Ever wondered how to combine the mystical world of oracle cards with cutting-edge AI? Today, I'm sharing how I built an intelligent Oracle Card reading system using OpenAI's function calling capabilities. This isn't just another chatbot - it's a full-fledged spiritual guidance agent that knows when to draw cards, interpret meanings, and even create personalized oracle cards with AI-generated artwork.
🔮 See It In Action
Check out the live implementation at Tarotap - Oracle Cards to experience AI-powered spiritual guidance firsthand.
🎯 What Makes This Special?
Unlike traditional random card generators, this system uses OpenAI's function calling to create an autonomous agent that:
- Intelligently decides when to draw oracle cards based on context
- Provides personalized interpretations considering the full conversation
- Creates custom oracle cards with unique AI-generated imagery
- Maintains conversation flow naturally between card readings
🏗️ The Architecture
Let's dive into the code. The core magic happens through OpenAI's function calling API, which allows the LLM to autonomously choose when and how to interact with our oracle card system.
Setting Up OpenAI with Function Calling
import OpenAI from 'openai';
import { ChatCompletionMessageParam, ChatCompletionTool } from 'openai/resources/chat';
const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
// Define our oracle card tools
const oracleTools: ChatCompletionTool[] = [
{
type: 'function',
function: {
name: 'drawOracleCard',
description: 'Draw a random oracle card when the user asks for guidance or a reading',
parameters: {
type: 'object',
properties: {
question: {
type: 'string',
description: 'The user\'s question or area of focus for the reading'
}
},
required: ['question']
}
}
},
{
type: 'function',
function: {
name: 'createPersonalOracleCard',
description: 'Create a personalized oracle card with AI-generated imagery',
parameters: {
type: 'object',
properties: {
cardName: {
type: 'string',
description: 'The name of the oracle card'
},
cardMeaning: {
type: 'string',
description: 'The spiritual meaning and guidance of the card'
},
artDescription: {
type: 'string',
description: 'Description for the AI image generation'
}
},
required: ['cardName', 'cardMeaning', 'artDescription']
}
}
}
];
The Core Agent Loop
Here's where the magic happens - our agent decides autonomously when to use which tool:
async function processOracleReading(messages: ChatCompletionMessageParam[]) {
try {
const response = await openai.chat.completions.create({
model: 'gpt-4-turbo-preview',
messages: messages,
tools: oracleTools,
tool_choice: 'auto', // Let the model decide when to use tools
temperature: 0.7,
max_tokens: 1000,
});
const message = response.choices[0]?.message;
if (!message) {
throw new Error('No response from OpenAI');
}
// Handle tool calls
if (message.tool_calls) {
const toolResults = await Promise.all(
message.tool_calls.map(async (toolCall) => {
if (toolCall.function.name === 'drawOracleCard') {
const args = JSON.parse(toolCall.function.arguments);
const card = await drawRandomOracleCard(args.question);
return {
tool_call_id: toolCall.id,
role: 'tool' as const,
content: JSON.stringify(card)
};
}
if (toolCall.function.name === 'createPersonalOracleCard') {
const args = JSON.parse(toolCall.function.arguments);
const imageUrl = await generateOracleCardImage(args);
return {
tool_call_id: toolCall.id,
role: 'tool' as const,
content: JSON.stringify({
success: true,
imageUrl,
cardName: args.cardName
})
};
}
})
);
// Continue the conversation with tool results
messages.push(message);
messages.push(...toolResults);
// Get final response
return await processOracleReading(messages);
}
return message;
} catch (error) {
console.error('Oracle reading error:', error);
throw error;
}
}
Implementing the Oracle Card Drawing Logic
interface OracleCard {
id: string;
name: string;
message: string;
keywords: string[];
}
// Sample oracle cards data
const oracleCards: OracleCard[] = [
{
id: '1',
name: 'New Beginnings',
message: 'A fresh start awaits you. Trust in the journey ahead and embrace the unknown with open arms.',
keywords: ['fresh start', 'opportunity', 'courage']
},
{
id: '2',
name: 'Inner Wisdom',
message: 'Look within for the answers you seek. Your intuition is your greatest guide.',
keywords: ['intuition', 'self-trust', 'meditation']
},
// ... more cards
];
async function drawRandomOracleCard(question: string): Promise<OracleCard> {
// You could add more sophisticated logic here
// For example, selecting cards based on keywords in the question
const randomIndex = Math.floor(Math.random() * oracleCards.length);
return oracleCards[randomIndex];
}
Creating Custom Oracle Cards with DALL-E
One of the coolest features is the ability to create personalized oracle cards:
async function generateOracleCardImage(params: {
cardName: string;
cardMeaning: string;
artDescription: string;
}): Promise<string> {
try {
const imageResponse = await openai.images.generate({
model: 'dall-e-3',
prompt: `Create a mystical oracle card illustration: ${params.artDescription}.
