Input path: /home/debian/html/nutritwin/output_llm/66e6aada2e292/input.json Output path: /home/debian/html/nutritwin/output_llm/66e6aada2e292/output.json Input text: Ce matin j'ai mangé deux Vache Qui Rit 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: Ce matin j'ai mangé deux Vache Qui Rit ================================================================================================================================== ==================================== 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: ###Ce matin j'ai mangé deux Vache Qui Rit###. 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 : """Ce matin j'ai mangé deux Vache Qui Rit""" 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": "Vache Qui Rit", "quantity": "deux", "type": "food", "time of the day": "breakfast", "event": "declaration", "brand": "Vache Qui Rit", "company": "" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Vache Qui Rit", "quantity": "deux", "type": "food", "time of the day": "breakfast", "event": "declaration", "brand": "Vache Qui Rit", "company": "" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Vache Qui Rit", "quantity": "deux", "type": "food", "time of the day": "breakfast", "event": "declaration", "brand": "Vache Qui Rit", "company": "" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Vache Qui Rit', 'quantity': 'deux', 'type': 'food', 'time of the day': 'breakfast', 'event': 'declaration', 'brand': 'Vache Qui Rit', 'company': ''}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Vache Qui Rit', 'quantity': 'deux', 'type': 'food', 'time of the day': 'breakfast', 'event': 'declaration', 'brand': 'Vache Qui Rit', 'company': ''} 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 '% vache qui rit %' AND V_NormTrademark LIKE '%vache qui rit%' Second 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_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%vache qui rit%' ------------------------------------------- ------ERROR-------------------------------- No solution for query: 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_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%vache qui rit%' ------------------------------------------- ------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "Ce matin j'ai mangé deux Vache Qui Rit", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 1.926175832748413} ---------------------------------------------------------------------------------- LLM CPU Time: 1.926175832748413