feat: allow image uploads for AI invoice analysis

Modified `LLMService` to detect image attachments (PNG, JPEG, etc.) and send them as base64-encoded `image_url` content to OpenAI, instead of uploading them as files. This resolves the 400 error for unsupported MIME types when uploading PNGs.

Maintained existing behavior for PDF files.

Added unit tests to cover both image and PDF scenarios.

Author: google-labs-jules[bot]

src/backend/services/llm.service.spec.ts

@@ -116,6 +116,102 @@ describe('LLMService', () => {
 				'OpenAI API key not configured',
 			);
 		});
+
+		it('should handle image attachments correctly using image_url', async () => {
+			mockCreate.mockResolvedValueOnce({
+				choices: [{ message: { content: 'Image analysis' } }],
+				usage: { prompt_tokens: 10, completion_tokens: 5, total_tokens: 15 },
+			});
+			const config: LLMConfig = {
+				provider: 'openai',
+				apiKey: 'test-key',
+				model: 'gpt-4-vision',
+			};
+			const service = new LLMService(
+				new MockConfigService(config) as unknown as ConfigService,
+			);
+
+			const request: LLMRequest = {
+				prompt: 'Analyze this image',
+				files: [
+					{
+						name: 'test.png',
+						content: Buffer.from('fake-image-data'),
+						mimeType: 'image/png',
+					},
+				],
+			};
+
+			await service.sendRequest(request);
+
+			expect(mockCreate).toHaveBeenCalledWith(
+				expect.objectContaining({
+					messages: expect.arrayContaining([
+						expect.objectContaining({
+							role: 'user',
+							content: [
+								{
+									type: 'image_url',
+									image_url: {
+										url: `data:image/png;base64,${Buffer.from('fake-image-data').toString('base64')}`,
+									},
+								},
+							],
+						}),
+					]),
+				}),
+			);
+			expect(mockFilesCreate).not.toHaveBeenCalled();
+		});
+
+		it('should handle PDF attachments correctly using file upload', async () => {
+			mockCreate.mockResolvedValueOnce({
+				choices: [{ message: { content: 'PDF analysis' } }],
+				usage: { prompt_tokens: 10, completion_tokens: 5, total_tokens: 15 },
+			});
+			mockFilesCreate.mockResolvedValueOnce({ id: 'file-123' });
+
+			const config: LLMConfig = {
+				provider: 'openai',
+				apiKey: 'test-key',
+				model: 'gpt-4',
+			};
+			const service = new LLMService(
+				new MockConfigService(config) as unknown as ConfigService,
+			);
+
+			const request: LLMRequest = {
+				prompt: 'Analyze this PDF',
+				files: [
+					{
+						name: 'test.pdf',
+						content: Buffer.from('fake-pdf-data'),
+						mimeType: 'application/pdf',
+					},
+				],
+			};
+
+			await service.sendRequest(request);
+
+			expect(mockFilesCreate).toHaveBeenCalled();
+			expect(mockCreate).toHaveBeenCalledWith(
+				expect.objectContaining({
+					messages: expect.arrayContaining([
+						expect.objectContaining({
+							role: 'user',
+							content: [
+								{
+									type: 'file',
+									file: {
+										file_id: 'file-123',
+									},
+								},
+							],
+						}),
+					]),
+				}),
+			);
+		});
 	});
 
 	describe('Anthropic provider', () => {

src/backend/services/llm.service.ts

@@ -147,26 +147,42 @@ export class LLMService {
 
 		if (request.files?.length) {
 			for (const attachment of request.files) {
-				const file = new File([attachment.content], attachment.name, {
-					type: attachment.mimeType,
-				});
-
-				const fileID = await openai.files.create({
-					file,
-					purpose: 'user_data',
-				});
-
-				messages.push({
-					role: 'user',
-					content: [
-						{
-							type: 'file',
-							file: {
-								file_id: fileID.id,
+				if (attachment.mimeType.startsWith('image/')) {
+					messages.push({
+						role: 'user',
+						content: [
+							{
+								type: 'image_url',
+								image_url: {
+									url: `data:${attachment.mimeType};base64,${attachment.content.toString(
+										'base64',
+									)}`,
+								},
 							},
-						},
-					],
-				});
+						],
+					});
+				} else {
+					const file = new File([attachment.content], attachment.name, {
+						type: attachment.mimeType,
+					});
+
+					const fileID = await openai.files.create({
+						file,
+						purpose: 'user_data',
+					});
+
+					messages.push({
+						role: 'user',
+						content: [
+							{
+								type: 'file',
+								file: {
+									file_id: fileID.id,
+								},
+							},
+						],
+					});
+				}
 			}
 		}