mirror of
https://github.com/kjanat/livedash-node.git
synced 2026-01-16 20:12:08 +01:00
feat: complete tRPC integration and fix platform UI issues
- Implement comprehensive tRPC setup with type-safe API - Create tRPC routers for dashboard, admin, and auth endpoints - Migrate frontend components to use tRPC client - Fix platform dashboard Settings button functionality - Add platform settings page with profile and security management - Create OpenAI API mocking infrastructure for cost-safe testing - Update tests to work with new tRPC architecture - Sync database schema to fix AIBatchRequest table errors
This commit is contained in:
583
lib/mocks/openai-responses.ts
Normal file
583
lib/mocks/openai-responses.ts
Normal file
@ -0,0 +1,583 @@
|
||||
/**
|
||||
* OpenAI API Mock Response Templates
|
||||
*
|
||||
* Provides realistic response templates for cost-safe testing
|
||||
* and development without actual API calls.
|
||||
*/
|
||||
|
||||
export interface MockChatCompletion {
|
||||
id: string;
|
||||
object: "chat.completion";
|
||||
created: number;
|
||||
model: string;
|
||||
choices: Array<{
|
||||
index: number;
|
||||
message: {
|
||||
role: "assistant";
|
||||
content: string;
|
||||
};
|
||||
finish_reason: "stop" | "length" | "content_filter";
|
||||
}>;
|
||||
usage: {
|
||||
prompt_tokens: number;
|
||||
completion_tokens: number;
|
||||
total_tokens: number;
|
||||
};
|
||||
}
|
||||
|
||||
export interface MockBatchResponse {
|
||||
id: string;
|
||||
object: "batch";
|
||||
endpoint: string;
|
||||
errors: {
|
||||
object: "list";
|
||||
data: Array<{
|
||||
code: string;
|
||||
message: string;
|
||||
param?: string;
|
||||
type: string;
|
||||
}>;
|
||||
};
|
||||
input_file_id: string;
|
||||
completion_window: string;
|
||||
status:
|
||||
| "validating"
|
||||
| "in_progress"
|
||||
| "finalizing"
|
||||
| "completed"
|
||||
| "failed"
|
||||
| "expired"
|
||||
| "cancelling"
|
||||
| "cancelled";
|
||||
output_file_id?: string;
|
||||
error_file_id?: string;
|
||||
created_at: number;
|
||||
in_progress_at?: number;
|
||||
expires_at?: number;
|
||||
finalizing_at?: number;
|
||||
completed_at?: number;
|
||||
failed_at?: number;
|
||||
expired_at?: number;
|
||||
cancelling_at?: number;
|
||||
cancelled_at?: number;
|
||||
request_counts: {
|
||||
total: number;
|
||||
completed: number;
|
||||
failed: number;
|
||||
};
|
||||
metadata?: Record<string, string>;
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate realistic session analysis response matching the expected JSON schema
|
||||
*/
|
||||
export function generateSessionAnalysisResponse(
|
||||
text: string,
|
||||
sessionId: string
|
||||
): MockChatCompletion {
|
||||
// Extract session ID from the text if provided in system prompt
|
||||
const sessionIdMatch = text.match(/"session_id":\s*"([^"]+)"/);
|
||||
const extractedSessionId = sessionIdMatch?.[1] || sessionId;
|
||||
|
||||
// Simple sentiment analysis logic
|
||||
const positiveWords = [
|
||||
"good",
|
||||
"great",
|
||||
