generated from fahricansecer/boilerplate-be
main
This commit is contained in:
Harun CAN
56
package-lock.json
generated
56
package-lock.json
generated
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|
||||
"integrity": "sha512-2NxwbF/hZ0KpepYN0cNbo+FN6XoK7GaHlQhgx/hIZl6Va0bF45RQOOwhLIy8lQDbuCiadSLCBnH2CFYquit5bw==",
|
||||
"dev": true,
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"esrecurse": "^4.3.0",
|
||||
"estraverse": "^4.1.1"
|
||||
@@ -12710,6 +12684,7 @@
|
||||
"resolved": "https://registry.npmjs.org/estraverse/-/estraverse-4.3.0.tgz",
|
||||
"integrity": "sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==",
|
||||
"dev": true,
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">=4.0"
|
||||
}
|
||||
@@ -12718,13 +12693,15 @@
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz",
|
||||
"integrity": "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==",
|
||||
"dev": true
|
||||
"dev": true,
|
||||
"peer": true
|
||||
},
|
||||
"node_modules/webpack/node_modules/mime-db": {
|
||||
"version": "1.52.0",
|
||||
"resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
|
||||
"integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
|
||||
"dev": true,
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">= 0.6"
|
||||
}
|
||||
@@ -12734,6 +12711,7 @@
|
||||
"resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
|
||||
"integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
|
||||
"dev": true,
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"mime-db": "1.52.0"
|
||||
},
|
||||
@@ -12746,6 +12724,7 @@
|
||||
"resolved": "https://registry.npmjs.org/schema-utils/-/schema-utils-4.3.3.tgz",
|
||||
"integrity": "sha512-eflK8wEtyOE6+hsaRVPxvUKYCpRgzLqDTb8krvAsRIwOGlHoSgYLgBXoubGgLd2fT41/OUYdb48v4k4WWHQurA==",
|
||||
"dev": true,
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@types/json-schema": "^7.0.9",
|
||||
"ajv": "^8.9.0",
|
||||
@@ -12953,9 +12932,10 @@
|
||||
}
|
||||
},
|
||||
"node_modules/zod": {
|
||||
"version": "4.3.5",
|
||||
"resolved": "https://registry.npmjs.org/zod/-/zod-4.3.5.tgz",
|
||||
"integrity": "sha512-k7Nwx6vuWx1IJ9Bjuf4Zt1PEllcwe7cls3VNzm4CQ1/hgtFUK2bRNG3rvnpPUhFjmqJKAKtjV576KnUkHocg/g==",
|
||||
"version": "4.3.6",
|
||||
"resolved": "https://registry.npmjs.org/zod/-/zod-4.3.6.tgz",
|
||||
"integrity": "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==",
|
||||
"license": "MIT",
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/colinhacks"
|
||||
}
|
||||
|
||||
@@ -52,7 +52,7 @@
|
||||
"prisma": "^5.22.0",
|
||||
"reflect-metadata": "^0.2.2",
|
||||
"rxjs": "^7.8.1",
|
||||
"zod": "^4.3.5"
|
||||
"zod": "^4.3.6"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@eslint/eslintrc": "^3.2.0",
|
||||
|
||||
@@ -1,6 +1,11 @@
|
||||
import { Injectable, OnModuleInit, Logger } from '@nestjs/common';
|
||||
import { ConfigService } from '@nestjs/config';
|
||||
import { GoogleGenAI } from '@google/genai';
|
||||
import { ZodSchema, ZodError } from 'zod';
|
||||
|
||||
// ============================================
|
||||
// Types & Interfaces
|
||||
// ============================================
|
||||
|
||||
export interface GeminiGenerateOptions {
|
||||
model?: string;
|
||||
@@ -15,30 +20,72 @@ export interface GeminiChatMessage {
|
||||
content: string;
|
||||
}
|
||||
|
||||
export interface GeminiJSONOptions<T = any> extends GeminiGenerateOptions {
|
||||
/** Zod schema for runtime validation of the AI response */
|
||||
zodSchema?: ZodSchema<T>;
|
||||
/** Max retry attempts for JSON generation (default: 3) */
|
||||
maxRetries?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* Gemini AI Service
|
||||
* Error types for Gemini API failures
|
||||
*/
|
||||
export enum GeminiErrorType {
|
||||
RATE_LIMIT = 'RATE_LIMIT',
|
||||
QUOTA_EXCEEDED = 'QUOTA_EXCEEDED',
|
||||
SAFETY_BLOCKED = 'SAFETY_BLOCKED',
|
||||
INVALID_RESPONSE = 'INVALID_RESPONSE',
|
||||
JSON_PARSE_FAILED = 'JSON_PARSE_FAILED',
|
||||
TIMEOUT = 'TIMEOUT',
|
||||
UNAVAILABLE = 'UNAVAILABLE',
|
||||
UNKNOWN = 'UNKNOWN',
|
||||
}
|
||||
|
||||
/**
|
||||
* Custom exception for Gemini AI errors with rich context
|
||||
*/
|
||||
export class GeminiException extends Error {
|
||||
constructor(
|
||||
message: string,
|
||||
public readonly type: GeminiErrorType,
|
||||
public readonly originalError?: any,
|
||||
public readonly retryable: boolean = false,
|
||||
) {
|
||||
super(message);
|
||||
this.name = 'GeminiException';
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Service
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Gemini AI Service — Enhanced with Retry, JSON Recovery & Validation
|
||||
*
|
||||
* Provides AI-powered text generation using Google Gemini API.
|
||||
* Provides AI-powered text/JSON/image generation using Google Gemini API.
|
||||
* This service is globally available when ENABLE_GEMINI=true.
|
||||
*
|
||||
* Key improvements over v1:
|
||||
* - responseMimeType: "application/json" for native JSON output
|
||||
* - Exponential backoff retry (up to 3 attempts)
|
||||
* - Multi-strategy JSON extraction & recovery
|
||||
* - Optional Zod schema validation
|
||||
* - Typed GeminiException with error classification
|
||||
* - AI usage metrics logging
|
||||
*
|
||||
* @example
|
||||
* ```typescript
|
||||
* // Simple text generation
|
||||
* const response = await geminiService.generateText('Write a poem about coding');
|
||||
*
|
||||
* // With options
|
||||
* const response = await geminiService.generateText('Translate to Turkish', {
|
||||
* temperature: 0.7,
|
||||
* systemPrompt: 'You are a professional translator',
|
||||
* });
|
||||
*
|
||||
* // Chat conversation
|
||||
* const messages = [
|
||||
* { role: 'user', content: 'Hello!' },
|
||||
* { role: 'model', content: 'Hi there!' },
|
||||
* { role: 'user', content: 'What is 2+2?' },
|
||||
* ];
|
||||
* const response = await geminiService.chat(messages);
|
||||
* // JSON generation with Zod validation
|
||||
* import { z } from 'zod';
|
||||
* const schema = z.object({ title: z.string(), score: z.number() });
|
||||
* const result = await geminiService.generateJSON(
|
||||
* 'Analyze this script', '{ title, score }',
|
||||
* { zodSchema: schema }
|
||||
* );
|
||||
* ```
|
||||
*/
|
||||
@Injectable()
|
||||
@@ -87,6 +134,10 @@ export class GeminiService implements OnModuleInit {
|
||||
return this.isEnabled && this.client !== null;
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Text Generation
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Generate text content from a prompt
|
||||
*
|
||||
@@ -98,11 +149,10 @@ export class GeminiService implements OnModuleInit {
|
||||
prompt: string,
|
||||
options: GeminiGenerateOptions = {},
|
||||
): Promise<{ text: string; usage?: any }> {
|
||||
if (!this.isAvailable()) {
|
||||
throw new Error('Gemini AI is not available. Check your configuration.');
|
||||
}
|
||||
this.ensureAvailable();
|
||||
|
||||
const model = options.model || this.defaultModel;
|
||||
const startTime = Date.now();
|
||||
|
||||
try {
|
||||
const contents: any[] = [];
|
||||
@@ -134,16 +184,27 @@ export class GeminiService implements OnModuleInit {
|
||||
},
|
||||
});
|
||||
|
||||
const durationMs = Date.now() - startTime;
|
||||
this.logUsage('generateText', model, response.usageMetadata, durationMs);
|
||||
|
||||
return {
|
||||
text: (response.text || '').trim(),
|
||||
usage: response.usageMetadata,
|
||||
};
|
||||
} catch (error) {
|
||||
this.logger.error('Gemini generation failed', error);
|
||||
throw error;
|
||||
const durationMs = Date.now() - startTime;
|
||||
this.logger.error(
|
||||
`Gemini generation failed after ${durationMs}ms`,
|
||||
error,
|
||||
);
|
||||
throw this.classifyError(error);
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Chat
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Have a multi-turn chat conversation
|
||||
*
|
||||
@@ -155,11 +216,10 @@ export class GeminiService implements OnModuleInit {
|
||||
messages: GeminiChatMessage[],
|
||||
options: GeminiGenerateOptions = {},
|
||||
): Promise<{ text: string; usage?: any }> {
|
||||
if (!this.isAvailable()) {
|
||||
throw new Error('Gemini AI is not available. Check your configuration.');
|
||||
}
|
||||
this.ensureAvailable();
|
||||
|
||||
const model = options.model || this.defaultModel;
|
||||
const startTime = Date.now();
|
||||
|
||||
try {
|
||||
const contents = messages.map((msg) => ({
|
||||
@@ -190,55 +250,165 @@ export class GeminiService implements OnModuleInit {
|
||||
},
|
||||
});
|
||||
|
||||
const durationMs = Date.now() - startTime;
|
||||
this.logUsage('chat', model, response.usageMetadata, durationMs);
|
||||
|
||||
return {
|
||||
text: (response.text || '').trim(),
|
||||
usage: response.usageMetadata,
|
||||
};
|
||||
} catch (error) {
|
||||
this.logger.error('Gemini chat failed', error);
|
||||
throw error;
|
||||
throw this.classifyError(error);
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// JSON Generation (Enhanced)
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Generate structured JSON output
|
||||
* Generate structured JSON output with retry, recovery, and optional Zod validation.
