mirror of
https://github.com/lobehub/lobehub.git
synced 2026-03-27 13:29:15 +07:00
🐛 fix(model-runtime): handle Qwen tool_calls without initial arguments (#11211)
* 🐛 fix(model-runtime): handle Qwen tool_calls without initial arguments Qwen models (e.g., qwen3-vl-235b-a22b-thinking) send tool_calls in two separate chunks: 1. First chunk: {id, name} without arguments 2. Second chunk: {id, arguments} without name Previously, the code directly passed `value.function`, which caused undefined values for arguments/name in respective chunks. Changes: - Add default values for function.arguments (empty string) and function.name (null) in Qwen stream transformer - Align behavior with OpenAI/vLLM stream handling - Add test cases for split tool_call chunks scenario 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * 🐛 fix: fix openai parallel tools calling in chat competition * 💄 style: improve style --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Arvin Xu
@@ -47,7 +47,7 @@ export const SearchInspector = memo<BuiltinInspectorProps<SearchQuery, UniformSe
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)}
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>
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<span>{t('builtins.lobe-web-browsing.apiName.search')}: </span>
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{query && <span className={highlightTextStyles.gold}>{query}</span>}
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{query && <span className={highlightTextStyles.primary}>{query}</span>}
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{!isLoading &&
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!isArgumentsStreaming &&
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pluginState?.results &&
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@@ -28,7 +28,6 @@ const lobehubChatModels: AIChatModelCard[] = [
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releasedAt: '2025-12-11',
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settings: {
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extendParams: ['gpt5_2ReasoningEffort', 'textVerbosity'],
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searchImpl: 'params',
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},
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type: 'chat',
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},
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@@ -56,7 +55,6 @@ const lobehubChatModels: AIChatModelCard[] = [
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releasedAt: '2025-11-13',
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settings: {
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extendParams: ['gpt5_1ReasoningEffort', 'textVerbosity'],
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searchImpl: 'params',
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},
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type: 'chat',
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},
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@@ -84,7 +82,6 @@ const lobehubChatModels: AIChatModelCard[] = [
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releasedAt: '2025-08-07',
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settings: {
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extendParams: ['reasoningEffort'],
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searchImpl: 'params',
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},
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type: 'chat',
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},
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@@ -1203,6 +1203,173 @@ describe('OpenAIStream', () => {
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'thoughtSignature',
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);
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});
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it('should handle GPT-5.2 parallel tool calls with correct id mapping', async () => {
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// GPT-5.2 returns multiple tool calls in parallel with different indices
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// Each tool call starts with id+name, followed by arguments-only chunks
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// The key issue is that subsequent chunks without id should use the correct id
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// based on their index, not the first tool's id
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const streamData = [
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// Tool 0: first chunk with id
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [
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{
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id: 'call_tool0',
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type: 'function',
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function: { name: 'search', arguments: '' },
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index: 0,
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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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// Tool 0: arguments chunk
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [{ function: { arguments: '{"query":' }, index: 0 }],
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},
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},
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],
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},
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// Tool 1: first chunk with id (parallel tool call starts)
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [
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{
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id: 'call_tool1',
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type: 'function',
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function: { name: 'search', arguments: '' },
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index: 1,
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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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// Tool 0: more arguments (continuing tool 0)
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [{ function: { arguments: ' "test0"}' }, index: 0 }],
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},
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},
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],
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},
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// Tool 1: arguments chunk
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [{ function: { arguments: '{"query": "test1"}' }, index: 1 }],
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},
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},
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],
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},
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// Tool 2: first chunk with id
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [
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{
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id: 'call_tool2',
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type: 'function',
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function: { name: 'search', arguments: '' },
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index: 2,
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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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// Tool 2: arguments chunk
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{
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id: 'chatcmpl-test',
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [{ function: { arguments: '{"query": "test2"}' }, index: 2 }],
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},
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},
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],
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},
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// Finish
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{
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id: 'chatcmpl-test',
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choices: [{ index: 0, delta: {}, finish_reason: 'tool_calls' }],
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},
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];
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const mockOpenAIStream = new ReadableStream({
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start(controller) {
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streamData.forEach((data) => {
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controller.enqueue(data);
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});
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controller.close();
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},
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});
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const protocolStream = OpenAIStream(mockOpenAIStream);
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const decoder = new TextDecoder();
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const chunks: string[] = [];
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// @ts-ignore
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for await (const chunk of protocolStream) {
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chunks.push(decoder.decode(chunk, { stream: true }));
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}
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// Verify the exact output - each tool call chunk should have the correct id based on index
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expect(chunks).toEqual(
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[
