Input path: /home/debian/html/nutritwin/output_llm/68a721898d88d/input.json Output path: /home/debian/html/nutritwin/output_llm/68a721898d88d/output.json Input text: Une tomate vin. DB path: __deriveddata__/DerivedObjects/Data/KcalMeDB_fr.sl3 Picto path: __deriveddata__/DerivedObjects/Data/PictoMatcherNetNG_fr.json Sport grounding path: __deriveddata__/DerivedObjects/Data/DerivedSportMET.json ================================================================================================================================== Prompt from user: Une tomate vin. ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Identify food and beverage consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Une tomate vin.###. Format the result in JSON format: {"intents": []}. ========================================================================================= ------------------------------ LLM Raw response ----------------------------- {"intents": ["Identify food and beverage consumption or declaration"]} ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ {"intents": ["Identify food and beverage consumption or declaration"]} ------------------------------------------------------ ERROR: wrong object representation: {'intents': ['Identify food and beverage consumption or declaration']} ------------------------ After simplification ------------------------ { "intents": [ "Identify food and beverage consumption or declaration" ] } ---------------------------------------------------------------------- ==================================== Prompt ============================================= Convert this natural language query : """Une tomate vin.""" into an array of JSON. Ignore what it is not connected to nutrition, beverage or food. Provide a solution without explanation. Use the following ontology and only this ontology described in this Turtle/RDF model: """ @prefix food: . @prefix rdfs: . @prefix xsd: . @prefix owl: . @prefix prov: . food: a owl:Ontology ; rdfs:comment "Definition of the food archetype"@en . food:name a owl:DatatypeProperty; rdfs:label "name"@en; rdfs:comment """Food or beverage identifier, the name should not contain information related to quantity or container (like glass...). Ignore food or beverage when it is not consumed in the past, now or in the future. The cooking mode is not in the name. The name is only in french."""@en; rdfs:range xsd:string. food:quantity a owl:DatatypeProperty ; rdfs:label "quantity"@en; rdfs:comment "The quantity of food or drink that is or was consumed. Quantity is only in french. Here are examples: 'un quignon', 'un cornet', 'un verre', 'une tranche', 'une boule', 'un', 'deux', 'trois',... Keep the same language."@en; rdfs:range xsd:string. food:cookingMethod a owl:DatatypeProperty ; rdfs:label "cooking method"@en; rdfs:comment "The cooking method of food. The cooking method is in french."@en; rdfs:range xsd:string. food:type a owl:DatatypeProperty ; rdfs:label "type of food"@en; rdfs:comment "Identify the type of food."@en; rdfs:range xsd:string. food:food a food:type ; rdfs:label "food" . food:beverage a food:type ; rdfs:label "beverage" . food:timeOfTheDay a owl:DatatypeProperty ; rdfs:label "time of the day"@en; rdfs:comment "Time of the day when food or drink was consumed."@en; rdfs:range xsd:string. food:breakfast a food:timeOfTheDay ; rdfs:label "breakfast" . food:lunch a food:timeOfTheDay ; rdfs:label "lunch" . food:snacking a food:timeOfTheDay ; rdfs:label "snacking" . food:dinner a food:timeOfTheDay ; rdfs:label "dinner" . food:brand a owl:DatatypeProperty ; rdfs:label "Brand"@en; rdfs:comment """Food or beverage brand. The restaurants are not brand. When the name is very known (ex: Activia, Coca) and the brand is not mentioned, guess the brand."""