|
||||
*
|
||||
* Strategy:
|
||||
* 1. First attempt uses `responseMimeType: "application/json"` for native JSON
|
||||
* 2. If that fails, falls back to prompt-based JSON with multi-strategy extraction
|
||||
* 3. Up to `maxRetries` attempts with exponential backoff
|
||||
* 4. Optional Zod schema validation on the parsed result
|
||||
*
|
||||
* @param prompt - The prompt describing what JSON to generate
|
||||
* @param schema - JSON schema description for the expected output
|
||||
* @param options - Optional configuration for the generation
|
||||
* @returns Parsed JSON object
|
||||
* @param schema - JSON schema description for the expected output (human readable)
|
||||
* @param options - Optional configuration including zodSchema and maxRetries
|
||||
* @returns Parsed and optionally validated JSON object
|
||||
*/
|
||||
async generateJSON<T = any>(
|
||||
prompt: string,
|
||||
schema: string,
|
||||
options: GeminiGenerateOptions = {},
|
||||
options: GeminiJSONOptions<T> = {},
|
||||
): Promise<{ data: T; usage?: any }> {
|
||||
const fullPrompt = `${prompt}
|
||||
this.ensureAvailable();
|
||||
|
||||
const maxRetries = options.maxRetries ?? 3;
|
||||
const model = options.model || this.defaultModel;
|
||||
let lastError: Error | null = null;
|
||||
let lastUsage: any = undefined;
|
||||
|
||||
for (let attempt = 1; attempt <= maxRetries; attempt++) {
|
||||
const startTime = Date.now();
|
||||
|
||||
try {
|
||||
// Build the full prompt
|
||||
const fullPrompt = `${prompt}
|
||||
|
||||
Output the result as valid JSON that matches this schema:
|
||||
${schema}
|
||||
|
||||
IMPORTANT: Only output valid JSON, no markdown code blocks or other text.`;
|
||||
|
||||
const response = await this.generateText(fullPrompt, options);
|
||||
const contents: any[] = [];
|
||||
|
||||
try {
|
||||
// Try to extract JSON from the response
|
||||
let jsonStr = response.text;
|
||||
if (options.systemPrompt) {
|
||||
contents.push({
|
||||
role: 'user',
|
||||
parts: [{ text: options.systemPrompt }],
|
||||
});
|
||||
contents.push({
|
||||
role: 'model',
|
||||
parts: [
|
||||
{ text: 'Understood. I will follow these instructions.' },
|
||||
],
|
||||
});
|
||||
}
|
||||
|
||||
// Remove potential markdown code blocks
|
||||
const jsonMatch = jsonStr.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
if (jsonMatch) {
|
||||
jsonStr = jsonMatch[1].trim();
|
||||
contents.push({
|
||||
role: 'user',
|
||||
parts: [{ text: fullPrompt }],
|
||||
});
|
||||
|
||||
// Configure responseMimeType for native JSON (only when no tools — tools don't support it)
|
||||
const config: any = {
|
||||
temperature: options.temperature,
|
||||
maxOutputTokens: options.maxTokens,
|
||||
};
|
||||
|
||||
if (!options.tools || options.tools.length === 0) {
|
||||
config.responseMimeType = 'application/json';
|
||||
}
|
||||
|
||||
if (options.tools) {
|
||||
config.tools = options.tools;
|
||||
}
|
||||
|
||||
const response = await this.client!.models.generateContent({
|
||||
model,
|
||||
contents,
|
||||
config,
|
||||
});
|
||||
|
||||
const durationMs = Date.now() - startTime;
|
||||
lastUsage = response.usageMetadata;
|
||||
this.logUsage(
|
||||
`generateJSON (attempt ${attempt}/${maxRetries})`,
|
||||
model,
|
||||
response.usageMetadata,
|
||||
durationMs,
|
||||
);
|
||||
|
||||
const rawText = (response.text || '').trim();
|
||||
|
||||
// Try to extract and parse JSON
|
||||
const jsonStr = this.extractJSON(rawText);
|
||||
const data = JSON.parse(jsonStr) as T;
|
||||
|
||||
// Validate with Zod schema if provided
|
||||
if (options.zodSchema) {
|
||||
const validated = options.zodSchema.parse(data);
|
||||
return { data: validated as T, usage: lastUsage };
|
||||
}
|
||||
|
||||
return { data, usage: lastUsage };
|
||||
} catch (error) {
|
||||
lastError = error as Error;
|
||||
|
||||
const isParseError =
|
||||
error instanceof SyntaxError ||
|
||||
(error instanceof ZodError) ||
|
||||
(error instanceof Error &&
|
||||
error.message.includes('Failed to extract JSON'));
|
||||
|
||||
const isRetryable = isParseError || this.isRetryableError(error);
|
||||
|
||||
if (isRetryable && attempt < maxRetries) {
|
||||
const backoffMs = Math.min(1000 * Math.pow(2, attempt - 1), 8000);
|
||||
this.logger.warn(
|
||||
`JSON generation attempt ${attempt}/${maxRetries} failed (${error instanceof Error ? error.message : 'unknown'}). Retrying in ${backoffMs}ms...`,
|
||||
);
|
||||
await this.sleep(backoffMs);
|
||||
continue;
|
||||
}
|
||||
|
||||
// Log failure details
|
||||
if (error instanceof ZodError) {
|
||||
this.logger.error(
|
||||
`Zod validation failed after ${attempt} attempts: ${error.issues.map((e) => `${e.path.join('.')}: ${e.message}`).join(', ')}`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
const data = JSON.parse(jsonStr) as T;
|
||||
return { data, usage: response.usage };
|
||||
} catch (error) {
|
||||
this.logger.error('Failed to parse JSON response', error);
|
||||
throw new Error('Failed to parse AI response as JSON');
|
||||
}
|
||||
|
||||
// All retries exhausted
|
||||
throw new GeminiException(
|
||||
`Failed to generate valid JSON after ${maxRetries} attempts: ${lastError?.message}`,
|
||||
GeminiErrorType.JSON_PARSE_FAILED,
|
||||
lastError,
|
||||
false,
|
||||
);
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Image Generation
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Generate an image using Google Imagen (Nano Banana)
|
||||
*
|
||||
@@ -246,9 +416,9 @@ IMPORTANT: Only output valid JSON, no markdown code blocks or other text.`;
|
||||
* @returns Base64 encoded image data URI
|
||||
*/
|
||||
async generateImage(prompt: string): Promise<string> {
|
||||
if (!this.isAvailable()) {
|
||||
throw new Error('Gemini AI is not available. Check your configuration.');
|
||||
}
|
||||
this.ensureAvailable();
|
||||
|
||||
const startTime = Date.now();
|
||||
|
||||
try {
|
||||
// Use Imagen 3.0 (Nano Banana Pro)
|
||||
@@ -263,6 +433,11 @@ IMPORTANT: Only output valid JSON, no markdown code blocks or other text.`;
|
||||
},
|
||||
})) as any;
|
||||
|
||||
const durationMs = Date.now() - startTime;
|
||||
this.logger.log(
|
||||
`Image generated in ${durationMs}ms (model: ${model})`,
|
||||
);
|
||||
|
||||
if (
|
||||
response.images &&
|
||||
response.images.length > 0 &&
|
||||
@@ -272,11 +447,209 @@ IMPORTANT: Only output valid JSON, no markdown code blocks or other text.`;
|
||||
return `data:image/png;base64,${response.images[0].image}`;
|
||||
}
|
||||
|
||||
throw new Error('No image returned from Gemini');
|
||||
throw new GeminiException(
|
||||
'No image returned from Gemini',
|
||||
GeminiErrorType.INVALID_RESPONSE,
|
||||
);
|
||||
} catch (error) {
|
||||
if (error instanceof GeminiException) throw error;
|
||||
this.logger.error('Gemini image generation failed', error);
|
||||
// Fallback or rethrow
|
||||
throw error;
|
||||
throw this.classifyError(error);
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Private Helpers
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Ensure Gemini client is available, throw typed exception if not
|
||||
*/
|
||||
private ensureAvailable(): void {
|
||||
if (!this.isAvailable()) {
|
||||
throw new GeminiException(
|
||||
'Gemini AI is not available. Check your configuration.',
|
||||
GeminiErrorType.UNAVAILABLE,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract JSON from a raw AI response using multiple strategies:
|
||||
* 1. Direct parse (cleanest case)
|
||||
* 2. Strip markdown code blocks
|
||||
* 3. Find first { or [ and match to closing bracket
|
||||
* 4. Remove trailing commas and retry
|
||||
*/
|
||||
private extractJSON(raw: string): string {
|
||||
// Strategy 1: Direct parse attempt
|
||||
try {
|
||||
JSON.parse(raw);
|
||||
return raw;
|
||||
} catch {
