ? { background: cloneJson(rawItem.background) } : {}),
|
||||
...(rawItem.mime_type !== undefined ? { mime_type: cloneJson(rawItem.mime_type) } : {}),
|
||||
...(rawItem.output_format !== undefined ? { output_format: cloneJson(rawItem.output_format) } : {}),
|
||||
...(rawItem.quality !== undefined ? { quality: cloneJson(rawItem.quality) } : {}),
|
||||
...(rawItem.size !== undefined ? { size: cloneJson(rawItem.size) } : {}),
|
||||
...(rawItem.revised_prompt !== undefined ? { revised_prompt: cloneJson(rawItem.revised_prompt) } : {}),
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType.startsWith('mcp_')) {
|
||||
return cloneJson(rawItem);
|
||||
}
|
||||
|
||||
return null;
|
||||
})
|
||||
.filter((item): item is ResponsesOutputItem => item !== null);
|
||||
}
|
||||
|
||||
function collectOutputTextFromItems(items: ResponsesOutputItem[]): string {
|
||||
const textParts: string[] = [];
|
||||
|
||||
for (const item of items) {
|
||||
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 partType = asTrimmedString(part.type).toLowerCase();
|
||||
if ((partType === 'output_text' || partType === 'text') && typeof part.text === 'string') {
|
||||
textParts.push(part.text);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return textParts.join('');
|
||||
}
|
||||
|
||||
function extractSyntheticUsageDetails(upstreamPayload: unknown): {
|
||||
inputTokensDetails?: Record<string, unknown>;
|
||||
outputTokensDetails?: Record<string, unknown>;
|
||||
} {
|
||||
if (!isRecord(upstreamPayload) || !isRecord(upstreamPayload.usage)) {
|
||||
return {};
|
||||
}
|
||||
|
||||
const usage = upstreamPayload.usage;
|
||||
const promptDetails = (
|
||||
(isRecord(usage.input_tokens_details) ? usage.input_tokens_details : null)
|
||||
?? (isRecord(usage.prompt_tokens_details) ? usage.prompt_tokens_details : null)
|
||||
);
|
||||
const completionDetails = (
|
||||
(isRecord(usage.output_tokens_details) ? usage.output_tokens_details : null)
|
||||
?? (isRecord(usage.completion_tokens_details) ? usage.completion_tokens_details : null)
|
||||
);
|
||||
|
||||
return {
|
||||
...(promptDetails ? { inputTokensDetails: cloneJson(promptDetails) } : {}),
|
||||
...(completionDetails ? { outputTokensDetails: cloneJson(completionDetails) } : {}),
|
||||
};
|
||||
}
|
||||
|
||||
export function normalizeResponsesFinalPayload(
|
||||
payload: unknown,
|
||||
modelName: string,
|
||||
fallbackText = '',
|
||||
): NormalizedFinalResponse {
|
||||
return normalizeUpstreamFinalResponse(payload, modelName, fallbackText);
|
||||
}
|
||||
|
||||
function buildResponsesUsagePayload(
|
||||
usage: ResponsesUsageSummary,
|
||||
syntheticUsageDetails: {
|
||||
inputTokensDetails?: Record<string, unknown>;
|
||||
outputTokensDetails?: Record<string, unknown>;
|
||||
},
|
||||
): Record<string, unknown> {
|
||||
return {
|
||||
input_tokens: usage.promptTokens,
|
||||
output_tokens: usage.completionTokens,
|
||||
total_tokens: usage.totalTokens,
|
||||
...(usage.inputTokensDetails ?? syntheticUsageDetails.inputTokensDetails
|
||||
? { input_tokens_details: cloneJson(usage.inputTokensDetails ?? syntheticUsageDetails.inputTokensDetails) }
|
||||
: {}),
|
||||
...(usage.outputTokensDetails ?? syntheticUsageDetails.outputTokensDetails
|
||||
? { output_tokens_details: cloneJson(usage.outputTokensDetails ?? syntheticUsageDetails.outputTokensDetails) }
|
||||
: {}),
|
||||
};
|
||||
}
|
||||
|
||||
export function serializeResponsesFinalPayload(input: {
|
||||
upstreamPayload: unknown;
|
||||
normalized: NormalizedFinalResponse;
|
||||
usage: ResponsesUsageSummary;
|
||||
serializationMode?: ResponsesFinalSerializationMode;
|
||||
}): Record<string, unknown> {
|
||||
const {
|
||||
upstreamPayload,
|
||||
normalized,
|
||||
usage,
|
||||
serializationMode = 'response',
|
||||
} = input;
|
||||
if (isRecord(upstreamPayload)) {
|
||||
if (upstreamPayload.object === 'response.compaction') {
|
||||
return upstreamPayload;
|
||||
}
|
||||
if (serializationMode === 'response' && 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 syntheticOutput = extractSyntheticOutputItemsFromUpstream(upstreamPayload);
|
||||
const syntheticUsageDetails = extractSyntheticUsageDetails(upstreamPayload);
|
||||
const toolCalls = (() => {
|
||||
const extracted = extractToolCallsFromUpstream(upstreamPayload);
|
||||
if (extracted.length > 0) return extracted;
|
||||
return extractToolCallsFromNormalized(normalized);
|
||||
})();
|
||||
const rawReasoningSignature = typeof normalized.reasoningSignature === 'string'
|
||||
? normalized.reasoningSignature.trim()
|
||||
: '';
|
||||
const encryptedReasoning = decodeOpenAiEncryptedReasoning(rawReasoningSignature)
|
||||
?? (rawReasoningSignature && !rawReasoningSignature.startsWith('metapi:') ? rawReasoningSignature : null);
|
||||
const annotations = extractAnnotationsFromUpstream(upstreamPayload);
|
||||
|
||||
const output: Array<Record<string, unknown>> = syntheticOutput.map((item) => cloneJson(item));
|
||||
const hasReasoningItem = output.some((item) => asTrimmedString(item.type).toLowerCase() === 'reasoning');
|
||||
const hasMessageItem = output.some((item) => asTrimmedString(item.type).toLowerCase() === 'message');
|
||||
const hasToolLikeItem = output.some((item) => {
|
||||
const itemType = asTrimmedString(item.type).toLowerCase();
|
||||
return (
|
||||
itemType === 'function_call'
|
||||
|| itemType === 'custom_tool_call'
|
||||
|| itemType === 'function_call_output'
|
||||
|| itemType === 'custom_tool_call_output'
|
||||
|| itemType === 'image_generation_call'
|
||||
);
|
||||
});
|
||||
|
||||
if ((normalized.reasoningContent || encryptedReasoning) && !hasReasoningItem) {
|
||||
const reasoningItem: Record<string, unknown> = {
|
||||
id: ensureMessageId(`${normalizedId}_reasoning`),
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: normalized.reasoningContent
|
||||
? [{
|
||||
type: 'summary_text',
|
||||
text: normalized.reasoningContent,
|
||||
}]
|
||||
: [],
|
||||
};
|
||||
if (encryptedReasoning) {
|
||||
reasoningItem.encrypted_content = encryptedReasoning;
|
||||
}
|
||||
output.push(reasoningItem);
|
||||
}
|
||||
|
||||
if ((normalized.content || (!hasToolLikeItem && output.length === 0 && toolCalls.length === 0)) && !hasMessageItem) {
|
||||
const textPart: Record<string, unknown> = {
|
||||
type: 'output_text',
|
||||
text: normalized.content || '',
|
||||
};
|
||||
if (annotations.length > 0) {
|
||||
textPart.annotations = annotations;
|
||||
}
|
||||
output.push({
|
||||
id: messageId,
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [textPart],
|
||||
});
|
||||
}
|
||||
|
||||
if (toolCalls.length > 0 && !hasToolLikeItem) {
|
||||
for (const toolCall of toolCalls) {
|
||||
const mcpItem = decodeResponsesMcpCompatToolCall(toolCall.name, toolCall.arguments);
|
||||
if (mcpItem) {
|
||||
output.push(mcpItem);
|
||||
continue;
|
||||
}
|
||||
|
||||
output.push({
|
||||
id: toolCall.id,
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: toolCall.id,
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const usagePayload = buildResponsesUsagePayload(usage, syntheticUsageDetails);
|
||||
if (serializationMode === 'compact') {
|
||||
return {
|
||||
id: responseId,
|
||||
object: 'response.compaction',
|
||||
created_at: normalized.created,
|
||||
output,
|
||||
usage: usagePayload,
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
id: responseId,
|
||||
object: 'response',
|
||||
created_at: normalized.created,
|
||||
status: 'completed',
|
||||
model: normalized.model,
|
||||
output,
|
||||
output_text: normalized.content || collectOutputTextFromItems(output),
|
||||
usage: usagePayload,
|
||||
};
|
||||
}
|
||||
|
||||
export const openAiResponsesOutbound = {
|
||||
normalizeFinal: normalizeResponsesFinalPayload,
|
||||
serializeFinal: serializeResponsesFinalPayload,
|
||||
};
|
||||
|
||||
export type {
|
||||
ResponsesFinalSerializationMode,
|
||||
ResponsesToolCall,
|
||||
ResponsesUsageSummary,
|
||||
} from './responseBridge.js';
|
||||
export {
|
||||
serializeResponsesFinalPayload as toResponsesPayload,
|
||||
};
|
||||
openAiResponsesOutbound,
|
||||
openAiResponsesResponseBridge,
