{"product_id":"natural-language-nl-to-graph-db-1000-pair-ai-training-dataset-interior-design-csv-or-json-copy","title":"Interior Design NL to SQL 1000-Pair Dataset (CSV or JSON)","description":"\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\n\u003ch3 data-end=\"259\" data-start=\"180\"\u003e\u003c\/h3\u003e\n\u003ch3 data-end=\"259\" data-start=\"180\"\u003e🧠 Natural-Language-to-SQL Training Pairs for Furniture, Lighting \u0026amp; Decor\u003c\/h3\u003e\nTeach your LLM to convert real interior-design queries into database queries! Natural language (NL) to SQL1000 verified training pairs dataset for \u003cstrong class=\"Yjhzub\" data-processed=\"true\"\u003eNatural Language to Graph DB \u003c\/strong\u003e\u003cstrong\u003etranslation\u003c\/strong\u003e. 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. \u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\n\u003ch3 data-end=\"161\" data-start=\"144\"\u003e1000 new pairs:\u003c\/h3\u003e\n\u003cul data-end=\"392\" data-start=\"162\"\u003e\n\u003cli data-end=\"238\" data-start=\"162\"\u003e\n\u003cp data-end=\"238\" data-start=\"164\"\u003e\u003cspan data-state=\"closed\" class=\"\"\u003e\u003ca class=\"cursor-pointer\" data-end=\"236\" data-start=\"164\"\u003eDownload CSV\u003c\/a\u003e\u003c\/span\u003e\u003cspan data-state=\"closed\" class=\"\"\u003e\u003ca class=\"cursor-pointer\" data-end=\"313\" data-start=\"241\"\u003e, TXT\u003c\/a\u003e\u003c\/span\u003e\u003cspan data-state=\"closed\" class=\"\"\u003e\u003ca class=\"cursor-pointer\" data-end=\"392\" data-start=\"318\"\u003e or JSON.\u003c\/a\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cstrong\u003e✅ Human-readable SQL\u003cbr data-end=\"1664\" data-start=\"1661\"\u003e✅ Universal schema (no database required)\u003cbr data-end=\"1708\" data-start=\"1705\"\u003e✅ Perfect for fine-tuning, evaluation, or agent grounding\u003c\/strong\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cstrong\u003eThis training dataset is meant for \u003cspan class=\"T286Pc\" data-sfc-cp=\"\" data-processed=\"true\"\u003ebuilding intelligent search or question-answering systems using Large Language Models (LLMs) which rely on training data for prompt engineering or fine-tuning\u003c\/span\u003e.\u003c\/strong\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cstrong\u003e \u003c\/strong\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e A dataset of this magnitude might be used internally for training AI models. We constructed (knowledge) entities both by\u003cstrong\u003e generating synthetic pairs \u003c\/strong\u003eand by crowdsourcing questions\/ answers for a general \u003cstrong\u003einterior design-knowledge graph\u003c\/strong\u003e. \u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003eWhile 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.\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003eThe 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,\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\n\u003cp data-end=\"813\" data-start=\"261\"\u003e \u003c\/p\u003e\n\u003ch4 data-end=\"813\" data-start=\"261\"\u003e\u003cstrong\u003eMore\u003c\/strong\u003e\u003c\/h4\u003e\n\u003cp data-end=\"813\" data-start=\"261\"\u003eThis dataset contains \u003cstrong data-end=\"384\" data-start=\"344\"\u003e1000 expertly crafted NL→SQL examples\u003c\/strong\u003e built around furniture, lighting, and decor catalogs — exactly the kind of free-form, tag-driven language real designers use.\u003cbr data-end=\"513\" data-start=\"510\"\u003eEach pair helps models translate natural user requests (“Find brass arc floor lamps made in Denmark”) into clean, executable SQL queries.\u003cbr data-end=\"653\" data-start=\"650\"\u003e\u003c\/p\u003e\n\u003cp data-end=\"813\" data-start=\"261\"\u003eIncludes \u003cstrong data-end=\"733\" data-start=\"662\"\u003enegation, ambiguity, brand names, materials, colors, and style tags\u003c\/strong\u003e, plus 60 advanced “robust” pairs for handling vague or contradictory prompts.\u003c\/p\u003e\n\u003ch4 data-end=\"881\" data-start=\"815\"\u003e💡 Perfect for Building AI Apps That Understand Real People\u003c\/h4\u003e\n\u003cp data-end=\"1265\" data-start=\"882\"\u003eIdeal for \u003cstrong data-end=\"909\" data-start=\"892\"\u003eAI assistants\u003c\/strong\u003e, \u003cstrong data-end=\"927\" data-start=\"911\"\u003esearch tools\u003c\/strong\u003e, \u003cstrong data-end=\"948\" data-start=\"929\"\u003eproduct finders\u003c\/strong\u003e, and \u003cstrong data-end=\"974\" data-start=\"954\"\u003ecatalog chatbots\u003c\/strong\u003e in e-commerce, design, or retail.\u003cbr data-end=\"1011\" data-start=\"1008\"\u003e\u003c\/p\u003e\n\u003cp data-end=\"1265\" data-start=\"882\"\u003eWhether you’re creating an \u003cstrong data-end=\"1068\" data-start=\"1038\"\u003eAI interior design advisor\u003c\/strong\u003e, a \u003cstrong data-end=\"1106\" data-start=\"1072\"\u003evisual furniture search engine\u003c\/strong\u003e, or a \u003cstrong data-end=\"1140\" data-start=\"1113\"\u003esemantic product filter\u003c\/strong\u003e, these examples teach models to bridge the gap between how people describe what they want and how your database stores it.\u003c\/p\u003e\n\u003ch4 data-end=\"1316\" data-start=\"1267\"\u003e👩💻 Built for Developers \u0026amp; LLM Engineers\u003c\/h4\u003e\n\u003cp data-end=\"1639\" data-start=\"1317\"\u003eUse it to fine-tune or test models that convert \u003cstrong data-end=\"1407\" data-start=\"1365\"\u003enatural language to structured queries\u003c\/strong\u003e.\u003cbr data-end=\"1411\" data-start=\"1408\"\u003eEvery pair uses a \u003cstrong data-end=\"1453\" data-start=\"1429\"\u003euniversal SQL schema\u003c\/strong\u003e, so it works with any database (PostgreSQL, MySQL, Supabase, etc.).\u003cbr data-end=\"1524\" data-start=\"1521\"\u003e\u003c\/p\u003e\n\u003cp data-end=\"1639\" data-start=\"1317\"\u003eDesigned for \u003cstrong data-end=\"1601\" data-start=\"1537\"\u003eLLM evaluation, prompt-engineering, or fine-tuning pipelines\u003c\/strong\u003e where real-world ambiguity matters.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Token Ring","offers":[{"title":"csv","offer_id":47350744875256,"sku":null,"price":199.0,"currency_code":"USD","in_stock":true},{"title":"JSON","offer_id":47350744908024,"sku":null,"price":299.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0776\/7392\/2808\/files\/NL_to_SQL_Interior_Design_AI_Training_Pairs_-_JSON_2.png?v=1762113148","url":"https:\/\/9ynuzz-au.myshopify.com\/products\/natural-language-nl-to-graph-db-1000-pair-ai-training-dataset-interior-design-csv-or-json-copy","provider":"Token Ring","version":"1.0","type":"link"}