|
||||
// Continue to next strategy
|
||||
}
|
||||
|
||||
// Strategy 2: Strip markdown code blocks (```json ... ``` or ``` ... ```)
|
||||
const codeBlockMatch = raw.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
if (codeBlockMatch) {
|
||||
const extracted = codeBlockMatch[1].trim();
|
||||
try {
|
||||
JSON.parse(extracted);
|
||||
return extracted;
|
||||
} catch {
|
||||
// Continue with the extracted content for further cleaning
|
||||
raw = extracted;
|
||||
}
|
||||
}
|
||||
|
||||
// Strategy 3: Find the first { or [ and match to the last } or ]
|
||||
const objectStart = raw.indexOf('{');
|
||||
const arrayStart = raw.indexOf('[');
|
||||
|
||||
let start = -1;
|
||||
let endChar = '';
|
||||
|
||||
if (objectStart >= 0 && (arrayStart < 0 || objectStart < arrayStart)) {
|
||||
start = objectStart;
|
||||
endChar = '}';
|
||||
} else if (arrayStart >= 0) {
|
||||
start = arrayStart;
|
||||
endChar = ']';
|
||||
}
|
||||
|
||||
if (start >= 0) {
|
||||
const end = raw.lastIndexOf(endChar);
|
||||
if (end > start) {
|
||||
const candidate = raw.substring(start, end + 1);
|
||||
try {
|
||||
JSON.parse(candidate);
|
||||
return candidate;
|
||||
} catch {
|
||||
// Strategy 4: Remove trailing commas and retry
|
||||
const cleaned = candidate
|
||||
.replace(/,\s*([\]}])/g, '$1') // Remove trailing commas
|
||||
.replace(/'/g, '"') // Replace single quotes with double quotes
|
||||
.replace(/(\w+)\s*:/g, '"$1":') // Quote unquoted keys
|
||||
.replace(/""(\w+)""/g, '"$1"'); // Fix double-quoted keys
|
||||
|
||||
try {
|
||||
JSON.parse(cleaned);
|
||||
return cleaned;
|
||||
} catch {
|
||||
// Last resort: return the candidate anyway, caller will handle error
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error(
|
||||
`Failed to extract JSON from AI response (length: ${raw.length})`,
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Classify an error into a typed GeminiException
|
||||
*/
|
||||
private classifyError(error: any): GeminiException {
|
||||
if (error instanceof GeminiException) return error;
|
||||
|
||||
const message = error?.message || String(error);
|
||||
const status = error?.status || error?.statusCode;
|
||||
|
||||
// Rate limiting
|
||||
if (status === 429 || message.includes('429') || message.includes('RATE_LIMIT') || message.includes('rate limit')) {
|
||||
return new GeminiException(
|
||||
'Gemini API rate limit exceeded. Please wait before retrying.',
|
||||
GeminiErrorType.RATE_LIMIT,
|
||||
error,
|
||||
true,
|
||||
);
|
||||
}
|
||||
|
||||
// Quota
|
||||
if (message.includes('QUOTA') || message.includes('quota') || status === 403) {
|
||||
return new GeminiException(
|
||||
'Gemini API quota exceeded.',
|
||||
GeminiErrorType.QUOTA_EXCEEDED,
|
||||
error,
|
||||
false,
|
||||
);
|
||||
}
|
||||
|
||||
// Safety
|
||||
if (message.includes('SAFETY') || message.includes('safety') || message.includes('blocked')) {
|
||||
return new GeminiException(
|
||||
'Content was blocked by safety filters. Try rephrasing the prompt.',
|
||||
GeminiErrorType.SAFETY_BLOCKED,
|
||||
error,
|
||||
false,
|
||||
);
|
||||
}
|
||||
|
||||
// Timeout
|
||||
if (message.includes('TIMEOUT') || message.includes('timeout') || message.includes('DEADLINE_EXCEEDED')) {
|
||||
return new GeminiException(
|
||||
'Gemini API request timed out.',
|
||||
GeminiErrorType.TIMEOUT,
|
||||
error,
|
||||
true,
|
||||
);
|
||||
}
|
||||
|
||||
// Generic
|
||||
return new GeminiException(
|
||||
`Gemini API error: ${message}`,
|
||||
GeminiErrorType.UNKNOWN,
|
||||
error,
|
||||
true,
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if an error is retryable
|
||||
*/
|
||||
private isRetryableError(error: any): boolean {
|
||||
if (error instanceof GeminiException) return error.retryable;
|
||||
const message = error?.message || '';
|
||||
return (
|
||||
message.includes('429') ||
|
||||
message.includes('RATE_LIMIT') ||
|
||||
message.includes('TIMEOUT') ||
|
||||
message.includes('DEADLINE_EXCEEDED') ||
|
||||
message.includes('UNAVAILABLE') ||
|
||||
message.includes('INTERNAL')
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Log AI usage metrics for monitoring
|
||||
*/
|
||||
private logUsage(
|
||||
operation: string,
|
||||
model: string,
|
||||
usage: any,
|
||||
durationMs: number,
|
||||
): void {
|
||||
if (usage) {
|
||||
this.logger.log(
|
||||
`AI Usage [${operation}] model=${model} ` +
|
||||
`prompt=${usage.promptTokenCount || '?'} ` +
|
||||
`completion=${usage.candidatesTokenCount || '?'} ` +
|
||||
`total=${usage.totalTokenCount || '?'} ` +
|
||||
`duration=${durationMs}ms`,
|
||||
);
|
||||
} else {
|
||||
this.logger.log(
|
||||
`AI Usage [${operation}] model=${model} duration=${durationMs}ms`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Sleep helper for retry backoff
|
||||
*/
|
||||
private sleep(ms: number): Promise<void> {
|
||||
return new Promise((resolve) => setTimeout(resolve, ms));
|
||||
}
|
||||
}
|
||||
|
||||
191
src/modules/skriptai/prompts/analysis.prompt.ts
Normal file
191
src/modules/skriptai/prompts/analysis.prompt.ts
Normal file
@@ -0,0 +1,191 @@
|
||||
/**
|
||||
* Analysis Prompt Builders
|
||||
*
|
||||
* Prompts for AI-powered content analysis:
|
||||
* - Neuro Marketing Analysis (Cialdini's 6 Principles)
|
||||
* - YouTube Algorithm Audit
|
||||
* - Commercial Brief (Sponsorship Analysis)
|
||||
* - Visual Asset Keywords
|
||||
*
|
||||
* Used in: AnalysisService
|
||||
*/
|
||||
|
||||
// ============================================
|
||||
// Neuro Marketing Analysis
|
||||
// ============================================
|
||||
|
||||
export interface NeuroAnalysisInput {
|
||||
fullScript: string;
|
||||
}
|
||||
|
||||
export function buildNeuroAnalysisPrompt(input: NeuroAnalysisInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Analyze this script using Consumer Neuroscience and Cialdini's 6 Principles of Persuasion.
|
||||
|
||||
Script:
|
||||
${input.fullScript.substring(0, 10000)}
|
||||
|
||||
Provide:
|
||||
1. Engagement Score (0-100): How well does it capture attention?
|
||||
2. Dopamine Score (0-100): Does it create anticipation & reward loops?
|
||||
3. Clarity Score (0-100): Is the message clear and memorable?
|
||||
|
||||
4. Cialdini's Persuasion Metrics (0-100 each):
|
||||
- Reciprocity: Does it give value first?
|
||||
- Scarcity: Does it create urgency?
|
||||
- Authority: Does it establish credibility?
|
||||
- Consistency: Does it align with viewer beliefs?
|
||||
- Liking: Is the tone likeable/relatable?
|
||||
- Social Proof: Does it reference others' actions?
|
||||
|
||||
5. Neuro Metrics:
|
||||
- Attention Hooks: Moments that grab attention
|
||||
- Emotional Triggers: Points that evoke emotion
|
||||
- Memory Anchors: Unique/memorable elements
|
||||
- Action Drivers: CTAs or challenges
|
||||
|
||||
6. Suggestions: 3-5 specific improvements`,
|
||||
|
||||
temperature: 0.6,
|
||||
|
||||
schema: `{
|
||||
"engagementScore": 0,
|
||||
"dopamineScore": 0,
|
||||
"clarityScore": 0,
|
||||
"persuasionMetrics": {
|
||||
"reciprocity": 0, "scarcity": 0, "authority": 0,
|
||||
"consistency": 0, "liking": 0, "socialProof": 0
|
||||
},
|
||||
"neuroMetrics": {
|
||||
"attentionHooks": ["..."], "emotionalTriggers": ["..."],
|
||||
"memoryAnchors": ["..."], "actionDrivers": ["..."]
|
||||
},
|
||||
"suggestions": ["..."]
|
||||
}`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// YouTube Audit
|
||||
// ============================================
|
||||
|
||||
export interface YoutubeAuditInput {
|
||||
topic: string;
|
||||
fullScript: string;
|
||||
}
|
||||
|
||||
export function buildYoutubeAuditPrompt(input: YoutubeAuditInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Perform a YouTube Algorithm Audit on this script for topic "${input.topic}".