|
||||
buildNormalizedFinalToOpenAiResponsesPayload as serializeResponsesFinalPayload,
|
||||
buildNormalizedFinalToOpenAiResponsesPayload as toResponsesPayload,
|
||||
normalizeOpenAiResponsesFinalToNormalized as normalizeResponsesFinalPayload,
|
||||
} from './responseBridge.js';
|
||||
|
||||
@@ -1,8 +1,13 @@
|
||||
import { createProxyStreamLifecycle } from '../../shared/protocolLifecycle.js';
|
||||
import { type ParsedSseEvent } from '../../shared/normalized.js';
|
||||
import { completeResponsesStream, createOpenAiResponsesAggregateState, failResponsesStream, serializeConvertedResponsesEvents } from './aggregator.js';
|
||||
import { openAiResponsesOutbound } from './outbound.js';
|
||||
import { openAiResponsesStream } from './stream.js';
|
||||
import {
|
||||
hasMeaningfulResponsesOutputItem,
|
||||
hasMeaningfulResponsesPayloadOutput,
|
||||
openAiResponsesStream,
|
||||
preserveMeaningfulResponsesTerminalPayload,
|
||||
serializeResponsesUpstreamFinalAsStream,
|
||||
} from './streamBridge.js';
|
||||
import { config } from '../../../config.js';
|
||||
|
||||
type StreamReader = {
|
||||
@@ -45,42 +50,8 @@ function asTrimmedString(value: unknown): string {
|
||||
return typeof value === 'string' ? value.trim() : '';
|
||||
}
|
||||
|
||||
function hasNonEmptyString(value: unknown): boolean {
|
||||
return typeof value === 'string' && value.trim().length > 0;
|
||||
}
|
||||
|
||||
function hasMeaningfulContentPart(part: unknown): boolean {
|
||||
if (!isRecord(part)) return false;
|
||||
const partType = typeof part.type === 'string' ? part.type.trim().toLowerCase() : '';
|
||||
if (partType === 'output_text' || partType === 'text') {
|
||||
return hasNonEmptyString(part.text);
|
||||
}
|
||||
return partType.length > 0;
|
||||
}
|
||||
|
||||
function hasMeaningfulOutputItem(item: unknown): boolean {
|
||||
if (!isRecord(item)) return false;
|
||||
const itemType = typeof item.type === 'string' ? item.type.trim().toLowerCase() : '';
|
||||
if (itemType === 'message') {
|
||||
return Array.isArray(item.content) && item.content.some((part) => hasMeaningfulContentPart(part));
|
||||
}
|
||||
if (itemType === 'reasoning') {
|
||||
return (
|
||||
(Array.isArray(item.summary) && item.summary.some((part) => hasMeaningfulContentPart(part)))
|
||||
|| hasNonEmptyString(item.encrypted_content)
|
||||
);
|
||||
}
|
||||
return itemType.length > 0;
|
||||
}
|
||||
|
||||
function hasMeaningfulResponsesPayloadOutput(payload: unknown): boolean {
|
||||
if (!isRecord(payload)) return false;
|
||||
if (hasNonEmptyString(payload.output_text)) return true;
|
||||
return Array.isArray(payload.output) && payload.output.some((item) => hasMeaningfulOutputItem(item));
|
||||
}
|
||||
|
||||
function hasMeaningfulAggregateOutput(state: ReturnType<typeof createOpenAiResponsesAggregateState>): boolean {
|
||||
return state.outputItems.some((item) => hasMeaningfulOutputItem(item));
|
||||
return state.outputItems.some((item) => hasMeaningfulResponsesOutputItem(item));
|
||||
}
|
||||
|
||||
function shouldFailEmptyResponsesCompletion(input: {
|
||||
@@ -116,59 +87,6 @@ function getResponsesStreamFailureMessage(payload: unknown, fallback = 'upstream
|
||||
return fallback;
|
||||
}
|
||||
|
||||
function preserveMeaningfulTerminalResponsesPayload(
|
||||
lines: string[],
|
||||
eventType: 'response.completed' | 'response.incomplete',
|
||||
payload: Record<string, unknown>,
|
||||
): string[] {
|
||||
const responsePayload = isRecord(payload.response) ? payload.response : null;
|
||||
if (!responsePayload || !hasMeaningfulResponsesPayloadOutput(responsePayload)) {
|
||||
return lines;
|
||||
}
|
||||
|
||||
const parsed = openAiResponsesStream.pullSseEvents(lines.join(''));
|
||||
let replaced = false;
|
||||
const nextLines: string[] = [];
|
||||
|
||||
for (const event of parsed.events) {
|
||||
if (event.data === '[DONE]') {
|
||||
nextLines.push('data: [DONE]\n\n');
|
||||
continue;
|
||||
}
|
||||
|
||||
let parsedPayload: unknown = null;
|
||||
try {
|
||||
parsedPayload = JSON.parse(event.data);
|
||||
} catch {
|
||||
nextLines.push(`${event.event ? `event: ${event.event}\n` : ''}data: ${event.data}\n\n`);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (
|
||||
isRecord(parsedPayload)
|
||||
&& asTrimmedString(parsedPayload.type) === eventType
|
||||
&& isRecord(parsedPayload.response)
|
||||
&& !hasMeaningfulResponsesPayloadOutput(parsedPayload.response)
|
||||
) {
|
||||
replaced = true;
|
||||
nextLines.push(
|
||||
`event: ${event.event || eventType}\ndata: ${JSON.stringify({
|
||||
...parsedPayload,
|
||||
response: {
|
||||
...parsedPayload.response,
|
||||
...responsePayload,
|
||||
},
|
||||
})}\n\n`,
|
||||
);
|
||||
continue;
|
||||
}
|
||||
|
||||
nextLines.push(`${event.event ? `event: ${event.event}\n` : ''}data: ${event.data}\n\n`);
|
||||
}
|
||||
|
||||
return replaced ? nextLines : lines;
|
||||
}
|
||||
|
||||
export function createResponsesProxyStreamSession(input: ResponsesProxyStreamSessionInput) {
|
||||
const streamContext = openAiResponsesStream.createContext(input.modelName);
|
||||
const responsesState = createOpenAiResponsesAggregateState(input.modelName);
|
||||
@@ -268,9 +186,9 @@ export function createResponsesProxyStreamSession(input: ResponsesProxyStreamSes
|
||||
usage: input.getUsage(),
|
||||
});
|
||||
if (isIncompleteEvent) {
|
||||
convertedLines = preserveMeaningfulTerminalResponsesPayload(convertedLines, 'response.incomplete', parsedPayload);
|
||||
convertedLines = preserveMeaningfulResponsesTerminalPayload(convertedLines, 'response.incomplete', parsedPayload);
|
||||
} else if (eventBlock.event === 'response.completed' || payloadType === 'response.completed') {
|
||||
convertedLines = preserveMeaningfulTerminalResponsesPayload(convertedLines, 'response.completed', parsedPayload);
|
||||
convertedLines = preserveMeaningfulResponsesTerminalPayload(convertedLines, 'response.completed', parsedPayload);
|
||||
}
|
||||
if (
|
||||
(eventBlock.event === 'response.completed' || payloadType === 'response.completed')
|
||||
@@ -314,27 +232,22 @@ export function createResponsesProxyStreamSession(input: ResponsesProxyStreamSes
|
||||
const payloadType = (isRecord(payload) && typeof payload.type === 'string')
|
||||
? payload.type
|
||||
: '';
|
||||
const payloadStatus = isRecord(payload) && typeof payload.status === 'string'
|
||||
? payload.status
|
||||
: '';
|
||||
if (payloadType === 'error' || payloadType === 'response.failed') {
|
||||
fail(payload);
|
||||
response?.end();
|
||||
return terminalResult;
|
||||
}
|
||||
|
||||
const normalizedFinal = openAiResponsesOutbound.normalizeFinal(payload, input.modelName, fallbackText);
|
||||
const serializedFinal = serializeResponsesUpstreamFinalAsStream({
|
||||
payload,
|
||||
modelName: input.modelName,
|
||||
fallbackText,
|
||||
usage: input.getUsage(),
|
||||
});
|
||||
const { normalizedFinal, streamPayload, isIncompletePayload, lines } = serializedFinal;
|
||||
streamContext.id = normalizedFinal.id;
|
||||
streamContext.model = normalizedFinal.model;
|
||||
streamContext.created = normalizedFinal.created;
|
||||
|
||||
const streamPayload = openAiResponsesOutbound.serializeFinal({
|
||||
upstreamPayload: payload,
|
||||
normalized: normalizedFinal,
|
||||
usage: input.getUsage(),
|
||||
serializationMode: 'response',
|
||||
});
|
||||
const isIncompletePayload = payloadType === 'response.incomplete' || payloadStatus === 'incomplete';
|
||||
if (!isIncompletePayload && shouldFailEmptyResponsesCompletion({
|
||||
payload: { type: 'response.completed', response: streamPayload },
|
||||
state: responsesState,
|
||||
@@ -349,36 +262,13 @@ export function createResponsesProxyStreamSession(input: ResponsesProxyStreamSes
|
||||
response?.end();
|
||||
return terminalResult;
|
||||
}
|
||||
const createdPayload = {
|
||||
...streamPayload,
|
||||
status: 'in_progress',
|
||||
output: [],
|
||||
output_text: '',
|
||||
};
|
||||
|
||||
if (isIncompletePayload) {
|
||||
finalized = true;
|
||||
terminalResult = {
|
||||
status: 'completed',
|
||||
errorMessage: null,
|
||||
};
|
||||
input.writeLines([
|
||||
