I let Palantir's AIP plan my meals

Summary

Recipe Rescue is an AI-powered app that helps users decide what to cook based on the ingredients in their fridge. By simply taking a photo, the app detects ingredients using a vision model and suggests recipes based on them. Users can also manually add ingredients. The backend uses a combination of large language models and vector databases to generate recipe suggestions. Built with a low-code/no-code tool, the app’s seamless process lets users quickly turn their fridge contents into meal ideas, making it a must-have for anyone looking to simplify meal planning.

Takeaways

• 😀 Recipe Rescue helps solve the problem of deciding what to cook using AI to analyze the contents of your fridge.
• 😀 The app uses computer vision to detect ingredients in photos and suggests recipes based on those ingredients.
• 😀 Users can manually add ingredients that the vision model may miss, such as chicken or other specific items.
• 😀 The app allows users to specify additional details like meal time or desired cooking duration.
• 😀 A large language model (LLM) processes the ingredients and searches a recipe database for relevant dishes.
• 😀 The app delivers recipe options along with required ingredients, cooking time, and detailed instructions.
• 😀 The backend uses Foundry’s AIP to embed recipes into vector format, optimizing recipe search through similarity matching.
• 😀 Data transformation in the app is simplified using a low-code/no-code tool called Pipeline Builder.
• 😀 Recipe data is cleaned, transformed, and embedded into the system using powerful AI tools before being stored for fast access.
• 😀 Vision models are used to process images of ingredients, with options to add custom models for better detection.
• 😀 The entire process of taking a photo, generating ingredient lists, and suggesting recipes happens in seconds, providing users with fast meal ideas.

Q & A

What is Recipe Rescue?

Recipe Rescue is an app designed to help users decide what to cook by analyzing photos of their fridge or ingredients. It uses AI models to detect ingredients and suggests recipes based on what is available.

How does Recipe Rescue detect ingredients in a photo?

The app uses a vision model that analyzes the photo of the fridge or ingredients, identifying and listing items such as vegetables, proteins, or leftovers present in the image.

What happens if the vision model misses an ingredient in the photo?

Users can manually add missing ingredients to the app, such as chicken or other items that may not have been detected by the model.

How does the app suggest recipes after detecting ingredients?

Once the ingredients are detected, an LLM (Large Language Model) processes the list and performs a similarity search over a database of recipes. It then provides the user with recipe suggestions that match the detected ingredients.

Can users customize their search criteria in the app?

Yes, users can customize the app by specifying factors like the desired cooking time for the recipe, in addition to the ingredients.

What is the role of the vision model in the app?

The vision model is responsible for detecting and listing the ingredients visible in the user's photo. It helps the app identify what ingredients are available for use in recipes.

How does the app handle different recipe formats?

The app transforms raw recipes into structured, embeddable text by renaming columns, casting data types, and cleaning up the data before embedding it for search and comparison.

What technology does the app use to embed and search recipes?

The app uses a vector database optimized for text embeddings. The recipes are embedded using machine learning models, which allows the app to perform efficient similarity searches to find the best matching recipes.

What is Pipeline Builder and how does it fit into the app?

Pipeline Builder is a low-code, no-code tool used to manage and transform data in the app. It helps users quickly move from raw data to production-ready datasets by providing a visual interface for creating transformations and embeddings.

How does the app process images to generate recipe suggestions?

The app takes an image of the ingredients, uses a vision model to detect them, and then passes the ingredient list through AI logic. This logic performs a similarity search over the recipe database and returns relevant recipes.