IA e pequenas coisas a funcionar
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test/rag_services_simple_test.dart
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224
test/rag_services_simple_test.dart
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import 'package:flutter_test/flutter_test.dart';
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import '../lib/core/services/vector_service.dart';
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import '../lib/core/services/rag_service.dart';
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import '../lib/core/services/rag_ai_service.dart';
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import '../lib/core/models/content_chunk.dart';
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void main() {
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group('AI Tutor RAG Services Tests', () {
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test('VectorService - Generate Embedding', () async {
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print('🔍 Testing VectorService embedding generation...');
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const testText = 'A fotossíntese é o processo pelo qual as plantas convertem luz solar em energia química.';
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final embedding = VectorService.generateEmbedding(testText);
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// Verify embedding properties
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expect(embedding.length, equals(384)); // Standard embedding size
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expect(embedding.every((x) => x >= -1.0 && x <= 1.0), isTrue); // Normalized
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print('✅ Embedding generated successfully: ${embedding.length} dimensions');
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});
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test('VectorService - Cosine Similarity', () {
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print('🔍 Testing cosine similarity calculation...');
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const text1 = 'A fotossíntese é importante para as plantas.';
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const text2 = 'As plantas usam fotossíntese para produzir energia.';
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const text3 = 'Os animais precisam de comida para sobreviver.';
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final embedding1 = VectorService.generateEmbedding(text1);
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final embedding2 = VectorService.generateEmbedding(text2);
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final embedding3 = VectorService.generateEmbedding(text3);
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final similarity12 = VectorService.cosineSimilarity(embedding1, embedding2);
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final similarity13 = VectorService.cosineSimilarity(embedding1, embedding3);
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// Similar texts should have higher similarity
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expect(similarity12, greaterThan(similarity13));
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expect(similarity12, greaterThan(0.5)); // Should be reasonably similar
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expect(similarity13, lessThan(0.3)); // Should be less similar
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print('✅ Cosine similarity working correctly');
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print(' Similar (fotossíntese): ${similarity12.toStringAsFixed(3)}');
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print(' Different (plantas vs animais): ${similarity13.toStringAsFixed(3)}');
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});
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test('VectorService - Search by Text', () async {
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print('🔍 Testing vector search by text...');
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// This would normally search the database, but we'll test the embedding part
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const query = 'O que é fotossíntese?';
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final results = await VectorService.searchByText(
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query: query,
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subject: 'Biologia',
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concept: 'Fotossíntese',
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grade: 10,
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k: 3,
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);
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// Results might be empty if no data in database, but the call should work
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expect(results, isA<List<ContentChunk>>());
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print('✅ Vector search completed');
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print(' Found ${results.length} results for: "$query"');
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});
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test('RAGAIService - Service Availability', () async {
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print('🔍 Testing Ollama service availability...');
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try {
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final isAvailable = await RAGAIService.isServiceAvailable();
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if (isAvailable) {
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print('✅ Ollama service is available');
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// Test model info
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final modelInfo = await RAGAIService.getModelInfo();
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if (modelInfo != null) {
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print(' Model: ${modelInfo['name']}');
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print(' Size: ${modelInfo['size']}');
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print(' Modified: ${modelInfo['modified']}');
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}
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// Test simple query
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final testResponse = await RAGAIService.testService();
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print(' Test response: "$testResponse"');
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} else {
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print('⚠️ Ollama service is not available');
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print(' This is expected if the service is not running');
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}
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} catch (e) {
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print('❌ Error testing service availability: $e');
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}
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});
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test('RAG Pipeline - End-to-End Simulation', () async {
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print('🔍 Testing complete RAG pipeline simulation...');
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try {
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const userQuery = 'O que é fotossíntese?';
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const mode = TutorMode.explanation;
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// Step 1: Test query embedding
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final queryEmbedding = VectorService.generateEmbedding(userQuery);
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print(' ✅ Query embedding generated (${queryEmbedding.length} dims)');
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// Step 2: Test vector search
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final searchResults = await VectorService.searchSimilar(
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queryEmbedding: queryEmbedding,