`event: response.created\ndata: ${JSON.stringify({ type: 'response.created', response: createdPayload })}\n\n`,
|
||||
`event: response.incomplete\ndata: ${JSON.stringify({ type: 'response.incomplete', response: streamPayload })}\n\n`,
|
||||
'data: [DONE]\n\n',
|
||||
]);
|
||||
} else {
|
||||
finalized = true;
|
||||
terminalResult = {
|
||||
status: 'completed',
|
||||
errorMessage: null,
|
||||
};
|
||||
input.writeLines([
|
||||
`event: response.created\ndata: ${JSON.stringify({ type: 'response.created', response: createdPayload })}\n\n`,
|
||||
`event: response.completed\ndata: ${JSON.stringify({ type: 'response.completed', response: streamPayload })}\n\n`,
|
||||
'data: [DONE]\n\n',
|
||||
]);
|
||||
}
|
||||
finalized = true;
|
||||
terminalResult = {
|
||||
status: 'completed',
|
||||
errorMessage: null,
|
||||
};
|
||||
input.writeLines(lines);
|
||||
response?.end();
|
||||
return terminalResult;
|
||||
},
|
||||
|
||||
@@ -0,0 +1,126 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
buildCanonicalRequestToOpenAiResponsesBody,
|
||||
parseOpenAiResponsesRequestToCanonical,
|
||||
} from './requestBridge.js';
|
||||
|
||||
describe('openai responses request bridge', () => {
|
||||
it('parses OpenAI Responses bodies into canonical envelopes', () => {
|
||||
const result = parseOpenAiResponsesRequestToCanonical({
|
||||
model: 'gpt-5',
|
||||
input: 'hello',
|
||||
previous_response_id: 'resp_prev_1',
|
||||
prompt_cache_key: 'cache-key',
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
},
|
||||
});
|
||||
|
||||
expect(result.error).toBeUndefined();
|
||||
expect(result.value).toMatchObject({
|
||||
operation: 'generate',
|
||||
surface: 'openai-responses',
|
||||
requestedModel: 'gpt-5',
|
||||
continuation: {
|
||||
previousResponseId: 'resp_prev_1',
|
||||
promptCacheKey: 'cache-key',
|
||||
},
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
},
|
||||
});
|
||||
});
|
||||
|
||||
it('builds OpenAI Responses bodies from canonical envelopes', () => {
|
||||
const body = buildCanonicalRequestToOpenAiResponsesBody({
|
||||
operation: 'generate',
|
||||
surface: 'openai-responses',
|
||||
cliProfile: 'codex',
|
||||
requestedModel: 'gpt-5',
|
||||
stream: true,
|
||||
messages: [{ role: 'user', parts: [{ type: 'text', text: 'hello' }] }],
|
||||
continuation: {
|
||||
previousResponseId: 'resp_prev_1',
|
||||
promptCacheKey: 'cache-key',
|
||||
},
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
},
|
||||
});
|
||||
|
||||
expect(body).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
stream: true,
|
||||
store: false,
|
||||
previous_response_id: 'resp_prev_1',
|
||||
prompt_cache_key: 'cache-key',
|
||||
include: ['reasoning.encrypted_content'],
|
||||
reasoning: {
|
||||
effort: 'high',
|
||||
},
|
||||
});
|
||||
});
|
||||
|
||||
it('builds Responses tool-turn history from canonical assistant reasoning and tool results', () => {
|
||||
const body = buildCanonicalRequestToOpenAiResponsesBody({
|
||||
operation: 'generate',
|
||||
surface: 'openai-responses',
|
||||
cliProfile: 'codex',
|
||||
requestedModel: 'gpt-5',
|
||||
stream: false,
|
||||
messages: [
|
||||
{
|
||||
role: 'assistant',
|
||||
parts: [
|
||||
{
|
||||
type: 'text',
|
||||
text: 'plan quietly',
|
||||
thought: true,
|
||||
},
|
||||
{
|
||||
type: 'tool_call',
|
||||
id: 'call_1',
|
||||
name: 'Glob',
|
||||
argumentsJson: '{"pattern":"README*"}',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
role: 'tool',
|
||||
parts: [{
|
||||
type: 'tool_result',
|
||||
toolCallId: 'call_1',
|
||||
resultText: 'README.md',
|
||||
}],
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
expect(body).toMatchObject({
|
||||
model: 'gpt-5',
|
||||
store: false,
|
||||
include: ['reasoning.encrypted_content'],
|
||||
input: [
|
||||
{
|
||||
type: 'reasoning',
|
||||
summary: [{
|
||||
type: 'summary_text',
|
||||
text: 'plan quietly',
|
||||
}],
|
||||
},
|
||||
{
|
||||
type: 'function_call',
|
||||
call_id: 'call_1',
|
||||
name: 'Glob',
|
||||
arguments: '{"pattern":"README*"}',
|
||||
},
|
||||
{
|
||||
type: 'function_call_output',
|
||||
call_id: 'call_1',
|
||||
output: 'README.md',
|
||||
},
|
||||
],
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,87 @@
|
||||
import { canonicalRequestFromOpenAiBody, canonicalRequestToOpenAiChatBody } from '../../canonical/openAiRequestBridge.js';
|
||||
import type { CanonicalRequestEnvelope } from '../../canonical/types.js';
|
||||
import type { ProtocolParseContext } from '../../contracts.js';
|
||||
import {
|
||||
convertOpenAiBodyToResponsesBody,
|
||||
convertResponsesBodyToOpenAiBody,
|
||||
} from './conversion.js';
|
||||
import { openAiResponsesInbound } from './inbound.js';
|
||||
|
||||
function normalizeIncludeList(value: unknown): string[] {
|
||||
if (typeof value === 'string') {
|
||||
const trimmed = value.trim();
|
||||
return trimmed ? [trimmed] : [];
|
||||
}
|
||||
if (!Array.isArray(value)) return [];
|
||||
return value
|
||||
.map((item) => (typeof item === 'string' ? item.trim() : ''))
|
||||
.filter((item) => item.length > 0);
|
||||
}
|
||||
|
||||
export function parseOpenAiResponsesRequestToCanonical(
|
||||
body: unknown,
|
||||
ctx?: ProtocolParseContext,
|
||||
): { value?: CanonicalRequestEnvelope; error?: { statusCode: number; payload: unknown } } {
|
||||
const parsed = openAiResponsesInbound.parse(body, {
|
||||
defaultEncryptedReasoningInclude: ctx?.defaultEncryptedReasoningInclude,
|
||||
});
|
||||
if (parsed.error) {
|
||||
return { error: parsed.error };
|
||||
}
|
||||
if (!parsed.value) {
|
||||
return {
|
||||
error: {
|
||||
statusCode: 400,
|
||||
payload: {
|
||||
error: {
|
||||
message: 'invalid responses request',
|
||||
type: 'invalid_request_error',
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
const responsesBody = parsed.value.parsed.normalizedBody;
|
||||
const openAiBody = convertResponsesBodyToOpenAiBody(
|
||||
responsesBody,
|
||||
typeof responsesBody.model === 'string' ? responsesBody.model : parsed.value.model,
|
||||
responsesBody.stream === true,
|
||||
{ defaultEncryptedReasoningInclude: ctx?.defaultEncryptedReasoningInclude },
|
||||
);
|
||||
|
||||
return {
|
||||
value: canonicalRequestFromOpenAiBody({
|
||||
body: openAiBody,
|
||||
surface: 'openai-responses',
|
||||
cliProfile: ctx?.cliProfile,
|
||||
operation: ctx?.operation,
|
||||
metadata: ctx?.metadata,
|
||||
passthrough: ctx?.passthrough,
|
||||
continuation: ctx?.continuation,
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
export function buildCanonicalRequestToOpenAiResponsesBody(
|
||||
request: CanonicalRequestEnvelope,
|
||||
): Record<string, unknown> {
|
||||
const openAiBody = canonicalRequestToOpenAiChatBody(request);
|
||||
if (request.reasoning) {
|
||||
openAiBody.reasoning = {
|
||||
...(request.reasoning.effort ? { effort: request.reasoning.effort } : {}),
|
||||
...(request.reasoning.budgetTokens !== undefined ? { budget_tokens: request.reasoning.budgetTokens } : {}),
|
||||
...(request.reasoning.summary ? { summary: request.reasoning.summary } : {}),
|
||||
};
|
||||
}
|
||||
const body = convertOpenAiBodyToResponsesBody(openAiBody, request.requestedModel, request.stream);
|
||||
const mergedInclude = Array.from(new Set([
|
||||
'reasoning.encrypted_content',
|
||||
...normalizeIncludeList(body.include),
|
||||
]));
|
||||
return {
|
||||
...body,
|
||||
include: mergedInclude,
|
||||
store: false,
|
||||
};
|
||||
}
|
||||
@@ -0,0 +1,217 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
buildNormalizedFinalToOpenAiResponsesPayload,
|
||||
normalizeOpenAiResponsesFinalToNormalized,
|
||||
openAiResponsesResponseBridge,
|
||||
} from './responseBridge.js';
|
||||
import { openAiResponsesOutbound } from './outbound.js';
|
||||
|
||||
describe('openai responses response bridge', () => {
|
||||
it('normalizes upstream responses finals into normalized final responses', () => {
|
||||
const normalized = normalizeOpenAiResponsesFinalToNormalized({
|
||||
id: 'resp_native_1',
|
||||
object: 'response',
|
||||
created_at: 1700000000,
|
||||
model: 'gpt-5',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: 'hello',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
