Real-World Scenarios

@Service
public class CustomerSupportService {
    private final ChatModel chatModel;
    private final ChatMemory chatMemory;
    
    public String handleCustomerQuery(String customerId, String query) {
        // Get conversation history
        List<Message> history = chatMemory.get(customerId);
        
        // Add system instruction
        if (history.isEmpty()) {
            history = new ArrayList<>();
            history.add(new SystemMessage(
                "You are a helpful customer support agent for TechCorp. " +
                "Be professional, empathetic, and solution-oriented."
            ));
        }
        
        // Add user query
        UserMessage userMessage = new UserMessage(query);
        history.add(userMessage);
        
        // Create prompt with options
        ChatOptions options = ChatOptions.builder()
            .temperature(0.7)
            .maxTokens(500)
            .build();
        
        Prompt prompt = new Prompt(history, options);
        
        // Get response
        ChatResponse response = chatModel.call(prompt);
        AssistantMessage assistant = response.getResult().getOutput();
        
        // Update memory
        chatMemory.add(customerId, userMessage);
        chatMemory.add(customerId, assistant);
        
        return assistant.getText();
    }
}

@Service
public class DocumentQAService {
    private final ChatModel chatModel;
    private final EmbeddingModel embeddingModel;
    private final List<Document> documentStore;
    
    public String answerQuestion(String question) {
        // 1. Embed the question
        float[] questionEmbedding = embeddingModel.embed(question);
        
        // 2. Find relevant documents (simplified similarity search)
        List<Document> relevantDocs = findSimilarDocuments(questionEmbedding, 3);
        
        // 3. Build context from documents
        String context = relevantDocs.stream()
            .map(Document::getText)
            .collect(Collectors.joining("\n\n"));
        
        // 4. Create prompt with context
        String promptText = String.format("""
            Context information:
            %s
            
            Question: %s
            
            Answer the question based only on the context above.
            """, context, question);
        
        // 5. Get answer
        return chatModel.call(promptText);
    }
    
    private List<Document> findSimilarDocuments(float[] queryEmbedding, int topK) {
        return documentStore.stream()
            .map(doc -> new ScoredDoc(doc, cosineSimilarity(queryEmbedding, doc.getEmbedding())))
            .sorted((a, b) -> Double.compare(b.score, a.score))
            .limit(topK)
            .map(sd -> sd.doc)
            .toList();
    }
    
    private double cosineSimilarity(float[] a, float[] b) {
        double dotProduct = 0.0, normA = 0.0, normB = 0.0;
        for (int i = 0; i < a.length; i++) {
            dotProduct += a[i] * b[i];
            normA += a[i] * a[i];
            normB += b[i] * b[i];
        }
        return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
    }
    
    record ScoredDoc(Document doc, double score) {}
}

@Service
public class DataExtractionService {
    private final ChatModel chatModel;
    
    public InvoiceData extractInvoiceData(String invoiceText) {
        // Create converter
        BeanOutputConverter<InvoiceData> converter = 
            new BeanOutputConverter<>(InvoiceData.class);
        
        // Build prompt with format instructions
        String promptText = String.format("""
            Extract invoice information from the following text:
            
            %s
            
            %s
            """, invoiceText, converter.getFormat());
        
        // Get structured response
        String response = chatModel.call(promptText);
        
        // Convert to object
        return converter.convert(response);
    }
    
    record InvoiceData(
        String invoiceNumber,
        String date,
        String vendor,
        double amount,
        String currency,
        List<LineItem> items
    ) {}
    
    record LineItem(String description, int quantity, double price) {}
}

@Service
public class AgentService {
    private final ChatModel chatModel;
    private final ToolCallbackResolver toolResolver;
    
    public String executeAgentTask(String task) {
        List<Message> conversation = new ArrayList<>();
        conversation.add(new UserMessage(task));
        
        int maxIterations = 10;
        int iteration = 0;
        
        while (iteration < maxIterations) {
            // Call model
            Prompt prompt = new Prompt(conversation);
            ChatResponse response = chatModel.call(prompt);
            AssistantMessage assistant = response.getResult().getOutput();
            
            conversation.add(assistant);
            
