Input path: /home/debian/html/nutritwin/output_llm/667c14745137c/input.json Output path: /home/debian/html/nutritwin/output_llm/667c14745137c/output.json Input text: Un steak haché 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: Un steak haché ================================================================================================================================== ==================================== 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: ###Un steak haché###. 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 : """Un steak haché""" 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": "steak haché", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "steak haché", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "steak haché", "quantity": "", "cookingMethod": "", "type": "food", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'steak haché', 'quantity': '', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': '', 'brand': '', 'company': '', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'steak haché', '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 '% steak hache %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Steak Haché - steak hache - steak haché - - 0 - - - KCA#959303ed3534b9c5c79e9cca14656f10 Steak Haché de Veau - steak hache de veau - de veau - - 0 - - - KCA#b2ad2dc15a31de3a5e4c82f59493b374 Steak Haché Pur Boeuf - steak hache pur boeuf - cru 5% MG - - 503 - - - KCA#938fbe35bce49b6b3be37bd88dd8dbda Steak Haché Pur Boeuf - steak hache pur boeuf - cru 15% MG - - 976 - - - KCA#c535c91b3321042c8f4db636820e9bec Steak Haché Pur Boeuf - steak hache pur boeuf - cuit 5% MG - - 346 - - - KCA#204bae7f16652a41d4ce4eb14e3b7c2a Steak Haché Pur Boeuf - steak hache pur boeuf - cru 10% MG - - 35 - - - KCA#76f5adade8613cd175ce7fe7a4571b4e Steak Haché Pur Boeuf - steak hache pur boeuf - cru 20% MG - - 30 - - - KCA#52243626b34baa1a5cbe130cce524dab Steak Haché Pur Boeuf - steak hache pur boeuf - cuit 15% MG - - 430 - - - KCA#b469a95dae325ba42b0e819b093f6d25 Steak Haché Pur Boeuf - steak hache pur boeuf - cuit 20% MG - - 251 - - - KCA#de368fdaccc0ddb574e394f9cd58e8cd Steak Haché Pur Boeuf - steak hache pur boeuf - cuit 10% MG - - 163 - - - KCA#a5358d1ed4bfa56e5af765e0b20efb32 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Un steak haché', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Steak Haché', 'normName': ' steak hache ', 'comment': 'steak haché', 'normComment': ' steak hache ', 'rank': 0, 'id': 'KCA#959303ed3534b9c5c79e9cca14656f10', 'quantity': '', 'quantityLem': '', 'pack': ['STH.w100', 'ST2.w100'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.2884562015533447} ---------------------------------------------------------------------------------- LLM CPU Time: 2.2884562015533447