}, 'gpt-5');
|
||||
|
||||
expect(normalized).toMatchObject({
|
||||
id: 'resp_native_1',
|
||||
model: 'gpt-5',
|
||||
content: 'hello',
|
||||
});
|
||||
});
|
||||
|
||||
it('builds responses payloads from normalized final responses', () => {
|
||||
const payload = buildNormalizedFinalToOpenAiResponsesPayload({
|
||||
upstreamPayload: {
|
||||
id: 'opaque_1',
|
||||
model: 'gpt-5',
|
||||
},
|
||||
normalized: {
|
||||
id: 'opaque_1',
|
||||
model: 'gpt-5',
|
||||
created: 1700000000,
|
||||
content: 'hello',
|
||||
reasoningContent: '',
|
||||
finishReason: 'stop',
|
||||
toolCalls: [],
|
||||
},
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
expect(payload).toEqual({
|
||||
id: 'resp_opaque_1',
|
||||
object: 'response',
|
||||
created_at: 1700000000,
|
||||
status: 'completed',
|
||||
model: 'gpt-5',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_opaque_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: 'hello',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
output_text: 'hello',
|
||||
usage: {
|
||||
input_tokens: 11,
|
||||
output_tokens: 7,
|
||||
total_tokens: 18,
|
||||
},
|
||||
});
|
||||
});
|
||||
|
||||
it('preserves function-call chains from response-like upstream payloads instead of flattening them into text', () => {
|
||||
const payload = buildNormalizedFinalToOpenAiResponsesPayload({
|
||||
upstreamPayload: {
|
||||
id: 'opaque_tool_chain_1',
|
||||
model: 'gpt-5',
|
||||
output: [
|
||||
{
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [
|
||||
{
|
||||
type: 'summary_text',
|
||||
text: 'plan first',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
id: 'fc_1',
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
{
|
||||
id: 'fco_1',
|
||||
type: 'function_call_output',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
output: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: '22C',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
id: 'msg_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: 'done',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
normalized: {
|
||||
id: 'opaque_tool_chain_1',
|
||||
model: 'gpt-5',
|
||||
created: 1700000000,
|
||||
content: 'done',
|
||||
reasoningContent: 'plan first',
|
||||
finishReason: 'stop',
|
||||
toolCalls: [{
|
||||
id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
}],
|
||||
},
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
expect(payload).toMatchObject({
|
||||
id: 'resp_opaque_tool_chain_1',
|
||||
object: 'response',
|
||||
output_text: 'done',
|
||||
output: [
|
||||
{
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [
|
||||
{
|
||||
type: 'summary_text',
|
||||
text: 'plan first',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
id: 'fc_1',
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
{
|
||||
id: 'fco_1',
|
||||
type: 'function_call_output',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
output: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: '22C',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
id: 'msg_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [
|
||||
{
|
||||
type: 'output_text',
|
||||
text: 'done',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
});
|
||||
});
|
||||
|
||||
it('keeps the outbound facade pointed at the response bridge object', () => {
|
||||
expect(openAiResponsesOutbound).toBe(openAiResponsesResponseBridge);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,453 @@
|
||||
import {
|
||||
normalizeUpstreamFinalResponse,
|
||||
type NormalizedFinalResponse,
|
||||
} from '../../shared/normalized.js';
|
||||
import { decodeOpenAiEncryptedReasoning } from '../../shared/reasoningTransport.js';
|
||||
import { decodeResponsesMcpCompatToolCall } from './mcpCompatibility.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 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_${trimmed}`;
|
||||
}
|
||||
|
||||
function ensureMessageId(rawId: string): string {
|
||||
const trimmed = rawId.trim() || `msg_${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_${trimmed}`;
|
||||
}
|
||||
|
||||
function stringifyToolInput(input: unknown): string {
|
||||
if (typeof input === 'string') return input;
|
||||
if (!input || typeof input !== 'object') return '';
|
||||
try {
|
||||
return JSON.stringify(input);
|
||||
} catch {
|
||||
return '';
|
||||
}
|
||||
}
|
||||
|
||||
export type ResponsesUsageSummary = {
|
||||
promptTokens: number;
|
||||
completionTokens: number;
|
||||
totalTokens: number;
|
||||
inputTokensDetails?: Record<string, unknown>;
|
||||
outputTokensDetails?: Record<string, unknown>;
|
||||
};
|
||||
|
||||
export type ResponsesToolCall = {
|
||||
id: string;
|
||||
name: string;
|
||||
arguments: string;
|
||||
};
|
||||
|
||||
export type ResponsesFinalSerializationMode = 'response' | 'compact';
|
||||
|
||||
type ResponsesOutputItem = Record<string, unknown>;
|
||||
|
||||
function extractToolCallsFromUpstream(payload: unknown): ResponsesToolCall[] {
|
||||
if (!isRecord(payload)) return [];
|
||||
|
||||
if (Array.isArray(payload.choices)) {
|
||||
const choice = payload.choices[0];
|
||||
const message = isRecord((choice as any)?.message) ? (choice as any).message : {};
|
||||
const toolCalls = Array.isArray((message as any).tool_calls) ? (message as any).tool_calls : [];
|
||||
return toolCalls
|
||||
.map((item: unknown, index: number) => {
|
||||
if (!isRecord(item)) return null;
|
||||
const fn = isRecord(item.function) ? item.function : {};
|
||||
const id = typeof item.id === 'string' && item.id.trim().length > 0
|
||||
? item.id
|
||||
: `call_${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 [];
|
||||
}
|
||||
|
||||
function extractToolCallsFromNormalized(normalized: NormalizedFinalResponse): ResponsesToolCall[] {
|
||||
return Array.isArray(normalized.toolCalls)
|
||||
? normalized.toolCalls
|
||||
.map((item) => {
|
||||
const id = ensureFunctionCallId(asTrimmedString(item.id));
|
||||
const name = asTrimmedString(item.name);
|
||||
if (!name) return null;
|
||||
return {
|
||||
id,
|
||||
name,
|
||||
arguments: typeof item.arguments === 'string' ? item.arguments : '',
|
||||
};
|
||||
})
|
||||
.filter((item): item is ResponsesToolCall => !!item)
|
||||
: [];
|
||||
}
|
||||
|
||||
function extractAnnotationsFromUpstream(payload: unknown): unknown[] {
|
||||
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 annotations = Array.isArray((message as any).annotations) ? (message as any).annotations : [];
|
||||
return annotations.map((item) => cloneJson(item));
|
||||
}
|
||||
|
||||
if (payload.type === 'message' && Array.isArray(payload.content)) {
|
||||
const annotations = payload.content
|
||||
.filter((item): item is Record<string, unknown> => isRecord(item))
|
||||
.flatMap((item) => (Array.isArray(item.annotations) ? item.annotations : []));
|
||||
return annotations.map((item) => cloneJson(item));
|
||||
}
|
||||
|
||||
return [];
|
||||
}
|
||||
|
||||
function extractSyntheticOutputItemsFromUpstream(payload: unknown): ResponsesOutputItem[] {
|
||||
if (!isRecord(payload) || !Array.isArray(payload.output)) return [];
|
||||
|
||||
return payload.output
|
||||
.map((rawItem): ResponsesOutputItem | null => {
|
||||
if (!isRecord(rawItem)) return null;
|
||||
const itemType = asTrimmedString(rawItem.type).toLowerCase();
|
||||
if (!itemType) return null;
|
||||
|
||||
if (itemType === 'message') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: 'message',
|
||||
role: asTrimmedString(rawItem.role) || 'assistant',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
content: Array.isArray(rawItem.content) ? cloneJson(rawItem.content) : [],
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'reasoning') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: 'reasoning',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
encrypted_content: rawItem.encrypted_content,
|
||||
summary: Array.isArray(rawItem.summary) ? cloneJson(rawItem.summary) : [],
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'function_call') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureFunctionCallId(''),
|
||||
type: 'function_call',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
