mirror of
https://github.com/QwenLM/qwen-code.git
synced 2026-07-10 01:29:17 +00:00
fix(core): enforce adjacent tool results (#4622)
* fix(core): enforce adjacent tool results * fix(core): handle orphan cleanup edge cases
This commit is contained in:
Yufeng He
GitHub
parent
5743c2a05c
commit
9dafd60d5d
2 changed files with 675 additions and 113 deletions
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@ -1474,6 +1474,504 @@ describe('OpenAIContentConverter', () => {
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});
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});
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it('should drop tool responses that are not adjacent to their assistant tool call', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: {
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id: 'call_a',
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name: 'read_file',
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args: { path: 'a.txt' },
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},
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},
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{
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functionCall: {
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id: 'call_b',
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name: 'grep',
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args: { pattern: 'needle' },
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},
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_a',
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name: 'read_file',
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response: { output: 'A' },
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},
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},
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],
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},
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{
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role: 'user',
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parts: [{ text: 'history text inserted between tool results' }],
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},
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{
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role: 'model',
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parts: [
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{
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functionCall: {
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id: 'call_c',
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name: 'list_files',
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args: {},
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},
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_c',
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name: 'list_files',
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response: { output: 'C' },
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},
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},
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{
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functionResponse: {
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id: 'call_b',
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name: 'grep',
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response: { output: 'B' },
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},
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},
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],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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const assistantWithCallA = messages.find(
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(message): message is OpenAI.Chat.ChatCompletionAssistantMessageParam =>
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message.role === 'assistant' &&
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'tool_calls' in message &&
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Array.isArray(message.tool_calls) &&
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message.tool_calls.some((toolCall) => toolCall.id === 'call_a'),
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);
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expect(
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assistantWithCallA?.tool_calls?.map((toolCall) => toolCall.id),
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).toEqual(['call_a']);
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const toolCallIds = messages
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.filter(
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(message): message is OpenAI.Chat.ChatCompletionToolMessageParam =>
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message.role === 'tool' && 'tool_call_id' in message,
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)
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.map((message) => message.tool_call_id);
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expect(toolCallIds).toEqual(['call_a', 'call_c']);
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});
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it('should keep assistant text when all tool calls are orphaned', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{ text: 'I can answer without the tool.' },
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{
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functionCall: {
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id: 'call_missing',
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name: 'read_file',
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args: { path: 'missing.txt' },
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},
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},
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],
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},
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{
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role: 'user',
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parts: [{ text: 'continue' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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const assistant = messages.find(
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(message): message is OpenAI.Chat.ChatCompletionAssistantMessageParam =>
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message.role === 'assistant',
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);
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expect(assistant?.content).toBe('I can answer without the tool.');
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expect('tool_calls' in (assistant ?? {})).toBe(false);
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expect(messages.some((message) => message.role === 'tool')).toBe(false);
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});
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it('should drop assistant-only tool calls when all responses are orphaned', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: {
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id: 'call_missing',
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name: 'read_file',
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args: { path: 'missing.txt' },
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},
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},
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],
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},
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{
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role: 'user',
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parts: [{ text: 'break adjacency' }],
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},
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{
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role: 'user',
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parts: [{ text: 'continue' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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expect(messages.some((message) => message.role === 'assistant')).toBe(
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false,
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);
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expect(messages.some((message) => message.role === 'tool')).toBe(false);
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});
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it('should keep a tool response after an empty-id tool message', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: {
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id: 'call_a',
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name: 'read_file',
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args: { path: 'a.txt' },
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},
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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name: 'empty_id',
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response: { output: 'no id' },
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},
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},
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{
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functionResponse: {
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id: 'call_a',
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name: 'read_file',
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response: { output: 'A' },
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},
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},
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],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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const toolCallIds = messages
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.filter(
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(message): message is OpenAI.Chat.ChatCompletionToolMessageParam =>
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message.role === 'tool' && 'tool_call_id' in message,
