Input path: /home/debian/html/nutritwin/output_llm/66b895a9cf9e3/input.json Output path: /home/debian/html/nutritwin/output_llm/66b895a9cf9e3/output.json Input text: Je viens de manger un melon 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: Je viens de manger un melon ================================================================================================================================== ==================================== 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: ###Je viens de manger un melon###. 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 : """Je viens de manger un melon""" 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": "melon", "quantity": "", "cooking method": "", "type of food": "food", "time of the day": "", "brand": "", "company": "", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "melon", "quantity": "", "cooking method": "", "type of food": "food", "time of the day": "", "brand": "", "company": "", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "melon", "quantity": "", "cooking method": "", "type of food": "food", "time of the day": "", "brand": "", "company": "", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'melon', 'quantity': '', 'cooking method': '', 'type of food': 'food', 'time of the day': '', 'brand': '', 'company': '', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'melon', 'quantity': '', 'cooking method': '', 'type of food': 'food', 'time of the day': '', 'brand': '', 'company': '', '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 '% melon %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Melon - melon - gros - - 0 - - - KCA#5cc523eef9e42851707c24552b47f6af Melon - melon - petit - - 15997 - - - KCA#885237474ee2442ff374f85fc6fb6e49 Melon - melon - blanc - - 1433 - - - KCA#10bed80aac0f11015597dd722b0402d0 Melon au Cassis - melon cassi - - - 59 - - - KCA#f42b7e69e07e21854350a0f4e9d88060 Melon au Pastis - melon pasti - - - 14 - - - KCA#cc977c7170a74c39f55770239fe8c34d Melon Cantaloup - melon cantaloup - par ex.: Charentais, de Cavaillon, pulpe, cru - - 0 - - - CIQ#2f89188f745a5f9eb9b09cf8af524649 Melons au Muscat - melon muscat - - - 5 - - - KCA#93d92ba1896cc0c9247829b9a00ac517 Melon des Bénédictins - melon de benedictin - - - 5 - - - KCA#b276e8efcf8fb1fbfabc9ee62e3e711e Compote Melon - compote melon - - - 9 - - - KCA#84d506540870d2b2a4b3c6d33069092a Billes de Melon - bille de melon - - - 206 - - - KCA#d8157715bf2f44357e5a4eeaaf6d2069 Jus d'Orange, Ananas et Glace au Melon - ju orange anana glace melon - - - 21 - - - KCA#3e4e71456576da23059304f3eba50c9c ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Je viens de manger un melon', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Melon', 'normName': ' melon ', 'comment': 'gros', 'normComment': ' gro ', 'rank': 0, 'id': 'KCA#5cc523eef9e42851707c24552b47f6af', 'quantity': '', 'quantityLem': '', 'pack': ['MEL.w1000', 'CUB.w10'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.3556463718414307} ---------------------------------------------------------------------------------- LLM CPU Time: 2.3556463718414307