Search By Ingredients -- Semantic Classification

Classifies recipe ingredients as vegan and/or keto. Handles the messy real-world stuff: compound ingredients, hidden animal products (L-cysteine, gelatin, carmine), and regional synonyms. Runs locally. No cloud API required.

~0.6s per uncached ingredient. >90% cache hit rate in practice, so most ingredients resolve in <10ms.

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How It Works

Four stages in sequence:

1. Parse -- ingredient-parser-nlp extracts the core ingredient name from noisy recipe text (97.8% word-level accuracy). "3 pounds pork shoulder, trimmed and cut into 2-inch chunks" becomes "pork shoulder".

2. Retrieve -- Arctic Text2SQL generates a semantic LIKE query against two databases. Prioritizes raw forms over processed variants to avoid contaminated nutritional data. Falls back to RapidFuzz if the semantic query returns nothing.

3. Dual databases -- Nutrition facts DB (8,789 ingredients) for keto analysis. Vegan ontology (236+ terms) for detecting hidden animal products that nutrition data misses.

4. Judge -- Qwen 0.5B weighs the retrieved evidence and outputs is_vegan, is_keto, and a plain-language reason.

135x faster than the previous brute-force API baseline (82s -> 0.6s per ingredient).

---

Architecture

graph TD
    subgraph "Input"
        A["Raw Recipe Ingredient List<br/><i>e.g., '2 tbsp extra virgin olive oil'</i>"]
    end

    subgraph "1. Parsing Stage"
        B["ingredient-parser-nlp<br/>(97.8% Accuracy)"]
        A --> B
    end

    subgraph "2. Semantic Retrieval Stage"
        C["Cleaned Ingredient<br/><i>e.g., 'olive oil'</i>"]
        D["Arctic Text2SQL Engine"]
        E["<b>Nutrition Database</b><br/>(8,789 ingredients)"]
        F["<b>Vegan Ontology</b><br/>(236+ terms)"]
        G["RapidFuzz Fallback"]
        B --> C --> D
        D -- "Generates Semantic<br/>LIKE Query" --> E
        D -- "Generates Semantic<br/>LIKE Query" --> F
        C -.->|If Semantic Search Fails| G
        G --> E
        G --> F
    end

    subgraph "3. Reasoning & Caching Stage"
        H["Retrieved Evidence<br/>- Nutritional Data<br/>- Animal Product Flags"]
        I["Qwen 0.5B Judge Model"]
        J["Redis Cache"]
        K["Final Classification<br/><i>{is_keto: true, is_vegan: true}</i>"]
        E --> H
        F --> H
        H --> I
        I -- "Writes to & Reads from" --> J
        I --> K
    end

    style A fill:#FFF,stroke:#333,stroke-width:2px
    style B fill:#E3F2FD,stroke:#333,stroke-width:2px
    style D fill:#E3F2FD,stroke:#333,stroke-width:2px
    style I fill:#E3F2FD,stroke:#333,stroke-width:2px
    style E fill:#FFF3E0,stroke:#333,stroke-width:1px
    style F fill:#FFF3E0,stroke:#333,stroke-width:1px
    style J fill:#D1C4E9,stroke:#333,stroke-width:1px
    style K fill:#C8E6C9,stroke:#333,stroke-width:2px

Loading

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Usage

import asyncio
from ingredient_sense import Classifier

classifier = Classifier()

recipe_ingredients = [
    "3 pounds pork shoulder, cut into chunks",
    "2 tbsp extra virgin olive oil",
    "1 large onion, chopped",
    "4 cloves garlic, minced",
    "1 cup almond flour"
]

async def main():
    result = await classifier.classify_recipe(recipe_ingredients)
    print(f"Is Vegan: {result['is_vegan']}")
    print(f"Is Keto: {result['is_keto']}")
    print(f"Reasoning: {result['reasoning']}")

    for item in result['details']:
        print(f"{item['original']} -> {item['parsed']}: vegan={item['is_vegan']}, keto={item['is_keto']}")

asyncio.run(main())

Output:

Is Vegan: False
Is Keto: True
Reasoning: Not vegan -- contains pork shoulder. Keto-friendly -- all ingredients are low-carb.

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Performance

Metric	Result
End-to-end latency (uncached)	~0.6s / ingredient
End-to-end latency (cached)	<10ms / ingredient
Cache hit rate	>90%
Semantic match quality	85%+ excellent/good
Data loading time	~1.4s (Polars, 8,789 records)

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Stack

Polars, Redis, Ollama (Arctic Text2SQL + Qwen 0.5B), ingredient-parser-nlp, RapidFuzz, MLflow, Numba, uv.

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Setup

Prerequisites: Python 3.11+, Ollama, Redis running locally.

# Pull models
ollama pull snowflake/arctic-text2sql-artefact
ollama pull qwen:0.5b

# Install
git clone https://github.com/ShovalBenjer/argmax_solution.git
cd argmax_solution
python -m venv .venv && source .venv/bin/activate
pip install uv
uv pip sync requirements.txt

# Run
python main.py

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License

MIT.