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)
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.map((message) => message.tool_call_id);
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expect(toolCallIds).toEqual(['call_a']);
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});
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it('should clean after merging consecutive assistant turns', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: {
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id: 'call_a',
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name: 'read_file',
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args: { path: 'a.txt' },
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},
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},
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],
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},
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{
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role: 'model',
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parts: [{ text: 'A short follow-up.' }],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_a',
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name: 'read_file',
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response: { output: 'A' },
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},
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},
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],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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expect(messages[0]).toMatchObject({
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role: 'assistant',
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content: 'A short follow-up.',
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});
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expect(
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(
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messages[0] as OpenAI.Chat.ChatCompletionAssistantMessageParam
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).tool_calls?.map((toolCall) => toolCall.id),
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).toEqual(['call_a']);
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expect(messages[1]).toMatchObject({
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role: 'tool',
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tool_call_id: 'call_a',
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});
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});
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it('should keep split media after all adjacent tool responses across content items', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: { id: 'call_a', name: 'shot_a', args: {} },
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},
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{
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functionCall: { id: 'call_b', name: 'shot_b', args: {} },
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_a',
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name: 'shot_a',
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response: { output: 'A' },
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parts: [
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{ inlineData: { mimeType: 'image/png', data: 'aaa' } },
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],
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},
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_b',
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name: 'shot_b',
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response: { output: 'B' },
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parts: [
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{ inlineData: { mimeType: 'image/png', data: 'bbb' } },
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],
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},
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},
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],
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},
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],
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};
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const strictContext: RequestContext = {
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...requestContext,
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splitToolMedia: true,
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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strictContext,
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);
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const assistantIndex = messages.findIndex(
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(message) => message.role === 'assistant',
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);
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expect(messages[assistantIndex + 1]).toMatchObject({
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role: 'tool',
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tool_call_id: 'call_a',
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});
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expect(messages[assistantIndex + 2]).toMatchObject({
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role: 'tool',
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tool_call_id: 'call_b',
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});
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expect(messages[assistantIndex + 3]?.role).toBe('user');
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expect(messages[assistantIndex + 4]?.role).toBe('user');
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});
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it('should not keep split media from orphaned tool responses', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: { id: 'call_a', name: 'shot_a', args: {} },
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_x',
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name: 'shot_x',
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response: { output: 'X' },
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parts: [
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{ inlineData: { mimeType: 'image/png', data: 'xxx' } },
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],
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},
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_a',
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name: 'shot_a',
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response: { output: 'A' },
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},
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},
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],
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},
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],
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};
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const strictContext: RequestContext = {
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...requestContext,
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splitToolMedia: true,
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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strictContext,
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);
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expect(messages.map((message) => message.role)).toEqual([
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'assistant',
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'tool',
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]);
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expect(messages[1]).toMatchObject({
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role: 'tool',
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tool_call_id: 'call_a',
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});
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});
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it('should merge assistant turns created by orphan cleanup', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{
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functionCall: { id: 'call_a', name: 'read_file', args: {} },
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_a',
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name: 'read_file',
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response: { output: 'A' },
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},
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},
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],
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},
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{
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role: 'model',
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parts: [{ text: 'Next I will call another tool.' }],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_orphan',
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name: 'stale_tool',
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response: { output: 'stale' },
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},
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},
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],
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},
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{
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role: 'model',
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parts: [
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{
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functionCall: { id: 'call_b', name: 'grep', args: {} },
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},
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],
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},
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{
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role: 'user',
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parts: [
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{
