Input path: /home/debian/html/nutritwin/output_llm/66cc447420ee9/input.json Output path: /home/debian/html/nutritwin/output_llm/66cc447420ee9/output.json Input text: donne moi le nombre de calories dans un plat de lasagne 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: donne moi le nombre de calories dans un plat de lasagne ================================================================================================================================== ==================================== 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: ###donne moi le nombre de calories dans un plat de lasagne###. Format the result in JSON format: {intents: []}. ========================================================================================= ------------------------------ LLM Raw response ----------------------------- ```json { "intents": ["Answer a nutrition question"] } ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json { "intents": ["Answer a nutrition question"] } ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ { "intents": ["Answer a nutrition question"]} ---------------------------------------------------------------------- ==================================== Prompt ============================================= Convert this natural language query : """donne moi le nombre de calories dans un plat de lasagne""" 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": "lasagne", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "lasagne", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "lasagne", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'lasagne', 'quantity': '', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': '', 'brand': '', 'company': '', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'lasagne', '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 '% lasagne %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Lasagnes - lasagne - - - 1771 - - - KCA#5a9913cd5d8f6ef7ea5e79b3badab9fb Lasagne de Légumes - lasagne de legume - - - 533 - - - KCA#c25549bab455e69212b09dd4817dadc1 Lasagnes à la Bolognaise - lasagne bolognaise - - - 1698 - - - KCA#5a66257f42cd461e2320c7231ac66577 Lasagnes Chèvre Epinards - lasagne chevre epinard - 'maison' - - 202 - - - KCA#bb87f36466b476d493686d067f7a309f Lasagne aux Légumes Roties - lasagne au legume rotie - - - 68 - - - KCA#1e8b78670ada1767ead09c33e90d05d0 Lasagnes de Pomme de Terre - lasagne de pomme de terre - - - 1 - - - KCA#94741aae46d590e45df619fc50e20189 Lasagnes au Butternut et au Chèvre - lasagne butternut chevre - - - 19 - - - KCA#3977b3f77a2ef9e0a5c943bcd8d54b08 Lasagnes ou Cannellonis au Poisson - lasagne ou cannelloni poisson - - - 0 - - - CIQ#6ab465cbb5d31e6b2db7d8750c6bce1b Lasagnes ou Cannelloni à la Viande - lasagne ou cannelloni viande - bolognaise - - 0 - - - CIQ#20b6fcf1b706aae309473c5b9fe06fed Lasagnes ou Cannellonis aux Légumes - lasagne ou cannelloni au legume - - - 0 - - - CIQ#3ecb40a251e08e2467c8f6a1a53b031c Lasagnes ou Cannellonis aux Légumes - lasagne ou cannelloni au legume - - - 0 - - - CIQ#3ecb40a251e08e2467c8f6a1a53b031c Lasagnes ou Cannellonis au Fromage et aux Épinards - lasagne ou cannelloni fromage au epinard - - - 0 - - - CIQ#a20fb36d8b1180d91a04c94f2de13ac4 Lasagnes ou Cannellonis aux Légumes et au Fromage de Chèvre - lasagne ou cannelloni au legume fromage de chevre - - - 0 - - - CIQ#5d42729119eba2b6dc2e0ff8d4ba716d ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution BA1.w250 ==================================== Prompt ============================================= Here is all known information: For "Lasagnes", here are the nutrition values: name: Lasagnes GTIN: none brand: none calorie: 153.0Kcal per 100g reference weight for a unity: 250g salt: -1.0g per 100g sugar: -1.0g per 100g NutriScore: none EcoScore: none allergens: none allergen traces: none data source: KcalMe Answer in less than 50 words to this question with a short explanation if needed: "donne moi le nombre de calories dans un plat de lasagne" " + "Mention the data source in the response if it exists. The answer must be in the same language than the question ========================================================================================= ------------------------------ LLM Raw response ----------------------------- Un plat de lasagne contient 382,5 Kcal (153 Kcal par 100g pour une portion de 250g). Source des données : KcalMe. ----------------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': 'Un plat de lasagne contient 382,5 Kcal (153 Kcal par 100g pour une portion de 250g). Source des données : KcalMe.', 'cost': 0.0} -------------------------------------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'donne moi le nombre de calories dans un plat de lasagne', 'intents': ['Answer a nutrition question'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Lasagnes', 'normName': ' lasagne ', 'comment': '', 'normComment': '', 'rank': 1771, 'id': 'KCA#5a9913cd5d8f6ef7ea5e79b3badab9fb', 'quantity': '', 'quantityLem': '', 'pack': ['BA1.w250'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {'type': 'text', 'data': 'Un plat de lasagne contient 382,5 Kcal (153 Kcal par 100g pour une portion de 250g). Source des données : KcalMe.'}}, 'cputime': 3.037231206893921} ---------------------------------------------------------------------------------- LLM CPU Time: 3.037231206893921