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Backport: feat(open-responses): add option to pass reasoning summary for OpenResponses (#14208)
This is an automated backport of #14115 to the release-v6.0 branch. FYI @aayush-kapoor This backport has conflicts that need to be resolved manually. ### `git cherry-pick` output ``` Auto-merging packages/open-responses/src/open-responses-provider.ts Auto-merging packages/open-responses/src/responses/open-responses-language-model.test.ts CONFLICT (content): Merge conflict in packages/open-responses/src/responses/open-responses-language-model.test.ts Auto-merging packages/open-responses/src/responses/open-responses-language-model.ts CONFLICT (content): Merge conflict in packages/open-responses/src/responses/open-responses-language-model.ts error: could not apply e69a836... feat(open-responses): add option to pass reasoning summary for OpenResponses (#14115) hint: After resolving the conflicts, mark them with hint: "git add/rm <pathspec>", then run hint: "git cherry-pick --continue". hint: You can instead skip this commit with "git cherry-pick --skip". hint: To abort and get back to the state before "git cherry-pick", hint: run "git cherry-pick --abort". hint: Disable this message with "git config set advice.mergeConflict false" ``` --------- Co-authored-by: Aayush Kapoor <83492835+aayush-kapoor@users.noreply.github.com> Co-authored-by: Aayush Kapoor <aayushkapoor34@gmail.com>
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‎.changeset/stale-berries-push.md‎

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---
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"@ai-sdk/open-responses": patch
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---
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feat(open-responses): add option to pass reasoning summary for OpenResponses

‎examples/ai-functions/src/generate-text/open-responses/reasoning.ts‎

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import { createOpenResponses } from '@ai-sdk/open-responses';
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import { generateText } from 'ai';
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import { run } from '../../lib/run';
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const lmstudio = createOpenResponses({
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name: 'lmstudio',
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url: 'https://api.openai.com/v1/responses',
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apiKey: process.env.OPENAI_API_KEY,
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});
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run(async () => {
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const result = await generateText({
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model: lmstudio('gpt-5'),
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prompt: 'Invent a new holiday and describe its traditions.',
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maxOutputTokens: 100,
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providerOptions: {
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lmstudio: {
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reasoningEffort: 'high',
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reasoningSummary: 'detailed',
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},
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},
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});
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console.log(JSON.stringify(result.response.body, null, 2));
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console.log('Reasoning:', result.reasoning);
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console.log(result.text);
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console.log();
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console.log('Token usage:', result.usage);
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console.log('Finish reason:', result.finishReason);
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console.log('Request:', JSON.stringify(result.request, null, 2));
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});

‎packages/open-responses/src/index.ts‎

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export { VERSION } from './version';
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export { createOpenResponses } from './open-responses-provider';
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export type { OpenResponsesOptions } from './responses/open-responses-options';

‎packages/open-responses/src/open-responses-provider.ts‎

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@@ -65,6 +65,7 @@ export function createOpenResponses(
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const createResponsesModel = (modelId: string) => {
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return new OpenResponsesLanguageModel(modelId, {
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provider: `${providerName}.responses`,
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providerOptionsName: providerName,
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headers: getHeaders,
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url: options.url,
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fetch: options.fetch,

‎packages/open-responses/src/responses/open-responses-config.ts‎

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@@ -2,6 +2,7 @@ import { FetchFunction } from '@ai-sdk/provider-utils';
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export type OpenResponsesConfig = {
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provider: string;
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providerOptionsName: string;
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url: string;
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headers: () => Record<string, string | undefined>;
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fetch?: FetchFunction;