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functionResponse: {
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id: 'call_b',
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name: 'grep',
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response: { output: 'B' },
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},
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},
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],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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for (let index = 1; index < messages.length; index += 1) {
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expect([messages[index - 1].role, messages[index].role]).not.toEqual([
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'assistant',
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'assistant',
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]);
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}
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expect(
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messages
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.filter(
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(message): message is OpenAI.Chat.ChatCompletionToolMessageParam =>
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message.role === 'tool' && 'tool_call_id' in message,
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)
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.map((message) => message.tool_call_id),
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).toEqual(['call_a', 'call_b']);
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});
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describe('assistant message with reasoning-only content (issue #3421)', () => {
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/**
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* Regression tests for https://github.com/QwenLM/qwen-code/issues/3421
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@ -1514,6 +2012,41 @@ describe('OpenAIContentConverter', () => {
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).toBe('I reasoned about it.');
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});
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it('should keep reasoning content when orphaned tool calls are removed', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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contents: [
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{
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role: 'model',
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parts: [
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{ text: 'I need to inspect this.', thought: true },
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{
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functionCall: {
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id: 'call_missing',
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name: 'read_file',
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args: {},
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},
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},
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],
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},
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{ role: 'user', parts: [{ text: 'break adjacency' }] },
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(
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request,
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requestContext,
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);
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const assistantMsg = messages.find((m) => m.role === 'assistant');
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expect(assistantMsg).toBeDefined();
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expect((assistantMsg as { content: unknown }).content).toBe('');
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expect(
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(assistantMsg as { reasoning_content?: string }).reasoning_content,
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).toBe('I need to inspect this.');
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expect('tool_calls' in (assistantMsg ?? {})).toBe(false);
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});
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it('should keep content null when assistant has only tool_calls and no reasoning', () => {
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const request: GenerateContentParameters = {
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model: 'models/test',
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|
|
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|
|
@ -29,6 +29,7 @@ import {
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} from '../../utils/schemaConverter.js';
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const debugLogger = createDebugLogger('CONVERTER');
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const SPLIT_TOOL_MEDIA_TEXT = '(attached media from previous tool call)';
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/**
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* Extended usage type that supports both OpenAI standard format and alternative formats
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|
|
@ -380,11 +381,11 @@ export function convertGeminiRequestToOpenAI(
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// Handle contents
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processContents(request.contents, messages, requestContext);
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// Clean up orphaned tool calls and merge consecutive assistant messages
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messages = mergeConsecutiveAssistantMessages(messages);
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if (options.cleanOrphanToolCalls) {
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messages = cleanOrphanedToolCalls(messages);
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messages = mergeConsecutiveAssistantMessages(messages);
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}
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messages = mergeConsecutiveAssistantMessages(messages);
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return messages;
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||||
}
|
||||
|
|
@ -645,7 +646,7 @@ function processContent(
|
|||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: '(attached media from previous tool call)',
|
||||
text: SPLIT_TOOL_MEDIA_TEXT,
|
||||
},
|
||||
...accumulatedSplitMedia,
|
||||
] as unknown as OpenAI.Chat.ChatCompletionContentPartText[],
|
||||
|
|
@ -1387,51 +1388,126 @@ function mapGeminiFinishReasonToOpenAI(
|
|||
}
|
||||
}
|
||||
|
||||
/** Type guard: is this an assistant message with at least one tool call? */
|
||||
function hasToolCalls(
|
||||
message: OpenAI.Chat.ChatCompletionMessageParam,
|
||||
): message is OpenAI.Chat.ChatCompletionAssistantMessageParam & {
|
||||
tool_calls: OpenAI.Chat.ChatCompletionMessageToolCall[];
|
||||
} {
|
||||
return (
|
||||
message.role === 'assistant' &&
|
||||
'tool_calls' in message &&
|
||||
Array.isArray(message.tool_calls) &&
|
||||
message.tool_calls.length > 0
|
||||
);
|
||||
}
|
||||
|
||||
function isSplitToolMediaMessage(
|
||||
message: OpenAI.Chat.ChatCompletionMessageParam,
|
||||
): boolean {
|
||||
if (
|
||||
message.role !== 'user' ||
|
||||
!('content' in message) ||
|
||||
!Array.isArray(message.content)
|
||||
) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const firstPart = message.content[0] as
|
||||
| { type?: string; text?: string }
|
||||
| undefined;
|
||||
return firstPart?.type === 'text' && firstPart.text === SPLIT_TOOL_MEDIA_TEXT;
|
||||
}
|
||||
|
||||
/**
|
||||
* Clean up orphaned tool calls from message history to prevent OpenAI API errors.
|
||||
*
|
||||
* Assumes consecutive assistant messages have already been merged.
|
||||
*/
|
||||
function cleanOrphanedToolCalls(
|
||||
messages: OpenAI.Chat.ChatCompletionMessageParam[],
|
||||
): OpenAI.Chat.ChatCompletionMessageParam[] {
|
||||
const cleaned: OpenAI.Chat.ChatCompletionMessageParam[] = [];
|
||||
const toolCallIds = new Set<string>();
|
||||
const toolResponseIds = new Set<string>();
|
||||
const adjacentToolResponseIdsByAssistant = new Map<number, Set<string>>();
|
||||
const validToolResponseIndexesByAssistant = new Map<number, number[]>();
|
||||
const splitMediaIndexesByAssistant = new Map<number, number[]>();
|
||||
const emittedWithAssistant = new Set<number>();
|
||||
|
||||
// First pass: collect all tool call IDs and tool response IDs
|
||||
for (const message of messages) {
|
||||
if (
|
||||
message.role === 'assistant' &&
|
||||
'tool_calls' in message &&
|
||||
message.tool_calls
|
||||
) {
|
||||
for (const toolCall of message.tool_calls) {
|
||||
if (toolCall.id) {
|
||||
toolCallIds.add(toolCall.id);
|
||||
for (let index = 0; index < messages.length; index += 1) {
|
||||
const message = messages[index];
|
||||
if (hasToolCalls(message)) {
|
||||
const toolCallIds = new Set(
|
||||
message.tool_calls
|
||||
.map((toolCall) => toolCall.id)
|
||||
.filter((id): id is string => Boolean(id)),
|
||||
);
|
||||
const adjacentToolResponseIds = new Set<string>();
|
||||
const toolResponseIndexes: number[] = [];
|
||||
const splitMediaIndexes: number[] = [];
|
||||
let lastToolResponseMatchesAssistant = false;
|
||||
|
||||
for (
|
||||
let nextIndex = index + 1;
|
||||
nextIndex < messages.length;
|
||||
nextIndex += 1
|
||||
) {
|
||||
const nextMessage = messages[nextIndex];
|
||||
if (nextMessage.role === 'tool' && 'tool_call_id' in nextMessage) {
|
||||
if (!nextMessage.tool_call_id) {
|
||||
lastToolResponseMatchesAssistant = false;
|
||||
continue;
|
||||
}
|
||||
|
||||
if (toolCallIds.has(nextMessage.tool_call_id)) {
|
||||
adjacentToolResponseIds.add(nextMessage.tool_call_id);
|
||||
toolResponseIndexes.push(nextIndex);
|
||||
lastToolResponseMatchesAssistant = true;
|
||||
} else {
|
||||
lastToolResponseMatchesAssistant = false;
|
||||
}
|
||||
|
||||
// Other tool responses in this block may belong to another assistant.