call_id: asTrimmedString(rawItem.call_id) || ensureFunctionCallId(asTrimmedString(rawItem.id)),
|
||||
name: asTrimmedString(rawItem.name),
|
||||
arguments: typeof rawItem.arguments === 'string' ? rawItem.arguments : '',
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'custom_tool_call') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureFunctionCallId(''),
|
||||
type: 'custom_tool_call',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
call_id: asTrimmedString(rawItem.call_id) || ensureFunctionCallId(asTrimmedString(rawItem.id)),
|
||||
name: asTrimmedString(rawItem.name),
|
||||
input: typeof rawItem.input === 'string' ? rawItem.input : '',
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'function_call_output' || itemType === 'custom_tool_call_output') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: itemType,
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
call_id: asTrimmedString(rawItem.call_id),
|
||||
output: cloneJson(rawItem.output),
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType === 'image_generation_call') {
|
||||
return {
|
||||
id: asTrimmedString(rawItem.id) || ensureMessageId(''),
|
||||
type: 'image_generation_call',
|
||||
status: asTrimmedString(rawItem.status) || 'completed',
|
||||
result: cloneJson(rawItem.result),
|
||||
partial_images: Array.isArray(rawItem.partial_images) ? cloneJson(rawItem.partial_images) : [],
|
||||
...(rawItem.background !== undefined ? { background: cloneJson(rawItem.background) } : {}),
|
||||
...(rawItem.mime_type !== undefined ? { mime_type: cloneJson(rawItem.mime_type) } : {}),
|
||||
...(rawItem.output_format !== undefined ? { output_format: cloneJson(rawItem.output_format) } : {}),
|
||||
...(rawItem.quality !== undefined ? { quality: cloneJson(rawItem.quality) } : {}),
|
||||
...(rawItem.size !== undefined ? { size: cloneJson(rawItem.size) } : {}),
|
||||
...(rawItem.revised_prompt !== undefined ? { revised_prompt: cloneJson(rawItem.revised_prompt) } : {}),
|
||||
};
|
||||
}
|
||||
|
||||
if (itemType.startsWith('mcp_')) {
|
||||
return cloneJson(rawItem);
|
||||
}
|
||||
|
||||
return null;
|
||||
})
|
||||
.filter((item): item is ResponsesOutputItem => item !== null);
|
||||
}
|
||||
|
||||
function collectOutputTextFromItems(items: ResponsesOutputItem[]): string {
|
||||
const textParts: string[] = [];
|
||||
|
||||
for (const item of items) {
|
||||
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 partType = asTrimmedString(part.type).toLowerCase();
|
||||
if ((partType === 'output_text' || partType === 'text') && typeof part.text === 'string') {
|
||||
textParts.push(part.text);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return textParts.join('');
|
||||
}
|
||||
|
||||
function extractSyntheticUsageDetails(upstreamPayload: unknown): {
|
||||
inputTokensDetails?: Record<string, unknown>;
|
||||
outputTokensDetails?: Record<string, unknown>;
|
||||
} {
|
||||
if (!isRecord(upstreamPayload) || !isRecord(upstreamPayload.usage)) {
|
||||
return {};
|
||||
}
|
||||
|
||||
const usage = upstreamPayload.usage;
|
||||
const promptDetails = (
|
||||
(isRecord(usage.input_tokens_details) ? usage.input_tokens_details : null)
|
||||
?? (isRecord(usage.prompt_tokens_details) ? usage.prompt_tokens_details : null)
|
||||
);
|
||||
const completionDetails = (
|
||||
(isRecord(usage.output_tokens_details) ? usage.output_tokens_details : null)
|
||||
?? (isRecord(usage.completion_tokens_details) ? usage.completion_tokens_details : null)
|
||||
);
|
||||
|
||||
return {
|
||||
...(promptDetails ? { inputTokensDetails: cloneJson(promptDetails) } : {}),
|
||||
...(completionDetails ? { outputTokensDetails: cloneJson(completionDetails) } : {}),
|
||||
};
|
||||
}
|
||||
|
||||
export function normalizeOpenAiResponsesFinalToNormalized(
|
||||
payload: unknown,
|
||||
modelName: string,
|
||||
fallbackText = '',
|
||||
): NormalizedFinalResponse {
|
||||
return normalizeUpstreamFinalResponse(payload, modelName, fallbackText);
|
||||
}
|
||||
|
||||
function buildResponsesUsagePayload(
|
||||
usage: ResponsesUsageSummary,
|
||||
syntheticUsageDetails: {
|
||||
inputTokensDetails?: Record<string, unknown>;
|
||||
outputTokensDetails?: Record<string, unknown>;
|
||||
},
|
||||
): Record<string, unknown> {
|
||||
return {
|
||||
input_tokens: usage.promptTokens,
|
||||
output_tokens: usage.completionTokens,
|
||||
total_tokens: usage.totalTokens,
|
||||
...(usage.inputTokensDetails ?? syntheticUsageDetails.inputTokensDetails
|
||||
? { input_tokens_details: cloneJson(usage.inputTokensDetails ?? syntheticUsageDetails.inputTokensDetails) }
|
||||
: {}),
|
||||
...(usage.outputTokensDetails ?? syntheticUsageDetails.outputTokensDetails
|
||||
? { output_tokens_details: cloneJson(usage.outputTokensDetails ?? syntheticUsageDetails.outputTokensDetails) }
|
||||
: {}),
|
||||
};
|
||||
}
|
||||
|
||||
export function buildNormalizedFinalToOpenAiResponsesPayload(input: {
|
||||
upstreamPayload: unknown;
|
||||
normalized: NormalizedFinalResponse;
|
||||
usage: ResponsesUsageSummary;
|
||||
serializationMode?: ResponsesFinalSerializationMode;
|
||||
}): Record<string, unknown> {
|
||||
const {
|
||||
upstreamPayload,
|
||||
normalized,
|
||||
usage,
|
||||
serializationMode = 'response',
|
||||
} = input;
|
||||
if (isRecord(upstreamPayload)) {
|
||||
if (upstreamPayload.object === 'response.compaction') {
|
||||
return upstreamPayload;
|
||||
}
|
||||
if (serializationMode === 'response' && 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 syntheticOutput = extractSyntheticOutputItemsFromUpstream(upstreamPayload);
|
||||
const syntheticUsageDetails = extractSyntheticUsageDetails(upstreamPayload);
|
||||
const toolCalls = (() => {
|
||||
const extracted = extractToolCallsFromUpstream(upstreamPayload);
|
||||
if (extracted.length > 0) return extracted;
|
||||
return extractToolCallsFromNormalized(normalized);
|
||||
})();
|
||||
const rawReasoningSignature = typeof normalized.reasoningSignature === 'string'
|
||||
? normalized.reasoningSignature.trim()
|
||||
: '';
|
||||
const encryptedReasoning = decodeOpenAiEncryptedReasoning(rawReasoningSignature)
|
||||
?? (rawReasoningSignature && !rawReasoningSignature.startsWith('metapi:') ? rawReasoningSignature : null);
|
||||
const annotations = extractAnnotationsFromUpstream(upstreamPayload);
|
||||
|
||||
const output: Array<Record<string, unknown>> = syntheticOutput.map((item) => cloneJson(item));
|
||||
const hasReasoningItem = output.some((item) => asTrimmedString(item.type).toLowerCase() === 'reasoning');
|
||||
const hasMessageItem = output.some((item) => asTrimmedString(item.type).toLowerCase() === 'message');
|
||||
const hasToolLikeItem = output.some((item) => {
|
||||
const itemType = asTrimmedString(item.type).toLowerCase();
|
||||
return (
|
||||
itemType === 'function_call'
|
||||
|| itemType === 'custom_tool_call'
|
||||
|| itemType === 'function_call_output'
|
||||
|| itemType === 'custom_tool_call_output'
|
||||
|| itemType === 'image_generation_call'
|
||||
);
|
||||
});
|
||||
|
||||
if ((normalized.reasoningContent || encryptedReasoning) && !hasReasoningItem) {
|
||||
const reasoningItem: Record<string, unknown> = {
|
||||
id: ensureMessageId(`${normalizedId}_reasoning`),
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: normalized.reasoningContent
|
||||
? [{
|
||||
type: 'summary_text',
|
||||
text: normalized.reasoningContent,
|
||||
}]
|
||||
: [],
|
||||
};
|
||||
if (encryptedReasoning) {
|
||||
reasoningItem.encrypted_content = encryptedReasoning;
|
||||
}
|
||||
output.push(reasoningItem);
|
||||
}
|
||||
|
||||
if ((normalized.content || (!hasToolLikeItem && output.length === 0 && toolCalls.length === 0)) && !hasMessageItem) {
|
||||
const textPart: Record<string, unknown> = {
|
||||
type: 'output_text',
|
||||
text: normalized.content || '',
|
||||
};
|
||||
if (annotations.length > 0) {
|
||||
textPart.annotations = annotations;
|
||||
}
|
||||
output.push({
|
||||
id: messageId,
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [textPart],
|
||||
});
|
||||
}
|
||||
|
||||
if (toolCalls.length > 0 && !hasToolLikeItem) {
|
||||