|
||||
|
||||
Script:
|
||||
${input.fullScript.substring(0, 10000)}
|
||||
|
||||
Analyze and provide:
|
||||
1. Hook Score (0-100): First 10 seconds effectiveness
|
||||
2. Pacing Score (0-100): Does it maintain momentum?
|
||||
3. Viral Potential (0-100): Shareability factor
|
||||
|
||||
4. Retention Analysis: 3-5 potential drop-off points with time, issue, suggestion, severity (High/Medium/Low)
|
||||
|
||||
5. Thumbnail Concepts: 3 high-CTR thumbnail ideas with:
|
||||
- Concept name, Visual description, Text overlay
|
||||
- Color psychology, Emotion target, AI generation prompt
|
||||
|
||||
6. Title Options: 5 clickable titles (curiosity gap, numbers, power words)
|
||||
7. Community Post: Engaging post to tease the video
|
||||
8. Pinned Comment: Engagement-driving first comment
|
||||
9. SEO Description: Optimized video description with keywords
|
||||
10. Keywords: 10 relevant search keywords`,
|
||||
|
||||
temperature: 0.7,
|
||||
|
||||
schema: `{
|
||||
"hookScore": 0, "pacingScore": 0, "viralPotential": 0,
|
||||
"retentionAnalysis": [{ "time": "0:30", "issue": "...", "suggestion": "...", "severity": "High" }],
|
||||
"thumbnails": [{ "conceptName": "...", "visualDescription": "...", "textOverlay": "...", "colorPsychology": "...", "emotionTarget": "...", "aiPrompt": "..." }],
|
||||
"titles": ["..."],
|
||||
"communityPost": "...", "pinnedComment": "...",
|
||||
"description": "...", "keywords": ["..."]
|
||||
}`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Commercial Brief
|
||||
// ============================================
|
||||
|
||||
export interface CommercialBriefInput {
|
||||
topic: string;
|
||||
targetAudience: string[];
|
||||
contentType: string;
|
||||
fullScript: string;
|
||||
}
|
||||
|
||||
export function buildCommercialBriefPrompt(input: CommercialBriefInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Analyze this content for commercial viability and sponsorship opportunities.
|
||||
|
||||
Topic: "${input.topic}"
|
||||
Audience: ${input.targetAudience.join(', ')}
|
||||
Content Type: ${input.contentType}
|
||||
|
||||
Script excerpt:
|
||||
${input.fullScript.substring(0, 5000)}
|
||||
|
||||
Provide:
|
||||
1. Viability Score (1-10 scale as string): "8/10"
|
||||
2. Viability Reason: Why this content is commercially viable
|
||||
|
||||
3. Sponsor Suggestions (3-5 potential sponsors):
|
||||
- Company name, Industry
|
||||
- Match reason (why this sponsor fits)
|
||||
- Email draft (outreach template)`,
|
||||
|
||||
temperature: 0.6,
|
||||
|
||||
schema: `{
|
||||
"viabilityScore": "8/10",
|
||||
"viabilityReason": "...",
|
||||
"sponsors": [{ "name": "...", "industry": "...", "matchReason": "...", "emailDraft": "..." }]
|
||||
}`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Visual Asset Keywords
|
||||
// ============================================
|
||||
|
||||
export interface VisualAssetKeywordsInput {
|
||||
topic: string;
|
||||
count: number;
|
||||
}
|
||||
|
||||
export function buildVisualAssetKeywordsPrompt(
|
||||
input: VisualAssetKeywordsInput,
|
||||
): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Generate ${input.count} specific, simple visual keywords for an image generator about "${input.topic}".
|
||||
Format: "subject action context style". Keep it English, concise, no special chars.`,
|
||||
|
||||
temperature: 0.8,
|
||||
|
||||
schema: '["keyword1", "keyword2", ...]',
|
||||
};
|
||||
}
|
||||
38
src/modules/skriptai/prompts/character-generation.prompt.ts
Normal file
38
src/modules/skriptai/prompts/character-generation.prompt.ts
Normal file
@@ -0,0 +1,38 @@
|
||||
/**
|
||||
* Character Generation Prompt Builder
|
||||
*
|
||||
* Uses Alan C. Hueth's "Triunity of Character" model to create
|
||||
* rich character profiles for video content.
|
||||
*
|
||||
* Used in: ResearchService.generateCharacters()
|
||||
*/
|
||||
|
||||
export interface CharacterGenerationInput {
|
||||
contentType: string;
|
||||
topic: string;
|
||||
language: string;
|
||||
}
|
||||
|
||||
export function buildCharacterGenerationPrompt(
|
||||
input: CharacterGenerationInput,
|
||||
): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Create Character Profiles for a ${input.contentType} about "${input.topic}".
|
||||
Use Alan C. Hueth's "Triunity of Character" model:
|
||||
1. Values (Inner belief)
|
||||
2. Traits (Personality)
|
||||
3. Mannerisms (External behavior)
|
||||
|
||||
If format is non-fiction (Youtube Doc), create a 'Host/Narrator' persona and potentially an 'Antagonist' (e.g., The Problem, Time, A Rival).
|
||||
Language: ${input.language}.`,
|
||||
|
||||
temperature: 0.8,
|
||||
|
||||
schema:
|
||||
'[{ "name": "Name", "role": "Protagonist", "values": "...", "traits": "...", "mannerisms": "..." }]',
|
||||
};
|
||||
}
|
||||
51
src/modules/skriptai/prompts/deep-research.prompt.ts
Normal file
51
src/modules/skriptai/prompts/deep-research.prompt.ts
Normal file
@@ -0,0 +1,51 @@
|
||||
/**
|
||||
* Deep Research Prompt Builders
|
||||
*
|
||||
* Two-stage prompts:
|
||||
* 1. Generate search queries for a topic
|
||||
* 2. Find high-quality web sources for each query
|
||||
*
|
||||
* Used in: ResearchService.performDeepResearch()
|
||||
*/
|
||||
|
||||
export interface SearchQueryInput {
|
||||
topic: string;
|
||||
briefContext: string;
|
||||
language: string;
|
||||
}
|
||||
|
||||
export function buildSearchQueryPrompt(input: SearchQueryInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Generate 5 specific Google Search queries for "${input.topic}".
|
||||
Context: ${input.briefContext}. Language: ${input.language}.
|
||||
Return strictly a JSON array of strings.`,
|
||||
|
||||
temperature: 0.7,
|
||||
|
||||
schema: '["query1", "query2", ...]',
|
||||
};
|
||||
}
|
||||
|
||||
export interface SourceSearchInput {
|
||||
query: string;
|
||||
language: string;
|
||||
}
|
||||
|
||||
export function buildSourceSearchPrompt(input: SourceSearchInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Find 3 high-quality web sources for: ${input.query}. Language: ${input.language}.
|
||||
Return JSON array: [{ "title": string, "url": string, "snippet": string, "type": "article" }]`,
|
||||
|
||||
temperature: 0.5,
|
||||
|
||||
schema: '[{ "title": "...", "url": "...", "snippet": "...", "type": "article" }]',
|
||||
};
|
||||
}
|
||||
53
src/modules/skriptai/prompts/discovery-questions.prompt.ts
Normal file
53
src/modules/skriptai/prompts/discovery-questions.prompt.ts
Normal file
@@ -0,0 +1,53 @@
|
||||
/**
|
||||
* Discovery Questions Prompt Builder
|
||||
*
|
||||
* Generates provocative "Screenwriter's Room" style questions
|
||||
* to help shape the narrative arc for a given topic.
|
||||
*
|
||||
* Used in: ResearchService.generateDiscoveryQuestions()
|
||||
*/
|
||||
|
||||
export interface DiscoveryQuestionsInput {
|
||||
topic: string;
|
||||
language: string;
|
||||
existingQuestions?: string[];
|
||||
}
|
||||
|
||||
export function buildDiscoveryQuestionsPrompt(
|
||||
input: DiscoveryQuestionsInput,
|
||||
): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
const existingContext =
|
||||
input.existingQuestions && input.existingQuestions.length > 0
|
||||
? `Avoid these questions: ${input.existingQuestions.join(', ')}`
|
||||
: '';
|
||||
|
||||
return {
|
||||
prompt: `You are an expert Screenwriter and Creative Director. Topic: "${input.topic}".
|
||||
|
||||
PHASE 1: DEEP DIVE
|
||||
Think like a filmmaker. We are not just making a video; we are telling a story.
|
||||
Analyze the topic "${input.topic}" to find the drama, the conflict, and the human element.
|
||||
|
||||
PHASE 2: INTERROGATION
|
||||
Ask 3-4 provocative, "Screenwriter's Room" style questions to help shape the narrative arc.
|
||||
|
||||
DO NOT ASK: "What is the goal?" or "Who is the audience?".
|
||||
|
||||
INSTEAD ASK (Examples):
|
||||
- "What is the 'Inciting Incident' that makes this topic urgent right now?"
|
||||
- "If this topic was a character, what would be its fatal flaw?"
|
||||
- "What is the 'Villain' (opposing force or misconception) we are fighting against?"
|
||||
- "What is the emotional climax you want the viewer to feel at the end?"
|
||||
|
||||
${existingContext}
|
||||
Output Language: ${input.language}.`,
|
||||
|
||||
temperature: 0.9,
|
||||
|
||||
schema: '{ "questions": ["Question 1", "Question 2", "Question 3", "Question 4"] }',
|
||||
};
|
||||
}
|
||||
54
src/modules/skriptai/prompts/index.ts
Normal file
54
src/modules/skriptai/prompts/index.ts
Normal file
@@ -0,0 +1,54 @@
|
||||
/**
|
||||
* SkriptAI Prompt Index
|
||||
*
|
||||
* Centralized exports for all AI prompt builders.