|
||||
continue;
|
||||
}
|
||||
|
||||
if (isSplitToolMediaMessage(nextMessage)) {
|
||||
if (lastToolResponseMatchesAssistant) {
|
||||
splitMediaIndexes.push(nextIndex);
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
if (nextMessage.role === 'assistant' && !hasToolCalls(nextMessage)) {
|
||||
// Consecutive assistant turns are merged before cleanup.
|
||||
continue;
|
||||
}
|
||||
|
||||
break;
|
||||
}
|
||||
} else if (
|
||||
message.role === 'tool' &&
|
||||
'tool_call_id' in message &&
|
||||
message.tool_call_id
|
||||
) {
|
||||
toolResponseIds.add(message.tool_call_id);
|
||||
|
||||
adjacentToolResponseIdsByAssistant.set(index, adjacentToolResponseIds);
|
||||
validToolResponseIndexesByAssistant.set(index, toolResponseIndexes);
|
||||
splitMediaIndexesByAssistant.set(index, splitMediaIndexes);
|
||||
}
|
||||
}
|
||||
|
||||
// Second pass: filter out orphaned messages
|
||||
for (const message of messages) {
|
||||
if (
|
||||
message.role === 'assistant' &&
|
||||
'tool_calls' in message &&
|
||||
message.tool_calls
|
||||
) {
|
||||
// Filter out tool calls that don't have corresponding responses
|
||||
for (let index = 0; index < messages.length; index += 1) {
|
||||
if (emittedWithAssistant.has(index)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const message = messages[index];
|
||||
if (hasToolCalls(message)) {
|
||||
const reasoningContent = (
|
||||
message as ExtendedChatCompletionAssistantMessageParam
|
||||
).reasoning_content;
|
||||
const adjacentToolResponseIds =
|
||||
adjacentToolResponseIdsByAssistant.get(index) ?? new Set<string>();
|
||||
const validToolCalls = message.tool_calls.filter(
|
||||
(toolCall) => toolCall.id && toolResponseIds.has(toolCall.id),
|
||||
(toolCall) => toolCall.id && adjacentToolResponseIds.has(toolCall.id),
|
||||
);
|
||||
|
||||
if (validToolCalls.length > 0) {
|
||||
// Keep the message but only with valid tool calls
|
||||
const cleanedMessage = { ...message };
|
||||
(
|
||||
cleanedMessage as OpenAI.Chat.ChatCompletionMessageParam & {
|
||||
|
|
@ -1439,11 +1515,30 @@ function cleanOrphanedToolCalls(
|
|||
}
|
||||
).tool_calls = validToolCalls;
|
||||
cleaned.push(cleanedMessage);
|
||||
|
||||
for (const toolResponseIndex of validToolResponseIndexesByAssistant.get(
|
||||
index,
|
||||
) ?? []) {
|
||||
const toolResponse = messages[toolResponseIndex];
|
||||
if (toolResponse) {
|
||||
cleaned.push(toolResponse);
|
||||
emittedWithAssistant.add(toolResponseIndex);
|
||||
}
|
||||
}
|
||||
|
||||
for (const splitMediaIndex of splitMediaIndexesByAssistant.get(index) ??
|
||||
[]) {
|
||||
const splitMediaMessage = messages[splitMediaIndex];
|
||||
if (splitMediaMessage) {
|
||||
cleaned.push(splitMediaMessage);
|
||||
emittedWithAssistant.add(splitMediaIndex);
|
||||
}
|
||||
}
|
||||
} else if (
|
||||
typeof message.content === 'string' &&
|
||||
message.content.trim()
|
||||
(typeof message.content === 'string' && message.content.trim()) ||
|
||||
reasoningContent
|
||||
) {
|
||||
// Keep the message if it has text content, but remove tool calls
|
||||
// Keep text/reasoning content, but remove orphaned tool calls.