for (const toolCall of toolCalls) {
|
||||
const mcpItem = decodeResponsesMcpCompatToolCall(toolCall.name, toolCall.arguments);
|
||||
if (mcpItem) {
|
||||
output.push(mcpItem);
|
||||
continue;
|
||||
}
|
||||
|
||||
output.push({
|
||||
id: toolCall.id,
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: toolCall.id,
|
||||
name: toolCall.name,
|
||||
arguments: toolCall.arguments,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const usagePayload = buildResponsesUsagePayload(usage, syntheticUsageDetails);
|
||||
if (serializationMode === 'compact') {
|
||||
return {
|
||||
id: responseId,
|
||||
object: 'response.compaction',
|
||||
created_at: normalized.created,
|
||||
output,
|
||||
usage: usagePayload,
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
id: responseId,
|
||||
object: 'response',
|
||||
created_at: normalized.created,
|
||||
status: 'completed',
|
||||
model: normalized.model,
|
||||
output,
|
||||
output_text: normalized.content || collectOutputTextFromItems(output),
|
||||
usage: usagePayload,
|
||||
};
|
||||
}
|
||||
|
||||
export {
|
||||
normalizeOpenAiResponsesFinalToNormalized as normalizeResponsesFinalPayload,
|
||||
buildNormalizedFinalToOpenAiResponsesPayload as serializeResponsesFinalPayload,
|
||||
buildNormalizedFinalToOpenAiResponsesPayload as toResponsesPayload,
|
||||
};
|
||||
|
||||
export const openAiResponsesResponseBridge = {
|
||||
normalizeFinal: normalizeOpenAiResponsesFinalToNormalized,
|
||||
serializeFinal: buildNormalizedFinalToOpenAiResponsesPayload,
|
||||
};
|
||||
|
||||
export const openAiResponsesOutbound = openAiResponsesResponseBridge;
|
||||
@@ -38,6 +38,7 @@ type UpstreamResponse = Exclude<EndpointRecoverResult, null>['upstream'];
|
||||
type CreateResponsesEndpointStrategyInput = {
|
||||
isStream: boolean;
|
||||
requiresNativeResponsesFileUrl: boolean;
|
||||
sitePlatform?: string;
|
||||
dispatchRequest: (
|
||||
request: CompatibilityRequest,
|
||||
targetUrl?: string,
|
||||
@@ -51,12 +52,47 @@ export function createResponsesEndpointStrategy(input: CreateResponsesEndpointSt
|
||||
endpoint: ctx.request.endpoint,
|
||||
status: ctx.response.status,
|
||||
rawErrText: ctx.rawErrText,
|
||||
body: ctx.request.body,
|
||||
})) {
|
||||
const compatibilityBodies = buildResponsesCompatibilityBodies(ctx.request.body);
|
||||
const normalizedSitePlatform = String(input.sitePlatform || '').trim().toLowerCase();
|
||||
const compatibilityBodies = buildResponsesCompatibilityBodies(ctx.request.body, {
|
||||
sitePlatform: input.sitePlatform,
|
||||
});
|
||||
const compatibilityHeaders = buildResponsesCompatibilityHeaderCandidates(
|
||||
ctx.request.headers,
|
||||
input.isStream,
|
||||
{
|
||||
sitePlatform: input.sitePlatform,
|
||||
},
|
||||
);
|
||||
const alternateCompatibilityHeaders = compatibilityHeaders.slice(1);
|
||||
|
||||
if (
|
||||
normalizedSitePlatform === 'sub2api'
|
||||
&& compatibilityBodies.length === 0
|
||||
&& alternateCompatibilityHeaders.length > 0
|
||||
) {
|
||||
for (const compatibilityHeadersCandidate of alternateCompatibilityHeaders) {
|
||||
const compatibilityRequest = {
|
||||
...ctx.request,
|
||||
headers: compatibilityHeadersCandidate,
|
||||
};
|
||||
const compatibilityResponse = await input.dispatchRequest(
|
||||
compatibilityRequest,
|
||||
ctx.targetUrl,
|
||||
);
|
||||
if (compatibilityResponse.ok) {
|
||||
return {
|
||||
upstream: compatibilityResponse,
|
||||
upstreamPath: compatibilityRequest.path,
|
||||
};
|
||||
}
|
||||
|
||||
ctx.request = compatibilityRequest;
|
||||
ctx.response = compatibilityResponse;
|
||||
ctx.rawErrText = await compatibilityResponse.text().catch(() => 'unknown error');
|
||||
}
|
||||
}
|
||||
|
||||
for (const compatibilityHeadersCandidate of compatibilityHeaders) {
|
||||
for (const compatibilityBody of compatibilityBodies) {
|
||||
|
||||
@@ -1,81 +1,11 @@
|
||||
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.incomplete',
|
||||
'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 type {
|
||||
OpenAiResponsesStreamEvent,
|
||||
ResponsesStreamState,
|
||||
} from './streamBridge.js';
|
||||
export {
|
||||
completeResponsesStream,
|
||||
createResponsesStreamState,
|
||||
failResponsesStream,
|
||||
openAiResponsesStream,
|
||||
serializeConvertedResponsesEvents,
|
||||
};
|
||||
} from './streamBridge.js';
|
||||
|
||||
@@ -0,0 +1,420 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import {
|
||||
openAiResponsesStream,
|
||||
preserveMeaningfulResponsesTerminalPayload,
|
||||
serializeResponsesUpstreamFinalAsStream,
|
||||
} from './streamBridge.js';
|
||||
|
||||
function parseSseEvents(lines: string[]): Array<{ event: string | null; payload: Record<string, unknown> | '[DONE]' }> {
|
||||
return lines
|
||||
.flatMap((line) => line.split('\n\n').filter((block) => block.trim().length > 0))
|
||||
.map((block) => {
|
||||
const eventLine = block
|
||||
.split('\n')
|
||||
.find((line) => line.startsWith('event: '));
|
||||
const dataLine = block
|
||||
.split('\n')
|
||||
.find((line) => line.startsWith('data: '));
|
||||
if (!dataLine) return null;
|
||||
if (dataLine === 'data: [DONE]') {
|
||||
return {
|
||||
event: eventLine ? eventLine.slice('event: '.length) : null,
|
||||
payload: '[DONE]' as const,
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
event: eventLine ? eventLine.slice('event: '.length) : null,
|
||||
payload: JSON.parse(dataLine.slice('data: '.length)) as Record<string, unknown>,
|
||||
};
|
||||
})
|
||||
.filter((item): item is { event: string | null; payload: Record<string, unknown> | '[DONE]' } => !!item);
|
||||
}
|
||||
|
||||
describe('openai responses stream bridge', () => {
|
||||
it('normalizes response stream payload metadata onto normalized events', () => {
|
||||
const context = openAiResponsesStream.createContext('gpt-5');
|
||||
const payload = {
|
||||
type: 'response.completed',
|
||||
response: {
|
||||
id: 'resp_1',
|
||||
status: 'completed',
|
||||
},
|
||||
usage: {
|
||||
input_tokens: 11,
|
||||
output_tokens: 7,
|
||||
total_tokens: 18,
|
||||
},
|
||||
};
|
||||
|
||||
const normalized = openAiResponsesStream.normalizeEvent(payload, context, 'gpt-5');
|
||||
|
||||
expect(normalized.responsesEventType).toBe('response.completed');
|
||||
expect(normalized.responsesPayload).toEqual(payload);
|
||||
expect(normalized.usagePayload).toEqual(payload.usage);
|
||||
});
|
||||
|
||||
it('pulls response SSE events and preserves the DONE terminator', () => {
|
||||
const parsed = openAiResponsesStream.pullSseEvents([
|
||||
'event: response.completed',
|
||||
'data: {"type":"response.completed"}',
|
||||
'',
|
||||
'data: [DONE]',
|
||||
'',
|
||||
'',
|
||||
].join('\n'));
|
||||
|
||||
expect(parsed.events).toEqual([
|
||||
{
|
||||
event: 'response.completed',
|
||||
data: '{"type":"response.completed"}',
|
||||
},
|
||||
{
|
||||
event: '',
|
||||
data: '[DONE]',
|
||||
},
|
||||
]);
|
||||
expect(parsed.rest).toBe('');
|
||||
});
|
||||
|
||||
it('serializes upstream final payloads into response.created and response.completed SSE lines', () => {
|
||||
const serialized = serializeResponsesUpstreamFinalAsStream({
|
||||
payload: {
|
||||
id: 'resp_fallback_1',
|
||||
object: 'response',
|
||||
status: 'completed',
|
||||
model: 'gpt-5',
|
||||
output_text: 'hello from fallback',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_fallback_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [{ type: 'output_text', text: 'hello from fallback' }],
|
||||
},
|
||||
],
|
||||
},
|
||||
modelName: 'gpt-5',
|
||||
fallbackText: 'hello from fallback',
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
expect(serialized.isIncompletePayload).toBe(false);
|
||||
expect(serialized.lines.join('')).toContain('event: response.created');
|
||||
expect(serialized.lines.join('')).toContain('event: response.completed');
|
||||
expect(serialized.lines.join('')).toContain('"output_text":"hello from fallback"');
|
||||
expect(serialized.lines.join('')).toContain('data: [DONE]');
|
||||
});
|
||||
|
||||
it('serializes fallback final tool and reasoning output items as canonical Responses SSE events before completion', () => {
|
