Input path: /home/debian/html/nutritwin/output_llm/66d5aa81519bf/input.json Output path: /home/debian/html/nutritwin/output_llm/66d5aa81519bf/output.json Input text: Combien de calories dans 3 bananes? 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: Combien de calories dans 3 bananes? ================================================================================================================================== ==================================== 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: ###Combien de calories dans 3 bananes?###. 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 : """Combien de calories dans 3 bananes?""" 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": "banane", "quantity": "trois", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "banane", "quantity": "trois", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "banane", "quantity": "trois", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'banane', 'quantity': 'trois', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': '', 'brand': '', 'company': '', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'banane', 'quantity': 'trois', '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 '% banane %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Banane - banane - pulpe, crue - - 57967 - - - CIQ#6066b5bb884711efc0e44c9446b96aa3 Banane Sèche - banane seche - - - 346 - - - KCA#2e3e40d3b1ae9f793251e9948142d784 Bananes en Robe - banane en robe - - - 14 - - - KCA#b274666ef64f762c58695191d4286b85 Banane Plantain - banane plantain - - - 2 - - - CIQ#1055a76a23712202f3c842fba09fa691 Bananes Barbecue - banane barbecue - - - 33 - - - KCA#1d31fb8efe54f0bc7765a60cc9f8c324 Bananes au Jambon - banane jambon - - - 4 - - - KCA#e21d980b838ba89f4e9ba1d85f593c95 Smoothie Banane et Lait de Soja - smoothie banane lait de soja - de soja - - 0 - - - KCA#dc0b16a02e5290892f9adee7419ec0e7 Crème Glacée Banane, Pomme et Noix de Macadamia - creme glacee banane pomme noix de macadamia - - - 34 - - - KCA#3233d39965b7baa31d10a301ac541ffa Bruschette à la Fraise, à la Banane et à la Ricotta - bruschette fraise banane ricotta - - - 2 - - - KCA#fd9db147f698ab1c84b0905704258a5f ---------------------------------------------------- BAN.w100 ==================================== Prompt ============================================= Here is all known information: For "Banane", here are the nutrition values: name: Banane GTIN: none brand: none calorie: 90.5Kcal per 100g reference weight for a unity: 100g salt: 0.013g per 100g sugar: 15.6g per 100g NutriScore: none EcoScore: none allergens: none allergen traces: none data source: Ciqual Answer in less than 50 words to this question with a short explanation if needed: "Combien de calories dans 3 bananes?" " + "Mention the data source in the response if it exists. The answer must be in the same language than the question ========================================================================================= ------------------------------ LLM Raw response ----------------------------- Il y a environ 271.5 Kcal dans 3 bananes (90.5 Kcal par 100g, chaque banane pesant environ 100g). Source des données : Ciqual. ----------------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': 'Il y a environ 271.5 Kcal dans 3 bananes (90.5 Kcal par 100g, chaque banane pesant environ 100g). Source des données : Ciqual.', 'cost': 0.0} -------------------------------------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'Combien de calories dans 3 bananes?', 'intents': ['Answer a nutrition question'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Banane', 'normName': ' banane ', 'comment': 'pulpe, crue', 'normComment': ' pulpe crue ', 'rank': 57967, 'id': 'CIQ#6066b5bb884711efc0e44c9446b96aa3', 'quantity': 'trois', 'quantityLem': '3', 'pack': ['BAN.w100'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': 'BAN-300', 'posiNormName': 0}], 'activity': [], 'response': {'type': 'text', 'data': 'Il y a environ 271.5 Kcal dans 3 bananes (90.5 Kcal par 100g, chaque banane pesant environ 100g). Source des données : Ciqual.'}}, 'cputime': 2.8664474487304688} ---------------------------------------------------------------------------------- LLM CPU Time: 2.8664474487304688