|
||||
const cleanedMessage = { ...message };
|
||||
delete (
|
||||
cleanedMessage as OpenAI.Chat.ChatCompletionMessageParam & {
|
||||
|
|
@ -1451,91 +1546,25 @@ function cleanOrphanedToolCalls(
|
|||
}
|
||||
).tool_calls;
|
||||
cleaned.push(cleanedMessage);
|
||||
} else {
|
||||
debugLogger.debug(
|
||||
`cleanOrphanedToolCalls: dropping assistant with ${message.tool_calls.length} orphaned tool call(s) and no text/reasoning content`,
|
||||
);
|
||||
}
|
||||
// If no valid tool calls and no content, skip the message entirely
|
||||
} else if (
|
||||
message.role === 'tool' &&
|
||||
'tool_call_id' in message &&
|
||||
message.tool_call_id
|
||||
) {
|
||||
// Only keep tool responses that have corresponding tool calls
|
||||
if (toolCallIds.has(message.tool_call_id)) {
|
||||
cleaned.push(message);
|
||||
}
|
||||
} else if (message.role === 'tool' && 'tool_call_id' in message) {
|
||||
debugLogger.debug(
|
||||
`cleanOrphanedToolCalls: dropping orphaned tool response ${message.tool_call_id || '<empty>'}`,
|
||||
);
|
||||
} else if (isSplitToolMediaMessage(message)) {
|
||||
debugLogger.debug(
|
||||
'cleanOrphanedToolCalls: dropping orphaned split tool media message',
|
||||
);
|
||||
} else {
|
||||
// Keep all other messages as-is
|
||||
cleaned.push(message);
|
||||
}
|
||||
}
|
||||
|
||||
// Final validation: ensure every assistant message with tool_calls has corresponding tool responses
|
||||
const finalCleaned: OpenAI.Chat.ChatCompletionMessageParam[] = [];
|
||||
const finalToolCallIds = new Set<string>();
|
||||
|
||||
// Collect all remaining tool call IDs
|
||||
for (const message of cleaned) {
|
||||
if (
|
||||
message.role === 'assistant' &&
|
||||
'tool_calls' in message &&
|
||||
message.tool_calls
|
||||
) {
|
||||
for (const toolCall of message.tool_calls) {
|
||||
if (toolCall.id) {
|
||||
finalToolCallIds.add(toolCall.id);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Verify all tool calls have responses
|
||||
const finalToolResponseIds = new Set<string>();
|
||||
for (const message of cleaned) {
|
||||
if (
|
||||
message.role === 'tool' &&
|
||||
'tool_call_id' in message &&
|
||||
message.tool_call_id
|
||||
) {
|
||||
finalToolResponseIds.add(message.tool_call_id);
|
||||
}
|
||||
}
|
||||
|
||||
// Remove any remaining orphaned tool calls
|
||||
for (const message of cleaned) {
|
||||
if (
|
||||
message.role === 'assistant' &&
|
||||
'tool_calls' in message &&
|
||||
message.tool_calls
|
||||
) {
|
||||
const finalValidToolCalls = message.tool_calls.filter(
|
||||
(toolCall) => toolCall.id && finalToolResponseIds.has(toolCall.id),
|
||||
);
|
||||
|
||||
if (finalValidToolCalls.length > 0) {
|
||||
const cleanedMessage = { ...message };
|
||||
(
|
||||
cleanedMessage as OpenAI.Chat.ChatCompletionMessageParam & {
|
||||
tool_calls?: OpenAI.Chat.ChatCompletionMessageToolCall[];
|
||||
}
|
||||
).tool_calls = finalValidToolCalls;
|
||||
finalCleaned.push(cleanedMessage);
|
||||
} else if (
|
||||
typeof message.content === 'string' &&
|
||||
message.content.trim()
|
||||
) {
|
||||
const cleanedMessage = { ...message };
|
||||
delete (
|
||||
cleanedMessage as OpenAI.Chat.ChatCompletionMessageParam & {
|
||||
tool_calls?: OpenAI.Chat.ChatCompletionMessageToolCall[];
|
||||
}
|
||||
).tool_calls;
|
||||
finalCleaned.push(cleanedMessage);
|
||||
}
|
||||
} else {
|
||||
finalCleaned.push(message);
|
||||
}
|
||||
}
|
||||
|
||||
return finalCleaned;
|
||||
return cleaned;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue