Input path: /home/debian/html/nutritwin/output_llm/66c4ac8b6ce55/input.json Output path: /home/debian/html/nutritwin/output_llm/66c4ac8b6ce55/output.json Input text: J'ai mangé une crème au chocolat noir mamie Nova 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: J'ai mangé une crème au chocolat noir mamie Nova ================================================================================================================================== ==================================== 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: ###J'ai mangé une crème au chocolat noir mamie Nova###. 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 : """J'ai mangé une crème au chocolat noir mamie Nova""" 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": "crème au chocolat noir", "quantity": "une", "type": "food", "brand": "mamie Nova", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "crème au chocolat noir", "quantity": "une", "type": "food", "brand": "mamie Nova", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "crème au chocolat noir", "quantity": "une", "type": "food", "brand": "mamie Nova", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'crème au chocolat noir', 'quantity': 'une', 'type': 'food', 'brand': 'mamie Nova', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'crème au chocolat noir', 'quantity': 'une', 'type': 'food', 'brand': 'mamie Nova', '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 '% creme chocolat noir %' AND V_NormTrademark LIKE '%mamie nova%' ------------- Found solution (max 20) -------------- Crème au Chocolat Noir - creme chocolat noir - - Mamie Nova - 0 - 3456770913205 - 3456770913205 - OFF#719d88b82b57d71adc9d0458f6f18444 Crème Chocolat Noir Intense 70% Cacao - creme chocolat noir intense 70% cacao - - Mamie Nova - 0 - 3456770035655 - 3456770035655 - OFF#40aedcf0e191067bef3fe1521ec37777 Offre Maxi Crème Chocolat Noir Intense - offre maxi creme chocolat noir intense - - Mamie Nova - 0 - 3456770914356 - 3456770914356 - OFF#f6c555837acd4aa3d28e69ff73f071d3 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "J'ai mangé une crème au chocolat noir mamie Nova", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Crème au Chocolat Noir', 'normName': ' creme chocolat noir ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#719d88b82b57d71adc9d0458f6f18444', 'quantity': 'une', 'quantityLem': '1', 'pack': ['GA5.w100'], 'type': 'food', 'gtin': '3456770913205', 'gtinRef': '3456770913205', 'brand': 'Mamie Nova', 'time': '', 'event': 'declaration', 'serving': 'GA5-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.8817946910858154} ---------------------------------------------------------------------------------- LLM CPU Time: 2.8817946910858154