Input path: /home/debian/html/nutritwin/output_llm/6681a7e9d1903/input.json Output path: /home/debian/html/nutritwin/output_llm/6681a7e9d1903/output.json Input text: Verre de vin rouge 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: Verre de vin rouge ================================================================================================================================== ==================================== 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: ###Verre de vin rouge###. 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 : """Verre de vin rouge""" 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": "vin rouge", "quantity": "un verre", "type": "beverage", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "vin rouge", "quantity": "un verre", "type": "beverage", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "vin rouge", "quantity": "un verre", "type": "beverage", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'vin rouge', 'quantity': 'un verre', 'type': 'beverage', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'vin rouge', 'quantity': 'un verre', 'type': 'beverage', '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 '% vin rouge %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Vin Rouge - vin rouge - - - 0 - - - CIQ#0247898eabeefe3884ee430550359cfb Vin Rouge 9° - vin rouge 9° - - - 235 - - - KCA#9db74ea9610e574a4b5fd169739808d7 Vin Rouge 13° - vin rouge 13° - - - 27665 - - - KCA#f965995ec93171a22515f7141f3fcaec Vin Rouge 12° - vin rouge 12° - - - 13758 - - - KCA#6576b07568c57c226d3a8a15baa81be6 Vin Rouge 10° - vin rouge 10° - - - 1065 - - - KCA#8c95a77df14a31cf02c05e7b2258cdf9 Vin Rouge 11° - vin rouge 11° - - - 996 - - - KCA#4defe56e99d409c448e479743de50aad Vin Rouge 14° - vin rouge 14° - - - 891 - - - KCA#7b7cb654b939b936970e863f0cf9a707 Vin Rouge 15° - vin rouge 15° - - - 285 - - - KCA#522430b440ab36f2b30e37915271d575 Poule au Vin Rouge - poule vin rouge - - - 0 - - - KCA#16152425348f25d2abe48e2d55c22eca Vinaigre de Vin Rouge - vinaigre de vin rouge - - - 0 - - - CIQ#0e65f9a58f80513c4123cfe859bb81f5 Filets de Sole au Vin Rouge - filet de sole vin rouge - - - 3 - - - KCA#623ccf9a58c32a1884f4e7799961e816 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'Verre de vin rouge', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Vin Rouge', 'normName': ' vin rouge ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'CIQ#0247898eabeefe3884ee430550359cfb', 'quantity': 'un verre', 'quantityLem': '1 verre', 'pack': ['VAV'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'VAV-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.4307663440704346} ---------------------------------------------------------------------------------- LLM CPU Time: 1.4307663440704346