|
||||
* Each prompt is a pure function that takes typed input and returns
|
||||
* { prompt, temperature, schema } — ready to pass to GeminiService methods.
|
||||
*/
|
||||
|
||||
// Discovery & Research
|
||||
export {
|
||||
buildDiscoveryQuestionsPrompt,
|
||||
type DiscoveryQuestionsInput,
|
||||
} from './discovery-questions.prompt';
|
||||
|
||||
export {
|
||||
buildSearchQueryPrompt,
|
||||
buildSourceSearchPrompt,
|
||||
type SearchQueryInput,
|
||||
type SourceSearchInput,
|
||||
} from './deep-research.prompt';
|
||||
|
||||
// Characters & Logline
|
||||
export {
|
||||
buildCharacterGenerationPrompt,
|
||||
type CharacterGenerationInput,
|
||||
} from './character-generation.prompt';
|
||||
|
||||
export { buildLoglinePrompt, type LoglineInput } from './logline.prompt';
|
||||
|
||||
// Script Generation
|
||||
export {
|
||||
buildScriptOutlinePrompt,
|
||||
buildChapterSegmentPrompt,
|
||||
buildSegmentRewritePrompt,
|
||||
buildSegmentImagePrompt,
|
||||
calculateTargetWordCount,
|
||||
calculateEstimatedChapters,
|
||||
type ScriptOutlineInput,
|
||||
type ChapterSegmentInput,
|
||||
type SegmentRewriteInput,
|
||||
type SegmentImagePromptInput,
|
||||
} from './script-generation.prompt';
|
||||
|
||||
// Analysis
|
||||
export {
|
||||
buildNeuroAnalysisPrompt,
|
||||
buildYoutubeAuditPrompt,
|
||||
buildCommercialBriefPrompt,
|
||||
buildVisualAssetKeywordsPrompt,
|
||||
type NeuroAnalysisInput,
|
||||
type YoutubeAuditInput,
|
||||
type CommercialBriefInput,
|
||||
type VisualAssetKeywordsInput,
|
||||
} from './analysis.prompt';
|
||||
30
src/modules/skriptai/prompts/logline.prompt.ts
Normal file
30
src/modules/skriptai/prompts/logline.prompt.ts
Normal file
@@ -0,0 +1,30 @@
|
||||
/**
|
||||
* Logline & High Concept Prompt Builder
|
||||
*
|
||||
* Uses Hollywood Producer persona with Dallas Jones formula
|
||||
* to create compelling loglines and high concept premises.
|
||||
*
|
||||
* Used in: ResearchService.generateLogline()
|
||||
*/
|
||||
|
||||
export interface LoglineInput {
|
||||
topic: string;
|
||||
sourceContext: string;
|
||||
language: string;
|
||||
}
|
||||
|
||||
export function buildLoglinePrompt(input: LoglineInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Act as a Hollywood Producer. Topic: ${input.topic}. Material: ${input.sourceContext}.
|
||||
Create a "High Concept" premise and a "Logline" (Max 25 words, Dallas Jones formula).
|
||||
Language: ${input.language}.`,
|
||||
|
||||
temperature: 0.9,
|
||||
|
||||
schema: '{ "logline": "...", "highConcept": "..." }',
|
||||
};
|
||||
}
|
||||
191
src/modules/skriptai/prompts/script-generation.prompt.ts
Normal file
191
src/modules/skriptai/prompts/script-generation.prompt.ts
Normal file
@@ -0,0 +1,191 @@
|
||||
/**
|
||||
* Script Generation Prompt Builders
|
||||
*
|
||||
* Two-phase script generation:
|
||||
* - Phase 1: Content outline (chapters, SEO, thumbnails)
|
||||
* - Phase 2: Per-chapter segment generation
|
||||
* - Segment rewrite with style change
|
||||
* - Segment image prompt generation
|
||||
*
|
||||
* Used in: ScriptsService.generateScript(), rewriteSegment(), generateSegmentImage()
|
||||
*/
|
||||
|
||||
// ============================================
|
||||
// Phase 1: Outline
|
||||
// ============================================
|
||||
|
||||
export interface ScriptOutlineInput {
|
||||
topic: string;
|
||||
logline: string;
|
||||
characterContext: string;
|
||||
speechStyles: string[];
|
||||
targetAudience: string[];
|
||||
contentType: string;
|
||||
targetDuration: string;
|
||||
targetWordCount: number;
|
||||
estimatedChapters: number;
|
||||
sourceContext: string;
|
||||
briefContext: string;
|
||||
}
|
||||
|
||||
export function buildScriptOutlinePrompt(input: ScriptOutlineInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Create a CONTENT OUTLINE.
|
||||
Topic: "${input.topic}"
|
||||
Logline: "${input.logline}"
|
||||
Characters: ${input.characterContext}
|
||||
Styles: ${input.speechStyles.join(', ')}. Audience: ${input.targetAudience.join(', ')}.
|
||||
Format: ${input.contentType}. Target Duration: ${input.targetDuration}. Target Total Word Count: ${input.targetWordCount}.
|
||||
Generate exactly ${input.estimatedChapters} chapters.
|
||||
Material: ${input.sourceContext.substring(0, 15000)}
|
||||
Brief: ${input.briefContext}`,
|
||||
|
||||
temperature: 0.7,
|
||||
|
||||
schema: `{
|
||||
"title": "Title",
|
||||
"seoDescription": "Desc",
|
||||
"tags": ["tag1"],
|
||||
"thumbnailIdeas": ["Idea 1"],
|
||||
"chapters": [{ "title": "Chap 1", "focus": "Summary", "type": "Intro" }]
|
||||
}`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Phase 2: Chapter → Segments
|
||||
// ============================================
|
||||
|
||||
export interface ChapterSegmentInput {
|
||||
chapterIndex: number;
|
||||
totalChapters: number;
|
||||
chapterTitle: string;
|
||||
chapterFocus: string;
|
||||
chapterType: string;
|
||||
speechStyles: string[];
|
||||
targetAudience: string[];
|
||||
characterContext: string;
|
||||
language: string;
|
||||
}
|
||||
|
||||
export function buildChapterSegmentPrompt(input: ChapterSegmentInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Write Script Segment ${input.chapterIndex + 1}/${input.totalChapters}.
|
||||
Chapter: "${input.chapterTitle}". Focus: ${input.chapterFocus}.
|
||||
Style: ${input.speechStyles.join(', ')}.
|
||||
Audience: ${input.targetAudience.join(', ')}.
|
||||
Characters: ${input.characterContext}.
|
||||
Target Length: ~200 words.
|
||||
Language: ${input.language}.`,
|
||||
|
||||
temperature: 0.8,
|
||||
|
||||
schema: `[{
|
||||
"segmentType": "${input.chapterType || 'Body'}",
|
||||
"narratorScript": "Full text...",
|
||||
"visualDescription": "Detailed visual explanation...",
|
||||
"videoPrompt": "Cinematic shot of [subject], 4k...",
|
||||
"imagePrompt": "Hyper-realistic photo of [subject]...",
|
||||
"onScreenText": "Overlay text...",
|
||||
"stockQuery": "Pexels keyword",
|
||||
"audioCues": "SFX..."
|
||||
}]`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Segment Rewrite
|
||||
// ============================================
|
||||
|
||||
export interface SegmentRewriteInput {
|
||||
currentScript: string;
|
||||
newStyle: string;
|
||||
topic: string;
|
||||
language: string;
|
||||
}
|
||||
|
||||
export function buildSegmentRewritePrompt(input: SegmentRewriteInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Rewrite this script segment.
|
||||
Current Text: "${input.currentScript}"
|
||||
Goal: Change style to "${input.newStyle}".
|
||||
Context: Topic is "${input.topic}". Language: ${input.language}.
|
||||
Principles: Show Don't Tell, Subtext.`,
|
||||
|
||||
temperature: 0.85,
|
||||
|
||||
schema: `{
|
||||
"narratorScript": "New text...",
|
||||
"visualDescription": "Updated visual...",
|
||||
"onScreenText": "Updated overlay...",
|
||||
"audioCues": "Updated audio..."
|
||||
}`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Segment Image Prompt
|
||||
// ============================================
|
||||
|
||||
export interface SegmentImagePromptInput {
|
||||
topic: string;
|
||||
narratorScript: string;
|
||||
visualDescription: string;
|
||||
}
|
||||
|
||||
export function buildSegmentImagePrompt(input: SegmentImagePromptInput): {
|
||||
prompt: string;
|
||||
temperature: number;
|
||||
schema: string;
|
||||
} {
|
||||
return {
|
||||
prompt: `Create a detailed AI Image Generation Prompt and a Video Generation Prompt for this script segment.
|
||||
Topic: "${input.topic}"
|
||||
Segment Content: "${input.narratorScript}"
|
||||
Visual Context: "${input.visualDescription}"
|
||||
|
||||
Goal: Create a highly detailed, cinematic, and artistic prompt optimized for tools like Midjourney, Flux, or Runway.
|
||||
Style: Cinematic, highly detailed, 8k, professional lighting.`,
|
||||
|
||||
temperature: 0.7,
|
||||
|
||||
schema: `{
|
||||
"imagePrompt": "Full detailed image prompt...",
|
||||
"videoPrompt": "Full detailed video prompt..."
