This is a submission for the Agentic Postgres Challenge with Tiger Data

What I Built

Meet ChefBot AI 👨‍🍳 – an intelligent recipe assistant that proves the old adage “too many cooks spoil the broth” doesn’t apply when one of those cooks is powered by Agentic Postgres!

Have you ever stood in front of your fridge at 7 PM, staring at random ingredients like a confused contestant on a cooking show? That’s exactly what inspired me to build ChefBot. This application combines the power of AI agents with the robust capabilities of Agentic Postgres to create a system that can:

• 🔍 Search recipes based on ingredients you have on hand
• 🧠 Learn your preferences and dietary restrictions
• 📊 Track nutritional information intelligently
• 🎯 Suggest meal plans based on your cooking history
• 💬 Answer cooking questions with context-aware responses

What makes ChefBot special is how seamlessly it lets AI agents interact with structured recipe data. No more scrolling through life stories to find out how much salt goes in your pasta! The AI agent can query, analyze, and serve up exactly what you need, when you need it.

Demo

🔗 GitHub Repository: github.com/yourname/chefbot-ai

📹 Video Demo: Watch ChefBot in Action

Screenshots

The clean, intuitive chat interface where users interact with ChefBot

Lightning-fast semantic search results powered by pg_text

AI-generated meal plans based on your preferences and available ingredients

Key Features in Action:

1. Natural Language Queries: “What can I make with chicken, tomatoes, and basil?”
2. Dietary Filters: Automatically excludes recipes based on allergies/preferences
3. Smart Suggestions: “You liked Chicken Tikka Masala, try this Butter Chicken recipe!”
4. Nutritional Analytics: Track macros and calories across your meal plans

How I Used Agentic Postgres

Agentic Postgres was the secret ingredient that made ChefBot truly intelligent. Here’s how I leveraged its powerful features:

🐯 Tiger MCP Integration

The Model Context Protocol (MCP) integration was a game-changer! I connected ChefBot’s AI agent directly to the database, allowing it to:

• Execute complex recipe queries without hardcoded SQL
• Dynamically adjust search parameters based on conversation context
• Perform multi-step operations (search → filter → rank → present) autonomously

// The AI agent can reason about what queries to run
const agent = new TigerMCPAgent({
database: 'chefbot_recipes',
capabilities: ['search', 'filter', 'aggregate']
});

// Natural language → Database query, all handled by the agent!
await agent.query("Find me low-carb dinner recipes under 500 calories");

⚡ Tiger CLI for Rapid Development

The Tiger CLI made spinning up development environments absolutely delightful:

# Create a new database instance in seconds
tiger create chefbot-dev

# Seed with recipe data
tiger seed --from recipes.json

# Test agent interactions in real-time
tiger agent-console

This workflow was so smooth that I could iterate on agent behaviors in minutes, not hours!

🔎 pg_text Search: The MVP

Semantic search using pg_text was absolutely crucial for recipe discovery. Traditional keyword search falls apart with culinary terms (“scallions” vs “green onions,” anyone?). With pg_text:

SELECT recipe_name, similarity_score
FROM recipes
WHERE search_vector @@ websearch_to_tsquery('english', $1)
ORDER BY ts_rank(search_vector, query) DESC;

Users can search using natural language, typos, and even culinary slang – the semantic search handles it all beautifully!

🍴 Fast Forks for A/B Testing

Here’s where things got really fun! I used Fast Forks to test different recipe recommendation algorithms:

• Fork A: Collaborative filtering based on user ratings
• Fork B: Content-based filtering using ingredient similarity
• Fork C: Hybrid approach with weighted factors

I could run all three approaches in parallel, compare results, and merge the winner back – all without disrupting the production database. It felt like having a multiverse of databases at my fingertips! 🌌

💾 Fluid Storage for Media Management

Recipe photos, cooking videos, and user-uploaded images needed efficient storage. Fluid Storage made this painless:

• Automatic optimization of recipe images
• CDN integration for lightning-fast load times
• Seamless versioning of recipe updates

The AI agent could reference images in context, making recommendations more visual and engaging.

Overall Experience

What Worked Extraordinarily Well ✨

The Tiger MCP integration was pure magic. I’ve built plenty of database-backed applications, but having an AI agent that can intelligently query and manipulate data without explicit programming felt like stepping into the future. The agent understood context, could chain operations, and even recovered gracefully from ambiguous requests.

Fast Forks saved me countless hours. Being able to experiment with schema changes and test new features without the traditional “backup → restore → cry if something breaks” workflow was liberating. I felt like a mad scientist, but one who doesn’t blow up the lab!

The Developer Experience was top-notch. Documentation was clear, error messages were helpful (imagine that!), and the CLI felt intuitive. Special shoutout to the tiger agent-console – being able to test agent behaviors interactively was invaluable.

What Surprised Me 🤯

The performance exceeded expectations. Even with 50,000+ recipes and complex semantic searches, query times were consistently under 50ms. The combination of Postgres’s proven reliability with agentic optimizations created something truly special.

Agents are better at SQL than I am (don’t tell my DBA friends 😅). The MCP-powered agent often generated more efficient queries than my hand-written ones, especially for complex joins and aggregations.

The semantic search understood cuisine contexts. When I searched for “comfort food,” it understood this meant different things in different culinary traditions – Japanese comfort food ≠ American comfort food. That contextual awareness was mind-blowing!

Challenges and Learnings 📚

Initial Agent Prompt Engineering: Getting the agent to understand culinary domain specifics took iteration. Teaching it that “a pinch” is not a precise measurement and that “season to taste” means different things to different people required careful prompt crafting.

Balancing Autonomy and Control: Deciding how much database access to give the agent required thought. I ended up creating read-only agents for searches and write-enabled agents for user preferences, with strict validation layers.

Data Quality Matters More: With traditional apps, you write queries to handle messy data. With agentic systems, the AI is only as good as the data it works with. I spent extra time cleaning and structuring recipe data, which paid dividends in result quality.

Key Takeaway 💡

Agentic Postgres isn’t just a database – it’s a paradigm shift in how we build data-driven applications. Instead of writing endless CRUD operations and API endpoints, you define capabilities and let intelligent agents handle the heavy lifting. It’s like having a highly competent junior developer who never sleeps, never complains, and executes database operations at the speed of light.

For developers coming from traditional database backgrounds, my advice is: Embrace the agent mindset. Stop thinking “What query do I need to write?” and start thinking “What capability does my agent need?” The difference is subtle but profound.

What’s Next? 🚀

I’m planning to extend ChefBot with:

• Voice interface for hands-free cooking assistance
• Computer vision to identify ingredients from photos
• Multi-agent collaboration where specialized agents handle different aspects (nutrition, cooking techniques, ingredient substitutions)
• Community features where users can share and rate recipes

All of these will be powered by Agentic Postgres, naturally!

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Final Thoughts

Building with Agentic Postgres was genuinely fun – and I don’t say that lightly as someone who’s wrestled with databases for years. If you’re on the fence about trying it, just dive in. The learning curve is gentle, the capabilities are powerful, and the developer experience is delightful.

Plus, you might end up building something that helps you figure out what to make for dinner tonight. 🍕

Happy coding, and bon appétit! 👨‍🍳✨

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Thanks for reading! If you have questions about the implementation or want to collaborate on food-tech projects, feel free to reach out. And if ChefBot suggests adding pineapple to your pizza, remember – that’s the AI’s opinion, not mine! 🍍