            // Check if tool calls needed
            if (!assistant.hasToolCalls()) {
                // Task complete
                return assistant.getText();
            }
            
            // Execute tool calls
            List<ToolResponse> toolResponses = new ArrayList<>();
            for (AssistantMessage.ToolCall toolCall : assistant.getToolCalls()) {
                ToolCallback tool = toolResolver.resolve(toolCall.name());
                String result = tool.call(toolCall.arguments());
                
                toolResponses.add(new ToolResponseMessage.ToolResponse(
                    toolCall.id(),
                    toolCall.name(),
                    result
                ));
            }
            
            // Add tool responses to conversation
            conversation.add(new ToolResponseMessage(toolResponses));
            iteration++;
        }
        
        return "Task exceeded maximum iterations";
    }
}

@Component
public class AgentTools {
    @Tool(description = "Search the web for information")
    public String webSearch(@ToolParam(description = "Search query") String query) {
        // Implementation
        return "Search results for: " + query;
    }
    
    @Tool(description = "Calculate mathematical expressions")
    public String calculate(@ToolParam(description = "Expression") String expression) {
        // Implementation
        return "Result: " + evaluateExpression(expression);
    }
    
    @Tool(description = "Query database")
    public String queryDatabase(@ToolParam(description = "SQL query") String query) {
        // Implementation
        return "Database results";
    }
}

@Service
public class ImageAnalysisService {
    private final ChatModel chatModel;
    
    public String analyzeImage(String imageUrl, String question) {
        // Create media for image
        Media imageMedia = new Media(MimeType.IMAGE_JPEG, imageUrl);
        
        // Create user message with image and question
        UserMessage userMessage = new UserMessage(
            question,
            List.of(imageMedia)
        );
        
        // Call model
        Prompt prompt = new Prompt(userMessage);
        ChatResponse response = chatModel.call(prompt);
        
        return response.getResult().getOutput().getText();
    }
    
    public String compareImages(String imageUrl1, String imageUrl2) {
        Media image1 = new Media(MimeType.IMAGE_JPEG, imageUrl1);
        Media image2 = new Media(MimeType.IMAGE_JPEG, imageUrl2);
        
        UserMessage message = new UserMessage(
            "Compare these two images and describe the key differences.",
            List.of(image1, image2)
        );
        
        return chatModel.call(new Prompt(message))
            .getResult().getOutput().getText();
    }
}

@Service
public class ContentModerationPipeline {
    private final ModerationModel moderationModel;
    private final ChatModel chatModel;
    
    public ProcessedContent processUserContent(String content) {
        // Step 1: Check content safety
        ModerationPrompt moderationPrompt = new ModerationPrompt(content);
        ModerationResponse modResponse = moderationModel.call(moderationPrompt);
        Moderation moderation = modResponse.getResult().getOutput();
        
        if (moderation.isFlagged()) {
            Categories categories = moderation.getCategories();
            List<String> violations = new ArrayList<>();
            
            if (categories.isHate()) violations.add("hate speech");
            if (categories.isViolence()) violations.add("violence");
            if (categories.isSexual()) violations.add("sexual content");
            
            return new ProcessedContent(
                content,
                false,
                "Content blocked: " + String.join(", ", violations),
                null
            );
        }
        
        // Step 2: Process safe content
        String processed = chatModel.call(
            "Summarize this user content: " + content
        );
        
        return new ProcessedContent(content, true, "Approved", processed);
    }
    
    record ProcessedContent(
        String original,
        boolean approved,
        String status,
        String processed
    ) {}
}