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":"","name":"search"},"id":"call_tool0","index":0,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":"{\\"query\\":","name":null},"id":"call_tool0","index":0,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":"","name":"search"},"id":"call_tool1","index":1,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":" \\"test0\\"}","name":null},"id":"call_tool0","index":0,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":"{\\"query\\": \\"test1\\"}","name":null},"id":"call_tool1","index":1,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":"","name":"search"},"id":"call_tool2","index":2,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: tool_calls',
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`data: [{"function":{"arguments":"{\\"query\\": \\"test2\\"}","name":null},"id":"call_tool2","index":2,"type":"function"}]\n`,
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'id: chatcmpl-test',
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'event: stop',
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`data: "tool_calls"\n`,
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].map((i) => `${i}\n`),
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);
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});
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});
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describe('Reasoning', () => {
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@@ -157,13 +157,35 @@ const transformOpenAIStream = (
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);
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if (tool_calls.length > 0) {
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// Validate tool calls - function must exist for valid tool calls
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// This ensures proper error handling for malformed chunks
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const hasInvalidToolCall = item.delta.tool_calls.some((tc) => tc.function === null);
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if (hasInvalidToolCall) {
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throw new Error('Invalid tool call: function is null');
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}
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return {
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data: item.delta.tool_calls.map((value, index): StreamToolCallChunkData => {
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if (streamContext && !streamContext.tool) {
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data: item.delta.tool_calls.map((value, mapIndex): StreamToolCallChunkData => {
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// Determine the actual tool index
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const toolIndex = typeof value.index !== 'undefined' ? value.index : mapIndex;
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// Store tool info by index for parallel tool calls (e.g., GPT-5.2)
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// When a chunk has id and name, it's the start of a new tool call
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if (streamContext && value.id && value.function?.name) {
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if (!streamContext.tools) streamContext.tools = {};
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streamContext.tools[toolIndex] = {
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id: value.id,
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index: toolIndex,
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name: value.function.name,
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};
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}
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// Also maintain backward compatibility with single tool context
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if (streamContext && !streamContext.tool && value.id) {
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streamContext.tool = {
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id: value.id!,
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index: value.index,
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name: value.function!.name!,
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index: toolIndex,
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name: value.function?.name ?? '',
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};
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}
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@@ -172,10 +194,12 @@ const transformOpenAIStream = (
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arguments: value.function?.arguments ?? '',
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name: value.function?.name ?? null,
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},
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// Priority: explicit id > tools map by index > single tool fallback > generated id
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id:
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value.id ||
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streamContext?.tools?.[toolIndex]?.id ||
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streamContext?.tool?.id ||
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generateToolCallId(index, value.function?.name),
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generateToolCallId(mapIndex, value.function?.name),
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// mistral's tool calling don't have index and function field, it's data like:
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// [{"id":"xbhnmTtY7","function":{"name":"lobe-image-designer____text2image____builtin","arguments":"{\"prompts\": [\"A photo of a small, fluffy dog with a playful expression and wagging tail.\", \"A watercolor painting of a small, energetic dog with a glossy coat and bright eyes.\", \"A vector illustration of a small, adorable dog with a short snout and perky ears.\", \"A drawing of a small, scruffy dog with a mischievous grin and a wagging tail.\"], \"quality\": \"standard\", \"seeds\": [123456, 654321, 111222, 333444], \"size\": \"1024x1024\", \"style\": \"vivid\"}"}}]
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@@ -184,7 +208,7 @@ const transformOpenAIStream = (
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// [{"id":"call_function_4752059746","type":"function","function":{"name":"lobe-image-designer____text2image____builtin","arguments":"{\"prompts\": [\"一个流浪的地球，背景是浩瀚"}}]
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// so we need to add these default values
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index: typeof value.index !== 'undefined' ? value.index : index,
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index: toolIndex,
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type: value.type || 'function',
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};
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@@ -59,6 +59,11 @@ export interface StreamContext {
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name: string;
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};
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toolIndex?: number;
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/**
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* Map of tool information by index for parallel tool calls
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* Used when multiple tools are called in parallel (e.g., GPT-5.2 parallel search)
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*/
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tools?: Record<number, { id: string; index: number; name: string }>;
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usage?: ModelUsage;
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}
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@@ -131,7 +131,7 @@ describe('QwenAIStream', () => {
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expect(chunks).toEqual([
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'id: 2\n',
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'event: tool_calls\n',
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`data: [{"function":{"name":"tool1","arguments":"{}"},"id":"call_1","index":0,"type":"function"},{"function":{"name":"tool2","arguments":"{}"},"id":"call_2","index":1,"type":"function"}]\n\n`,
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`data: [{"function":{"arguments":"{}","name":"tool1"},"id":"call_1","index":0,"type":"function"},{"function":{"arguments":"{}","name":"tool2"},"id":"call_2","index":1,"type":"function"}]\n\n`,
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]);
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expect(onToolCallMock).toHaveBeenCalledTimes(1);
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@@ -347,7 +347,136 @@ describe('QwenAIStream', () => {
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expect(chunks).toEqual([
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'id: 5\n',
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'event: tool_calls\n',
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`data: [{"function":{"name":"tool1","arguments":"{}"},"id":"call_1","index":0,"type":"function"},{"function":{"name":"tool2","arguments":"{}"},"id":"call_2","index":1,"type":"function"}]\n\n`,
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`data: [{"function":{"arguments":"{}","name":"tool1"},"id":"call_1","index":0,"type":"function"},{"function":{"arguments":"{}","name":"tool2"},"id":"call_2","index":1,"type":"function"}]\n\n`,
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]);
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});
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// Test case for Qwen models sending tool_calls in two separate chunks:
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// 1. First chunk: {id, name} without arguments
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// 2. Second chunk: {id, arguments} without name
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// This behavior is observed in qwen3-vl-235b-a22b-thinking model
