mirror of
https://github.com/QwenLM/qwen-code.git
synced 2026-01-06 17:09:14 +00:00
support merge ChatCompletionContentPart && add filterEmptyMessages
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@@ -542,4 +542,206 @@ describe('OpenAIContentConverter', () => {
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expect(original).toEqual(originalCopy);
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});
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});
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describe('mergeConsecutiveAssistantMessages', () => {
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it('should merge two consecutive assistant messages with string content', () => {
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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: [{ text: 'First part' }],
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},
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{
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role: 'model',
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parts: [{ text: 'Second part' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(request);
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expect(messages).toHaveLength(1);
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expect(messages[0].role).toBe('assistant');
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const content = messages[0]
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.content as OpenAI.Chat.ChatCompletionContentPart[];
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expect(content).toHaveLength(2);
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expect(content[0]).toEqual({ type: 'text', text: 'First part' });
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expect(content[1]).toEqual({ type: 'text', text: 'Second part' });
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});
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it('should merge multiple consecutive assistant messages', () => {
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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: [{ text: 'Part 1' }],
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},
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{
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role: 'model',
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parts: [{ text: 'Part 2' }],
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},
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{
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role: 'model',
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parts: [{ text: 'Part 3' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(request);
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expect(messages).toHaveLength(1);
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expect(messages[0].role).toBe('assistant');
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const content = messages[0]
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.content as OpenAI.Chat.ChatCompletionContentPart[];
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expect(content).toHaveLength(3);
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});
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it('should merge tool_calls from consecutive assistant messages', () => {
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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_1',
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name: 'tool_1',
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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_1',
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name: 'tool_1',
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response: { output: 'result_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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role: 'model',
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parts: [
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{
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functionCall: {
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id: 'call_2',
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name: 'tool_2',
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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_2',
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name: 'tool_2',
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response: { output: 'result_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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const messages = converter.convertGeminiRequestToOpenAI(request);
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// Should have: assistant (tool_call_1), tool (result_1), assistant (tool_call_2), tool (result_2)
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expect(messages).toHaveLength(4);
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expect(messages[0].role).toBe('assistant');
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expect(messages[1].role).toBe('tool');
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expect(messages[2].role).toBe('assistant');
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expect(messages[3].role).toBe('tool');
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});
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it('should not merge assistant messages separated by user messages', () => {
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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: [{ text: 'First assistant' }],
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},
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{
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role: 'user',
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parts: [{ text: 'User message' }],
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},
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{
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role: 'model',
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parts: [{ text: 'Second assistant' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(request);
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expect(messages).toHaveLength(3);
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expect(messages[0].role).toBe('assistant');
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expect(messages[1].role).toBe('user');
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expect(messages[2].role).toBe('assistant');
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});
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it('should handle merging when one message has array content and another has string', () => {
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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: [{ text: 'Text part' }],
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},
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{
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role: 'model',
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parts: [{ text: 'Another text' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(request);
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expect(messages).toHaveLength(1);
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const content = messages[0]
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.content as OpenAI.Chat.ChatCompletionContentPart[];
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expect(Array.isArray(content)).toBe(true);
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expect(content).toHaveLength(2);
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});
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it('should merge empty content correctly', () => {
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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: [{ text: 'First' }],
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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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{
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role: 'model',
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parts: [{ text: 'Second' }],
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},
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],
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};
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const messages = converter.convertGeminiRequestToOpenAI(request);
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// Empty messages should be filtered out
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expect(messages).toHaveLength(1);
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const content = messages[0]
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.content as OpenAI.Chat.ChatCompletionContentPart[];
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expect(content).toHaveLength(2);
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expect(content[0]).toEqual({ type: 'text', text: 'First' });
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expect(content[1]).toEqual({ type: 'text', text: 'Second' });
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});
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});
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});
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@@ -250,7 +250,7 @@ export class OpenAIContentConverter {
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const mergedMessages =
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this.mergeConsecutiveAssistantMessages(cleanedMessages);
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return mergedMessages;
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return this.filterEmptyMessages(mergedMessages);
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}
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/**
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@@ -1124,12 +1124,44 @@ export class OpenAIContentConverter {
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// If the last message is also an assistant message, merge them
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if (lastMessage.role === 'assistant') {
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// Combine content
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const combinedContent = [
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typeof lastMessage.content === 'string' ? lastMessage.content : '',
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typeof message.content === 'string' ? message.content : '',
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]
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.filter(Boolean)
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.join('');
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const lastContent = lastMessage.content;
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const currentContent = message.content;
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// Determine if we should use array format (if either content is an array)
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const useArrayFormat =
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Array.isArray(lastContent) || Array.isArray(currentContent);
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let combinedContent:
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| string
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| OpenAI.Chat.ChatCompletionContentPart[]
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| null;
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if (useArrayFormat) {
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// Convert both to array format and merge
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const lastParts = Array.isArray(lastContent)
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? lastContent
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: typeof lastContent === 'string' && lastContent
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? [{ type: 'text' as const, text: lastContent }]
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: [];
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const currentParts = Array.isArray(currentContent)
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? currentContent
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: typeof currentContent === 'string' && currentContent
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? [{ type: 'text' as const, text: currentContent }]
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: [];
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combinedContent = [
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...lastParts,
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...currentParts,
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] as OpenAI.Chat.ChatCompletionContentPart[];
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} else {
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// Both are strings or null, merge as strings
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const lastText = typeof lastContent === 'string' ? lastContent : '';
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const currentText =
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typeof currentContent === 'string' ? currentContent : '';
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const mergedText = [lastText, currentText].filter(Boolean).join('');
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combinedContent = mergedText || null;
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}
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// Combine tool calls
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const lastToolCalls =
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@@ -1141,14 +1173,17 @@ export class OpenAIContentConverter {
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// Update the last message with combined data
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(
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lastMessage as OpenAI.Chat.ChatCompletionMessageParam & {
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content: string | null;
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content: string | OpenAI.Chat.ChatCompletionContentPart[] | null;
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tool_calls?: OpenAI.Chat.ChatCompletionMessageToolCall[];
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}
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).content = combinedContent || null;
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if (combinedToolCalls.length > 0) {
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(
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lastMessage as OpenAI.Chat.ChatCompletionMessageParam & {
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content: string | null;
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content:
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| string
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| OpenAI.Chat.ChatCompletionContentPart[]
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| null;
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tool_calls?: OpenAI.Chat.ChatCompletionMessageToolCall[];
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}
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).tool_calls = combinedToolCalls;
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@@ -1164,4 +1199,43 @@ export class OpenAIContentConverter {
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return merged;
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}
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/**
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* Filter out messages that have neither content nor tool_calls
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* to prevent API errors
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*/
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private filterEmptyMessages(
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messages: OpenAI.Chat.ChatCompletionMessageParam[],
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): OpenAI.Chat.ChatCompletionMessageParam[] {
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return messages.filter((message) => {
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// Keep system, user, and tool messages if they have content
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if (
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message.role === 'system' ||
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message.role === 'user' ||
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message.role === 'tool'
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) {
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return (
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message.content !== null &&
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message.content !== undefined &&
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message.content !== ''
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);
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}
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// For assistant messages, keep if they have content or tool_calls
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if (message.role === 'assistant') {
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const hasContent =
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message.content !== null &&
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message.content !== undefined &&
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message.content !== '';
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const hasToolCalls =
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'tool_calls' in message &&
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message.tool_calls &&
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message.tool_calls.length > 0;
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return hasContent || hasToolCalls;
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}
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// Keep other message types by default
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return true;
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});
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}
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}
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