Style: Ethereal, spiritual, watercolor with gold accents.
Include sacred geometry and soft cosmic elements.`,
size: '1024x1024',
quality: 'standard',
n: 1,
});
const imageUrl = imageResponse.data[0]?.url;
if (!imageUrl) {
throw new Error('Failed to generate image');
}
// In production, you'd want to save this image to your storage
return imageUrl;
} catch (error) {
console.error('Image generation error:', error);
throw error;
}
}
Building the Conversation Flow
Here's how we maintain context and create meaningful oracle readings:
class OracleReadingSession {
private messages: ChatCompletionMessageParam[] = [];
constructor() {
// Initialize with system prompt
this.messages.push({
role: 'system',
content: `You are a wise and intuitive oracle card reader. You provide
compassionate, insightful guidance through oracle cards. When users
seek guidance, you naturally know when to draw cards or suggest
creating personalized ones. Always interpret cards in context of
the user's situation.`
});
}
async processUserMessage(userInput: string): Promise<string> {
// Add user message
this.messages.push({
role: 'user',
content: userInput
});
// Process with our oracle agent
const response = await processOracleReading(this.messages);
// Add assistant response to history
this.messages.push(response);
return response.content || '';
}
getConversationHistory(): ChatCompletionMessageParam[] {
return [...this.messages];
}
}
Handling Streaming Responses
For a better user experience, implement streaming:
async function* streamOracleReading(messages: ChatCompletionMessageParam[]) {
const stream = await openai.chat.completions.create({
model: 'gpt-4-turbo-preview',
messages: messages,
tools: oracleTools,
tool_choice: 'auto',
stream: true,
});
for await (const chunk of stream) {
const delta = chunk.choices[0]?.delta;
if (delta?.content) {
yield { type: 'content', data: delta.content };
}
if (delta?.tool_calls) {
yield { type: 'tool_call', data: delta.tool_calls };
}
}
}
🎨 Advanced Features
Multi-Language Support
The system dynamically loads oracle cards based on user locale:
const loadOracleCardsByLocale = async (locale: string) => {
const cardSets = {
'en': await import('./cards/en.json'),
'es': await import('./cards/es.json'),
'ja': await import('./cards/ja.json'),
// Add more languages
};
return cardSets[locale] || cardSets['en'];
};
Contextual Card Interpretations
The AI considers the full conversation context when interpreting cards:
const enhancedSystemPrompt = `
You are an experienced oracle card reader who:
- Remembers previous cards drawn in the session
- Connects card meanings to the user's specific situation
- Offers practical advice alongside spiritual guidance
- Recognizes patterns across multiple card draws
`;
🚀 Performance Optimizations
Implementing Caching
Cache frequently drawn cards to reduce latency:
class OracleCardCache {
private cache = new Map<string, { card: OracleCard; timestamp: number }>();
private readonly TTL = 3600000; // 1 hour
get(id: string): OracleCard | null {
const cached = this.cache.get(id);
if (cached && Date.now() - cached.timestamp < this.TTL) {
return cached.card;
}
return null;
}
set(id: string, card: OracleCard): void {
this.cache.set(id, { card, timestamp: Date.now() });
}
}
Rate Limiting and Error Handling
Implement proper error handling for production:
class OracleAPIClient {
private retryCount = 3;
private retryDelay = 1000;
async makeRequest<T>(
fn: () => Promise<T>,
attempt = 1
): Promise<T> {
try {
return await fn();
} catch (error) {
if (attempt < this.retryCount) {
await new Promise(resolve =>
setTimeout(resolve, this.retryDelay * attempt)
);
return this.makeRequest(fn, attempt + 1);
}
throw error;
}
}
}
🎯 Key Takeaways
Function Calling is Powerful: OpenAI's function calling transforms LLMs into autonomous agents that can intelligently decide when and how to use external tools.
Context Matters: Maintaining conversation history allows for more meaningful and personalized oracle readings.
User Experience: Streaming responses and proper error handling create a smooth, professional experience.
Extensibility: The architecture easily supports adding new card decks, languages, and features.
🔮 What's Next?
Some ideas for extending this system:
- Add voice interactions for a more immersive experience
- Implement card spread layouts (like Celtic Cross)
- Create a learning system that adapts to user preferences
- Build a community feature for sharing custom oracle cards
🌟 Try It Yourself
Experience the full Oracle Card reading system at Tarotap - Oracle Cards. Whether you're seeking guidance, exploring spirituality, or just curious about AI-powered divination, give it a try!
Have you built something interesting with OpenAI's function calling? I'd love to hear about your experiences in the comments! Drop a 🔮 if you found this helpful.
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