||||
const serialized = serializeResponsesUpstreamFinalAsStream({
|
||||
payload: {
|
||||
id: 'resp_semantic_fallback_1',
|
||||
object: 'response',
|
||||
status: 'completed',
|
||||
model: 'gpt-5',
|
||||
output_text: 'final answer',
|
||||
output: [
|
||||
{
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
status: 'completed',
|
||||
summary: [{ type: 'summary_text', text: 'plan quietly' }],
|
||||
},
|
||||
{
|
||||
id: 'call_1',
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
{
|
||||
id: 'msg_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [{ type: 'output_text', text: 'final answer' }],
|
||||
},
|
||||
],
|
||||
},
|
||||
modelName: 'gpt-5',
|
||||
fallbackText: 'final answer',
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
const events = parseSseEvents(serialized.lines);
|
||||
|
||||
expect(events.map((entry) => entry.event)).toEqual([
|
||||
'response.created',
|
||||
'response.reasoning_summary_text.done',
|
||||
'response.reasoning_summary_part.done',
|
||||
'response.output_item.done',
|
||||
'response.function_call_arguments.done',
|
||||
'response.output_item.done',
|
||||
'response.output_text.done',
|
||||
'response.content_part.done',
|
||||
'response.output_item.done',
|
||||
'response.completed',
|
||||
null,
|
||||
]);
|
||||
expect(events[4]?.payload).toMatchObject({
|
||||
type: 'response.function_call_arguments.done',
|
||||
call_id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
});
|
||||
expect(events[9]?.payload).toMatchObject({
|
||||
type: 'response.completed',
|
||||
response: {
|
||||
id: 'resp_semantic_fallback_1',
|
||||
output_text: 'final answer',
|
||||
output: [
|
||||
{
|
||||
id: 'rs_1',
|
||||
type: 'reasoning',
|
||||
},
|
||||
{
|
||||
id: 'call_1',
|
||||
type: 'function_call',
|
||||
call_id: 'call_1',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
{
|
||||
id: 'msg_1',
|
||||
type: 'message',
|
||||
},
|
||||
],
|
||||
},
|
||||
});
|
||||
expect(events[10]?.payload).toBe('[DONE]');
|
||||
});
|
||||
|
||||
it('preserves meaningful terminal response payload output when the synthesized terminal event is empty', () => {
|
||||
const preserved = preserveMeaningfulResponsesTerminalPayload(
|
||||
[
|
||||
'event: response.completed\n',
|
||||
`data: ${JSON.stringify({
|
||||
type: 'response.completed',
|
||||
response: {
|
||||
id: 'resp_empty',
|
||||
status: 'completed',
|
||||
output: [],
|
||||
output_text: '',
|
||||
},
|
||||
})}\n\n`,
|
||||
'data: [DONE]\n\n',
|
||||
],
|
||||
'response.completed',
|
||||
{
|
||||
type: 'response.completed',
|
||||
response: {
|
||||
id: 'resp_full',
|
||||
status: 'completed',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_full',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [{ type: 'output_text', text: 'restored text' }],
|
||||
},
|
||||
],
|
||||
output_text: 'restored text',
|
||||
},
|
||||
},
|
||||
);
|
||||
|
||||
expect(preserved.join('')).toContain('"output_text":"restored text"');
|
||||
expect(preserved.join('')).toContain('data: [DONE]');
|
||||
});
|
||||
|
||||
it('keeps response tool-call chains intact in completed SSE fallback payloads', () => {
|
||||
const serialized = serializeResponsesUpstreamFinalAsStream({
|
||||
payload: {
|
||||
id: 'resp_tool_stream_1',
|
||||
object: 'response',
|
||||
status: 'completed',
|
||||
model: 'gpt-5',
|
||||
output_text: 'done',
|
||||
output: [
|
||||
{
|
||||
id: 'fc_1',
|
||||
type: 'function_call',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
name: 'lookup_weather',
|
||||
arguments: '{"city":"Shanghai"}',
|
||||
},
|
||||
{
|
||||
id: 'fco_1',
|
||||
type: 'function_call_output',
|
||||
status: 'completed',
|
||||
call_id: 'call_1',
|
||||
output: '22C',
|
||||
},
|
||||
{
|
||||
id: 'msg_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
status: 'completed',
|
||||
content: [{ type: 'output_text', text: 'done' }],
|
||||
},
|
||||
],
|
||||
},
|
||||
modelName: 'gpt-5',
|
||||
fallbackText: 'done',
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
expect(serialized.lines.join('')).toContain('event: response.completed');
|
||||
expect(serialized.lines.join('')).toContain('"type":"function_call"');
|
||||
expect(serialized.lines.join('')).toContain('"type":"function_call_output"');
|
||||
expect(serialized.lines.join('')).toContain('"call_id":"call_1"');
|
||||
expect(serialized.lines.join('')).toContain('"output_text":"done"');
|
||||
});
|
||||
|
||||
it('keeps incomplete fallback item statuses aligned with the terminal response status when item statuses are omitted', () => {
|
||||
const serialized = serializeResponsesUpstreamFinalAsStream({
|
||||
payload: {
|
||||
id: 'resp_incomplete_1',
|
||||
object: 'response',
|
||||
status: 'incomplete',
|
||||
model: 'gpt-5',
|
||||
output_text: 'partial',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_partial_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
content: [{ type: 'output_text', text: 'partial' }],
|
||||
},
|
||||
],
|
||||
},
|
||||
modelName: 'gpt-5',
|
||||
fallbackText: 'partial',
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
const events = parseSseEvents(serialized.lines);
|
||||
|
||||
expect(events.map((entry) => entry.event)).toEqual([
|
||||
'response.created',
|
||||
'response.output_text.done',
|
||||
'response.content_part.done',
|
||||
'response.output_item.done',
|
||||
'response.incomplete',
|
||||
null,
|
||||
]);
|
||||
expect(events[3]?.payload).toMatchObject({
|
||||
type: 'response.output_item.done',
|
||||
item: {
|
||||
id: 'msg_partial_1',
|
||||
type: 'message',
|
||||
status: 'incomplete',
|
||||
},
|
||||
});
|
||||
expect(events[4]?.payload).toMatchObject({
|
||||
type: 'response.incomplete',
|
||||
response: {
|
||||
id: 'resp_incomplete_1',
|
||||
status: 'incomplete',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_partial_1',
|
||||
type: 'message',
|
||||
status: 'incomplete',
|
||||
},
|
||||
],
|
||||
},
|
||||
});
|
||||
});
|
||||
|
||||
it('serializes failed fallback wrappers as response.failed and preserves the upstream error payload', () => {
|
||||
const serialized = serializeResponsesUpstreamFinalAsStream({
|
||||
payload: {
|
||||
type: 'response.failed',
|
||||
error: {
|
||||
message: 'upstream exploded',
|
||||
type: 'upstream_error',
|
||||
},
|
||||
response: {
|
||||
id: 'resp_failed_1',
|
||||
status: 'failed',
|
||||
model: 'gpt-5',
|
||||
output_text: 'partial',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_failed_1',
|
||||
type: 'message',
|
||||
role: 'assistant',
|
||||
content: [{ type: 'output_text', text: 'partial' }],
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
modelName: 'gpt-5',
|
||||
fallbackText: 'partial',
|
||||
usage: {
|
||||
promptTokens: 11,
|
||||
completionTokens: 7,
|
||||
totalTokens: 18,
|
||||
},
|
||||
});
|
||||
|
||||
const events = parseSseEvents(serialized.lines);
|
||||
|
||||
expect(events.map((entry) => entry.event)).toEqual([
|
||||
'response.created',
|
||||
'response.output_text.done',
|
||||
'response.content_part.done',
|
||||
'response.output_item.done',
|
||||
'response.failed',
|
||||
null,
|
||||
]);
|
||||
expect(events[3]?.payload).toMatchObject({
|
||||
type: 'response.output_item.done',
|
||||
item: {
|
||||
id: 'msg_failed_1',
|
||||
type: 'message',
|
||||
status: 'failed',
|
||||
},
|
||||
});
|
||||
expect(events[4]?.payload).toMatchObject({
|
||||
type: 'response.failed',
|
||||
error: {
|
||||
message: 'upstream exploded',
|
||||
type: 'upstream_error',
|
||||
},
|
||||
response: {
|
||||
id: 'resp_failed_1',
|
||||
status: 'failed',
|
||||
output: [
|
||||
{
|
||||
id: 'msg_failed_1',
|
||||
type: 'message',
|
||||
status: 'failed',
|
||||
},
|
||||
],
|
||||
},
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,305 @@
|
||||
import {
|
||||
createStreamTransformContext,
|
||||
normalizeUpstreamStreamEvent,
|
||||
pullSseEventsWithDone,
|
||||
type NormalizedStreamEvent,
|
||||
type StreamTransformContext,
|
||||
} from '../../shared/normalized.js';
|
||||
import {
|
||||
completeResponsesStream,
|
||||
createOpenAiResponsesAggregateState,
|
||||
failResponsesStream,
|
||||
serializeConvertedResponsesEvents,
|
||||