|
||||
}`,
|
||||
};
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// Helpers
|
||||
// ============================================
|
||||
|
||||
/**
|
||||
* Calculate target word count based on duration string
|
||||
*/
|
||||
export function calculateTargetWordCount(targetDuration: string): number {
|
||||
if (targetDuration.includes('Short')) return 140;
|
||||
if (targetDuration.includes('Standard')) return 840;
|
||||
if (targetDuration.includes('Long')) return 1680;
|
||||
if (targetDuration.includes('Deep Dive')) return 2800;
|
||||
return 840;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate estimated chapters based on word count
|
||||
*/
|
||||
export function calculateEstimatedChapters(targetWordCount: number): number {
|
||||
return Math.ceil(targetWordCount / 200);
|
||||
}
|
||||
@@ -2,6 +2,12 @@ import { Injectable, Logger, NotFoundException } from '@nestjs/common';
|
||||
import { PrismaService } from '../../../database/prisma.service';
|
||||
import { GeminiService } from '../../gemini/gemini.service';
|
||||
import { NeuroAnalysisResult, YoutubeAudit } from '../types/skriptai.types';
|
||||
import {
|
||||
buildNeuroAnalysisPrompt,
|
||||
buildYoutubeAuditPrompt,
|
||||
buildCommercialBriefPrompt,
|
||||
buildVisualAssetKeywordsPrompt,
|
||||
} from '../prompts';
|
||||
|
||||
/**
|
||||
* AnalysisService
|
||||
@@ -43,56 +49,12 @@ export class AnalysisService {
|
||||
.map((s) => s.narratorScript)
|
||||
.join('\n\n');
|
||||
|
||||
const prompt = `Analyze this script using Consumer Neuroscience and Cialdini's 6 Principles of Persuasion.
|
||||
|
||||
Script:
|
||||
${fullScript.substring(0, 10000)}
|
||||
|
||||
Provide:
|
||||
1. Engagement Score (0-100): How well does it capture attention?
|
||||
2. Dopamine Score (0-100): Does it create anticipation & reward loops?
|
||||
3. Clarity Score (0-100): Is the message clear and memorable?
|
||||
|
||||
4. Cialdini's Persuasion Metrics (0-100 each):
|
||||
- Reciprocity: Does it give value first?
|
||||
- Scarcity: Does it create urgency?
|
||||
- Authority: Does it establish credibility?
|
||||
- Consistency: Does it align with viewer beliefs?
|
||||
- Liking: Is the tone likeable/relatable?
|
||||
- Social Proof: Does it reference others' actions?
|
||||
|
||||
5. Neuro Metrics:
|
||||
- Attention Hooks: Moments that grab attention
|
||||
- Emotional Triggers: Points that evoke emotion
|
||||
- Memory Anchors: Unique/memorable elements
|
||||
- Action Drivers: CTAs or challenges
|
||||
|
||||
6. Suggestions: 3-5 specific improvements
|
||||
|
||||
Return JSON: {
|
||||
"engagementScore": number,
|
||||
"dopamineScore": number,
|
||||
"clarityScore": number,
|
||||
"persuasionMetrics": {
|
||||
"reciprocity": number,
|
||||
"scarcity": number,
|
||||
"authority": number,
|
||||
"consistency": number,
|
||||
"liking": number,
|
||||
"socialProof": number
|
||||
},
|
||||
"neuroMetrics": {
|
||||
"attentionHooks": ["..."],
|
||||
"emotionalTriggers": ["..."],
|
||||
"memoryAnchors": ["..."],
|
||||
"actionDrivers": ["..."]
|
||||
},
|
||||
"suggestions": ["..."]
|
||||
}`;
|
||||
const promptData = buildNeuroAnalysisPrompt({ fullScript });
|
||||
|
||||
const resp = await this.gemini.generateJSON<NeuroAnalysisResult>(
|
||||
prompt,
|
||||
'{ engagementScore, dopamineScore, clarityScore, persuasionMetrics, neuroMetrics, suggestions }',
|
||||
promptData.prompt,
|
||||
promptData.schema,
|
||||
{ temperature: promptData.temperature },
|
||||
);
|
||||
|
||||
// Save to project
|
||||
@@ -124,52 +86,15 @@ Return JSON: {
|
||||
.map((s) => s.narratorScript)
|
||||
.join('\n\n');
|
||||
|
||||
const prompt = `Perform a YouTube Algorithm Audit on this script for topic "${project.topic}".
|
||||
|
||||
Script:
|
||||
${fullScript.substring(0, 10000)}
|
||||
|
||||
Analyze and provide:
|
||||
1. Hook Score (0-100): First 10 seconds effectiveness
|
||||
2. Pacing Score (0-100): Does it maintain momentum?
|
||||
3. Viral Potential (0-100): Shareability factor
|
||||
|
||||
4. Retention Analysis: 3-5 potential drop-off points with time, issue, suggestion, severity (High/Medium/Low)
|
||||
|
||||
5. Thumbnail Concepts: 3 high-CTR thumbnail ideas with:
|
||||
- Concept name
|
||||
- Visual description
|
||||
- Text overlay
|
||||
- Color psychology
|
||||
- Emotion target
|
||||
- AI generation prompt
|
||||
|
||||
6. Title Options: 5 clickable titles (curiosity gap, numbers, power words)
|
||||
|
||||
7. Community Post: Engaging post to tease the video
|
||||
|
||||
8. Pinned Comment: Engagement-driving first comment
|
||||
|
||||
9. SEO Description: Optimized video description with keywords
|
||||
|
||||
10. Keywords: 10 relevant search keywords
|
||||
|
||||
Return JSON: {
|
||||
"hookScore": number,
|
||||
"pacingScore": number,
|
||||
"viralPotential": number,
|
||||
"retentionAnalysis": [{ "time": "0:30", "issue": "...", "suggestion": "...", "severity": "High" }],
|
||||
"thumbnails": [{ "conceptName": "...", "visualDescription": "...", "textOverlay": "...", "colorPsychology": "...", "emotionTarget": "...", "aiPrompt": "..." }],
|
||||
"titles": ["..."],
|
||||
"communityPost": "...",
|
||||
"pinnedComment": "...",
|
||||
"description": "...",
|
||||
"keywords": ["..."]
|
||||
}`;
|
||||
const promptData = buildYoutubeAuditPrompt({
|
||||
topic: project.topic,
|
||||
fullScript,
|
||||
});
|
||||
|
||||
const resp = await this.gemini.generateJSON<YoutubeAudit>(
|
||||
prompt,
|
||||
'{ hookScore, pacingScore, viralPotential, retentionAnalysis, thumbnails, titles, communityPost, pinnedComment, description, keywords }',
|
||||
promptData.prompt,
|
||||
promptData.schema,
|
||||
{ temperature: promptData.temperature },
|
||||
);
|
||||
|
||||
// Save to project
|
||||
@@ -201,37 +126,12 @@ Return JSON: {
|
||||
.map((s) => s.narratorScript)
|
||||
.join('\n\n');
|
||||
|
||||
const prompt = `Analyze this content for commercial viability and sponsorship opportunities.
|
||||
|
||||
Topic: "${project.topic}"
|
||||
Audience: ${project.targetAudience.join(', ')}
|
||||
Content Type: ${project.contentType}
|
||||
|
||||
Script excerpt:
|
||||
${fullScript.substring(0, 5000)}
|
||||
|
||||
Provide:
|
||||
1. Viability Score (1-10 scale as string): "8/10"
|
||||
2. Viability Reason: Why this content is commercially viable
|
||||
|
||||
3. Sponsor Suggestions (3-5 potential sponsors):
|
||||
- Company name
|
||||
- Industry
|
||||
- Match reason (why this sponsor fits)
|
||||
- Email draft (outreach template)
|
||||
|
||||
Return JSON: {
|
||||
"viabilityScore": "8/10",
|
||||
"viabilityReason": "...",
|
||||
"sponsors": [
|
||||
{
|
||||
"name": "Company Name",
|
||||
"industry": "Tech/Finance/etc",
|
||||
"matchReason": "...",
|
||||
"emailDraft": "..."
|
||||
}
|
||||
]
|
||||
}`;
|
||||
const promptData = buildCommercialBriefPrompt({
|
||||
topic: project.topic,
|
||||
targetAudience: project.targetAudience,
|
||||
contentType: project.contentType,
|
||||
fullScript,
|
||||
});
|
||||
|
||||
const resp = await this.gemini.generateJSON<{
|
||||
viabilityScore: string;
|
||||
@@ -242,7 +142,9 @@ Return JSON: {
|
||||
matchReason: string;
|
||||
emailDraft: string;
|
||||
}[];
|
||||
}>(prompt, '{ viabilityScore, viabilityReason, sponsors }');
|
||||
}>(promptData.prompt, promptData.schema, {
|
||||
temperature: promptData.temperature,
|
||||
});
|
||||
|
||||
// Save to project
|
||||
await this.prisma.scriptProject.update({
|
||||
@@ -253,12 +155,6 @@ Return JSON: {
|
||||
return resp.data;
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate thumbnails using external image service
|
||||
*
|
||||
* @param prompt - Image generation prompt
|
||||
* @returns Generated image URL
|
||||
*/
|
||||
/**
|
||||
* Generate thumbnails using external image service
|
||||
* Applies "Nano Banana" prompt enrichment for high-quality results.
