Input path: /home/debian/html/nutritwin/output_llm/66a5e672340d8/input.json Output path: /home/debian/html/nutritwin/output_llm/66a5e672340d8/output.json Input text: Pain de mie sans sucre 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: Pain de mie sans sucre ================================================================================================================================== ==================================== 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: ###Pain de mie sans sucre###. 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 : """Pain de mie sans sucre""" 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": "Pain de mie sans sucre", "type": "food", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Pain de mie sans sucre", "type": "food", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Pain de mie sans sucre", "type": "food", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Pain de mie sans sucre', 'type': 'food', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Pain de mie sans sucre', 'type': 'food', '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 '% pain de mie san sucre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) Second 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_NormAggr LIKE '% pain de mie san sucre %' AND V_NormTrademark LIKE '%%' ------------- Found solution (max 20) -------------- Pain de Mie - pain de mie - - Limagrain - 0 - 3029330071181 - 3029330003366 - OFF#07f398a9196f071c4ec4e5ca6d17f8b0 Tartine P'Tit Déj Nature - tartine tit dej nature - - Limagrain - 0 - 3029330069959 - 3029330003649 - OFF#f155af17710bc2965d24e8e0a3fc90f9 Tartine P'Tit Dej Nature Ssa - tartine tit dej nature ssa - - Limagrain - 0 - 3029330069911 - 3029330069911 - OFF#14612653aadf394ef7ba4c1a13231c36 Pain de Mie Nature sans Sucres Ajoutés - pain de mie nature san sucre ajoute - - Limagrain - 0 - 3029330001027 - 3029330001027 - OFF#8ecb2a196475dbe7b8fda7402fb27a2f Maxi Tranches sans Sucre Ajouté Nature - maxi tranche san sucre ajoute nature - - Limagrain - 0 - 3029330022411 - 3029330022411 - OFF#09c308d94156e55ee782be5e27e1bae2 Crousti Moelleux Nature sans Sucres Ajoutés - crousti moelleu nature san sucre ajoute - - Limagrain - 0 - 3029330003526 - 3029330003526 - OFF#fe3a6637ee2e9ec8ff53260b28b81c07 Pain Mie 32 Tranches Nature sans Sucre Ajouté - pain mie 32 tranche nature san sucre ajoute - - Limagrain - 0 - 3029330071136 - 3029330071136 - OFF#1410f9447b6d122bffcebbcf8b0fa314 24 Petites Tranches Nature sans Sucres Ajoutés - 24 petite tranche nature san sucre ajoute - - Limagrain - 0 - 3029330071129 - 3029330071129 - OFF#d8bd537727ebf0c378983ceedba2640a Maxi Tranches Nature sans Sucre Ajouté 25% Offert8 Tranches - maxi tranche nature san sucre ajoute 25% offert8 tranche - - Limagrain - 0 - 3029330071204 - 3029330071204 - OFF#a580e0678830841f3035efff39b08839 Pain de Mie sans Sucres Ajoutés - pain de mie san sucre ajoute - - Limagrain - 0 - 3029330071174 - 3029330071174 - OFF#bdb3a9c436e82ee5c65f8b484ab6272a Pain de Mie sans Sucres Ajoutes - pain de mie san sucre ajoute - - Limagrain - 0 - 66338031 - 3029330071174 - OFF#2a297c962903ed95937136bdb9f1d83c ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Pain de mie sans sucre', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Pain de Mie', 'normName': ' pain de mie ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#07f398a9196f071c4ec4e5ca6d17f8b0', 'quantity': '', 'quantityLem': '', 'pack': ['TR1.w25'], 'type': 'food', 'gtin': '3029330071181', 'gtinRef': '3029330003366', 'brand': 'Limagrain', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 1.9455444812774658} ---------------------------------------------------------------------------------- LLM CPU Time: 1.9455444812774658