type OpenAiResponsesAggregateState,
|
||||
} from './aggregator.js';
|
||||
import {
|
||||
buildNormalizedFinalToOpenAiResponsesPayload,
|
||||
normalizeOpenAiResponsesFinalToNormalized,
|
||||
type ResponsesUsageSummary,
|
||||
} from './responseBridge.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 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 hasNonEmptyString(value: unknown): boolean {
|
||||
return typeof value === 'string' && value.trim().length > 0;
|
||||
}
|
||||
|
||||
function hasMeaningfulContentPart(part: unknown): boolean {
|
||||
if (!isRecord(part)) return false;
|
||||
const partType = asTrimmedString(part.type).toLowerCase();
|
||||
if (partType === 'output_text' || partType === 'text') {
|
||||
return hasNonEmptyString(part.text);
|
||||
}
|
||||
return partType.length > 0;
|
||||
}
|
||||
|
||||
export function hasMeaningfulResponsesOutputItem(item: unknown): boolean {
|
||||
if (!isRecord(item)) return false;
|
||||
const itemType = asTrimmedString(item.type).toLowerCase();
|
||||
if (itemType === 'message') {
|
||||
return Array.isArray(item.content) && item.content.some((part) => hasMeaningfulContentPart(part));
|
||||
}
|
||||
if (itemType === 'reasoning') {
|
||||
return (
|
||||
(Array.isArray(item.summary) && item.summary.some((part) => hasMeaningfulContentPart(part)))
|
||||
|| hasNonEmptyString(item.encrypted_content)
|
||||
);
|
||||
}
|
||||
return itemType.length > 0;
|
||||
}
|
||||
|
||||
export function hasMeaningfulResponsesPayloadOutput(payload: unknown): boolean {
|
||||
if (!isRecord(payload)) return false;
|
||||
if (hasNonEmptyString(payload.output_text)) return true;
|
||||
return Array.isArray(payload.output) && payload.output.some((item) => hasMeaningfulResponsesOutputItem(item));
|
||||
}
|
||||
|
||||
export function preserveMeaningfulResponsesTerminalPayload(
|
||||
lines: string[],
|
||||
eventType: 'response.completed' | 'response.incomplete',
|
||||
payload: Record<string, unknown>,
|
||||
): string[] {
|
||||
const responsePayload = isRecord(payload.response) ? payload.response : null;
|
||||
if (!responsePayload || !hasMeaningfulResponsesPayloadOutput(responsePayload)) {
|
||||
return lines;
|
||||
}
|
||||
|
||||
const parsed = openAiResponsesStream.pullSseEvents(lines.join(''));
|
||||
let replaced = false;
|
||||
const nextLines: string[] = [];
|
||||
|
||||
for (const event of parsed.events) {
|
||||
if (event.data === '[DONE]') {
|
||||
nextLines.push('data: [DONE]\n\n');
|
||||
continue;
|
||||
}
|
||||
|
||||
let parsedPayload: unknown = null;
|
||||
try {
|
||||
parsedPayload = JSON.parse(event.data);
|
||||
} catch {
|
||||
nextLines.push(`${event.event ? `event: ${event.event}\n` : ''}data: ${event.data}\n\n`);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (
|
||||
isRecord(parsedPayload)
|
||||
&& asTrimmedString(parsedPayload.type) === eventType
|
||||
&& isRecord(parsedPayload.response)
|
||||
&& !hasMeaningfulResponsesPayloadOutput(parsedPayload.response)
|
||||
) {
|
||||
replaced = true;
|
||||
nextLines.push(
|
||||
`event: ${event.event || eventType}\ndata: ${JSON.stringify({
|
||||
...parsedPayload,
|
||||
response: {
|
||||
...parsedPayload.response,
|
||||
...responsePayload,
|
||||
},
|
||||
})}\n\n`,
|
||||
);
|
||||
continue;
|
||||
}
|
||||
|
||||
nextLines.push(`${event.event ? `event: ${event.event}\n` : ''}data: ${event.data}\n\n`);
|
||||
}
|
||||
|
||||
return replaced ? nextLines : lines;
|
||||
}
|
||||
|
||||
export function serializeResponsesUpstreamFinalAsStream(input: {
|
||||
payload: unknown;
|
||||
modelName: string;
|
||||
fallbackText: string;
|
||||
usage: ResponsesUsageSummary;
|
||||
}): {
|
||||
normalizedFinal: ReturnType<typeof normalizeOpenAiResponsesFinalToNormalized>;
|
||||
streamPayload: Record<string, unknown>;
|
||||
createdPayload: Record<string, unknown>;
|
||||
isIncompletePayload: boolean;
|
||||
lines: string[];
|
||||
} {
|
||||
const { payload, modelName, fallbackText, usage } = input;
|
||||
const normalizedFinal = normalizeOpenAiResponsesFinalToNormalized(payload, modelName, fallbackText);
|
||||
const streamPayload = buildNormalizedFinalToOpenAiResponsesPayload({
|
||||
upstreamPayload: payload,
|
||||
normalized: normalizedFinal,
|
||||
usage,
|
||||
serializationMode: 'response',
|
||||
});
|
||||
const payloadType = (isRecord(payload) && typeof payload.type === 'string')
|
||||
? payload.type
|
||||
: '';
|
||||
const payloadStatus = isRecord(payload) && typeof payload.status === 'string'
|
||||
? payload.status
|
||||
: '';
|
||||
const isFailedPayload = payloadType === 'response.failed' || payloadStatus === 'failed';
|
||||
const isIncompletePayload = payloadType === 'response.incomplete' || payloadStatus === 'incomplete';
|
||||
const terminalStatus = isFailedPayload ? 'failed' : (isIncompletePayload ? 'incomplete' : 'completed');
|
||||
const upstreamTerminalResponse = isRecord(payload) && isRecord(payload.response)
|
||||
? cloneJson(payload.response)
|
||||
: null;
|
||||
const terminalResponsePayload = upstreamTerminalResponse
|
||||
? {
|
||||
...streamPayload,
|
||||
...upstreamTerminalResponse,
|
||||
...(Array.isArray(upstreamTerminalResponse.output) ? { output: cloneJson(upstreamTerminalResponse.output) } : {}),
|
||||
...(typeof upstreamTerminalResponse.output_text === 'string'
|
||||
? { output_text: upstreamTerminalResponse.output_text }
|
||||
: {}),
|
||||
status: terminalStatus,
|
||||
}
|
||||
: {
|
||||
...streamPayload,
|
||||
status: terminalStatus,
|
||||
};
|
||||
const createdPayload = {
|
||||
...terminalResponsePayload,
|
||||
status: 'in_progress',
|
||||
output: [],
|
||||
output_text: '',
|
||||
};
|
||||
|
||||
const terminalEventType = isFailedPayload
|
||||
? 'response.failed'
|
||||
: (isIncompletePayload ? 'response.incomplete' : 'response.completed');
|
||||
const fallbackState = createOpenAiResponsesAggregateState(normalizedFinal.model || modelName);
|
||||
const fallbackContext = createStreamTransformContext(normalizedFinal.model || modelName);
|
||||
fallbackContext.id = normalizedFinal.id;
|
||||
fallbackContext.model = normalizedFinal.model;
|
||||
fallbackContext.created = normalizedFinal.created;
|
||||
|
||||
const lines = [
|
||||
`event: response.created\ndata: ${JSON.stringify({ type: 'response.created', response: createdPayload })}\n\n`,
|
||||
];
|
||||
|
||||
const outputItems = Array.isArray(terminalResponsePayload.output) ? terminalResponsePayload.output : [];
|
||||
for (let outputIndex = 0; outputIndex < outputItems.length; outputIndex += 1) {
|
||||
const item = outputItems[outputIndex];
|
||||
if (!isRecord(item)) continue;
|
||||
const eventItem = cloneJson(item);
|
||||
const itemStatus = asTrimmedString(eventItem.status).toLowerCase();
|
||||
if ((!itemStatus || itemStatus === 'in_progress') && terminalStatus !== 'completed') {
|
||||
eventItem.status = terminalStatus;
|
||||
}
|
||||
lines.push(...serializeConvertedResponsesEvents({
|
||||
state: fallbackState,
|
||||
streamContext: fallbackContext,
|
||||
usage,
|
||||
event: {
|
||||
responsesEventType: 'response.output_item.done',
|
||||
responsesPayload: {
|
||||
type: 'response.output_item.done',
|
||||
output_index: outputIndex,
|
||||
item: eventItem,
|
||||
},
|
||||
},
|
||||
}));
|
||||
}
|
||||
|
||||
lines.push(...serializeConvertedResponsesEvents({
|
||||
state: fallbackState,
|
||||
streamContext: fallbackContext,
|
||||
usage,
|
||||
event: {
|
||||
responsesEventType: terminalEventType,
|
||||
responsesPayload: {
|
||||
type: terminalEventType,
|
||||
response: terminalResponsePayload,
|
||||
...(terminalEventType === 'response.failed' && isRecord(payload) && payload.error !== undefined
|
||||
? { error: cloneJson(payload.error) }
|
||||
: {}),
|
||||
},
|
||||
},
|
||||
}));
|
||||
lines.push('data: [DONE]\n\n');
|
||||
|
||||
return {
|
||||
normalizedFinal,
|
||||
streamPayload,
|
||||
createdPayload,
|
||||
isIncompletePayload,
|
||||
lines,
|
||||
};
|
||||
}
|
||||
|
||||
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.incomplete',
|
||||
'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,
|
||||
};
|
||||
Reference in New Issue
Block a user