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MessageRoleSchema, + MessageContentTypeSchema, + MessageContentSchema, + FunctionCallSchema, + ToolCallSchema, + ConversationMessageSchema, + TokenBudgetStrategySchema, + TokenBudgetConfigSchema, + TokenUsageStatsSchema, + ConversationContextSchema, + ConversationSessionSchema, + ConversationSummarySchema, + MessagePruningEventSchema, + ConversationAnalyticsSchema, + type ConversationMessage, + type ConversationSession, + type TokenBudgetConfig, +} from './conversation.zod'; + +describe('MessageRoleSchema', () => { + it('should accept all valid roles', () => { + const roles = ['system', 'user', 'assistant', 'function', 'tool'] as const; + + roles.forEach(role => { + expect(() => MessageRoleSchema.parse(role)).not.toThrow(); + }); + }); + + it('should reject invalid roles', () => { + expect(() => MessageRoleSchema.parse('invalid')).toThrow(); + }); +}); + +describe('MessageContentTypeSchema', () => { + it('should accept all valid content types', () => { + const types = ['text', 'image', 'file', 'code', 'structured'] as const; + + types.forEach(type => { + expect(() => MessageContentTypeSchema.parse(type)).not.toThrow(); + }); + }); +}); + +describe('MessageContentSchema', () => { + it('should accept text content', () => { + const content = { + type: 'text', + text: 'Hello world', + }; + expect(() => MessageContentSchema.parse(content)).not.toThrow(); + }); + + it('should accept image content', () => { + const content = { + type: 'image', + imageUrl: 'https://example.com/image.png', + mimeType: 'image/png', + }; + expect(() => MessageContentSchema.parse(content)).not.toThrow(); + }); + + it('should default to text type', () => { + const content = { + text: 'Hello', + }; + const result = MessageContentSchema.parse(content); + expect(result.type).toBe('text'); + }); +}); + +describe('ConversationMessageSchema', () => { + it('should accept minimal message', () => { + const message: ConversationMessage = { + id: 'msg-1', + timestamp: '2024-01-15T10:00:00Z', + role: 'user', + content: 'Hello, how are you?', + }; + expect(() => ConversationMessageSchema.parse(message)).not.toThrow(); + }); + + it('should accept message with multimodal content', () => { + const message: ConversationMessage = { + id: 'msg-2', + timestamp: '2024-01-15T10:01:00Z', + role: 'user', + content: [ + { type: 'text', text: 'What is in this image?' }, + { type: 'image', imageUrl: 'https://example.com/photo.jpg' }, + ], + }; + expect(() => ConversationMessageSchema.parse(message)).not.toThrow(); + }); + + it('should accept assistant message with token stats', () => { + const message: ConversationMessage = { + id: 'msg-3', + timestamp: '2024-01-15T10:02:00Z', + role: 'assistant', + content: 'I am doing well, thank you!', + tokens: { + prompt: 15, + completion: 8, + total: 23, + }, + }; + expect(() => ConversationMessageSchema.parse(message)).not.toThrow(); + }); + + it('should accept message with tool calls', () => { + const message: ConversationMessage = { + id: 'msg-4', + timestamp: '2024-01-15T10:03:00Z', + role: 'assistant', + content: '', + toolCalls: [ + { + id: 'call-1', + type: 'function', + function: { + name: 'get_weather', + arguments: '{"location": "San Francisco"}', + }, + }, + ], + }; + expect(() => ConversationMessageSchema.parse(message)).not.toThrow(); + }); + + it('should accept pinned message with importance', () => { + const message: ConversationMessage = { + id: 'msg-5', + timestamp: '2024-01-15T10:04:00Z', + role: 'system', + content: 'You are a helpful assistant.', + pinned: true, + importance: 1.0, + }; + const result = ConversationMessageSchema.parse(message); + expect(result.pinned).toBe(true); + expect(result.importance).toBe(1.0); + }); + + it('should accept message with embedding', () => { + const message: ConversationMessage = { + id: 'msg-6', + timestamp: '2024-01-15T10:05:00Z', + role: 'user', + content: 'Important information', + embedding: [0.1, 0.2, 0.3, 0.4, 0.5], + }; + expect(() => ConversationMessageSchema.parse(message)).not.toThrow(); + }); +}); + +describe('TokenBudgetStrategySchema', () => { + it('should accept all valid strategies', () => { + const strategies = ['fifo', 'importance', 'semantic', 'sliding_window', 'summary'] as const; + + strategies.forEach(strategy => { + expect(() => TokenBudgetStrategySchema.parse(strategy)).not.toThrow(); + }); + }); +}); + +describe('TokenBudgetConfigSchema', () => { + it('should accept minimal budget config', () => { + const config: TokenBudgetConfig = { + maxTokens: 4096, + }; + const result = TokenBudgetConfigSchema.parse(config); + expect(result.strategy).toBe('sliding_window'); + expect(result.reserveTokens).toBe(500); + expect(result.bufferPercentage).toBe(0.1); + expect(result.warnThreshold).toBe(0.8); + }); + + it('should accept full budget config with sliding window', () => { + const config: TokenBudgetConfig = { + maxTokens: 8192, + maxPromptTokens: 6144, + maxCompletionTokens: 2048, + reserveTokens: 1000, + bufferPercentage: 0.15, + strategy: 'sliding_window', + slidingWindowSize: 10, + warnThreshold: 0.9, + enableSummarization: false, + }; + expect(() => TokenBudgetConfigSchema.parse(config)).not.toThrow(); + }); + + it('should accept importance-based strategy', () => { + const config: TokenBudgetConfig = { + maxTokens: 4096, + strategy: 'importance', + minImportanceScore: 0.3, + }; + expect(() => TokenBudgetConfigSchema.parse(config)).not.toThrow(); + }); + + it('should accept semantic strategy', () => { + const config: TokenBudgetConfig = { + maxTokens: 4096, + strategy: 'semantic', + semanticThreshold: 0.75, + }; + expect(() => TokenBudgetConfigSchema.parse(config)).not.toThrow(); + }); + + it('should accept summarization config', () => { + const config: TokenBudgetConfig = { + maxTokens: 16000, + strategy: 'summary', + enableSummarization: true, + summarizationThreshold: 12000, + summaryModel: 'gpt-3.5-turbo', + }; + expect(() => TokenBudgetConfigSchema.parse(config)).not.toThrow(); + }); +}); + +describe('TokenUsageStatsSchema', () => { + it('should accept valid usage stats', () => { + const stats = { + promptTokens: 1500, + completionTokens: 800, + totalTokens: 2300, + budgetLimit: 4096, + budgetUsed: 2300, + budgetRemaining: 1796, + budgetPercentage: 0.56, + messageCount: 10, + prunedMessageCount: 2, + summarizedMessageCount: 0, + }; + expect(() => TokenUsageStatsSchema.parse(stats)).not.toThrow(); + }); + + it('should default to zero for optional counts', () => { + const stats = { + budgetLimit: 4096, + budgetRemaining: 4096, + budgetPercentage: 0, + }; + const result = TokenUsageStatsSchema.parse(stats); + expect(result.promptTokens).toBe(0); + expect(result.completionTokens).toBe(0); + expect(result.totalTokens).toBe(0); + }); +}); + +describe('ConversationContextSchema', () => { + it('should accept minimal context', () => { + const context = { + sessionId: 'session-123', + }; + expect(() => ConversationContextSchema.parse(context)).not.toThrow(); + }); + + it('should accept full context', () => { + const context = { + sessionId: 'session-123', + userId: 'user-456', + agentId: 'support_agent', + object: 'case', + recordId: 'case-789', + scope: { + department: 'support', + priority: 'high', + }, + systemMessage: 'You are a helpful