"excellent",
|
||||
"happy",
|
||||
"satisfied",
|
||||
"wonderful",
|
||||
"amazing",
|
||||
"pleased",
|
||||
"thanks",
|
||||
];
|
||||
const negativeWords = [
|
||||
"bad",
|
||||
"terrible",
|
||||
"awful",
|
||||
"unhappy",
|
||||
"disappointed",
|
||||
"frustrated",
|
||||
"angry",
|
||||
"upset",
|
||||
"problem",
|
||||
];
|
||||
|
||||
const words = text.toLowerCase().split(/\s+/);
|
||||
const positiveCount = words.filter((word) =>
|
||||
positiveWords.some((pos) => word.includes(pos))
|
||||
).length;
|
||||
const negativeCount = words.filter((word) =>
|
||||
negativeWords.some((neg) => word.includes(neg))
|
||||
).length;
|
||||
|
||||
let sentiment: "POSITIVE" | "NEUTRAL" | "NEGATIVE";
|
||||
if (positiveCount > negativeCount) {
|
||||
sentiment = "POSITIVE";
|
||||
} else if (negativeCount > positiveCount) {
|
||||
sentiment = "NEGATIVE";
|
||||
} else {
|
||||
sentiment = "NEUTRAL";
|
||||
}
|
||||
|
||||
// Simple category classification
|
||||
const categories: Record<string, string[]> = {
|
||||
SCHEDULE_HOURS: ["schedule", "hours", "time", "shift", "working", "clock"],
|
||||
LEAVE_VACATION: [
|
||||
"vacation",
|
||||
"leave",
|
||||
"time off",
|
||||
"holiday",
|
||||
"pto",
|
||||
"days off",
|
||||
],
|
||||
SICK_LEAVE_RECOVERY: [
|
||||
"sick",
|
||||
"ill",
|
||||
"medical",
|
||||
"health",
|
||||
"doctor",
|
||||
"recovery",
|
||||
],
|
||||
SALARY_COMPENSATION: [
|
||||
"salary",
|
||||
"pay",
|
||||
"compensation",
|
||||
"money",
|
||||
"wage",
|
||||
"payment",
|
||||
],
|
||||
CONTRACT_HOURS: ["contract", "agreement", "terms", "conditions"],
|
||||
ONBOARDING: [
|
||||
"onboard",
|
||||
"new",
|
||||
"start",
|
||||
"first day",
|
||||
"welcome",
|
||||
"orientation",
|
||||
],
|
||||
OFFBOARDING: ["leaving", "quit", "resign", "last day", "exit", "farewell"],
|
||||
WORKWEAR_STAFF_PASS: [
|
||||
"uniform",
|
||||
"clothing",
|
||||
"badge",
|
||||
"pass",
|
||||
"equipment",
|
||||
"workwear",
|
||||
],
|
||||
TEAM_CONTACTS: ["contact", "phone", "email", "reach", "team", "colleague"],
|
||||
PERSONAL_QUESTIONS: ["personal", "family", "life", "private"],
|
||||
ACCESS_LOGIN: [
|
||||
"login",
|
||||
"password",
|
||||
"access",
|
||||
"account",
|
||||
"system",
|
||||
"username",
|
||||
],
|
||||
SOCIAL_QUESTIONS: ["social", "chat", "friendly", "casual", "weather"],
|
||||
};
|
||||
|
||||
const textLower = text.toLowerCase();
|
||||
let bestCategory: keyof typeof categories | "UNRECOGNIZED_OTHER" =
|
||||
"UNRECOGNIZED_OTHER";
|
||||
let maxMatches = 0;
|
||||
|
||||
for (const [category, keywords] of Object.entries(categories)) {
|
||||
const matches = keywords.filter((keyword) =>
|
||||
textLower.includes(keyword)
|
||||
).length;
|
||||
if (matches > maxMatches) {
|
||||
maxMatches = matches;
|
||||
bestCategory = category as keyof typeof categories;
|
||||
}
|
||||
}
|
||||
|
||||
// Extract questions (sentences ending with ?)
|
||||
const questions = text
|
||||
.split(/[.!]+/)
|
||||
.map((s) => s.trim())
|
||||
.filter((s) => s.endsWith("?"))