@Service
public class SemanticSearchService {
    private final EmbeddingModel embeddingModel;
    private final List<IndexedDocument> index = new ArrayList<>();
    
    public void indexDocuments(List<String> documents) {
        // Generate embeddings
        List<float[]> embeddings = embeddingModel.embed(documents);
        
        // Store in index
        for (int i = 0; i < documents.size(); i++) {
            index.add(new IndexedDocument(
                "doc_" + i,
                documents.get(i),
                embeddings.get(i)
            ));
        }
    }
    
    public List<SearchResult> search(String query, int topK) {
        // Embed query
        float[] queryEmbedding = embeddingModel.embed(query);
        
        // Score all documents
        return index.stream()
            .map(doc -> new SearchResult(
                doc.id(),
                doc.content(),
                cosineSimilarity(queryEmbedding, doc.embedding())
            ))
            .sorted((a, b) -> Double.compare(b.score(), a.score()))
            .limit(topK)
            .toList();
    }
    
    record IndexedDocument(String id, String content, float[] embedding) {}
    record SearchResult(String id, String content, double score) {}
}

@Service
public class ContentGenerationService {
    private final ChatModel chatModel;
    private final ImageModel imageModel;
    private final TextToSpeechModel ttsModel;
    
    public MultiModalContent generateContent(String topic) {
        // 1. Generate text
        String text = chatModel.call(
            "Write a 200-word article about: " + topic
        );
        
        // 2. Generate image
        ImagePrompt imagePrompt = new ImagePrompt(
            "Create an illustration for: " + topic
        );
        String imageUrl = imageModel.call(imagePrompt)
            .getResult().getOutput().getUrl();
        
        // 3. Generate audio narration
        byte[] audio = ttsModel.call(text);
        
        return new MultiModalContent(text, imageUrl, audio);
    }
    
    record MultiModalContent(String text, String imageUrl, byte[] audio) {}
}

@Service
public class BatchProcessingService {
    private final ChatModel chatModel;
    private static final long RATE_LIMIT_DELAY_MS = 1000;
    
    public List<ProcessedItem> processBatch(List<String> items) {
        List<ProcessedItem> results = new ArrayList<>();
        
        for (String item : items) {
            try {
                // Process item
                Prompt prompt = new Prompt("Process: " + item);
                ChatResponse response = chatModel.call(prompt);
                
                // Check rate limits
                RateLimit rateLimit = response.getMetadata().getRateLimit();
                if (rateLimit != null && rateLimit.getRequestsRemaining() != null) {
                    if (rateLimit.getRequestsRemaining() < 5) {
                        // Approaching limit, slow down
                        Thread.sleep(RATE_LIMIT_DELAY_MS * 2);
                    }
                }
                
                results.add(new ProcessedItem(
                    item,
                    true,
                    response.getResult().getOutput().getText(),
                    response.getMetadata().getUsage().getTotalTokens()
                ));
                
                // Brief delay between requests
                Thread.sleep(RATE_LIMIT_DELAY_MS);
                
            } catch (Exception e) {
                results.add(new ProcessedItem(item, false, e.getMessage(), 0));
            }
        }
        
        return results;
    }
    
    record ProcessedItem(String input, boolean success, String output, int tokensUsed) {}
}

@Service
public class AudioProcessingService {
    private final TranscriptionModel transcriptionModel;
    private final ChatModel chatModel;
    private final TextToSpeechModel ttsModel;
    
    public AudioProcessingResult processAudio(Resource audioFile) {
        // 1. Transcribe audio
        String transcription = transcriptionModel.transcribe(audioFile);
        
        // 2. Analyze transcription
        String analysis = chatModel.call(
            "Analyze this transcription and provide key insights: " + transcription
        );
        
        // 3. Generate summary
        String summary = chatModel.call(
            "Summarize in one sentence: " + transcription
        );
        
