Input path: /home/debian/html/nutritwin/output_llm/6718dbaf806dd/input.json Output path: /home/debian/html/nutritwin/output_llm/6718dbaf806dd/output.json Input text: Quatre petites pommes de terre bouillie. 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: Quatre petites pommes de terre bouillie. ================================================================================================================================== ==================================== 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: ###Quatre petites pommes de terre bouillie.###. 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 : """Quatre petites pommes de terre bouillie.""" 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": "pommes de terre", "quantity": "quatre petites", "cookingMethod": "bouillie", "type": "food", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "pommes de terre", "quantity": "quatre petites", "cookingMethod": "bouillie", "type": "food", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "pommes de terre", "quantity": "quatre petites", "cookingMethod": "bouillie", "type": "food", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'pommes de terre', 'quantity': 'quatre petites', 'cookingMethod': 'bouillie', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'pommes de terre', 'quantity': 'quatre petites', 'cookingMethod': 'bouillie', '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 '% pomme de terre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Pomme de Terre - pomme de terre - égouttée - - 26541 - - - CIQ#bbc0fd1495ed69b7aadd91d1d9b9ae69 Pomme de Terre - pomme de terre - aliment moyen - - 0 - - - CIQ#15f690b8140afc79288abfb96a139095 Pomme de Terre - pomme de terre - sans peau, crue - - 0 - - - CIQ#9d1dc4d850cf0a126428e8235097b299 Pomme de Terre - pomme de terre - rôtie/cuite au four - - 0 - - - CIQ#73642ae51d1ceb413f96f404c2e8fcc5 Pomme de Terre - pomme de terre - purée, aliment moyen - - 0 - - - CIQ#20c56d85dc4d344fdfb3594d5e93f5ff Pomme de Terre - pomme de terre - bouillie/cuite à l'eau - - 0 - - - CIQ#6997e933cb8bbe4ad6fb62b2f04c05c2 Pomme de Terre - pomme de terre - sans peau, rôtie/cuite au four - - 0 - - - CIQ#7c973fe7644a5cc7a5e1ac7f7690f91c Pomme de Terre - pomme de terre - purée, avec lait et beurre, non salée - - 54 - - - CIQ#f6d85f887fb7a88d451e7d1390b123ee Pomme de Terre - pomme de terre - flocons déshydratés, au lait ou à la crème - - 0 - - - CIQ#1450a8209d87032018367a76931b19ad Pomme de Terre - pomme de terre - purée à base de flocons, reconstituée avec lait entier, matière grasse - - 0 - - - CIQ#e310092ee2308f72f5d4eb70daa82fbc Pomme de Terre - pomme de terre - purée à base de flocons, reconstituée avec lait demi-écrémé et eau, non salée - - 0 - - - CIQ#3b12d13dfd318911c754bcb37b7b05ab Pomme de Terre Anna - pomme de terre anna - - - 43 - - - KCA#96fe2fadd9f331eb4549227f2e4a6267 Pomme de Terre Chips - pomme de terre chip - - - 42 - - - KCA#1deb7b7eab80f8586099ee58a6db9ea2 Pomme de Terre Purée - pomme de terre puree - - - 40 - - - KCA#0d4cd5387a20885448dbbf1f634017b3 Pomme de Terre Byron - pomme de terre byron - - - 4 - - - KCA#244d59f3080438c8160682d32b6ff789 Pomme de Terre Rôties - pomme de terre rotie - - - 1077 - - - KCA#797b578eb598e7082faea0ae30d34021 Pomme de Terre Frites - pomme de terre frite - - - 178 - - - KCA#d9391c743d3aee9e28d0940b17624718 Pomme de Terre Vapeur - pomme de terre vapeur - sous vide - - 0 - - - CIQ#d52218f9e63c6cb0bf8151b244a71afd Pomme de Terre Poêlée - pomme de terre poelee - avec matière grasse - - 0 - - - CIQ#b717c125ad32aa35b8cd673ba48f8c60 Pomme de Terre Sautées - pomme de terre sautee - - - 5854 - - - KCA#7e685fe608808c6ddb2b7b1edab93c82 ---------------------------------------------------- ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '2 petit' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Quatre petites pommes de terre bouillie.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Pomme de Terre', 'normName': ' pomme de terre ', 'comment': 'égouttée', 'normComment': ' egouttee ', 'rank': 26541, 'id': 'CIQ#bbc0fd1495ed69b7aadd91d1d9b9ae69', 'quantity': 'quatre petites', 'quantityLem': '4 petit', 'pack': ['PDT.w120'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.723714828491211} ---------------------------------------------------------------------------------- LLM CPU Time: 1.723714828491211