customer support agent.', + metadata: { + source: 'web', + }, + }; + expect(() => ConversationContextSchema.parse(context)).not.toThrow(); + }); +}); + +describe('ConversationSessionSchema', () => { + it('should accept minimal session', () => { + const session: ConversationSession = { + id: 'session-1', + context: { + sessionId: 'session-1', + }, + tokenBudget: { + maxTokens: 4096, + }, + createdAt: '2024-01-15T10:00:00Z', + updatedAt: '2024-01-15T10:00:00Z', + }; + const result = ConversationSessionSchema.parse(session); + expect(result.status).toBe('active'); + expect(result.messages).toEqual([]); + }); + + it('should accept full session with messages', () => { + const session: ConversationSession = { + id: 'session-1', + name: 'Support Chat - Case #123', + context: { + sessionId: 'session-1', + userId: 'user-1', + agentId: 'support_agent', + object: 'case', + recordId: 'case-123', + systemMessage: 'You are a support agent.', + }, + modelId: 'gpt-4-turbo', + tokenBudget: { + maxTokens: 8192, + strategy: 'sliding_window', + slidingWindowSize: 20, + enableSummarization: true, + }, + messages: [ + { + id: 'msg-1', + timestamp: '2024-01-15T10:00:00Z', + role: 'user', + content: 'I need help with my order', + }, + { + id: 'msg-2', + timestamp: '2024-01-15T10:01:00Z', + role: 'assistant', + content: 'I\'d be happy to help! What\'s your order number?', + }, + ], + tokens: { + promptTokens: 150, + completionTokens: 80, + totalTokens: 230, + budgetLimit: 8192, + budgetUsed: 230, + budgetRemaining: 7962, + budgetPercentage: 0.028, + messageCount: 2, + prunedMessageCount: 0, + summarizedMessageCount: 0, + }, + status: 'active', + createdAt: '2024-01-15T10:00:00Z', + updatedAt: '2024-01-15T10:01:00Z', + tags: ['support', 'order-inquiry'], + metadata: { + priority: 'normal', + }, + }; + expect(() => ConversationSessionSchema.parse(session)).not.toThrow(); + }); + + it('should accept session with expiry', () => { + const session: ConversationSession = { + id: 'session-1', + context: { + sessionId: 'session-1', + }, + tokenBudget: { + maxTokens: 4096, + }, + createdAt: '2024-01-15T10:00:00Z', + updatedAt: '2024-01-15T10:00:00Z', + expiresAt: '2024-01-16T10:00:00Z', + }; + expect(() => ConversationSessionSchema.parse(session)).not.toThrow(); + }); +}); + +describe('ConversationSummarySchema', () => { + it('should accept valid summary', () => { + const summary = { + summary: 'User asked about order status. Agent provided tracking information.', + keyPoints: [ + 'Order number: 12345', + 'Tracking provided', + 'Expected delivery: Jan 20', + ], + originalTokens: 1500, + summaryTokens: 150, + tokensSaved: 1350, + messageRange: { + startIndex: 0, + endIndex: 10, + }, + generatedAt: '2024-01-15T11:00:00Z', + modelId: 'gpt-3.5-turbo', + }; + expect(() => ConversationSummarySchema.parse(summary)).not.toThrow(); + }); +}); + +describe('MessagePruningEventSchema', () => { + it('should accept valid pruning event', () => { + const event = { + timestamp: '2024-01-15T10:30:00Z', + strategy: 'fifo' as const, + reason: 'Token budget exceeded', + prunedMessages: [ + { + messageId: 'msg-1', + role: 'user' as const, + tokens: 50, + }, + { + messageId: 'msg-2', + role: 'assistant' as const, + tokens: 75, + importance: 0.3, + }, + ], + tokensFreed: 125, + messagesRemoved: 2, + remainingTokens: 3500, + remainingMessages: 15, + }; + expect(() => MessagePruningEventSchema.parse(event)).not.toThrow(); + }); +}); + +describe('ConversationAnalyticsSchema', () => { + it('should accept full analytics', () => { + const analytics = { + sessionId: 'session-1', + totalMessages: 50, + userMessages: 25, + assistantMessages: 23, + systemMessages: 2, + totalTokens: 5000, + averageTokensPerMessage: 100, + peakTokenUsage: 4500, + pruningEvents: 3, + summarizationEvents: 1, + tokensSavedByPruning: 500, + tokensSavedBySummarization: 2000, + duration: 1800, + firstMessageAt: '2024-01-15T10:00:00Z', + lastMessageAt: '2024-01-15T10:30:00Z', + }; + expect(() => ConversationAnalyticsSchema.parse(analytics)).not.toThrow(); + }); + + it('should default efficiency metrics to zero', () => { + const analytics = { + sessionId: 'session-1', + totalMessages: 10, + userMessages: 5, + assistantMessages: 5, + systemMessages: 0, + totalTokens: 1000, + averageTokensPerMessage: 100, + peakTokenUsage: 1000, + }; + const result = ConversationAnalyticsSchema.parse(analytics); + expect(result.pruningEvents).toBe(0); + expect(result.summarizationEvents).toBe(0); + expect(result.tokensSavedByPruning).toBe(0); + expect(result.tokensSavedBySummarization).toBe(0); + }); +}); + +describe('Real-World Conversation Examples', () => { + it('should accept long customer support conversation', () => { + const session: ConversationSession = { + id: 'support-session-789', + name: 'Customer Support - Order Issue', + context: { + sessionId: 'support-session-789', + userId: 'customer-456', + agentId: 'support_bot', + object: 'case', + recordId: 'case-12345', + systemMessage: 'You are a helpful customer support agent specialized in order management.', + }, + modelId: 'gpt-4-turbo', + tokenBudget: { + maxTokens: 16000, + maxPromptTokens: 12000, + maxCompletionTokens: 4000, + reserveTokens: 1000, + bufferPercentage: 0.1, + strategy: 'sliding_window', + slidingWindowSize: 30, + enableSummarization: true, + summarizationThreshold: 12000, + summaryModel: 'gpt-3.5-turbo', + warnThreshold: 0.85, + }, + messages: [ + { + id: 'msg-system', + timestamp: '2024-01-15T09:00:00Z', + role: 'system', + content: 'You are a helpful customer support agent specialized in order management.', + pinned: true, + importance: 1.0, + }, + { + id: 'msg-1', + timestamp: '2024-01-15T09:00:05Z', + role: 'user', + content: 'Hi, I have an issue with my order #ORD-2024-001', + tokens: { prompt: 15, total: 15 }, + }, + { + id: 'msg-2', + timestamp: '2024-01-15T09:00:15Z', + role: 'assistant', + content: 'Hello! I\'d be happy to help you with your order. Let me look up the details for order #ORD-2024-001.', + tokens: { prompt: 30, completion: 25, total: 55 }, + }, + ], + tokens: { + promptTokens: 45, + completionTokens: 25, + totalTokens: 70, + budgetLimit: 16000, + budgetUsed: 70, + budgetRemaining: 15930, + budgetPercentage: 0.0044, + messageCount: 3, + prunedMessageCount: 0, + summarizedMessageCount: 0, + }, + status: 'active', + createdAt: '2024-01-15T09:00:00Z', + updatedAt: '2024-01-15T09:00:15Z', + tags: ['support', 'order-issue', 'high-priority'], + metadata: { + priority: 'high', + category: 'order_management', + language: 'en', + }, + }; + + expect(() => ConversationSessionSchema.parse(session)).not.toThrow(); + }); + + it('should accept code generation conversation with tool calls', () => { + const session: ConversationSession = { + id: 'code-session-123', + name: 'Code Generation Assistant', + context: { + sessionId: 'code-session-123', + userId: 'dev-789', + agentId: 'code_generator', + systemMessage: 'You are an expert software engineer.', + }, + modelId: 'gpt-4-turbo', + tokenBudget: { + maxTokens: 8192, + strategy: 'importance', + minImportanceScore: 0.4, + }, + messages: [ + { + id: 'msg-1', + timestamp: '2024-01-15T14:00:00Z', + role: 'user', + content: 'Write a Python function to calculate fibonacci numbers', + importance: 0.9, + }, + { + id: 'msg-2', + timestamp: '2024-01-15T14:00:10Z', + role: 'assistant', + content: '', + toolCalls: [ + { + id: 'call-1', + type: 'function', + function: { + name: 'generate_code', + arguments: '{"language": "python", "task": "fibonacci"}', + result: 'def fibonacci(n):\n if n <= 1:\n return n\n return fibonacci(n-1) + fibonacci(n-2)', + }, + }, + ], + importance: 0.8, + }, + ], + status: 'active', + createdAt: '2024-01-15T14:00:00Z', + updatedAt: '2024-01-15T14:00:10Z', + tags: ['code-generation', 'python'], + }; + + expect(() => ConversationSessionSchema.parse(session)).not.toThrow(); + }); +}); diff --git a/packages/spec/src/ai/conversation.zod.ts b/packages/spec/src/ai/conversation.zod.ts new file mode 100644 index 000000000..279e9feca --- /dev/null +++ b/packages/spec/src/ai/conversation.zod.ts @@ -0,0 +1,307 @@ +import { z } from 'zod'; + +/** + * AI Conversation Memory Protocol + * + * Multi-turn AI conversations with token budget management. + * Enables context preservation, conversation history, and token optimization. + */ + +/** + * Message Role + */ +export const MessageRoleSchema = z.enum([ + 'system', + 'user', + 'assistant', + 'function', + 'tool', +]); + +/** + * Message Content Type + */ +export const MessageContentTypeSchema = z.enum([ + 'text', + 'image', + 'file', + 'code', + 'structured', +]); + +/** + * Message Content + */ +export const MessageContentSchema = z.object({ + type: MessageContentTypeSchema.default('text'), + text: z.string().optional().describe('Text content'), + imageUrl: z.string().url().optional().describe('Image URL for vision models'), + fileUrl: z.string().url().optional().describe('File attachment URL'), + mimeType: z.string().optional().describe('MIME type for files'), + metadata: z.record(z.any()).optional().describe('Additional metadata'), +}); + +/** + * Function Call + */ +export const FunctionCallSchema = z.object({ + name: z.string().describe('Function name'), + arguments: z.string().describe('JSON string of function arguments'), + result: z.string().optional().describe('Function execution result'), +}); + +/** + * Tool Call + */ +export const ToolCallSchema = z.object({ + id: z.string().describe('Tool call ID'), + type: z.enum(['function']).default('function'), + function: FunctionCallSchema, +}); + +/** + * Conversation Message + */ +export const ConversationMessageSchema = z.object({ + /** Identity */ + id: z.string().describe('Unique message ID'), + timestamp: z.string().describe('ISO 8601 timestamp'), + + /** Content */ + role: MessageRoleSchema, + content: z.union([ + z.string(), + z.array(MessageContentSchema), + ]).describe('Message content (text or multimodal)'), + + /** Function/Tool Calls */ + functionCall: FunctionCallSchema.optional().describe('Legacy function call'), + toolCalls: z.array(ToolCallSchema).optional().describe('Tool calls'), + toolCallId: z.string().optional().describe('Tool call ID this message responds to'), + + /** Metadata */ + name: z.string().optional().describe('Name of the function/user'), + tokens: z.object({ + prompt: z.number().int().nonnegative().optional().describe('Tokens in prompt'), + completion: z.number().int().nonnegative().optional().describe('Tokens in completion'), + total: z.number().int().nonnegative().optional().describe('Total tokens'), + }).optional().describe('Token usage for this message'), + + /** Context Management */ + pinned: z.boolean().optional().default(false).describe('Prevent removal during pruning'), + importance: z.number().min(0).max(1).optional().describe('Importance score for pruning'), + embedding: z.array(z.number()).optional().describe('Vector embedding for semantic search'), + + /** Annotations */ + metadata: z.record(z.any()).optional(), +}); + +/** + * Token Budget Strategy + */ +export const TokenBudgetStrategySchema = z.enum([ + 'fifo', // First-in-first-out (oldest messages dropped) + 'importance', // Drop by importance score + 'semantic', // Keep semantically relevant messages + 'sliding_window', // Fixed window of recent messages + 'summary', // Summarize old context +]); + +/** + * Token Budget Configuration + */ +export const TokenBudgetConfigSchema = z.object({ + /** Budget Limits */ + maxTokens: z.number().int().positive().describe('Maximum total tokens'), + maxPromptTokens: z.number().int().positive().optional().describe('Max tokens for prompt'), + maxCompletionTokens: z.number().int().positive().optional().describe('Max tokens for completion'), + + /** Buffer & Reserves */ + reserveTokens: z.number().int().nonnegative().default(500).describe('Reserve tokens for system messages'), + bufferPercentage: z.number().min(0).max(1).default(0.1).describe('Buffer percentage (0.1 = 10%)'), + + /** Pruning Strategy */ + strategy: TokenBudgetStrategySchema.default('sliding_window'), + + /** Strategy-Specific Options */ + slidingWindowSize: z.number().int().positive().optional().describe('Number of recent messages to keep'), + minImportanceScore: z.number().min(0).max(1).optional().describe('Minimum importance to keep'), + semanticThreshold: z.number().min(0).max(1).optional().describe('Semantic similarity threshold'), + + /** Summarization */ + enableSummarization: z.boolean().default(false).describe('Enable context summarization'), + summarizationThreshold: z.number().int().positive().optional().describe('Trigger summarization at N tokens'), + summaryModel: z.string().optional().describe('Model ID for summarization'), + + /** Monitoring */ + warnThreshold: z.number().min(0).max(1).default(0.8).describe('Warn at % of budget (0.8 = 80%)'), +}); + +/** + * Token Usage Stats + */ +export const TokenUsageStatsSchema = z.object({ + promptTokens: z.number().int().nonnegative().default(0), + completionTokens: z.number().int().nonnegative().default(0), + totalTokens: z.number().int().nonnegative().default(0), + + /** Budget Status */ + budgetLimit: z.number().int().positive(), + budgetUsed: z.number().int().nonnegative().default(0), + budgetRemaining: z.number().int().nonnegative(), + budgetPercentage: z.number().min(0).max(1).describe('Usage as percentage of budget'), + + /** Message Stats */ + messageCount: z.number().int().nonnegative().default(0), + prunedMessageCount: z.number().int().nonnegative().default(0), + summarizedMessageCount: z.number().int().nonnegative().default(0), +}); + +/** + * Conversation Context + */ +export const ConversationContextSchema = z.object({ + /** Identity */ + sessionId: z.string().describe('Conversation session ID'), + userId: z.string().optional().describe('User identifier'), + agentId: z.string().optional().describe('AI agent identifier'), + + /** Context Data */ + object: z.string().optional().describe('Related object (e.g., "case", "project")'), + recordId: z.string().optional().describe('Related record ID'), + scope: z.record(z.any()).optional().describe('Additional context scope'), + + /** System Instructions */ + systemMessage: z.string().optional().describe('System prompt/instructions'), + + /** Metadata */ + metadata: z.record(z.any()).optional(), +}); + +/** + * Conversation Session + */ +export const ConversationSessionSchema = z.object({ + /** Identity */ + id: z.string().describe('Unique session ID'), + name: z.string().optional().describe('Session name/title'), + + /** Configuration */ + context: ConversationContextSchema, + modelId: z.string().optional().describe('AI model ID'), + tokenBudget: TokenBudgetConfigSchema, + + /** Messages */ + messages: z.array(ConversationMessageSchema).default([]), + + /** Token Tracking */ + tokens: TokenUsageStatsSchema.optional(), + + /** Session Status */ + status: z.enum(['active', 'paused', 'completed', 'archived']).default('active'), + + /** Timestamps */ + createdAt: z.string().describe('ISO 8601 timestamp'), + updatedAt: z.string().describe('ISO 8601 timestamp'), + expiresAt: z.string().optional().describe('ISO 8601 timestamp'), + + /** Metadata */ + tags: z.array(z.string()).optional(), + metadata: z.record(z.any()).optional(), +}); + +/** + * Conversation Summary + */ +export const ConversationSummarySchema = z.object({ + /** Summary Content */ + summary: z.string().describe('Conversation summary'), + keyPoints: z.array(z.string()).optional().describe('Key discussion points'), + + /** Token Savings */ + originalTokens: z.number().int().nonnegative().describe('Original token count'), + summaryTokens: z.number().int().nonnegative().describe('Summary token count'), + tokensSaved: z.number().int().nonnegative().describe('Tokens saved'), + + /** Source Messages */ + messageRange: z.object({ + startIndex: z.number().int().nonnegative(), + endIndex: z.number().int().nonnegative(), + }).describe('Range of messages summarized'), + + /** Metadata */ + generatedAt: z.string().describe('ISO 8601 timestamp'), + modelId: z.string().optional().describe('Model used for summarization'), +}); + +/** + * Message Pruning Event + */ +export const MessagePruningEventSchema = z.object({ + /** Event Details */ + timestamp: z.string().describe('ISO 8601 timestamp'), + strategy: TokenBudgetStrategySchema, + reason: z.string().describe('Reason for pruning'), + + /** Pruned Messages */ + prunedMessages: z.array(z.object({ + messageId: z.string(), + role: MessageRoleSchema, + tokens: z.number().int().nonnegative(), + importance: z.number().min(0).max(1).optional(), + })), + + /** Impact */ + tokensFreed: z.number().int().nonnegative(), + messagesRemoved: z.number().int().nonnegative(), + + /** Post-Pruning State */ + remainingTokens: z.number().int().nonnegative(), + remainingMessages: z.number().int().nonnegative(), +}); + +/** + * Conversation Analytics + */ +export const ConversationAnalyticsSchema = z.object({ + /** Session Info */ + sessionId: z.string(), + + /** Message Statistics */ + totalMessages: z.number().int().nonnegative(), + userMessages: z.number().int().nonnegative(), + assistantMessages: z.number().int().nonnegative(), + systemMessages: z.number().int().nonnegative(), + + /** Token Statistics */ + totalTokens: z.number().int().nonnegative(), + averageTokensPerMessage: z.number().nonnegative(), + peakTokenUsage: z.number().int().nonnegative(), + + /** Efficiency Metrics */ + pruningEvents: z.number().int().nonnegative().default(0), + summarizationEvents: z.number().int().nonnegative().default(0), + tokensSavedByPruning: z.number().int().nonnegative().default(0), + tokensSavedBySummarization: z.number().int().nonnegative().default(0), + + /** Duration */ + duration: z.number().nonnegative().optional().describe('Session duration in seconds'), + firstMessageAt: z.string().optional().describe('ISO 8601 timestamp'), + lastMessageAt: z.string().optional().describe('ISO 8601 timestamp'), +}); + +// Type exports +export type MessageRole = z.infer; +export type MessageContentType = z.infer; +export type MessageContent = z.infer; +export type FunctionCall = z.infer; +export type ToolCall = z.infer; +export type ConversationMessage = z.infer; +export type TokenBudgetStrategy = z.infer; +export type TokenBudgetConfig = z.infer; +export type TokenUsageStats = z.infer; +export type ConversationContext = z.infer; +export type ConversationSession = z.infer; +export type ConversationSummary = z.infer; +export type MessagePruningEvent = z.infer; +export type ConversationAnalytics = z.infer; diff --git a/packages/spec/src/ai/cost.test.ts b/packages/spec/src/ai/cost.test.ts new file mode 100644 index 000000000..bf9c10418 --- /dev/null +++ b/packages/spec/src/ai/cost.test.ts @@ -0,0 +1,763 @@ +import { describe, it, expect } from 'vitest'; +import { + CostMetricTypeSchema, + BillingPeriodSchema, + CostEntrySchema, + BudgetTypeSchema, + BudgetLimitSchema, + BudgetStatusSchema, + CostAlertTypeSchema, + CostAlertSchema, + CostBreakdownDimensionSchema, + CostBreakdownEntrySchema, + CostAnalyticsSchema, + CostOptimizationRecommendationSchema, + CostReportSchema, + CostQueryFiltersSchema, + type CostEntry, + type BudgetLimit, + type CostAlert, + type CostReport, +} from './cost.zod'; + +describe('CostMetricTypeSchema', () => { + it('should accept all valid metric types', () => { + const types = ['token', 'request', 'character', 'second', 'image', 'embedding'] as const; + + types.forEach(type => { + expect(() => CostMetricTypeSchema.parse(type)).not.toThrow(); + }); + }); +}); + +describe('BillingPeriodSchema', () => { + it('should accept all valid billing periods', () => { + const periods = ['hourly', 'daily', 'weekly', 'monthly', 'quarterly', 'yearly', 'custom'] as const; + + periods.forEach(period => { + expect(() => BillingPeriodSchema.parse(period)).not.toThrow(); + }); + }); +}); + +describe('CostEntrySchema', () => { + it('should accept minimal cost entry', () => { + const entry: CostEntry = { + id: 'cost-1', + timestamp: '2024-01-15T10:00:00Z', + modelId: 'gpt-4-turbo', + provider: 'openai', + operation: 'chat_completion', + totalCost: 0.05, + }; + const result = CostEntrySchema.parse(entry); + expect(result.currency).toBe('USD'); + expect(result.requestCount).toBe(1); + }); + + it('should accept full cost entry', () => { + const entry: CostEntry = { + id: 'cost-1', + timestamp: '2024-01-15T10:00:00Z', + modelId: 'gpt-4-turbo', + provider: 'openai', + operation: 'chat_completion', + promptTokens: 1500, + completionTokens: 800, + totalTokens: 2300, + requestCount: 1, + promptCost: 0.015, + completionCost: 0.024, + totalCost: 0.039, + currency: 'USD', + sessionId: 'session-123', + userId: 'user-456', + agentId: 'support_agent', + object: 'case', + recordId: 'case-789', + tags: ['production', 'support'], + metadata: { + department: 'customer-service', + }, + }; + expect(() => CostEntrySchema.parse(entry)).not.toThrow(); + }); +}); + +describe('BudgetTypeSchema', () => { + it('should accept all valid budget types', () => { + const types = ['global', 'user', 'agent', 'object', 'project', 'department'] as const; + + types.forEach(type => { + expect(() => BudgetTypeSchema.parse(type)).not.toThrow(); + }); + }); +}); + +describe('BudgetLimitSchema', () => { + it('should accept minimal budget limit', () => { + const limit: BudgetLimit = { + type: 'global', + maxCost: 1000, + period: 'monthly', + }; + const result = BudgetLimitSchema.parse(limit); + expect(result.currency).toBe('USD'); + expect(result.enforced).toBe(true); + expect(result.allowRollover).toBe(false); + expect(result.active).toBe(true); + }); + + it('should accept user budget with warnings', () => { + const limit: BudgetLimit = { + type: 'user', + scope: 'user-123', + maxCost: 100, + currency: 'USD', + period: 'monthly', + softLimit: 80, + warnThresholds: [0.5, 0.75, 