|
||||
.slice(0, 5);
|
||||
|
||||
// Generate summary (first sentence or truncated text)
|
||||
const sentences = text.split(/[.!?]+/).filter((s) => s.trim().length > 0);
|
||||
let summary = sentences[0]?.trim() || text.substring(0, 100);
|
||||
if (summary.length > 150) {
|
||||
summary = summary.substring(0, 147) + "...";
|
||||
}
|
||||
if (summary.length < 10) {
|
||||
summary = "User inquiry regarding company policies";
|
||||
}
|
||||
|
||||
// Detect language (simple heuristic)
|
||||
const dutchWords = [
|
||||
"het",
|
||||
"de",
|
||||
"een",
|
||||
"en",
|
||||
"van",
|
||||
"is",
|
||||
"dat",
|
||||
"te",
|
||||
"met",
|
||||
"voor",
|
||||
];
|
||||
const germanWords = [
|
||||
"der",
|
||||
"die",
|
||||
"das",
|
||||
"und",
|
||||
"ist",
|
||||
"mit",
|
||||
"zu",
|
||||
"auf",
|
||||
"für",
|
||||
"von",
|
||||
];
|
||||
const dutchCount = dutchWords.filter((word) =>
|
||||
textLower.includes(word)
|
||||
).length;
|
||||
const germanCount = germanWords.filter((word) =>
|
||||
textLower.includes(word)
|
||||
).length;
|
||||
|
||||
let language = "en"; // default to English
|
||||
if (dutchCount > 0 && dutchCount >= germanCount) {
|
||||
language = "nl";
|
||||
} else if (germanCount > 0) {
|
||||
language = "de";
|
||||
}
|
||||
|
||||
// Check for escalation indicators
|
||||
const escalated = /escalate|supervisor|manager|boss|higher up/i.test(text);
|
||||
const forwardedHr = /hr|human resources|personnel|legal/i.test(text);
|
||||
|
||||
const analysisResult = {
|
||||
language,
|
||||
sentiment,
|
||||
escalated,
|
||||
forwarded_hr: forwardedHr,
|
||||
category: bestCategory,
|
||||
questions,
|
||||
summary,
|
||||
session_id: extractedSessionId,
|
||||
};
|
||||
|
||||
const jsonContent = JSON.stringify(analysisResult);
|
||||
const promptTokens = Math.ceil(text.length / 4);
|
||||
const completionTokens = Math.ceil(jsonContent.length / 4);
|
||||
|
||||
return {
|
||||
id: `chatcmpl-mock-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
|
||||
object: "chat.completion",
|
||||
created: Math.floor(Date.now() / 1000),
|
||||
model: "gpt-4o-mini",
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
message: {
|
||||
role: "assistant",
|
||||
content: jsonContent,
|
||||
},
|
||||
finish_reason: "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: promptTokens,
|
||||
completion_tokens: completionTokens,
|
||||
total_tokens: promptTokens + completionTokens,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate realistic category classification response
|
||||
*/
|
||||
export function generateCategoryResponse(text: string): MockChatCompletion {
|
||||
// Simple category classification logic
|
||||
const categories: Record<string, string[]> = {
|
||||
SCHEDULE_HOURS: ["schedule", "hours", "time", "shift", "working"],
|
||||
LEAVE_VACATION: ["vacation", "leave", "time off", "holiday", "pto"],
|
||||
SICK_LEAVE_RECOVERY: ["sick", "ill", "medical", "health", "doctor"],
|
||||
SALARY_COMPENSATION: ["salary", "pay", "compensation", "money", "wage"],
|
||||
CONTRACT_HOURS: ["contract", "agreement", "terms", "conditions"],
|
||||
ONBOARDING: ["onboard", "new", "start", "first day", "welcome"],
|
||||
OFFBOARDING: ["leaving", "quit", "resign", "last day", "exit"],
|
||||
WORKWEAR_STAFF_PASS: ["uniform", "clothing", "badge", "pass", "equipment"],
|
||||
TEAM_CONTACTS: ["contact", "phone", "email", "reach", "team"],
|
||||
PERSONAL_QUESTIONS: ["personal", "family", "life", "private"],
|
||||
ACCESS_LOGIN: ["login", "password", "access", "account", "system"],
|
||||
SOCIAL_QUESTIONS: ["social", "chat", "friendly", "casual"],
|
||||
};
|
||||
|
||||
const textLower = text.toLowerCase();
|
||||
let bestCategory = "UNRECOGNIZED_OTHER";
|
||||
let maxMatches = 0;
|
||||
|
||||
for (const [category, keywords] of Object.entries(categories)) {
|
||||
const matches = keywords.filter((keyword) =>
|
||||
textLower.includes(keyword)
|
||||
).length;
|
||||
if (matches > maxMatches) {
|
||||
maxMatches = matches;
|
||||
bestCategory = category;
|
||||
}
|
||||
}
|
||||
|
||||