        // 4. Generate audio summary
        byte[] audioSummary = ttsModel.call(summary);
        
        return new AudioProcessingResult(
            transcription,
            analysis,
            summary,
            audioSummary
        );
    }
    
    record AudioProcessingResult(
        String transcription,
        String analysis,
        String summary,
        byte[] audioSummary
    ) {}
}

@Service
public class DocumentEnrichmentPipeline {
    private final ChatModel chatModel;
    private final EmbeddingModel embeddingModel;
    
    public List<EnrichedDocument> enrichDocuments(List<String> rawDocuments) {
        // Create documents
        List<Document> documents = rawDocuments.stream()
            .map(Document::new)
            .toList();
        
        // Step 1: Extract keywords
        KeywordMetadataEnricher keywordEnricher = 
            KeywordMetadataEnricher.builder(chatModel)
                .keywordCount(5)
                .build();
        documents = keywordEnricher.apply(documents);
        
        // Step 2: Generate summaries
        SummaryMetadataEnricher summaryEnricher = new SummaryMetadataEnricher(
            chatModel,
            List.of(SummaryType.CURRENT)
        );
        documents = summaryEnricher.apply(documents);
        
        // Step 3: Generate embeddings
        List<float[]> embeddings = embeddingModel.embed(
            documents.stream().map(Document::getText).toList()
        );
        
        for (int i = 0; i < documents.size(); i++) {
            documents.get(i).setEmbedding(embeddings.get(i));
        }
        
        // Return enriched documents
        return documents.stream()
            .map(doc -> new EnrichedDocument(
                doc.getId(),
                doc.getText(),
                (String) doc.getMetadata().get("excerpt_keywords"),
                (String) doc.getMetadata().get("section_summary"),
                doc.getEmbedding()
            ))
            .toList();
    }
    
    record EnrichedDocument(
        String id,
        String content,
        String keywords,
        String summary,
        float[] embedding
    ) {}
}

@RestController
@RequestMapping("/api/chat")
public class StreamingChatController {
    private final ChatModel chatModel;
    private final ChatMemory chatMemory;
    
    @GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ServerSentEvent<String>> streamChat(
        @RequestParam String userId,
        @RequestParam String message
    ) {
        // Get conversation history
        List<Message> history = new ArrayList<>(chatMemory.get(userId));
        UserMessage userMsg = new UserMessage(message);
        history.add(userMsg);
        
        // Save user message
        chatMemory.add(userId, userMsg);
        
        // Stream response
        Prompt prompt = new Prompt(history);
        
        StringBuilder fullResponse = new StringBuilder();
        
        return chatModel.stream(prompt)
            .map(response -> response.getResult().getOutput().getText())
            .map(text -> {
                fullResponse.append(text);
                return ServerSentEvent.builder(text)
                    .event("message")
                    .build();
            })
            .concatWith(Mono.fromCallable(() -> {
                // Save complete assistant message
                chatMemory.add(userId, new AssistantMessage(fullResponse.toString()));
                return ServerSentEvent.<String>builder()
                    .event("done")
                    .build();
            }));
    }
}

@Service
public class ImageGenerationService {
    private final ImageModel imageModel;
    private final StorageService storageService;
    
    public GeneratedImage generateAndStore(ImageRequest request) {
        // Configure image options
        ImageOptions options = ImageOptions.builder()
            .width(request.width())
            .height(request.height())
            .style(request.style())
            .n(1)
            .build();
        
        // Generate image
        ImagePrompt prompt = new ImagePrompt(request.description(), options);
        ImageResponse response = imageModel.call(prompt);
        Image image = response.getResult().getOutput();
        
        // Download and store
        byte[] imageData = downloadImage(image.getUrl());
        String storedPath = storageService.store(imageData, "image.png");
        
        return new GeneratedImage(
            storedPath,
            image.getUrl(),
            request.description()
        );
    }
    
    private byte[] downloadImage(String url) {
        try (InputStream in = new URL(url).openStream()) {
            return in.readAllBytes();
        } catch (Exception e) {
            throw new RuntimeException("Failed to download image", e);
        }
    }
    
    record ImageRequest(String description, int width, int height, String style) {}
    record GeneratedImage(String storedPath, String originalUrl, String prompt) {}
}

