forked from boos/BoosAPI
test: add 5 tests for thoughtSignature injection in OpenAI→Gemini path
This commit is contained in:
weijiafu14
@@ -0,0 +1,196 @@
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import { describe, it, expect } from 'vitest';
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import { buildGeminiGenerateContentRequestFromOpenAi } from './geminiCliCompat.js';
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describe('thoughtSignature injection in OpenAI→Gemini conversion', () => {
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it('injects thoughtSignature from provider_specific_fields into functionCall parts', () => {
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const result = buildGeminiGenerateContentRequestFromOpenAi({
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body: {
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model: 'gemini-3-flash-preview',
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messages: [
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{ role: 'user', content: 'What is the weather?' },
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{
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role: 'assistant',
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content: 'Let me check.',
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tool_calls: [
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{
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id: 'call_123',
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type: 'function',
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function: { name: 'get_weather', arguments: '{"city":"Tokyo"}' },
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provider_specific_fields: { thought_signature: 'real_sig_abc' },
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},
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],
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},
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{ role: 'tool', tool_call_id: 'call_123', content: '{"temp":"22C"}' },
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],
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},
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modelName: 'gemini-3-flash-preview',
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}) as Record<string, unknown>;
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const contents = result.contents as Array<Record<string, unknown>>;
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// Find the model message with functionCall
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const modelMsgs = contents.filter((c) => c.role === 'model');
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const fcParts = modelMsgs
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.flatMap((m) => (m.parts as Array<Record<string, unknown>>))
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.filter((p) => 'functionCall' in p);
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expect(fcParts.length).toBe(1);
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expect(fcParts[0].thoughtSignature).toBe('real_sig_abc');
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});
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it('splits text and signed functionCall parts into separate model messages', () => {
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const result = buildGeminiGenerateContentRequestFromOpenAi({
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body: {
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model: 'gemini-3-flash-preview',
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messages: [
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{ role: 'user', content: 'Read the file.' },
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{
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role: 'assistant',
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content: 'I will read it.',
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tool_calls: [
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{
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id: 'call_456',
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type: 'function',
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function: { name: 'Read', arguments: '{"path":"/tmp/x"}' },
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provider_specific_fields: { thought_signature: 'sig_split_test' },
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},
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],
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},
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{ role: 'tool', tool_call_id: 'call_456', content: 'file content here' },
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],
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},
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modelName: 'gemini-3-flash-preview',
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}) as Record<string, unknown>;
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const contents = result.contents as Array<Record<string, unknown>>;
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const modelMsgs = contents.filter((c) => c.role === 'model');
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// Should be 2 model messages: text (unsigned) + functionCall (signed)
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expect(modelMsgs.length).toBe(2);
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const firstParts = modelMsgs[0].parts as Array<Record<string, unknown>>;
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const secondParts = modelMsgs[1].parts as Array<Record<string, unknown>>;
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// First: text only, no thoughtSignature
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expect(firstParts.every((p) => 'text' in p)).toBe(true);
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expect(firstParts.every((p) => !('thoughtSignature' in p))).toBe(true);
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// Second: functionCall with thoughtSignature
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expect(secondParts.every((p) => 'functionCall' in p)).toBe(true);
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expect(secondParts[0].thoughtSignature).toBe('sig_split_test');
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});
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it('injects dummy sentinel when thinking enabled but no signature available', () => {
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const result = buildGeminiGenerateContentRequestFromOpenAi({
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body: {
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model: 'gemini-3-flash-preview',
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reasoning_effort: 'high',
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messages: [
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{ role: 'user', content: 'Do something.' },
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{
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role: 'assistant',
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tool_calls: [
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{
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id: 'call_no_sig',
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type: 'function',
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function: { name: 'Bash', arguments: '{"command":"ls"}' },
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// No provider_specific_fields — signature missing
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},
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],
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},
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{ role: 'tool', tool_call_id: 'call_no_sig', content: 'file1\nfile2' },
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],
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},
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modelName: 'gemini-3-flash-preview',
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}) as Record<string, unknown>;
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const contents = result.contents as Array<Record<string, unknown>>;
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const modelMsgs = contents.filter((c) => c.role === 'model');
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const fcParts = modelMsgs
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.flatMap((m) => (m.parts as Array<Record<string, unknown>>))
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.filter((p) => 'functionCall' in p);
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expect(fcParts.length).toBe(1);
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// Should have a dummy sentinel, not be missing
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expect(typeof fcParts[0].thoughtSignature).toBe('string');
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expect((fcParts[0].thoughtSignature as string).length).toBeGreaterThan(0);
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});
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it('does not inject thoughtSignature when thinking is not enabled', () => {
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const result = buildGeminiGenerateContentRequestFromOpenAi({
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body: {
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model: 'gemini-2.5-flash',
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messages: [
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{ role: 'user', content: 'Hello' },
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{
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role: 'assistant',
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tool_calls: [
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{
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id: 'call_no_think',
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type: 'function',
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function: { name: 'Read', arguments: '{"path":"/x"}' },
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},
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],
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},
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{ role: 'tool', tool_call_id: 'call_no_think', content: 'data' },
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],
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},
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modelName: 'gemini-2.5-flash',
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}) as Record<string, unknown>;
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const contents = result.contents as Array<Record<string, unknown>>;
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const modelMsgs = contents.filter((c) => c.role === 'model');
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const fcParts = modelMsgs
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.flatMap((m) => (m.parts as Array<Record<string, unknown>>))
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.filter((p) => 'functionCall' in p);
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expect(fcParts.length).toBe(1);
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// No thinking → no signature injected
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expect(fcParts[0].thoughtSignature).toBeUndefined();
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});
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it('preserves functionResponse count matching functionCall count', () => {
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const result = buildGeminiGenerateContentRequestFromOpenAi({
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body: {
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model: 'gemini-3-flash-preview',
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messages: [
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{ role: 'user', content: 'Read two files.' },
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{
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role: 'assistant',
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tool_calls: [
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{
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id: 'call_a',
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type: 'function',
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function: { name: 'Read', arguments: '{"path":"/a"}' },
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provider_specific_fields: { thought_signature: 'sig_a' },
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},
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{
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id: 'call_b',
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type: 'function',
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function: { name: 'Read', arguments: '{"path":"/b"}' },
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provider_specific_fields: { thought_signature: 'sig_b' },
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},
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],
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},
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{ role: 'tool', tool_call_id: 'call_a', content: 'content a' },
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{ role: 'tool', tool_call_id: 'call_b', content: 'content b' },
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],
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},
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modelName: 'gemini-3-flash-preview',
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}) as Record<string, unknown>;
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const contents = result.contents as Array<Record<string, unknown>>;
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// Count functionCall and functionResponse parts
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let fcCount = 0;
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let frCount = 0;
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for (const content of contents) {
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for (const part of (content.parts as Array<Record<string, unknown>>)) {
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if ('functionCall' in part) fcCount++;
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if ('functionResponse' in part) frCount++;
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}
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}
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expect(fcCount).toBe(2);
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expect(frCount).toBe(2);
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});
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});
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