|
||||
@@ -302,13 +198,15 @@ Return JSON: {
|
||||
throw new NotFoundException(`Project with ID ${projectId} not found`);
|
||||
}
|
||||
|
||||
const prompt = `Generate ${count} specific, simple visual keywords for an image generator about "${project.topic}".
|
||||
Format: "subject action context style". Keep it English, concise, no special chars.
|
||||
Return JSON array of strings.`;
|
||||
const promptData = buildVisualAssetKeywordsPrompt({
|
||||
topic: project.topic,
|
||||
count,
|
||||
});
|
||||
|
||||
const resp = await this.gemini.generateJSON<string[]>(
|
||||
prompt,
|
||||
'["keyword1", "keyword2", ...]',
|
||||
promptData.prompt,
|
||||
promptData.schema,
|
||||
{ temperature: promptData.temperature },
|
||||
);
|
||||
|
||||
// Generate image URLs and save to database
|
||||
|
||||
@@ -7,6 +7,13 @@ import {
|
||||
CreateCharacterDto,
|
||||
} from '../dto';
|
||||
import { CharacterRole } from '../types/skriptai.types';
|
||||
import {
|
||||
buildDiscoveryQuestionsPrompt,
|
||||
buildSearchQueryPrompt,
|
||||
buildSourceSearchPrompt,
|
||||
buildCharacterGenerationPrompt,
|
||||
buildLoglinePrompt,
|
||||
} from '../prompts';
|
||||
|
||||
/**
|
||||
* ResearchService
|
||||
@@ -85,12 +92,8 @@ export class ResearchService {
|
||||
: project.topic;
|
||||
|
||||
// Generate search queries
|
||||
const queryPrompt = `Generate 5 specific Google Search queries for "${topic}".
|
||||
Context: ${briefContext}. Language: ${project.language}.
|
||||
Return strictly a JSON array of strings.`;
|
||||
|
||||
let searchQueries: string[] = [];
|
||||
// Check if Gemini is available for queries
|
||||
|
||||
if (!this.gemini.isAvailable()) {
|
||||
this.logger.warn('Gemini is disabled. Using mock search queries.');
|
||||
searchQueries = [
|
||||
@@ -100,10 +103,19 @@ export class ResearchService {
|
||||
];
|
||||
} else {
|
||||
try {
|
||||
const queryPromptData = buildSearchQueryPrompt({
|
||||
topic,
|
||||
briefContext,
|
||||
language: project.language,
|
||||
});
|
||||
|
||||
const queryResp = await this.gemini.generateJSON<string[]>(
|
||||
queryPrompt,
|
||||
'["query1", "query2", ...]',
|
||||
{ tools: [{ googleSearch: {} }] },
|
||||
queryPromptData.prompt,
|
||||
queryPromptData.schema,
|
||||
{
|
||||
temperature: queryPromptData.temperature,
|
||||
tools: [{ googleSearch: {} }],
|
||||
},
|
||||
);
|
||||
searchQueries = queryResp.data;
|
||||
} catch {
|
||||
@@ -130,12 +142,15 @@ export class ResearchService {
|
||||
continue;
|
||||
}
|
||||
|
||||
const sourcePrompt = `Find 3 high-quality web sources for: ${query}. Language: ${project.language}.
|
||||
Return JSON array: [{ "title": string, "url": string, "snippet": string, "type": "article" }]`;
|
||||
const sourcePromptData = buildSourceSearchPrompt({
|
||||
query,
|
||||
language: project.language,
|
||||
});
|
||||
|
||||
const sourceResp = await this.gemini.generateJSON<
|
||||
{ title: string; url: string; snippet: string; type: string }[]
|
||||
>(sourcePrompt, '[{ title, url, snippet, type }]', {
|
||||
>(sourcePromptData.prompt, sourcePromptData.schema, {
|
||||
temperature: sourcePromptData.temperature,
|
||||
tools: [{ googleSearch: {} }],
|
||||
});
|
||||
|
||||
@@ -221,11 +236,6 @@ export class ResearchService {
|
||||
language: string,
|
||||
existingQuestions: string[] = [],
|
||||
) {
|
||||
const existingContext =
|
||||
existingQuestions.length > 0
|
||||
? `Avoid these questions: ${existingQuestions.join(', ')}`
|
||||
: '';
|
||||
|
||||
// Check if Gemini is available
|
||||
if (!this.gemini.isAvailable()) {
|
||||
this.logger.warn(
|
||||
@@ -239,30 +249,16 @@ export class ResearchService {
|
||||
];
|
||||
}
|
||||
|
||||
const prompt = `You are an expert Screenwriter and Creative Director. Topic: "${topic}".
|
||||
|
||||
PHASE 1: DEEP DIVE
|
||||
Think like a filmmaker. We are not just making a video; we are telling a story.
|
||||
Analyze the topic "${topic}" to find the drama, the conflict, and the human element.
|
||||
|
||||
PHASE 2: INTERROGATION
|
||||
Ask 3-4 provocative, "Screenwriter's Room" style questions to help shape the narrative arc.
|
||||
|
||||
DO NOT ASK: "What is the goal?" or "Who is the audience?".
|
||||
|
||||
INSTEAD ASK (Examples):
|
||||
- "What is the 'Inciting Incident' that makes this topic urgent right now?"
|
||||
- "If this topic was a character, what would be its fatal flaw?"
|
||||
- "What is the 'Villain' (opposing force or misconception) we are fighting against?"
|
||||
- "What is the emotional climax you want the viewer to feel at the end?"
|
||||
|
||||
${existingContext}
|
||||
Output Language: ${language}.
|
||||
Return JSON object: { "questions": ["Question 1", "Question 2", "Question 3", "Question 4"] }`;
|
||||
const promptData = buildDiscoveryQuestionsPrompt({
|
||||
topic,
|
||||
language,
|
||||
existingQuestions,
|
||||
});
|
||||
|
||||
const resp = await this.gemini.generateJSON<{ questions: string[] }>(
|
||||
prompt,
|
||||
'{ questions: string[] }',
|
||||
promptData.prompt,
|
||||
promptData.schema,
|
||||
{ temperature: promptData.temperature },
|
||||
);
|
||||
|
||||
return resp.data.questions;
|
||||
@@ -312,15 +308,11 @@ export class ResearchService {
|
||||
throw new NotFoundException(`Project with ID ${projectId} not found`);
|
||||
}
|
||||
|
||||
const prompt = `Create Character Profiles for a ${project.contentType} about "${project.topic}".
|
||||
Use Alan C. Hueth's "Triunity of Character" model:
|
||||
1. Values (Inner belief)
|
||||
2. Traits (Personality)
|
||||
3. Mannerisms (External behavior)
|
||||
|
||||
If format is non-fiction (Youtube Doc), create a 'Host/Narrator' persona and potentially an 'Antagonist' (e.g., The Problem, Time, A Rival).
|
||||
Language: ${project.language}.
|
||||
Return JSON Array: [{ "name": "Name", "role": "Protagonist", "values": "...", "traits": "...", "mannerisms": "..." }]`;
|
||||
const promptData = buildCharacterGenerationPrompt({
|
||||
contentType: project.contentType,
|
||||
topic: project.topic,
|
||||
language: project.language,
|
||||
});
|
||||
|
||||
const resp = await this.gemini.generateJSON<
|
||||
{
|
||||
@@ -330,7 +322,9 @@ export class ResearchService {
|
||||
traits: string;
|
||||
mannerisms: string;
|
||||
}[]
|
||||
>(prompt, '[{ name, role, values, traits, mannerisms }]');
|
||||
>(promptData.prompt, promptData.schema, {
|
||||
temperature: promptData.temperature,
|
||||
});
|
||||
|
||||
// Save characters to database
|
||||
const characters = await Promise.all(
|
||||
@@ -371,15 +365,18 @@ export class ResearchService {
|
||||
|
||||
const sourceContext = project.sources.map((s) => s.snippet).join('\n');
|
||||
|
||||
const prompt = `Act as a Hollywood Producer. Topic: ${project.topic}. Material: ${sourceContext}.
|
||||
Create a "High Concept" premise and a "Logline" (Max 25 words, Dallas Jones formula).
|
||||
Language: ${project.language}.