0.9], + enforced: true, + gracePeriodSeconds: 3600, + name: 'User Monthly Budget', + description: 'Monthly AI usage budget for individual users', + tags: ['user-budget'], + }; + expect(() => BudgetLimitSchema.parse(limit)).not.toThrow(); + }); + + it('should accept budget with rollover', () => { + const limit: BudgetLimit = { + type: 'department', + scope: 'engineering', + maxCost: 5000, + period: 'monthly', + allowRollover: true, + maxRolloverPercentage: 0.2, + }; + expect(() => BudgetLimitSchema.parse(limit)).not.toThrow(); + }); + + it('should accept custom period budget', () => { + const limit: BudgetLimit = { + type: 'project', + scope: 'project-xyz', + maxCost: 2000, + period: 'custom', + customPeriodDays: 90, + }; + expect(() => BudgetLimitSchema.parse(limit)).not.toThrow(); + }); +}); + +describe('BudgetStatusSchema', () => { + it('should accept budget status', () => { + const status = { + budgetId: 'budget-1', + type: 'user' as const, + scope: 'user-123', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + currentCost: 75, + maxCost: 100, + currency: 'USD', + percentageUsed: 0.75, + remainingCost: 25, + isExceeded: false, + isWarning: true, + projectedCost: 95, + projectedOverage: 0, + lastUpdated: '2024-01-15T10:00:00Z', + }; + expect(() => BudgetStatusSchema.parse(status)).not.toThrow(); + }); + + it('should accept exceeded budget', () => { + const status = { + budgetId: 'budget-2', + type: 'agent' as const, + scope: 'code_generator', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + currentCost: 1200, + maxCost: 1000, + percentageUsed: 1.2, + remainingCost: -200, + isExceeded: true, + isWarning: true, + projectedCost: 1500, + projectedOverage: 500, + lastUpdated: '2024-01-15T10:00:00Z', + }; + expect(() => BudgetStatusSchema.parse(status)).not.toThrow(); + }); +}); + +describe('CostAlertTypeSchema', () => { + it('should accept all valid alert types', () => { + const types = [ + 'threshold_warning', + 'threshold_critical', + 'limit_exceeded', + 'anomaly_detected', + 'projection_exceeded', + ] as const; + + types.forEach(type => { + expect(() => CostAlertTypeSchema.parse(type)).not.toThrow(); + }); + }); +}); + +describe('CostAlertSchema', () => { + it('should accept threshold warning alert', () => { + const alert: CostAlert = { + id: 'alert-1', + timestamp: '2024-01-15T10:00:00Z', + type: 'threshold_warning', + severity: 'warning', + budgetId: 'budget-1', + budgetType: 'user', + scope: 'user-123', + message: 'User budget at 75% of limit', + currentCost: 75, + maxCost: 100, + threshold: 0.75, + currency: 'USD', + }; + const result = CostAlertSchema.parse(alert); + expect(result.acknowledged).toBe(false); + expect(result.resolved).toBe(false); + }); + + it('should accept limit exceeded alert with recommendations', () => { + const alert: CostAlert = { + id: 'alert-2', + timestamp: '2024-01-15T11:00:00Z', + type: 'limit_exceeded', + severity: 'critical', + budgetId: 'budget-2', + budgetType: 'agent', + scope: 'code_generator', + message: 'Agent budget exceeded by 20%', + currentCost: 1200, + maxCost: 1000, + currency: 'USD', + recommendations: [ + 'Consider switching to a more cost-effective model', + 'Review token usage patterns', + 'Enable response caching', + ], + }; + expect(() => CostAlertSchema.parse(alert)).not.toThrow(); + }); + + it('should accept acknowledged alert', () => { + const alert: CostAlert = { + id: 'alert-3', + timestamp: '2024-01-15T12:00:00Z', + type: 'anomaly_detected', + severity: 'warning', + message: 'Unusual spending pattern detected', + currentCost: 500, + acknowledged: true, + acknowledgedBy: 'admin-user', + acknowledgedAt: '2024-01-15T12:30:00Z', + resolved: true, + }; + expect(() => CostAlertSchema.parse(alert)).not.toThrow(); + }); +}); + +describe('CostBreakdownDimensionSchema', () => { + it('should accept all valid dimensions', () => { + const dimensions = ['model', 'provider', 'user', 'agent', 'object', 'operation', 'date', 'hour', 'tag'] as const; + + dimensions.forEach(dimension => { + expect(() => CostBreakdownDimensionSchema.parse(dimension)).not.toThrow(); + }); + }); +}); + +describe('CostBreakdownEntrySchema', () => { + it('should accept breakdown by model', () => { + const entry = { + dimension: 'model' as const, + value: 'gpt-4-turbo', + totalCost: 150.50, + requestCount: 1000, + totalTokens: 1500000, + percentageOfTotal: 0.65, + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + }; + expect(() => CostBreakdownEntrySchema.parse(entry)).not.toThrow(); + }); +}); + +describe('CostAnalyticsSchema', () => { + it('should accept comprehensive analytics', () => { + const analytics = { + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + totalCost: 2500.75, + totalRequests: 50000, + totalTokens: 75000000, + currency: 'USD', + averageCostPerRequest: 0.05, + averageCostPerToken: 0.0000333, + averageRequestsPerDay: 1612, + costTrend: 'increasing' as const, + trendPercentage: 15.5, + byModel: [ + { + dimension: 'model' as const, + value: 'gpt-4-turbo', + totalCost: 1625.50, + requestCount: 30000, + totalTokens: 48750000, + percentageOfTotal: 0.65, + }, + { + dimension: 'model' as const, + value: 'gpt-3.5-turbo', + totalCost: 625.15, + requestCount: 15000, + totalTokens: 20625000, + percentageOfTotal: 0.25, + }, + ], + byProvider: [ + { + dimension: 'provider' as const, + value: 'openai', + totalCost: 2250.65, + requestCount: 45000, + percentageOfTotal: 0.90, + }, + ], + topModels: [ + { + dimension: 'model' as const, + value: 'gpt-4-turbo', + totalCost: 1625.50, + requestCount: 30000, + percentageOfTotal: 0.65, + }, + ], + tokensPerDollar: 30000, + requestsPerDollar: 20, + }; + expect(() => CostAnalyticsSchema.parse(analytics)).not.toThrow(); + }); +}); + +describe('CostOptimizationRecommendationSchema', () => { + it('should accept model switch recommendation', () => { + const recommendation = { + id: 'rec-1', + type: 'switch_model' as const, + title: 'Switch to GPT-3.5-turbo for simple queries', + description: 'Analysis shows 40% of requests could use a cheaper model without quality loss', + estimatedSavings: 500, + savingsPercentage: 0.20, + priority: 'high' as const, + effort: 'medium' as const, + actionable: true, + actionSteps: [ + 'Identify simple query patterns', + 'Update routing logic', + 'Monitor quality metrics', + ], + targetModel: 'gpt-4-turbo', + alternativeModel: 'gpt-3.5-turbo', + }; + const result = CostOptimizationRecommendationSchema.parse(recommendation); + expect(result.status).toBe('pending'); + }); + + it('should accept implemented recommendation', () => { + const recommendation = { + id: 'rec-2', + type: 'cache_results' as const, + title: 'Enable response caching', + description: 'Cache frequently asked questions to reduce redundant API calls', + estimatedSavings: 300, + savingsPercentage: 0.12, + priority: 'medium' as const, + effort: 'low' as const, + actionable: true, + status: 'implemented' as const, + implementedAt: '2024-01-10T00:00:00Z', + }; + expect(() => CostOptimizationRecommendationSchema.parse(recommendation)).not.toThrow(); + }); +}); + +describe('CostReportSchema', () => { + it('should accept summary report', () => { + const report: CostReport = { + id: 'report-1', + name: 'January 2024 Cost Report', + generatedAt: '2024-02-01T00:00:00Z', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + