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test: add 5 tests for thoughtSignature injection in OpenAI→Gemini path

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This commit is contained in:
weijiafu14
2026-03-17 16:14:09 +08:00
parent 8bfdbda251
commit d20417d708
2 changed files with 214 additions and 29 deletions
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package-lock.json
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+18 -29
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src/server/routes/proxy/geminiCliCompat.thoughtSignature.test.ts
+196
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@@ -0,0 +1,196 @@
import { describe, it, expect } from 'vitest';
import { buildGeminiGenerateContentRequestFromOpenAi } from './geminiCliCompat.js';
describe('thoughtSignature injection in OpenAI→Gemini conversion', () => {
it('injects thoughtSignature from provider_specific_fields into functionCall parts', () => {
const result = buildGeminiGenerateContentRequestFromOpenAi({
body: {
model: 'gemini-3-flash-preview',
messages: [
{ role: 'user', content: 'What is the weather?' },
{
role: 'assistant',
content: 'Let me check.',
tool_calls: [
{
id: 'call_123',
type: 'function',
function: { name: 'get_weather', arguments: '{"city":"Tokyo"}' },
provider_specific_fields: { thought_signature: 'real_sig_abc' },
},
],
},
{ role: 'tool', tool_call_id: 'call_123', content: '{"temp":"22C"}' },
],
},
modelName: 'gemini-3-flash-preview',
}) as Record<string, unknown>;
const contents = result.contents as Array<Record<string, unknown>>;
// Find the model message with functionCall
const modelMsgs = contents.filter((c) => c.role === 'model');
const fcParts = modelMsgs
.flatMap((m) => (m.parts as Array<Record<string, unknown>>))
.filter((p) => 'functionCall' in p);
expect(fcParts.length).toBe(1);
expect(fcParts[0].thoughtSignature).toBe('real_sig_abc');
});
it('splits text and signed functionCall parts into separate model messages', () => {
const result = buildGeminiGenerateContentRequestFromOpenAi({
body: {
model: 'gemini-3-flash-preview',
messages: [
{ role: 'user', content: 'Read the file.' },
{
role: 'assistant',
content: 'I will read it.',
tool_calls: [
{
id: 'call_456',
type: 'function',
function: { name: 'Read', arguments: '{"path":"/tmp/x"}' },
provider_specific_fields: { thought_signature: 'sig_split_test' },
},
],
},
{ role: 'tool', tool_call_id: 'call_456', content: 'file content here' },
],
},
modelName: 'gemini-3-flash-preview',
}) as Record<string, unknown>;
const contents = result.contents as Array<Record<string, unknown>>;
const modelMsgs = contents.filter((c) => c.role === 'model');
// Should be 2 model messages: text (unsigned) + functionCall (signed)
expect(modelMsgs.length).toBe(2);
const firstParts = modelMsgs[0].parts as Array<Record<string, unknown>>;
const secondParts = modelMsgs[1].parts as Array<Record<string, unknown>>;
// First: text only, no thoughtSignature
expect(firstParts.every((p) => 'text' in p)).toBe(true);
expect(firstParts.every((p) => !('thoughtSignature' in p))).toBe(true);
// Second: functionCall with thoughtSignature
expect(secondParts.every((p) => 'functionCall' in p)).toBe(true);
expect(secondParts[0].thoughtSignature).toBe('sig_split_test');
});
it('injects dummy sentinel when thinking enabled but no signature available', () => {
const result = buildGeminiGenerateContentRequestFromOpenAi({
body: {
model: 'gemini-3-flash-preview',
reasoning_effort: 'high',
messages: [
{ role: 'user', content: 'Do something.' },
{
role: 'assistant',
tool_calls: [
{
id: 'call_no_sig',
type: 'function',
function: { name: 'Bash', arguments: '{"command":"ls"}' },
// No provider_specific_fields — signature missing
},
],
},
{ role: 'tool', tool_call_id: 'call_no_sig', content: 'file1\nfile2' },
],
},
modelName: 'gemini-3-flash-preview',
}) as Record<string, unknown>;
const contents = result.contents as Array<Record<string, unknown>>;
const modelMsgs = contents.filter((c) => c.role === 'model');
const fcParts = modelMsgs
.flatMap((m) => (m.parts as Array<Record<string, unknown>>))
.filter((p) => 'functionCall' in p);
expect(fcParts.length).toBe(1);
// Should have a dummy sentinel, not be missing
expect(typeof fcParts[0].thoughtSignature).toBe('string');
expect((fcParts[0].thoughtSignature as string).length).toBeGreaterThan(0);
});
it('does not inject thoughtSignature when thinking is not enabled', () => {
const result = buildGeminiGenerateContentRequestFromOpenAi({
body: {
model: 'gemini-2.5-flash',
messages: [
{ role: 'user', content: 'Hello' },
{
role: 'assistant',
tool_calls: [
{
id: 'call_no_think',
type: 'function',
function: { name: 'Read', arguments: '{"path":"/x"}' },
},
],
},
{ role: 'tool', tool_call_id: 'call_no_think', content: 'data' },
],
},
modelName: 'gemini-2.5-flash',
}) as Record<string, unknown>;
const contents = result.contents as Array<Record<string, unknown>>;
const modelMsgs = contents.filter((c) => c.role === 'model');
const fcParts = modelMsgs
.flatMap((m) => (m.parts as Array<Record<string, unknown>>))
.filter((p) => 'functionCall' in p);
expect(fcParts.length).toBe(1);
// No thinking → no signature injected
expect(fcParts[0].thoughtSignature).toBeUndefined();
});
it('preserves functionResponse count matching functionCall count', () => {
const result = buildGeminiGenerateContentRequestFromOpenAi({
body: {
model: 'gemini-3-flash-preview',
messages: [
{ role: 'user', content: 'Read two files.' },
{
role: 'assistant',
tool_calls: [
{
id: 'call_a',
type: 'function',
function: { name: 'Read', arguments: '{"path":"/a"}' },
provider_specific_fields: { thought_signature: 'sig_a' },
},
{
id: 'call_b',
type: 'function',
function: { name: 'Read', arguments: '{"path":"/b"}' },
provider_specific_fields: { thought_signature: 'sig_b' },
},
],
},
{ role: 'tool', tool_call_id: 'call_a', content: 'content a' },
{ role: 'tool', tool_call_id: 'call_b', content: 'content b' },
],
},
modelName: 'gemini-3-flash-preview',
}) as Record<string, unknown>;
const contents = result.contents as Array<Record<string, unknown>>;
// Count functionCall and functionResponse parts
let fcCount = 0;
let frCount = 0;
for (const content of contents) {
for (const part of (content.parts as Array<Record<string, unknown>>)) {
if ('functionCall' in part) fcCount++;
if ('functionResponse' in part) frCount++;
}
}
expect(fcCount).toBe(2);
expect(frCount).toBe(2);
});
});