const promptTokens = Math.ceil(text.length / 4);
|
||||
const completionTokens = bestCategory.length / 4;
|
||||
|
||||
return {
|
||||
id: `chatcmpl-mock-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
|
||||
object: "chat.completion",
|
||||
created: Math.floor(Date.now() / 1000),
|
||||
model: "gpt-4o-mini",
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
message: {
|
||||
role: "assistant",
|
||||
content: bestCategory,
|
||||
},
|
||||
finish_reason: "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: promptTokens,
|
||||
completion_tokens: completionTokens,
|
||||
total_tokens: promptTokens + completionTokens,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate realistic summary response
|
||||
*/
|
||||
export function generateSummaryResponse(text: string): MockChatCompletion {
|
||||
// Simple summarization - take first sentence or truncate
|
||||
const sentences = text.split(/[.!?]+/).filter((s) => s.trim().length > 0);
|
||||
let summary = sentences[0]?.trim() || text.substring(0, 100);
|
||||
|
||||
if (summary.length > 150) {
|
||||
summary = summary.substring(0, 147) + "...";
|
||||
}
|
||||
|
||||
const promptTokens = Math.ceil(text.length / 4);
|
||||
const completionTokens = Math.ceil(summary.length / 4);
|
||||
|
||||
return {
|
||||
id: `chatcmpl-mock-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
|
||||
object: "chat.completion",
|
||||
created: Math.floor(Date.now() / 1000),
|
||||
model: "gpt-4o-mini",
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
message: {
|
||||
role: "assistant",
|
||||
content: summary,
|
||||
},
|
||||
finish_reason: "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: promptTokens,
|
||||
completion_tokens: completionTokens,
|
||||
total_tokens: promptTokens + completionTokens,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate realistic sentiment analysis response
|
||||
*/
|
||||
export function generateSentimentResponse(text: string): MockChatCompletion {
|
||||
// Simple sentiment analysis logic
|
||||
const positiveWords = [
|
||||
"good",
|
||||
"great",
|
||||
"excellent",
|
||||
"happy",
|
||||
"satisfied",
|
||||
"wonderful",
|
||||
"amazing",
|
||||
"pleased",
|
||||
"thanks",
|
||||
];
|
||||
const negativeWords = [
|
||||
"bad",
|
||||
"terrible",
|
||||
"awful",
|
||||
"unhappy",
|
||||
"disappointed",
|
||||
"frustrated",
|
||||
"angry",
|
||||
"upset",
|
||||
"problem",
|
||||
];
|
||||
|
||||
const words = text.toLowerCase().split(/\s+/);
|
||||
const positiveCount = words.filter((word) =>
|
||||
positiveWords.some((pos) => word.includes(pos))
|
||||
).length;
|
||||
const negativeCount = words.filter((word) =>
|
||||
negativeWords.some((neg) => word.includes(neg))
|
||||
).length;
|
||||
|
||||
let sentiment: "POSITIVE" | "NEUTRAL" | "NEGATIVE";
|
||||
if (positiveCount > negativeCount) {
|
||||
sentiment = "POSITIVE";
|
||||
} else if (negativeCount > positiveCount) {
|
||||
sentiment = "NEGATIVE";
|
||||
} else {
|
||||
sentiment = "NEUTRAL";
|
||||
}
|
||||
|
||||
const promptTokens = Math.ceil(text.length / 4);
|
||||
const completionTokens = Math.ceil(sentiment.length / 4);
|
||||
|
||||
return {
|
||||
id: `chatcmpl-mock-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
|
||||
object: "chat.completion",
|
||||
created: Math.floor(Date.now() / 1000),
|
||||
model: "gpt-4o-mini",
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
message: {
|
||||
role: "assistant",
|
||||
content: sentiment,
|
||||
},
|
||||
finish_reason: "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: promptTokens,
|
||||
completion_tokens: completionTokens,
|
||||
total_tokens: promptTokens + completionTokens,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate realistic question extraction response
|
||||
*/
|
||||
export function generateQuestionExtractionResponse(
|
||||
text: string
|
||||
): MockChatCompletion {
|
||||
// Extract sentences that end with question marks
|
||||
const questions = text
|
||||
.split(/[.!]+/)
|
||||
.map((s) => s.trim())
|
||||
.filter((s) => s.endsWith("?"))