period: 'monthly', + analytics: { + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + totalCost: 2500.75, + totalRequests: 50000, + totalTokens: 75000000, + currency: 'USD', + averageCostPerRequest: 0.05, + averageCostPerToken: 0.0000333, + averageRequestsPerDay: 1612, + }, + }; + const result = CostReportSchema.parse(report); + expect(result.format).toBe('summary'); + expect(result.currency).toBe('USD'); + expect(result.activeAlertCount).toBe(0); + }); + + it('should accept detailed report with all sections', () => { + const report: CostReport = { + id: 'report-2', + name: 'Q1 2024 Executive Report', + generatedAt: '2024-04-01T00:00:00Z', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-03-31T23:59:59Z', + period: 'quarterly', + analytics: { + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-03-31T23:59:59Z', + totalCost: 7500, + totalRequests: 150000, + totalTokens: 225000000, + currency: 'USD', + averageCostPerRequest: 0.05, + averageCostPerToken: 0.0000333, + averageRequestsPerDay: 1666, + costTrend: 'increasing', + trendPercentage: 10, + }, + budgets: [ + { + budgetId: 'budget-global', + type: 'global', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-03-31T23:59:59Z', + currentCost: 7500, + maxCost: 10000, + percentageUsed: 0.75, + remainingCost: 2500, + isExceeded: false, + isWarning: true, + lastUpdated: '2024-03-31T23:59:59Z', + }, + ], + alerts: [ + { + id: 'alert-1', + timestamp: '2024-03-25T10:00:00Z', + type: 'threshold_warning', + severity: 'warning', + message: 'Global budget at 75%', + currentCost: 7500, + maxCost: 10000, + threshold: 0.75, + }, + ], + activeAlertCount: 1, + recommendations: [ + { + id: 'rec-1', + type: 'switch_model', + title: 'Optimize model selection', + description: 'Switch 30% of requests to cheaper models', + estimatedSavings: 1500, + savingsPercentage: 0.20, + priority: 'high', + effort: 'medium', + actionable: true, + }, + ], + previousPeriodCost: 6800, + costChange: 700, + costChangePercentage: 10.29, + forecastedCost: 8200, + forecastedBudgetStatus: 'at', + format: 'executive', + currency: 'USD', + }; + expect(() => CostReportSchema.parse(report)).not.toThrow(); + }); +}); + +describe('CostQueryFiltersSchema', () => { + it('should accept minimal filters', () => { + const filters = {}; + const result = CostQueryFiltersSchema.parse(filters); + expect(result.orderBy).toBe('timestamp'); + expect(result.orderDirection).toBe('desc'); + }); + + it('should accept comprehensive filters', () => { + const filters = { + startDate: '2024-01-01T00:00:00Z', + endDate: '2024-01-31T23:59:59Z', + modelIds: ['gpt-4-turbo', 'gpt-3.5-turbo'], + providers: ['openai'], + userIds: ['user-123', 'user-456'], + agentIds: ['support_agent', 'code_generator'], + operations: ['chat_completion', 'embedding'], + sessionIds: ['session-1', 'session-2'], + minCost: 0.01, + maxCost: 1.00, + tags: ['production', 'support'], + groupBy: ['model' as const, 'user' as const], + orderBy: 'cost' as const, + orderDirection: 'desc' as const, + limit: 100, + offset: 0, + }; + expect(() => CostQueryFiltersSchema.parse(filters)).not.toThrow(); + }); +}); + +describe('Real-World Cost Tracking Examples', () => { + it('should accept enterprise cost tracking scenario', () => { + const costEntry: CostEntry = { + id: 'cost-enterprise-1', + timestamp: '2024-01-15T14:35:22Z', + modelId: 'gpt-4-turbo', + provider: 'azure_openai', + operation: 'chat_completion', + promptTokens: 2500, + completionTokens: 1200, + totalTokens: 3700, + requestCount: 1, + promptCost: 0.025, + completionCost: 0.036, + totalCost: 0.061, + currency: 'USD', + sessionId: 'support-session-12345', + userId: 'agent-sarah', + agentId: 'customer_support_bot', + object: 'case', + recordId: 'case-67890', + tags: ['support', 'enterprise-customer', 'tier-1'], + metadata: { + department: 'customer-success', + region: 'us-east', + customer_tier: 'enterprise', + }, + }; + + expect(() => CostEntrySchema.parse(costEntry)).not.toThrow(); + }); + + it('should accept multi-tier budget system', () => { + const globalBudget: BudgetLimit = { + type: 'global', + maxCost: 50000, + period: 'monthly', + softLimit: 40000, + warnThresholds: [0.7, 0.85, 0.95], + enforced: true, + name: 'Global Monthly Budget', + description: 'Organization-wide AI budget', + }; + + const departmentBudget: BudgetLimit = { + type: 'department', + scope: 'customer-support', + maxCost: 15000, + period: 'monthly', + softLimit: 12000, + warnThresholds: [0.8, 0.9], + enforced: true, + allowRollover: true, + maxRolloverPercentage: 0.1, + name: 'Customer Support Budget', + }; + + const userBudget: BudgetLimit = { + type: 'user', + scope: 'power-user-123', + maxCost: 500, + period: 'monthly', + warnThresholds: [0.9], + enforced: false, + name: 'Power User Budget', + description: 'Budget for high-volume users', + }; + + expect(() => BudgetLimitSchema.parse(globalBudget)).not.toThrow(); + expect(() => BudgetLimitSchema.parse(departmentBudget)).not.toThrow(); + expect(() => BudgetLimitSchema.parse(userBudget)).not.toThrow(); + }); + + it('should accept comprehensive monthly cost report', () => { + const report: CostReport = { + id: 'monthly-report-2024-01', + name: 'January 2024 AI Cost Report', + generatedAt: '2024-02-01T00:00:00Z', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + period: 'monthly', + analytics: { + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + totalCost: 42350.50, + totalRequests: 850000, + totalTokens: 1275000000, + currency: 'USD', + averageCostPerRequest: 0.0498, + averageCostPerToken: 0.0000332, + averageRequestsPerDay: 27419, + costTrend: 'increasing', + trendPercentage: 12.5, + byModel: [ + { + dimension: 'model', + value: 'gpt-4-turbo', + totalCost: 27528, + requestCount: 340000, + totalTokens: 765000000, + percentageOfTotal: 0.65, + }, + { + dimension: 'model', + value: 'gpt-3.5-turbo', + totalCost: 10587.63, + requestCount: 425000, + totalTokens: 382500000, + percentageOfTotal: 0.25, + }, + { + dimension: 'model', + value: 'text-embedding-3-large', + totalCost: 4234.87, + requestCount: 85000, + totalTokens: 127500000, + percentageOfTotal: 0.10, + }, + ], + byProvider: [ + { + dimension: 'provider', + value: 'openai', + totalCost: 38115.45, + requestCount: 765000, + percentageOfTotal: 0.90, + }, + { + dimension: 'provider', + value: 'anthropic', + totalCost: 4235.05, + requestCount: 85000, + percentageOfTotal: 0.10, + }, + ], + topModels: [ + { + dimension: 'model', + value: 'gpt-4-turbo', + totalCost: 27528, + requestCount: 340000, + percentageOfTotal: 0.65, + }, + ], + topUsers: [ + { + dimension: 'user', + value: 'support-team', + totalCost: 18799.73, + requestCount: 382500, + percentageOfTotal: 0.444, + }, + ], + tokensPerDollar: 30106, + requestsPerDollar: 20, + }, + budgets: [ + { + budgetId: 'global-monthly', + type: 'global', + periodStart: '2024-01-01T00:00:00Z', + periodEnd: '2024-01-31T23:59:59Z', + currentCost: 42350.50, + maxCost: 50000, + percentageUsed: 0.847, + remainingCost: 7649.50, + isExceeded: false, + isWarning: true, + projectedCost: 46000, + lastUpdated: '2024-01-31T23:59:59Z', + }, + ], + alerts: [ + { + id: 'alert-budget-warning', + timestamp: '2024-01-28T10:00:00Z', + type: 'threshold_warning', + severity: 'warning', + budgetId: 