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it('should handle tool calls with name in first chunk and arguments in second chunk (Qwen behavior)', async () => {
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const mockOpenAIStream = new ReadableStream({
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start(controller) {
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// First chunk: has id and name, but no arguments
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controller.enqueue({
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choices: [
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{
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delta: {
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content: null,
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tool_calls: [
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{
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index: 0,
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id: 'call_4bde23783e314f219c6d65',
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type: 'function',
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function: { name: 'time____get_current_time____mcp' },
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},
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],
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},
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finish_reason: null,
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index: 0,
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},
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],
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id: 'chatcmpl-f574998f-e5b0-9b80-aac5-14b58e6978b5',
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});
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// Second chunk: same id, has arguments but no name
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controller.enqueue({
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choices: [
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{
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delta: {
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content: null,
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tool_calls: [
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{
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index: 0,
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id: 'call_4bde23783e314f219c6d65',
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type: 'function',
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function: { arguments: '{"timezone": "Asia/Shanghai"}' },
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},
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],
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},
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finish_reason: null,
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index: 0,
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},
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],
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id: 'chatcmpl-f574998f-e5b0-9b80-aac5-14b58e6978b5',
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});
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controller.close();
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},
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});
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const onToolCallMock = vi.fn();
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const protocolStream = QwenAIStream(mockOpenAIStream, {
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callbacks: {
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onToolsCalling: onToolCallMock,
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},
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});
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const decoder = new TextDecoder();
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const chunks = [];
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// @ts-ignore
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for await (const chunk of protocolStream) {
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chunks.push(decoder.decode(chunk, { stream: true }));
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}
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// First chunk should have name with empty arguments
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// Second chunk should have arguments with null name (same as OpenAI/vLLM behavior)
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expect(chunks).toEqual([
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'id: chatcmpl-f574998f-e5b0-9b80-aac5-14b58e6978b5\n',
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'event: tool_calls\n',
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`data: [{"function":{"arguments":"","name":"time____get_current_time____mcp"},"id":"call_4bde23783e314f219c6d65","index":0,"type":"function"}]\n\n`,
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'id: chatcmpl-f574998f-e5b0-9b80-aac5-14b58e6978b5\n',
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'event: tool_calls\n',
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`data: [{"function":{"arguments":"{\\"timezone\\": \\"Asia/Shanghai\\"}","name":null},"id":"call_4bde23783e314f219c6d65","index":0,"type":"function"}]\n\n`,
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]);
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expect(onToolCallMock).toHaveBeenCalledTimes(2);
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});
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it('should handle tool calls with only name (no arguments field)', async () => {
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const mockOpenAIStream = new ReadableStream({
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start(controller) {
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controller.enqueue({
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choices: [
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{
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delta: {
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tool_calls: [
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{
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index: 0,
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id: 'call_123',
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type: 'function',
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function: { name: 'get_weather' },
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},
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],
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},
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index: 0,
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},
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],
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id: '6',
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});
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controller.close();
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},
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});
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const protocolStream = QwenAIStream(mockOpenAIStream);
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const decoder = new TextDecoder();
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const chunks = [];
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// @ts-ignore
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for await (const chunk of protocolStream) {
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chunks.push(decoder.decode(chunk, { stream: true }));
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}
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// Should have empty string for arguments, not undefined
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expect(chunks).toEqual([
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'id: 6\n',
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'event: tool_calls\n',
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`data: [{"function":{"arguments":"","name":"get_weather"},"id":"call_123","index":0,"type":"function"}]\n\n`,
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]);
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});
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});
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@@ -72,7 +72,15 @@ export const transformQwenStream = (
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return {
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data: item.delta.tool_calls.map(
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(value, index): StreamToolCallChunkData => ({
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function: value.function,
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// Qwen models may send tool_calls in two separate chunks:
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// 1. First chunk: {id, name} without arguments
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// 2. Second chunk: {id, arguments} without name
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// We need to provide default values to handle both cases
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// Use null for missing name (same as OpenAI stream behavior)
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function: {
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arguments: value.function?.arguments ?? '',
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name: value.function?.name ?? null,
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},
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id: value.id || generateToolCallId(index, value.function?.name),
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index: typeof value.index !== 'undefined' ? value.index : index,
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type: value.type || 'function',
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