|
||||
Return JSON: { "logline": "...", "highConcept": "..." }`;
|
||||
const promptData = buildLoglinePrompt({
|
||||
topic: project.topic,
|
||||
sourceContext,
|
||||
language: project.language,
|
||||
});
|
||||
|
||||
const resp = await this.gemini.generateJSON<{
|
||||
logline: string;
|
||||
highConcept: string;
|
||||
}>(prompt, '{ logline, highConcept }');
|
||||
}>(promptData.prompt, promptData.schema, {
|
||||
temperature: promptData.temperature,
|
||||
});
|
||||
|
||||
// Update project
|
||||
await this.prisma.scriptProject.update({
|
||||
|
||||
@@ -2,10 +2,15 @@ import { Injectable, Logger, NotFoundException } from '@nestjs/common';
|
||||
import { PrismaService } from '../../../database/prisma.service';
|
||||
import { GeminiService } from '../../gemini/gemini.service';
|
||||
import { CreateSegmentDto, UpdateSegmentDto } from '../dto';
|
||||
|
||||
import { AnalysisService } from './analysis.service';
|
||||
|
||||
// AI_CONFIG is only used for model selection reference
|
||||
import {
|
||||
buildScriptOutlinePrompt,
|
||||
buildChapterSegmentPrompt,
|
||||
buildSegmentRewritePrompt,
|
||||
buildSegmentImagePrompt,
|
||||
calculateTargetWordCount,
|
||||
calculateEstimatedChapters,
|
||||
} from '../prompts';
|
||||
|
||||
/**
|
||||
* ScriptsService
|
||||
@@ -138,34 +143,24 @@ export class ScriptsService {
|
||||
)
|
||||
.join('\n');
|
||||
|
||||
// Calculate target word count based on duration
|
||||
let targetWordCount = 840;
|
||||
if (project.targetDuration.includes('Short')) targetWordCount = 140;
|
||||
else if (project.targetDuration.includes('Standard')) targetWordCount = 840;
|
||||
else if (project.targetDuration.includes('Long')) targetWordCount = 1680;
|
||||
else if (project.targetDuration.includes('Deep Dive'))
|
||||
targetWordCount = 2800;
|
||||
// Calculate target metrics
|
||||
const targetWordCount = calculateTargetWordCount(project.targetDuration);
|
||||
const estimatedChapters = calculateEstimatedChapters(targetWordCount);
|
||||
|
||||
const estimatedChapters = Math.ceil(targetWordCount / 200);
|
||||
|
||||
// PHASE 1: Generate Outline
|
||||
const outlinePrompt = `
|
||||
Create a CONTENT OUTLINE.
|
||||
Topic: "${project.topic}"
|
||||
Logline: "${project.logline || ''}"
|
||||
Characters: ${characterContext}
|
||||
Styles: ${project.speechStyle.join(', ')}. Audience: ${project.targetAudience.join(', ')}.
|
||||
Format: ${project.contentType}. Target Duration: ${project.targetDuration}. Target Total Word Count: ${targetWordCount}.
|
||||
Generate exactly ${estimatedChapters} chapters.
|
||||
Material: ${sourceContext.substring(0, 15000)}
|
||||
Brief: ${briefContext}
|
||||
|
||||
Return JSON: {
|
||||
"title": "Title", "seoDescription": "Desc", "tags": ["tag1"],
|
||||
"thumbnailIdeas": ["Idea 1"],
|
||||
"chapters": [{ "title": "Chap 1", "focus": "Summary", "type": "Intro" }]
|
||||
}
|
||||
`;
|
||||
// PHASE 1: Generate Outline using prompt builder
|
||||
const outlinePromptData = buildScriptOutlinePrompt({
|
||||
topic: project.topic,
|
||||
logline: project.logline || '',
|
||||
characterContext,
|
||||
speechStyles: project.speechStyle,
|
||||
targetAudience: project.targetAudience,
|
||||
contentType: project.contentType,
|
||||
targetDuration: project.targetDuration,
|
||||
targetWordCount,
|
||||
estimatedChapters,
|
||||
sourceContext,
|
||||
briefContext,
|
||||
});
|
||||
|
||||
const outlineResp = await this.gemini.generateJSON<{
|
||||
title: string;
|
||||
@@ -173,10 +168,9 @@ export class ScriptsService {
|
||||
tags: string[];
|
||||
thumbnailIdeas: string[];
|
||||
chapters: { title: string; focus: string; type: string }[];
|
||||
}>(
|
||||
outlinePrompt,
|
||||
'{ title, seoDescription, tags, thumbnailIdeas, chapters }',
|
||||
);
|
||||
}>(outlinePromptData.prompt, outlinePromptData.schema, {
|
||||
temperature: outlinePromptData.temperature,
|
||||
});
|
||||
|
||||
const outlineData = outlineResp.data;
|
||||
|
||||
@@ -191,38 +185,30 @@ export class ScriptsService {
|
||||
},
|
||||
});
|
||||
|
||||
// PHASE 2: Generate each chapter
|
||||
// PHASE 2: Generate each chapter using prompt builder
|
||||
const generatedSegments: any[] = [];
|
||||
let timeOffset = 0;
|
||||
|
||||
for (let i = 0; i < outlineData.chapters.length; i++) {
|
||||
const chapter = outlineData.chapters[i];
|
||||
|
||||
const chapterPrompt = `
|
||||
Write Script Segment ${i + 1}/${outlineData.chapters.length}.
|
||||
Chapter: "${chapter.title}". Focus: ${chapter.focus}.
|
||||
Style: ${project.speechStyle.join(', ')}.
|
||||
Audience: ${project.targetAudience.join(', ')}.
|
||||
Characters: ${characterContext}.
|
||||
Target Length: ~200 words.
|
||||
Language: ${project.language}.
|
||||
|
||||
Return JSON Array: [{
|
||||
"segmentType": "${chapter.type || 'Body'}",
|
||||
"narratorScript": "Full text...",
|
||||
"visualDescription": "Detailed visual explanation...",
|
||||
"videoPrompt": "Cinematic shot of [subject], 4k...",
|
||||
"imagePrompt": "Hyper-realistic photo of [subject]...",
|
||||
"onScreenText": "Overlay text...",
|
||||
"stockQuery": "Pexels keyword",
|
||||
"audioCues": "SFX..."
|
||||
}]
|
||||
`;
|
||||
const chapterPromptData = buildChapterSegmentPrompt({
|
||||
chapterIndex: i,
|
||||
totalChapters: outlineData.chapters.length,
|
||||
chapterTitle: chapter.title,
|
||||
chapterFocus: chapter.focus,
|
||||
chapterType: chapter.type,
|
||||
speechStyles: project.speechStyle,
|
||||
targetAudience: project.targetAudience,
|
||||
characterContext,
|
||||
language: project.language,
|
||||
});
|
||||
|
||||
try {
|
||||
const segmentResp = await this.gemini.generateJSON<any[]>(
|
||||
chapterPrompt,
|
||||
'[{ segmentType, narratorScript, visualDescription, videoPrompt, imagePrompt, onScreenText, stockQuery, audioCues }]',
|
||||
chapterPromptData.prompt,
|
||||
chapterPromptData.schema,
|
||||
{ temperature: chapterPromptData.temperature },
|
||||
);
|
||||
|
||||
for (const seg of segmentResp.data) {
|
||||
@@ -293,30 +279,21 @@ export class ScriptsService {
|
||||
throw new NotFoundException(`Segment with ID ${segmentId} not found`);
|
||||
}
|
||||
|
||||
const prompt = `
|
||||
Rewrite this script segment.
|
||||
Current Text: "${segment.narratorScript}"
|
||||
Goal: Change style to "${newStyle}".
|
||||
Context: Topic is "${segment.project.topic}". Language: ${segment.project.language}.
|
||||
Principles: Show Don't Tell, Subtext.
|
||||
|
||||
Return JSON: {
|
||||
"narratorScript": "New text...",
|
||||
"visualDescription": "Updated visual...",
|
||||
"onScreenText": "Updated overlay...",
|
||||
"audioCues": "Updated audio..."
|
||||
}
|
||||
`;
|
||||
const promptData = buildSegmentRewritePrompt({
|
||||
currentScript: segment.narratorScript || '',
|
||||
newStyle,
|
||||
topic: segment.project.topic,
|
||||
language: segment.project.language,
|
||||
});
|
||||
|
||||
const rewriteResp = await this.gemini.generateJSON<{
|
||||
narratorScript: string;
|
||||
visualDescription: string;
|
||||
onScreenText: string;
|
||||
audioCues: string;
|
||||
}>(
|
||||
prompt,
|
||||
'{ narratorScript, visualDescription, onScreenText, audioCues }',
|
||||
);
|
||||
}>(promptData.prompt, promptData.schema, {
|
||||
temperature: promptData.temperature,
|
||||
});
|
||||
|
||||
const data = rewriteResp.data;
|
||||
const words = data.narratorScript
|
||||
@@ -359,25 +336,18 @@ export class ScriptsService {
|
||||
}
|
||||
|
||||
// 1. Generate/Refine Image Prompt using LLM
|
||||
const promptGenPrompt = `
|
||||
Create a detailed AI Image Generation Prompt and a Video Generation Prompt for this script segment.
|
||||
Topic: "${segment.project.topic}"
|
||||
Segment Content: "${segment.narratorScript}"
|
||||
Visual Context: "${segment.visualDescription}"
|
||||
|
||||
Goal: Create a highly detailed, cinematic, and artistic prompt optimized for tools like Midjourney, Flux, or Runway.
|
||||
Style: Cinematic, highly detailed, 8k, professional lighting.
|
||||
|
||||
Return JSON: {
|
||||
"imagePrompt": "Full detailed image prompt...",
|
||||
"videoPrompt": "Full detailed video prompt..."
|
||||
}
|
||||
`;
|
||||
const promptData = buildSegmentImagePrompt({
|
||||
topic: segment.project.topic,
|
||||
narratorScript: segment.narratorScript || '',
|
||||
visualDescription: segment.visualDescription || '',
|
||||
});
|
||||
|
||||
const prompts = await this.gemini.generateJSON<{
|
||||
imagePrompt: string;
|
||||
videoPrompt: string;
|
||||
}>(promptGenPrompt, '{ imagePrompt, videoPrompt }');
|
||||
}>(promptData.prompt, promptData.schema, {
|
||||
temperature: promptData.temperature,
|
||||
});
|
||||
|
||||
// 2. Use the new image prompt for generation
|
||||
const imageUrl = await this.analysisService.generateThumbnailImage(
|
||||
|
||||
Reference in New Issue
Block a user