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subject: 'Biologia',
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concept: 'Fotossíntese',
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grade: 10,
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k: 3,
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);
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print(' ✅ Vector search completed (${searchResults.length} results)');
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// Step 3: Test RAG processing (with mock data if no real data)
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if (searchResults.isEmpty) {
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print(' ⚠️ No content found, creating mock data for testing...');
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// Create mock chunks for testing
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final mockChunks = [
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ContentChunk(
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id: 'mock1',
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contentId: 'mock_content1',
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text: 'A fotossíntese é o processo pelo qual as plantas convertem luz solar em energia química.',
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subject: 'Biologia',
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concept: 'Fotossíntese',
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unit: 'Processos Biológicos',
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difficulty: 0.6,
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grade: 10,
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embedding: VectorService.generateEmbedding('fotossíntese plantas energia'),
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sourceDocument: 'Biologia Manual.pdf',
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metadata: {'page': 45},
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createdAt: DateTime.now(),
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),
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];
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// Test RAG processing with mock data
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final ragResponse = await RAGService.processQuery(
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userQuery: userQuery,
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mode: mode,
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subject: 'Biologia',
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concept: 'Fotossíntese',
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grade: 10,
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maxSources: 3,
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);
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print(' ✅ RAG processing completed');
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print(' Answer: "${ragResponse.answer.substring(0, 100)}..."');
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print(' Confidence: ${ragResponse.confidence.toStringAsFixed(2)}');
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print(' Sources: ${ragResponse.sources.length}');
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print(' Related concepts: ${ragResponse.relatedConcepts.join(', ')}');
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} else {
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print(' ✅ Found real content for RAG processing');
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// Test with real data
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final ragResponse = await RAGService.processQuery(
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userQuery: userQuery,
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mode: mode,
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subject: 'Biologia',
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concept: 'Fotossíntese',
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grade: 10,
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maxSources: 3,
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);
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print(' ✅ RAG processing with real data completed');
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print(' Answer: "${ragResponse.answer.substring(0, 100)}..."');
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print(' Confidence: ${ragResponse.confidence.toStringAsFixed(2)}');
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print(' Sources: ${ragResponse.sources.length}');
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}
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print('✅ RAG pipeline simulation completed successfully');
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} catch (e) {
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print('❌ Error in RAG pipeline: $e');
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}
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});
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test('VectorService - Batch Embeddings', () async {
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print('🔍 Testing batch embedding generation...');
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final texts = [
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'A fotossíntese converte luz solar em energia.',
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'As plantas usam clorofila para capturar luz.',
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'O oxigénio é um subproduto da fotossíntese.',
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];
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final embeddings = await VectorService.batchGenerateEmbeddings(texts);
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expect(embeddings.length, equals(texts.length));
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expect(embeddings.every((e) => e.length == 384), isTrue);
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print('✅ Batch embeddings generated successfully');
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print(' Generated ${embeddings.length} embeddings');
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// Test similarities between batch embeddings
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for (int i = 0; i < embeddings.length; i++) {
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for (int j = i + 1; j < embeddings.length; j++) {
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final similarity = VectorService.cosineSimilarity(embeddings[i], embeddings[j]);
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print(' Similarity ${i+1}-${j+1}: ${similarity.toStringAsFixed(3)}');
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}
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}
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});
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test('VectorService - Statistics', () async {
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print('🔍 Testing vector statistics...');
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final stats = await VectorService.getVectorStats();
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expect(stats, isA<Map<String, dynamic>>());
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expect(stats.containsKey('totalChunks'), isTrue);
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expect(stats.containsKey('embeddingDimension'), isTrue);
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expect(stats['embeddingDimension'], equals(384));
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print('✅ Vector statistics retrieved');
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print(' Total chunks: ${stats['totalChunks']}');
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print(' Embedding dimension: ${stats['embeddingDimension']}');
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print(' Subjects: ${stats['subjects'].keys.length}');
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print(' Concepts: ${stats['concepts'].keys.length}');
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});
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});
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}
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