Input path: /home/debian/html/nutritwin/output_llm/6654edb24a24e/input.json Output path: /home/debian/html/nutritwin/output_llm/6654edb24a24e/output.json Input text: Andouillette 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: Andouillette ================================================================================================================================== ==================================== 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: ###Andouillette###. 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 : """Andouillette""" 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": "Andouillette", "type": "food", "quantity": "", "cookingMethod": "", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Andouillette", "type": "food", "quantity": "", "cookingMethod": "", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Andouillette", "type": "food", "quantity": "", "cookingMethod": "", "timeOfTheDay": "", "brand": "", "company": "", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Andouillette', 'type': 'food', 'quantity': '', 'cookingMethod': '', 'timeOfTheDay': '', 'brand': '', 'company': '', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Andouillette', 'type': 'food', 'quantity': '', 'cookingMethod': '', '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 '% andouillette %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Andouillette - andouillette - à cuire - - 0 - - - CIQ#3c8f75999ad145614b37b40e0fe79a73 Andouillette - andouillette - sautée/poêlée - - 0 - - - CIQ#149fd900adc4221c3e0c79782efebb8d Andouillette Crue - andouillette crue - - - 6 - - - KCA#fab82b6836bbddc90ef8f864a9c4d8b9 Andouillette Poêlée - andouillette poelee - - - 59 - - - KCA#79cce27109f55306041dd7525b04ad5e Andouillette Pouilly - andouillette pouilly - - - 5 - - - KCA#c805939a32656aa66eff5d5fdf18aef2 Andouillette de Troyes - andouillette de troye - à cuire - - 0 - - - CIQ#497eda9e3169962a832e63f5fc41e057 Andouillette Vallée d'Auge - andouillette vallee auge - - - 5 - - - KCA#454e8c06a49c9ee2257d531dcbfcc93a Andouillette à la Strasbourgeoise - andouillette strasbourgeoise - - - 0 - - - KCA#575ae848faea0723188d8f395cf816a2 ---------------------------------------------------- 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': 'Andouillette', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Andouillette', 'normName': ' andouillette ', 'comment': 'à cuire', 'normComment': ' cuire ', 'rank': 0, 'id': 'CIQ#3c8f75999ad145614b37b40e0fe79a73', 'quantity': '', 'quantityLem': '', 'pack': ['SA1.w500'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.670201301574707} ---------------------------------------------------------------------------------- LLM CPU Time: 2.670201301574707