'global-monthly', + budgetType: 'global', + message: 'Global budget has reached 85% of limit', + currentCost: 42350.50, + maxCost: 50000, + threshold: 0.85, + recommendations: [ + 'Review high-cost operations', + 'Consider switching to more cost-effective models for routine tasks', + ], + }, + ], + activeAlertCount: 1, + recommendations: [ + { + id: 'opt-rec-1', + type: 'switch_model', + title: 'Optimize support queries with GPT-3.5-turbo', + description: 'Analysis shows 45% of support queries could use GPT-3.5-turbo instead of GPT-4-turbo with minimal quality impact', + estimatedSavings: 8500, + savingsPercentage: 0.20, + priority: 'high', + effort: 'medium', + actionable: true, + targetModel: 'gpt-4-turbo', + alternativeModel: 'gpt-3.5-turbo', + actionSteps: [ + 'Identify simple support queries', + 'Update routing logic', + 'A/B test quality metrics', + 'Roll out to 50% of traffic', + ], + }, + { + id: 'opt-rec-2', + type: 'cache_results', + title: 'Enable embedding cache', + description: 'Cache embedding results for frequently searched content', + estimatedSavings: 2000, + savingsPercentage: 0.047, + priority: 'medium', + effort: 'low', + actionable: true, + }, + ], + previousPeriodCost: 37645, + costChange: 4705.50, + costChangePercentage: 12.5, + forecastedCost: 46000, + forecastedBudgetStatus: 'at', + format: 'detailed', + currency: 'USD', + }; + + expect(() => CostReportSchema.parse(report)).not.toThrow(); + }); +}); diff --git a/packages/spec/src/ai/cost.zod.ts b/packages/spec/src/ai/cost.zod.ts new file mode 100644 index 000000000..6db5c6936 --- /dev/null +++ b/packages/spec/src/ai/cost.zod.ts @@ -0,0 +1,400 @@ +import { z } from 'zod'; + +/** + * AI Cost Tracking Protocol + * + * Monitor and control AI API costs with budgets, alerts, and analytics. + * Provides cost optimization, budget enforcement, and financial reporting. + */ + +/** + * Cost Metric Type + */ +export const CostMetricTypeSchema = z.enum([ + 'token', // Cost per token + 'request', // Cost per API request + 'character', // Cost per character (e.g., TTS) + 'second', // Cost per second (e.g., speech) + 'image', // Cost per image + 'embedding', // Cost per embedding +]); + +/** + * Billing Period + */ +export const BillingPeriodSchema = z.enum([ + 'hourly', + 'daily', + 'weekly', + 'monthly', + 'quarterly', + 'yearly', + 'custom', +]); + +/** + * Cost Entry + */ +export const CostEntrySchema = z.object({ + /** Identity */ + id: z.string().describe('Unique cost entry ID'), + timestamp: z.string().describe('ISO 8601 timestamp'), + + /** Request Details */ + modelId: z.string().describe('AI model used'), + provider: z.string().describe('AI provider (e.g., "openai", "anthropic")'), + operation: z.string().describe('Operation type (e.g., "chat_completion", "embedding")'), + + /** Usage Metrics */ + promptTokens: z.number().int().nonnegative().optional(), + completionTokens: z.number().int().nonnegative().optional(), + totalTokens: z.number().int().nonnegative().optional(), + requestCount: z.number().int().positive().default(1), + + /** Cost Calculation */ + promptCost: z.number().nonnegative().optional().describe('Cost of prompt tokens'), + completionCost: z.number().nonnegative().optional().describe('Cost of completion tokens'), + totalCost: z.number().nonnegative().describe('Total cost in base currency'), + currency: z.string().default('USD'), + + /** Context */ + sessionId: z.string().optional().describe('Conversation session ID'), + userId: z.string().optional().describe('User who triggered the request'), + agentId: z.string().optional().describe('AI agent ID'), + object: z.string().optional().describe('Related object (e.g., "case", "project")'), + recordId: z.string().optional().describe('Related record ID'), + + /** Metadata */ + tags: z.array(z.string()).optional(), + metadata: z.record(z.any()).optional(), +}); + +/** + * Budget Type + */ +export const BudgetTypeSchema = z.enum([ + 'global', // Organization-wide budget + 'user', // Per-user budget + 'agent', // Per-agent budget + 'object', // Per-object budget (e.g., per case) + 'project', // Per-project budget + 'department', // Per-department budget +]); + +/** + * Budget Limit + */ +export const BudgetLimitSchema = z.object({ + /** Limit Configuration */ + type: BudgetTypeSchema, + scope: z.string().optional().describe('Scope identifier (userId, agentId, etc.)'), + + /** Limit Amount */ + maxCost: z.number().nonnegative().describe('Maximum cost limit'), + currency: z.string().default('USD'), + + /** Period */ + period: BillingPeriodSchema, + customPeriodDays: z.number().int().positive().optional().describe('Custom period in days'), + + /** Soft Limits & Warnings */ + softLimit: z.number().nonnegative().optional().describe('Soft limit for warnings'), + warnThresholds: z.array(z.number().min(0).max(1)).optional().describe('Warning thresholds (e.g., [0.5, 0.8, 0.95])'), + + /** Enforcement */ + enforced: z.boolean().default(true).describe('Block requests when exceeded'), + gracePeriodSeconds: z.number().int().nonnegative().default(0).describe('Grace period after limit exceeded'), + + /** Rollover */ + allowRollover: z.boolean().default(false).describe('Allow unused budget to rollover'), + maxRolloverPercentage: z.number().min(0).max(1).optional().describe('Max rollover as % of limit'), + + /** Metadata */ + name: z.string().optional().describe('Budget name'), + description: z.string().optional(), + active: z.boolean().default(true), + tags: z.array(z.string()).optional(), +}); + +/** + * Budget Status + */ +export const BudgetStatusSchema = z.object({ + /** Budget Reference */ + budgetId: z.string(), + type: BudgetTypeSchema, + scope: z.string().optional(), + + /** Current Period */ + periodStart: z.string().describe('ISO 8601 timestamp'), + periodEnd: z.string().describe('ISO 8601 timestamp'), + + /** Usage */ + currentCost: z.number().nonnegative().default(0), + maxCost: z.number().nonnegative(), + currency: z.string().default('USD'), + + /** Status */ + percentageUsed: z.number().nonnegative().describe('Usage as percentage (can exceed 1.0 if over budget)'), + remainingCost: z.number().describe('Remaining budget (can be negative if exceeded)'), + isExceeded: z.boolean().default(false), + isWarning: z.boolean().default(false), + + /** Projections */ + projectedCost: z.number().nonnegative().optional().describe('Projected cost for period'), + projectedOverage: z.number().nonnegative().optional().describe('Projected overage'), + + /** Last Update */ + lastUpdated: z.string().describe('ISO 8601 timestamp'), +}); + +/** + * Cost Alert Type + */ +export const CostAlertTypeSchema = z.enum([ + 'threshold_warning', // Warning threshold reached + 'threshold_critical', // Critical threshold reached + 'limit_exceeded', // Budget limit exceeded + 'anomaly_detected', // Unusual spending pattern + 'projection_exceeded', // Projected to exceed budget +]); + +/** + * Cost Alert + */ +export const CostAlertSchema = z.object({ + /** Alert Details */ + id: z.string(), + timestamp: z.string().describe('ISO 8601 timestamp'), + type: CostAlertTypeSchema, + severity: z.enum(['info', 'warning', 'critical']), + + /** Budget Context */ + budgetId: z.string().optional(), + budgetType: BudgetTypeSchema.optional(), + scope: z.string().optional(), + + /** Alert