Input path: /home/debian/html/nutritwin/output_llm/6668479b241e7/input.json Output path: /home/debian/html/nutritwin/output_llm/6668479b241e7/output.json Input text: Tomates 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: Tomates ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Identify food consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Tomates###. Format the result in JSON format: {intents: []}. ========================================================================================= ------------------------------ LLM Raw response ----------------------------- ```json { "intents": ["Identify food consumption or declaration"] } ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json { "intents": ["Identify food consumption or declaration"] } ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ { "intents": ["Identify food consumption or declaration"]} ---------------------------------------------------------------------- ==================================== Prompt ============================================= Convert this natural language query : """Tomates""" into an array in JSON of consumed foods and beverages. Provide a solution without explanation. Use only the 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 drink identifier, the name should not contain information related to quantity or container (like glass...). The cooking mode is not in the name. When the brand is very well-known (ex: Activia, Coca-Cola), the name is equal to the brand. Keep the same language"@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 examples in french: '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. Keep the same language"@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 'brand' is not specified and, the food or beverage is very well-known (like 'Coca-Cola'), provide the brand name in 'brand', otherwise set 'brand' to ''."@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. """ ========================================================================================= ------------------------------ LLM Raw response ----------------------------- ```json [ { "name": "Tomates", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Tomates", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Tomates", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Tomates', 'quantity': '', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': '', 'brand': '', 'company': '', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Tomates', 'quantity': '', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': '', 'brand': '', 'company': '', 'event': 'unknown'} 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 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Tomates', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Tomate', 'normName': ' tomate ', 'comment': 'crue', 'normComment': ' crue ', 'rank': 50564, 'id': 'CIQ#9019c33adc1aff1aeff07888f760e3dc', 'quantity': '', 'quantityLem': '', 'pack': ['TOM.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.9323673248291016} ---------------------------------------------------------------------------------- LLM CPU Time: 1.9323673248291016