Input path: /home/debian/html/nutritwin/output_llm/672cd15b36b52/input.json Output path: /home/debian/html/nutritwin/output_llm/672cd15b36b52/output.json Input text: Et cinq biscottes. 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: Et cinq biscottes. ================================================================================================================================== ==================================== 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: ###Et cinq biscottes.###. 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 : """Et cinq biscottes.""" 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 beverage identifier, the name should not contain information related to quantity or container (like glass...)."@en; rdfs:comment "Ignore food or beverage when it is not consumed in the past, now or in the future."@en; rdfs:comment "The cooking mode is not in the name. 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."@en; rdfs:comment "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. """ ========================================================================================= ------------------------------ LLM Raw response ----------------------------- ```json [ { "name": "biscottes", "quantity": "cinq", "type": "food", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "biscottes", "quantity": "cinq", "type": "food", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "biscottes", "quantity": "cinq", "type": "food", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'biscottes', 'quantity': 'cinq', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'biscottes', 'quantity': 'cinq', 'type': 'food', 'event': 'declaration'} 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 '% biscotte %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Biscotte - biscotte - - - 17910 - - - KCA#3c207d84f4fcd8c560d600d78cc70ffc Biscotte Complète - biscotte complete - - - 1621 - - - KCA#7f7dead31de24080ef78749af481251c Biscotte sans Sel - biscotte san sel - - - 600 - - - KCA#c5f3813b1a6d118cb63283dd5e3a901b Biscotte Briochée - biscotte briochee - - - 0 - - - CIQ#e844742600ba90ed3b132532c49b75bb Biscotte Classique - biscotte classique - - - 238 - - - KCA#4dc2d17bd9fad14b5e54595392b763c3 Biscotte au Gluten - biscotte gluten - - - 86 - - - KCA#9f3f141199ff82e673b330323c5fdf7d Biscotte Classique - biscotte classique - - - 0 - - - CIQ#4dc2d17bd9fad14b5e54595392b763c3 Biscotte Multicéréale - biscotte multicereale - - - 0 - - - CIQ#177c331355f19640f230bee547e8b22b Biscotte et 5g de Beurre - biscotte 5g de beurre - - - 1270 - - - KCA#48ffa58cea52bb4779419ff057ac8b76 Biscotte Riche en Fibres - biscotte riche en fibre - - - 685 - - - KCA#58d4b69923d318763c1c8dde9e4c1aee Biscotte sans Adjonction de Sel - biscotte san adjonction de sel - - - 54 - - - CIQ#6c2d738c1b082340d11535ecf3cc7b5b Biscotte Complète ou Riche en Fibres - biscotte complete ou riche en fibre - - - 0 - - - CIQ#b57099e4ec40969d291423aed8fd3ccb ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'Et cinq biscottes.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Biscotte', 'normName': ' biscotte ', 'comment': '', 'normComment': '', 'rank': 17910, 'id': 'KCA#3c207d84f4fcd8c560d600d78cc70ffc', 'quantity': 'cinq', 'quantityLem': '5', 'pack': ['TR1.w10'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'TR1-500', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.0506412982940674} ---------------------------------------------------------------------------------- LLM CPU Time: 2.0506412982940674