Information */ + message: z.string().describe('Alert message'), + currentCost: z.number().nonnegative(), + maxCost: z.number().nonnegative().optional(), + threshold: z.number().min(0).max(1).optional(), + currency: z.string().default('USD'), + + /** Recommendations */ + recommendations: z.array(z.string()).optional(), + + /** Status */ + acknowledged: z.boolean().default(false), + acknowledgedBy: z.string().optional(), + acknowledgedAt: z.string().optional(), + resolved: z.boolean().default(false), + + /** Metadata */ + metadata: z.record(z.any()).optional(), +}); + +/** + * Cost Breakdown Dimension + */ +export const CostBreakdownDimensionSchema = z.enum([ + 'model', + 'provider', + 'user', + 'agent', + 'object', + 'operation', + 'date', + 'hour', + 'tag', +]); + +/** + * Cost Breakdown Entry + */ +export const CostBreakdownEntrySchema = z.object({ + dimension: CostBreakdownDimensionSchema, + value: z.string().describe('Dimension value (e.g., model ID, user ID)'), + + /** Metrics */ + totalCost: z.number().nonnegative(), + requestCount: z.number().int().nonnegative(), + totalTokens: z.number().int().nonnegative().optional(), + + /** Share */ + percentageOfTotal: z.number().min(0).max(1), + + /** Time Range */ + periodStart: z.string().optional(), + periodEnd: z.string().optional(), +}); + +/** + * Cost Analytics + */ +export const CostAnalyticsSchema = z.object({ + /** Time Range */ + periodStart: z.string().describe('ISO 8601 timestamp'), + periodEnd: z.string().describe('ISO 8601 timestamp'), + + /** Summary Metrics */ + totalCost: z.number().nonnegative(), + totalRequests: z.number().int().nonnegative(), + totalTokens: z.number().int().nonnegative().optional(), + currency: z.string().default('USD'), + + /** Averages */ + averageCostPerRequest: z.number().nonnegative(), + averageCostPerToken: z.number().nonnegative().optional(), + averageRequestsPerDay: z.number().nonnegative(), + + /** Trends */ + costTrend: z.enum(['increasing', 'decreasing', 'stable']).optional(), + trendPercentage: z.number().optional().describe('% change vs previous period'), + + /** Breakdowns */ + byModel: z.array(CostBreakdownEntrySchema).optional(), + byProvider: z.array(CostBreakdownEntrySchema).optional(), + byUser: z.array(CostBreakdownEntrySchema).optional(), + byAgent: z.array(CostBreakdownEntrySchema).optional(), + byOperation: z.array(CostBreakdownEntrySchema).optional(), + byDate: z.array(CostBreakdownEntrySchema).optional(), + + /** Top Consumers */ + topModels: z.array(CostBreakdownEntrySchema).optional(), + topUsers: z.array(CostBreakdownEntrySchema).optional(), + topAgents: z.array(CostBreakdownEntrySchema).optional(), + + /** Efficiency Metrics */ + tokensPerDollar: z.number().nonnegative().optional(), + requestsPerDollar: z.number().nonnegative().optional(), +}); + +/** + * Cost Optimization Recommendation + */ +export const CostOptimizationRecommendationSchema = z.object({ + /** Recommendation Details */ + id: z.string(), + type: z.enum([ + 'switch_model', + 'reduce_tokens', + 'batch_requests', + 'cache_results', + 'adjust_parameters', + 'limit_usage', + ]), + + /** Impact */ + title: z.string(), + description: z.string(), + estimatedSavings: z.number().nonnegative().optional(), + savingsPercentage: z.number().min(0).max(1).optional(), + + /** Implementation */ + priority: z.enum(['low', 'medium', 'high']), + effort: z.enum(['low', 'medium', 'high']), + actionable: z.boolean().default(true), + actionSteps: z.array(z.string()).optional(), + + /** Context */ + targetModel: z.string().optional(), + alternativeModel: z.string().optional(), + affectedUsers: z.array(z.string()).optional(), + + /** Status */ + status: z.enum(['pending', 'accepted', 'rejected', 'implemented']).default('pending'), + implementedAt: z.string().optional(), +}); + +/** + * Cost Report + */ +export const CostReportSchema = z.object({ + /** Report Metadata */ + id: z.string(), + name: z.string(), + generatedAt: z.string().describe('ISO 8601 timestamp'), + + /** Time Range */ + periodStart: z.string().describe('ISO 8601 timestamp'), + periodEnd: z.string().describe('ISO 8601 timestamp'), + period: BillingPeriodSchema, + + /** Analytics */ + analytics: CostAnalyticsSchema, + + /** Budgets */ + budgets: z.array(BudgetStatusSchema).optional(), + + /** Alerts */ + alerts: z.array(CostAlertSchema).optional(), + activeAlertCount: z.number().int().nonnegative().default(0), + + /** Recommendations */ + recommendations: z.array(CostOptimizationRecommendationSchema).optional(), + + /** Comparisons */ + previousPeriodCost: z.number().nonnegative().optional(), + costChange: z.number().optional().describe('Change vs previous period'), + costChangePercentage: z.number().optional(), + + /** Forecasting */ + forecastedCost: z.number().nonnegative().optional(), + forecastedBudgetStatus: z.enum(['under', 'at', 'over']).optional(), + + /** Export */ + format: z.enum(['summary', 'detailed', 'executive']).default('summary'), + currency: z.string().default('USD'), +}); + +/** + * Cost Query Filters + */ +export const CostQueryFiltersSchema = z.object({ + /** Time Range */ + startDate: z.string().optional().describe('ISO 8601 timestamp'), + endDate: z.string().optional().describe('ISO 8601 timestamp'), + + /** Dimensions */ + modelIds: z.array(z.string()).optional(), + providers: z.array(z.string()).optional(), + userIds: z.array(z.string()).optional(), + agentIds: z.array(z.string()).optional(), + operations: z.array(z.string()).optional(), + sessionIds: z.array(z.string()).optional(), + + /** Cost Range */ + minCost: z.number().nonnegative().optional(), + maxCost: z.number().nonnegative().optional(), + + /** Tags */ + tags: z.array(z.string()).optional(), + + /** Aggregation */ + groupBy: z.array(CostBreakdownDimensionSchema).optional(), + + /** Sorting */ + orderBy: z.enum(['timestamp', 'cost', 'tokens']).optional().default('timestamp'), + orderDirection: z.enum(['asc', 'desc']).optional().default('desc'), + + /** Pagination */ + limit: z.number().int().positive().optional(), + offset: z.number().int().nonnegative().optional(), +}); + +// Type exports +export type CostMetricType = z.infer; +export type BillingPeriod = z.infer; +export type CostEntry = z.infer; +export type BudgetType = z.infer; +export type BudgetLimit = z.infer; +export type BudgetStatus = z.infer; +export type CostAlertType = z.infer; +export type CostAlert = z.infer; +export type CostBreakdownDimension = z.infer; +export type CostBreakdownEntry = z.infer; +export type CostAnalytics = z.infer; +export type CostOptimizationRecommendation = z.infer; +export type CostReport = z.infer; +export type CostQueryFilters = z.infer; diff --git a/packages/spec/src/ai/index.ts b/packages/spec/src/ai/index.ts index 431b93cd0..e7dcd39d8 100644 --- a/packages/spec/src/ai/index.ts +++ b/packages/spec/src/ai/index.ts @@ -8,6 +8,8 @@ * - Natural Language Query (NLQ) * - Workflow Automation * - Predictive Analytics + * - Conversation Memory & Token Management + * - Cost Tracking & Budget Management */ export * from './agent.zod'; @@ -16,3 +18,5 @@ export * from './rag-pipeline.zod'; export * from './nlq.zod'; export * from './workflow-automation.zod'; export * from './predictive.zod'; +export * from './conversation.zod'; +export * from './cost.zod';