@Service
public class ContextualToolService {
    private final ChatModel chatModel;
    
    public String processWithUserContext(String userId, String sessionId, String request) {
        // Build tool context
        Map<String, Object> toolContext = Map.of(
            "userId", userId,
            "sessionId", sessionId,
            "timestamp", System.currentTimeMillis()
        );
        
        // Configure options
        ToolCallingChatOptions options = ToolCallingChatOptions.builder()
            .toolCallbacks(getUserTools(), getDatabaseTools())
            .toolContext(toolContext)
            .internalToolExecutionEnabled(true)
            .temperature(0.7)
            .build();
        
        // Call model with tools
        Prompt prompt = new Prompt(request, options);
        ChatResponse response = chatModel.call(prompt);
        
        return response.getResult().getOutput().getText();
    }
    
    private List<ToolCallback> getUserTools() {
        return new MethodToolCallbackProvider(new UserTools()).getToolCallbacks();
    }
    
    private List<ToolCallback> getDatabaseTools() {
        return new MethodToolCallbackProvider(new DatabaseTools()).getToolCallbacks();
    }
}

@Component
class UserTools {
    @Tool(description = "Get user profile information")
    public String getUserProfile(@ToolParam(description = "User ID") String userId) {
        // Access userId from context if needed
        return "{\"name\": \"John Doe\", \"email\": \"john@example.com\"}";
    }
}

@Service
public class MonitoredAiService {
    private final ChatModel chatModel;
    private final MeterRegistry meterRegistry;
    private final ObservationRegistry observationRegistry;
    
    public String monitoredChat(String message) {
        Timer.Sample sample = Timer.start(meterRegistry);
        
        try {
            // Create observation context
            Prompt prompt = new Prompt(message);
            ChatModelObservationContext context = 
                new ChatModelObservationContext(prompt, chatModel);
            
            // Execute with observation
            return Observation.createNotStarted("ai.chat.call", context, observationRegistry)
                .observe(() -> {
                    ChatResponse response = chatModel.call(prompt);
                    context.setResponse(response);
                    
                    // Record metrics
                    Usage usage = response.getMetadata().getUsage();
                    meterRegistry.counter("ai.tokens.used", 
                        "type", "total").increment(usage.getTotalTokens());
                    
                    return response.getResult().getOutput().getText();
                });
        } finally {
            sample.stop(Timer.builder("ai.chat.duration")
                .tag("model", "chat")
                .register(meterRegistry));
        }
    }
}

@Service
public class ResilientAiService {
    private final ChatModel chatModel;
    
    public String chatWithRetry(String message) {
        int maxRetries = 3;
        int attempt = 0;
        long delay = 1000; // Start with 1 second
        
        while (attempt < maxRetries) {
            try {
                ChatResponse response = chatModel.call(new Prompt(message));
                
                // Check if we should slow down
                RateLimit rateLimit = response.getMetadata().getRateLimit();
                if (rateLimit != null && rateLimit.getRequestsRemaining() != null) {
                    if (rateLimit.getRequestsRemaining() < 10) {
                        Thread.sleep(2000); // Proactive slowdown
                    }
                }
                
                return response.getResult().getOutput().getText();
                
            } catch (Exception e) {
                attempt++;
                if (attempt >= maxRetries) {
                    throw new RuntimeException("Max retries exceeded", e);
                }
                
                try {
                    Thread.sleep(delay);
                    delay *= 2; // Exponential backoff
                } catch (InterruptedException ie) {
                    Thread.currentThread().interrupt();
                    throw new RuntimeException("Interrupted during retry", ie);
                }
            }
        }
        
        throw new RuntimeException("Unexpected retry loop exit");
    }
}