Interior Design NL to SQL 1000-Pair Dataset (CSV or JSON)

$199.00 USD

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🧠 Natural-Language-to-SQL Training Pairs for Furniture, Lighting & Decor

Teach your LLM to convert real interior-design queries into database queries! Natural language (NL) to SQL1000 verified training pairs dataset for Natural Language to Graph DB translation. It can be used for a product search in Interior Design for question-answering systems, building AI agents and for building generative AI apps. Works for vibe-coding.

1000 new pairs:

Download CSV, TXT or JSON.

✅ Human-readable SQL
✅ Universal schema (no database required)
✅ Perfect for fine-tuning, evaluation, or agent grounding

This training dataset is meant for building intelligent search or question-answering systems using Large Language Models (LLMs) which rely on training data for prompt engineering or fine-tuning.

A dataset of this magnitude might be used internally for training AI models. We constructed (knowledge) entities both by generating synthetic pairs and by crowdsourcing questions/ answers for a general interior design-knowledge graph.

While the datasets of this scale might exist in the interior design domain, but may not be specifically tied to the exact purpose of converting NL queries to database queries.

The dataset can be used for/in rapid development as a light-weight training for agents/ gen AI apps as well as can be added to your MCPs servers and RAGs,

This dataset contains 1000 expertly crafted NL→SQL examples built around furniture, lighting, and decor catalogs — exactly the kind of free-form, tag-driven language real designers use.
Each pair helps models translate natural user requests (“Find brass arc floor lamps made in Denmark”) into clean, executable SQL queries.

Includes negation, ambiguity, brand names, materials, colors, and style tags, plus 60 advanced “robust” pairs for handling vague or contradictory prompts.

💡 Perfect for Building AI Apps That Understand Real People

Ideal for AI assistants, search tools, product finders, and catalog chatbots in e-commerce, design, or retail.

Whether you’re creating an AI interior design advisor, a visual furniture search engine, or a semantic product filter, these examples teach models to bridge the gap between how people describe what they want and how your database stores it.

👩💻 Built for Developers & LLM Engineers

Use it to fine-tune or test models that convert natural language to structured queries.
Every pair uses a universal SQL schema, so it works with any database (PostgreSQL, MySQL, Supabase, etc.).

Designed for LLM evaluation, prompt-engineering, or fine-tuning pipelines where real-world ambiguity matters.

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Interior Design NL to SQL 1000-Pair Dataset (CSV or JSON)