|
||||
.slice(0, 5); // Limit to 5 questions
|
||||
|
||||
const result =
|
||||
questions.length > 0 ? questions.join("\n") : "No questions found.";
|
||||
|
||||
const promptTokens = Math.ceil(text.length / 4);
|
||||
const completionTokens = Math.ceil(result.length / 4);
|
||||
|
||||
return {
|
||||
id: `chatcmpl-mock-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`,
|
||||
object: "chat.completion",
|
||||
created: Math.floor(Date.now() / 1000),
|
||||
model: "gpt-4o-mini",
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
message: {
|
||||
role: "assistant",
|
||||
content: result,
|
||||
},
|
||||
finish_reason: "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: promptTokens,
|
||||
completion_tokens: completionTokens,
|
||||
total_tokens: promptTokens + completionTokens,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate mock batch job response
|
||||
*/
|
||||
export function generateBatchResponse(
|
||||
status: MockBatchResponse["status"] = "in_progress"
|
||||
): MockBatchResponse {
|
||||
const now = Math.floor(Date.now() / 1000);
|
||||
const batchId = `batch_mock_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
|
||||
|
||||
const result: MockBatchResponse = {
|
||||
id: batchId,
|
||||
object: "batch",
|
||||
endpoint: "/v1/chat/completions",
|
||||
errors: {
|
||||
object: "list",
|
||||
data: [],
|
||||
},
|
||||
input_file_id: `file-mock-input-${batchId}`,
|
||||
completion_window: "24h",
|
||||
status,
|
||||
created_at: now - 300, // 5 minutes ago
|
||||
expires_at: now + 86400, // 24 hours from now
|
||||
request_counts: {
|
||||
total: 100,
|
||||
completed:
|
||||
status === "completed" ? 100 : status === "in_progress" ? 75 : 0,
|
||||
failed: status === "failed" ? 25 : 0,
|
||||
},
|
||||
metadata: {
|
||||
company_id: "test-company",
|
||||
batch_type: "ai_processing",
|
||||
},
|
||||
};
|
||||
|
||||
// Set optional fields based on status
|
||||
if (status === "completed") {
|
||||
result.output_file_id = `file-mock-output-${batchId}`;
|
||||
result.completed_at = now - 30;
|
||||
}
|
||||
|
||||
if (status === "failed") {
|
||||
result.failed_at = now - 30;
|
||||
}
|
||||
|
||||
if (status !== "validating") {
|
||||
result.in_progress_at = now - 240; // 4 minutes ago
|
||||
}
|
||||
|
||||
if (status === "finalizing" || status === "completed") {
|
||||
result.finalizing_at = now - 60;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* Mock cost calculation for testing
|
||||
*/
|
||||
export function calculateMockCost(usage: {
|
||||
prompt_tokens: number;
|
||||
completion_tokens: number;
|
||||
}): number {
|
||||
// Mock pricing: $0.15 per 1K prompt tokens, $0.60 per 1K completion tokens (gpt-4o-mini rates)
|
||||
const promptCost = (usage.prompt_tokens / 1000) * 0.15;
|
||||
const completionCost = (usage.completion_tokens / 1000) * 0.6;
|
||||
return promptCost + completionCost;
|
||||
}
|
||||
|
||||
/**
|
||||
* Response templates for different AI processing types
|
||||
*/
|
||||
export const MOCK_RESPONSE_GENERATORS = {
|
||||
sentiment: generateSentimentResponse,
|
||||
category: generateCategoryResponse,
|
||||
summary: generateSummaryResponse,
|
||||
questions: generateQuestionExtractionResponse,
|
||||
} as const;
|
||||
|
||||
export type MockResponseType = keyof typeof MOCK_RESPONSE_GENERATORS;
|
||||
Reference in New Issue
Block a user