Input path: /home/debian/html/nutritwin/output_llm/6718dc0033038/input.json Output path: /home/debian/html/nutritwin/output_llm/6718dc0033038/output.json Input text: Une cuillère à café de beurre. 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: Une cuillère à café de beurre. ================================================================================================================================== ==================================== 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: ###Une cuillère à café de beurre.###. 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 : """Une cuillère à café de beurre.""" 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 beverage identifier, the name should not contain information related to quantity or container (like glass...)."@en; rdfs:comment "Ignore food or beverage when it is not consumed in the past, now or in the future."@en; rdfs:comment "The cooking mode is not in the name. 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."@en; rdfs:comment "When the name is very known (ex: Activia, Coca) and the brand is not mentioned, guess the brand."@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": "beurre", "quantity": "une cuillère à café", "type": "food", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "beurre", "quantity": "une cuillère à café", "type": "food", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "beurre", "quantity": "une cuillère à café", "type": "food", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'beurre', 'quantity': 'une cuillère à café', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'beurre', 'quantity': 'une cuillère à café', 'type': 'food', '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 '% beurre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Beurre Doux - beurre dou - - - 78338 - - - KCA#70e9c7551c333018468c7d83aa296ffb Beurre Allégé - beurre allege - - - 951 - - - KCA#e5fa3181212a47a965532c52b5386037 Beurre Demi-sel - beurre demi sel - - - 4320 - - - KCA#35fac54cc4123c12fb12b350abb9b457 Beurre à 80% MG - beurre 80% mg - salé - - 0 - - - CIQ#d5e469e83eef82ff717fa1e221cde650 Beurre à 82% MG - beurre 82% mg - doux - - 0 - - - CIQ#3a7a92623060e0f381d677274ed57211 Beurre à 80% MG - beurre 80% mg - demi-sel - - 0 - - - CIQ#35e38c233cccb53f6cefdd1ec56c4b5c Beurre à 82% MG - beurre 82% mg - doux, tendre - - 0 - - - CIQ#786b1aab29e436814daeb984092dd932 Beurre à 39-41% MG - beurre 39 41% mg - léger, doux - - 0 - - - CIQ#ff256be25c78202b0129214d377078fb Beurre à 60-62% MG - beurre 60 62% mg - à teneur réduite en matière grasse, doux - - 0 - - - CIQ#73ce45af94ffc409f4861c7199a2763b Beurre à 60-62% MG - beurre 60 62% mg - à teneur réduite en matière grasse, demi-sel - - 0 - - - CIQ#66500be73ce80bda379e9a7f7301e55e Beurre ou Assimilé Allégé - beurre ou assimile allege - léger ou à teneur reduite en matière grasse, doux, aliment moyen - - 0 - - - CIQ#39265899332bfa2f5a199208dbbcc2e4 Beurre Léger 39-41% MG Doux - beurre leger 39 41% mg dou - - - 148 - - - KCA#3c9a43dcdff911e58d4868ccce7295f5 Beurre Léger 39-41% MG Demi-sel - beurre leger 39 41% mg demi sel - - - 652 - - - KCA#1379376ecc87973b7f43402c587fe353 Beurre à Teneur en Matière Grasse Inconnue - beurre teneur en matiere grasse inconnue - allégé ou non, demi-sel, aliment moyen - - 0 - - - CIQ#b13b44b9cead6168da8f188d7acc178b Beurre ou Assimilé à Teneur en Matière Grasse Inconnue - beurre ou assimile teneur en matiere grasse inconnue - doux, aliment moyen - - 0 - - - CIQ#4b726ca38cd9d14e2956083e7e442827 Raie au Beurre Noir - raie beurre noir - - - 78 - - - KCA#89fb07f55a25aa8cbb884e6ce5baa12c Crème au Beurre - creme beurre - - - 43 - - - KCA#8239042903c3d4e3b577b2c276b39ee8 Crêpe Beurre Sucre - crepe beurre sucre - - - 1261 - - - KCA#adf06f3567a044e35baea32b960ad4dc Petit Beurre Industriel - petit beurre industriel - - - 41 - - - KCA#99d8a990698f19e6a4feca4736bb7a6c Haricot Beurre - haricot beurre - bouilli/cuit à l'eau - - 0 - - - CIQ#114be26033d424582a109fd9eea38878 ---------------------------------------------------- ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Une cuillère à café de beurre.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Beurre Doux', 'normName': ' beurre dou ', 'comment': '', 'normComment': '', 'rank': 78338, 'id': 'KCA#70e9c7551c333018468c7d83aa296ffb', 'quantity': 'une cuillère à café', 'quantityLem': '1 cuillere cafe', 'pack': ['NOI.w10', 'BEU.w15'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.3064675331115723} ---------------------------------------------------------------------------------- LLM CPU Time: 1.3064675331115723