‎packages/open-responses/src/responses/open-responses-language-model.test.ts‎

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@@ -25,6 +25,7 @@ describe('OpenResponsesLanguageModel', () => {
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function createModel(modelId: string = 'gemma-7b-it') {
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return new OpenResponsesLanguageModel(modelId, {
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provider: 'lmstudio',
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providerOptionsName: 'lmstudio',
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url: URL,
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headers: () => ({}),
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generateId: mockId(),
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});
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});
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describe('providerOptions reasoning', () => {
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beforeEach(() => {
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prepareJsonFixtureResponse('lmstudio-basic.1');
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});
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it('should send reasoning.effort via providerOptions', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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providerOptions: {
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lmstudio: { reasoningEffort: 'high' },
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},
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});
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expect((await server.calls[0].requestBodyJson).reasoning).toStrictEqual(
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{ effort: 'high' },
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);
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});
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it('should send reasoning.effort none via providerOptions', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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providerOptions: {
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lmstudio: { reasoningEffort: 'none' },
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},
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});
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expect((await server.calls[0].requestBodyJson).reasoning).toStrictEqual(
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{ effort: 'none' },
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);
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});
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it('should send reasoning.summary via providerOptions', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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providerOptions: {
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lmstudio: { reasoningSummary: 'detailed' },
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},
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});
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expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
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{
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"input": [
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{
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"content": [
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{
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"text": "Hello",
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"type": "input_text",
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},
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],
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"role": "user",
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"type": "message",
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},
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],
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"model": "gemma-7b-it",
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"reasoning": {
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"summary": "detailed",
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},
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}
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`);
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});
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it('should send reasoning.summary concise via providerOptions', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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providerOptions: {
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lmstudio: { reasoningSummary: 'concise' },
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},
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});
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expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
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{
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"input": [
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{
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"content": [
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{
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"text": "Hello",
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"type": "input_text",
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},
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],
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"role": "user",
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"type": "message",
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},
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],
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"model": "gemma-7b-it",
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"reasoning": {
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"summary": "concise",
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},
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}
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`);
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});
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it('should combine reasoning effort and summary from providerOptions', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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providerOptions: {
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lmstudio: { reasoningEffort: 'high', reasoningSummary: 'auto' },
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},
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});
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expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
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{
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"input": [
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{
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"content": [
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{
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"text": "Hello",
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"type": "input_text",
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},
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],
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"role": "user",
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"type": "message",
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},
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],
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"model": "gemma-7b-it",
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"reasoning": {
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"effort": "high",
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"summary": "auto",
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},
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}
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`);
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});
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it('should not set reasoning when providerOptions has no reasoning fields', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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providerOptions: {
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lmstudio: {},
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},
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});
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expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
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{
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"input": [
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{
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"content": [
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{
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"text": "Hello",
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"type": "input_text",
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},
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],
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"role": "user",
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"type": "message",
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},
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],
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"model": "gemma-7b-it",
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}
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`);
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});
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it('should not set reasoning when not specified', async () => {
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await createModel().doGenerate({
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prompt: TEST_PROMPT,
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});
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expect(
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(await server.calls[0].requestBodyJson).reasoning,
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).toBeUndefined();
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});
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});
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describe('tool call parsing', () => {
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let result: LanguageModelV3GenerateResult;
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‎packages/open-responses/src/responses/open-responses-language-model.ts‎

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createJsonErrorResponseHandler,
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createJsonResponseHandler,
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jsonSchema,
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parseProviderOptions,
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ParseResult,
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postJsonToApi,
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} from '@ai-sdk/provider-utils';
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} from './open-responses-api';
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import { mapOpenResponsesFinishReason } from './map-open-responses-finish-reason';
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import { OpenResponsesConfig } from './open-responses-config';
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import { openResponsesOptionsSchema } from './open-responses-options';
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export class OpenResponsesLanguageModel implements LanguageModelV3 {
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readonly specificationVersion = 'v3';
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}
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: undefined;
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const openResponsesOptions = await parseProviderOptions({
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provider: this.config.providerOptionsName,
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providerOptions,
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schema: openResponsesOptionsSchema,
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});
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return {
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body: {
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model: this.modelId,
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top_p: topP,
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presence_penalty: presencePenalty,
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frequency_penalty: frequencyPenalty,
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reasoning:
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openResponsesOptions?.reasoningEffort != null ||
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openResponsesOptions?.reasoningSummary != null
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? {
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...(openResponsesOptions?.reasoningEffort != null && {
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effort: openResponsesOptions.reasoningEffort,
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}),
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...(openResponsesOptions?.reasoningSummary != null && {
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summary: openResponsesOptions.reasoningSummary,
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}),
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}
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: undefined,
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tools: functionTools?.length ? functionTools : undefined,
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tool_choice: convertedToolChoice,
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...(textFormat != null && { text: { format: textFormat } }),

‎packages/open-responses/src/responses/open-responses-options.ts‎

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import { InferSchema, lazySchema, zodSchema } from '@ai-sdk/provider-utils';
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import { z } from 'zod/v4';
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export const openResponsesOptionsSchema = lazySchema(() =>
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zodSchema(
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z.object({
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reasoningEffort: z
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.enum(['none', 'low', 'medium', 'high', 'xhigh'])
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.nullish(),
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/**
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* Controls reasoning summary output from the model.
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* Valid values: 'concise', 'detailed', 'auto'.
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*/
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reasoningSummary: z.enum(['concise', 'detailed', 'auto']).nullish(),
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}),
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),
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);
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export type OpenResponsesOptions = InferSchema<
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typeof openResponsesOptionsSchema
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>;

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