@en; rdfs:range xsd:string. food:company a owl:DatatypeProperty ; rdfs:label "Company"@en; rdfs:comment "Product company."@en; rdfs:range xsd:string. food:enumEvent a rdfs:Class . food:event a owl:DatatypeProperty ; rdfs:label "event"@en; rdfs:comment "Event of eating or drinking. Each must have an event"@en; rdfs:range food:enumEvent. food:intent a food:enumEvent ; rdfs:label "intent" . rdfs:comment "When the event should happen"@en. food:declaration a food:enumEvent ; rdfs:label "declaration" . rdfs:comment "When the event has already occured"@en. food:unknownEvent a food:enumEvent ; rdfs:label "unknown" ; rdfs:comment "When the event is unknown in the day"@en. food:event a owl:DatatypeProperty ; rdfs:label "event"@en; rdfs:comment "Event of eating or drinking. Each must have an event"@en; rdfs:range food:enumEvent. food:intent a food:enumEvent ; rdfs:label "intent" . rdfs:comment "When the event should happen"@en. food:declaration a food:enumEvent ; rdfs:label "declaration" . rdfs:comment "When the event has already occured"@en. food:unknownEvent a food:enumEvent ; rdfs:label "unknown" ; rdfs:comment "When the event is unknown in the day"@en. """ Here is an example of result: [ { "name": "blanquette de veau", "quantity": "un plat", "cookingMethod": "mijot\u00e9", "timeOfTheDay": "lunch", "company": "Leclerc", "type": "food", "event": "declaration" }, { "name": "eau", "brand": "Evian", "company": "Danone", "timeOfTheDay": "breakfast", "quantity": "un verre", "type": "beverage", "event": "intent" } ] ========================================================================================= ------------------------------ LLM Raw response ----------------------------- [ { "name": "tomate", "quantity": "une", "type": "food", "event": "unknownEvent" }, { "name": "vin", "quantity": "un", "type": "beverage", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "tomate", "quantity": "une", "type": "food", "event": "unknownEvent" }, { "name": "vin", "quantity": "un", "type": "beverage", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "tomate", "quantity": "une", "type": "food", "event": "unknownEvent" }, { "name": "vin", "quantity": "un", "type": "beverage", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'tomate', 'quantity': 'une', 'type': 'food', 'event': 'unknownEvent'}, {'name': 'vin', 'quantity': 'un', 'type': 'beverage', 'event': 'unknownEvent'}], 'cost': 0.10085999999999999} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'tomate', 'quantity': 'une', 'type': 'food', 'event': 'unknownEvent'} First try: SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% tomate %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Tomate - tomate - crue - - 50564 - - - CIQ#9019c33adc1aff1aeff07888f760e3dc Tomate - tomate - purée - - 0 - - - CIQ#98e08e3b00fecca745d7da29e1015a95 Tomate - tomate - pulpe - - 0 - - - CIQ#fd785fdebdb36567c615d2cf46456ffd Tomate - tomate - concentré - - 0 - - - CIQ#7020e6d5e5bd9e09aaa1661220ba09b7 Tomate - tomate - pelée, égouttée - - 0 - - - CIQ#e42ed02a1db9c324a72333e04d401dc1 Tomate - tomate - double concentré - - 0 - - - CIQ#316f9d6fdf5ec84b18998fae96416e09 Tomate - tomate - séchée, à l'huile - - 0 - - - CIQ#b7e1592c157fef2c1429cdc04e65f429 Tomate - tomate - rôtie/cuite au four - - 0 - - - CIQ#abc1ee10e1ef1b8d9ea01e5cf5081ac9 Tomate - tomate - pulpe et peau, rôtie/cuite au four - - 0 - - - CIQ#a670b9fa38af8c6557b321a08d7ab367 Tomate Ronde - tomate ronde - crue - - 0 - - - CIQ#684dc9134dc864e3c83f5330fa9965d4 Tomate Farcie - tomate farcie - - - 1889 - - - CIQ#6662d127dcc7f87a176e7cda4540b6d5 Tomate Cerise - tomate cerise - crue - - 0 - - - CIQ#9f76e2172737f480f1c9b66f3627bfb0 Tomate Grappe - tomate grappe - crue - - 0 - - - CIQ#2bdccc054e39de9382dcb2ff97b1204d Tomate Cerise - tomate cerise - tomate cerise - - 0 - - - KCA#fc7d1647e177b261c9a22262037f6216 Tomate Séchée - tomate sechee - tomate séchée - - 0 - - - KCA#1dfa8e1ad113a5175e6a3ba4bee46416 Tomates au Four - tomate four - au four - - 0 - - - KCA#7bd06a9534bdcb97e7af0143ac0124d5 Tomates Farcies - tomate farcie - tomates farcies - - 0 - - - KCA#6e01a7596f6a74b9bca3e51ca2721e81 Tomates Tartares - tomate tartare - tomates tartares - - 0 - - - KCA#e15190c59aa8508125d81de65be88670 Tomate Concentrée - tomate concentree - tomate concentrée - - 0 - - - KCA#22854ad0ad81beeccc0841c1f0c5d66c Tomates Provençales - tomate provencale - tomates provençales - - 0 - - - KCA#799358a4b450be03bfd4014d3908c6dc ---------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'vin', 'quantity': 'un', 'type': 'beverage', 'event': 'unknownEvent'} First try: SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% vin %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Vin Cuit - vin cuit - - - 476 - - - KCA#413dce4eebdf3fdf2076ef50ae74590b Vin Blanc - vin blanc - - - 22924 - - - KCA#0a40d7fc7234085d12af2089c75f862b Vin Rouge - vin rouge - - - 0 - - - CIQ#0247898eabeefe3884ee430550359cfb Vin Rouge 9° - vin rouge 9° - - - 235 - - - KCA#9db74ea9610e574a4b5fd169739808d7 Vin Rouge 13° - vin rouge 13° - - - 27665 - - - KCA#f965995ec93171a22515f7141f3fcaec Vin Rouge 12° - vin rouge 12° - - - 13758 - - - KCA#6576b07568c57c226d3a8a15baa81be6 Vin Rouge 10° - vin rouge 10° - - - 1065 - - - KCA#8c95a77df14a31cf02c05e7b2258cdf9 Vin Rouge 11° - vin rouge 11° - - - 996 - - - KCA#4defe56e99d409c448e479743de50aad Vin Rouge 14° - vin rouge 14° - - - 891 - - - KCA#7b7cb654b939b936970e863f0cf9a707 Vin Rouge 15° - vin rouge 15° - - - 285 - - - KCA#522430b440ab36f2b30e37915271d575 Coq au Vin - coq vin - - - 97 - - - CIQ#588d397f09445cf782a6fd7abf34dd22 Bar au Vin Blanc - bar vin blanc - - - 30 - - - KCA#1ad8f1259ed6c3bf39ce51b22b7f6ec5 Pêches au Vin - peche vin - - - 39 - - - KCA#a59eaab7bfcb9b09ada234a6e2c1a7d3 Poule au Vin Rouge - poule vin rouge - - - 0 - - - KCA#16152425348f25d2abe48e2d55c22eca Dorade au Vin Blanc - dorade vin blanc - - - 140 - - - KCA#31a50d86b8de5651b38155aedb86fc12 Pruneaux au Vin - pruneau vin - - - 48 - - - KCA#0d4b4d95ca0c7588288992b65d0c876a Chipolatas au Vin Blanc - chipolata vin blanc - - - 8 - - - KCA#5fba9f7a50300f3dec74a85c0b8a3ab7 Steaks Sautés au Vin - steak saute vin - au vin - - 0 - - - KCA#3830e269f3a7cb20f83532fcaf5e9610 Vinaigre de Vin Rouge - vinaigre de vin rouge - - - 0 - - - CIQ#0e65f9a58f80513c4123cfe859bb81f5 Eau de Vie de Vin - eau de vie de vin - type armagnac, cognac - - 0 - - - CIQ#c0440021ea15aa2abf11853bbd2191a4 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'Une tomate vin.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Tomate', 'normName': ' tomate ', 'comment': 'crue', 'normComment': ' crue ', 'rank': 50564, 'id': 'CIQ#9019c33adc1aff1aeff07888f760e3dc', 'quantity': 'une', 'quantityLem': '1', 'pack': ['TOM.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknownEvent', 'serving': 'TOM-100', 'posiNormName': 0}, {'name': 'Vin Cuit', 'normName': ' vin cuit ', 'comment': '', 'normComment': '', 'rank': 476, 'id': 'KCA#413dce4eebdf3fdf2076ef50ae74590b', 'quantity': 'un', 'quantityLem': '1', 'pack': ['VAA'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknownEvent', 'serving': 'VAA-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.6338818073272705} ---------------------------------------------------------------------------------- LLM CPU Time: 2.6338818073272705