{"product_id":"natural-language-nl-to-graph-db-200-pair-ai-training-dataset-food-csv-or-json-copy","title":"Natural Language (NL) to Graph DB 1000-Pair AI Training Dataset - Interior Design (CSV or JSON)","description":"\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\u003e\n\u003ch3 data-start=\"233\" data-end=\"313\"\u003e🧠 Natural-Language-to-Graph Training Pairs for AI Design \u0026amp; Product Search\u003c\/h3\u003e\n\u003cp data-start=\"315\" data-end=\"660\"\u003eThis dataset gives you that foundation — \u003cstrong data-start=\"703\" data-end=\"762\"\u003e1000 expertly built Natural Language → Graph Query pairs\u003c\/strong\u003e created around \u003cstrong data-start=\"778\" data-end=\"830\"\u003ereal furniture, lighting, and decor vocabularies\u003c\/strong\u003e.\u003cbr data-start=\"831\" data-end=\"834\"\u003eIt’s trained on the messy, free-form language real designers use: brand names, styles, colors, materials, negations (“not made in China”), and layered filters (“Scandinavian oak chairs, in stock, under $800”).\u003cbr data-start=\"1043\" data-end=\"1046\"\u003eYou get \u003cstrong data-start=\"1054\" data-end=\"1106\"\u003eclean, human-readable Cypher-style graph queries\u003c\/strong\u003e that show your model exactly how to think in relationships — \u003cstrong\u003e\u003cspan style=\"text-decoration: underline;\"\u003enodes, edges, and properties\u003c\/span\u003e — not keywords\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp data-start=\"315\" data-end=\"660\"\u003eEvery smart interior-design AI begins here.\u003cbr data-start=\"358\" data-end=\"361\"\u003eIf your app needs to understand \u003cem data-start=\"393\" data-end=\"454\"\u003e“Show me modern walnut dining chairs from Italy under $600”\u003c\/em\u003e — it must first \u003cstrong data-start=\"471\" data-end=\"533\"\u003etranslate that natural language into a precise graph query\u003c\/strong\u003e.\u003cbr data-start=\"534\" data-end=\"537\"\u003eWithout this step, no AI wrapper, chatbot, or recommendation engine can reliably fetch the right item from your database.\u003c\/p\u003e\n\u003cp data-start=\"315\" data-end=\"660\"\u003e✅ Ready-to-train Cypher-like queries\u003cbr data-start=\"2086\" data-end=\"2089\"\u003e✅ Works with any knowledge graph schema\u003cbr data-start=\"2128\" data-end=\"2131\"\u003e✅ Unlocks the missing layer between human intent and database results.\u003c\/p\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=\"392\" data-start=\"318\"\u003eJSON.\u003c\/a\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch4 data-start=\"1216\" data-end=\"1280\"\u003e💡 Build AI Apps That Actually Understand Design Language\u003c\/h4\u003e\n\u003cp data-start=\"1281\" data-end=\"1627\"\u003eWhether you’re building a \u003cstrong data-start=\"1307\" data-end=\"1339\"\u003evisual furniture recommender\u003c\/strong\u003e, \u003cstrong data-start=\"1341\" data-end=\"1370\"\u003eAI interior search engine\u003c\/strong\u003e, or \u003cstrong data-start=\"1375\" data-end=\"1434\"\u003edesign assistant that queries a product knowledge graph\u003c\/strong\u003e, this dataset is your shortcut to a working prototype.\u003cbr data-start=\"1489\" data-end=\"1492\"\u003e\u003c\/p\u003e\n\u003cp data-start=\"1281\" data-end=\"1627\"\u003eIt enables AI models to convert user intent into structured graph logic — the essential step between \u003cem data-start=\"1593\" data-end=\"1608\"\u003e“describe it”\u003c\/em\u003e and \u003cem data-start=\"1613\" data-end=\"1625\"\u003e“find it.”\u003c\/em\u003e\u003c\/p\u003e\n\u003ch4 data-start=\"1629\" data-end=\"1700\"\u003e👩💻 Created for AI Developers, Graph Engineers \u0026amp; Product Teams\u003c\/h4\u003e\n\u003cp data-start=\"1701\" data-end=\"2048\"\u003eEach pair is \u003cstrong data-start=\"1714\" data-end=\"1735\"\u003edatabase-agnostic\u003c\/strong\u003e — works with \u003cstrong data-start=\"1749\" data-end=\"1808\"\u003eNeo4j, AWS Neptune, GraphQL, or custom knowledge graphs\u003c\/strong\u003e.\u003cbr data-start=\"1809\" data-end=\"1812\"\u003ePerfect for \u003cstrong data-start=\"1824\" data-end=\"1844\"\u003efine-tuning LLMs\u003c\/strong\u003e, building \u003cstrong data-start=\"1855\" data-end=\"1885\"\u003eembedding-based retrievers\u003c\/strong\u003e, or testing \u003cstrong data-start=\"1898\" data-end=\"1924\"\u003esemantic understanding\u003c\/strong\u003e pipelines.\u003cbr data-start=\"1935\" data-end=\"1938\"\u003eIncludes \u003cstrong data-start=\"1947\" data-end=\"1966\"\u003erobust examples\u003c\/strong\u003e with ambiguity, negation, and multi-property chaining to train reasoning depth.\u003c\/p\u003e\nNatural language (NL) to GRAPH DB 1000 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\u003c\/div\u003e\n\u003cdiv data-subtree=\"aimfl,mfl\" style=\"display: contents;\" data-processed=\"true\"\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\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\"\u003eA 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","brand":"Token Ring","offers":[{"title":"csv","offer_id":47350621536504,"sku":null,"price":199.0,"currency_code":"USD","in_stock":true},{"title":"JSON","offer_id":47350621569272,"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_Graph_Interior_Design_Training_Pairs_JSON.png?v=1762108297","url":"https:\/\/9ynuzz-au.myshopify.com\/products\/natural-language-nl-to-graph-db-200-pair-ai-training-dataset-food-csv-or-json-copy","provider":"Token Ring","version":"1.0","type":"link"}