Input path: /home/debian/html/nutritwin/output_llm/67c49f8726db5/input.json Output path: /home/debian/html/nutritwin/output_llm/67c49f8726db5/output.json Input text: Du son d'avoine et des graines de nigelle. 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: Du son d'avoine et des graines de nigelle. ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Identify food and beverage consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Du son d'avoine et des graines de nigelle.###. Format the result in JSON format: {"intents": []}. ========================================================================================= ------------------------------ LLM Raw response ----------------------------- {"intents": ["Identify food and beverage consumption or declaration"]} ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ {"intents": ["Identify food and beverage consumption or declaration"]} ------------------------------------------------------ ERROR: wrong object representation: {'intents': ['Identify food and beverage consumption or declaration']} ------------------------ After simplification ------------------------ { "intents": [ "Identify food and beverage consumption or declaration" ] } ---------------------------------------------------------------------- ==================================== Prompt ============================================= Convert this natural language query : """Du son d'avoine et des graines de nigelle.""" into an array of JSON. Ignore what it is not connected to nutrition, beverage or food. Provide a solution without explanation. Use the following ontology and only this 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...). Ignore food or beverage when it is not consumed in the past, now or in the future. The cooking mode is not in the name. The name is only in french."""@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 is only in french. Here are examples: '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. The cooking method is in french."@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 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. 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. """ Here is an example of result: [ { "name": "blanquette de veau", "quantity": "un plat", "cookingMethod": "mijot\u00e9", "timeOfTheDay": "lunch", "company": "Leclerc", "type": "food", "event": "declaration" }, { "name": "eau", "brand": "Evian", "company": "Danone", "timeOfTheDay": "breakfast", "quantity": "un verre", "type": "beverage", "event": "intent" } ] ========================================================================================= ------------------------------ LLM Raw response ----------------------------- [ { "name": "son d'avoine", "type": "food", "event": "unknownEvent" }, { "name": "graines de nigelle", "type": "food", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "son d'avoine", "type": "food", "event": "unknownEvent" }, { "name": "graines de nigelle", "type": "food", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "son d'avoine", "type": "food", "event": "unknownEvent" }, { "name": "graines de nigelle", "type": "food", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': "son d'avoine", 'type': 'food', 'event': 'unknownEvent'}, {'name': 'graines de nigelle', 'type': 'food', 'event': 'unknownEvent'}], 'cost': 0.10002} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': "son d'avoine", 'type': 'food', 'event': 'unknownEvent'} 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 '% son avoine %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Son d'Avoine - son avoine - son d'avoine - - 0 - - - KCA#ee0cc8aa1cfae65d8d93aa0a3d94e0ad Galette Son Avoine - galette son avoine - - - 1241 - - - KCA#51a9106c668964844b1bbe32742a539d ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ----------- result to be analyzed ----------- {'name': 'graines de nigelle', 'type': 'food', 'event': 'unknownEvent'} 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 '% graine de nigelle %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1223 seconds Word: Graines de Sesame - dist: 0.635574221611023 - row: 54795 Word: Graines de Chia Noir - dist: 0.6410996317863464 - row: 55681 Word: Graines de Sésame BIO - dist: 0.649505078792572 - row: 27585 Word: Graines de Sarrasin Grillées - dist: 0.6616731882095337 - row: 54867 Word: Graine de Sésame BIO - dist: 0.6622814536094666 - row: 46954 Found embedding word: Graines de Sesame Second try (embedded): 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_Name = 'Graines de Sesame' ------------- Found solution (max 20) -------------- Graines de Sesame - graine de sesame - - Carrefour - 0 - 5400101025240 - 5400101025240 - OFF#4a5c0282e8807f3877298d86f55b156b Graines de Sesame - graine de sesame - - Spar - 0 - 5400210188645 - 5400210188645 - OFF#dee5d08a33fa125a63b2bef9a35fbbae ---------------------------------------------------- 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': "Du son d'avoine et des graines de nigelle.", 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': "Son d'Avoine", 'normName': ' son avoine ', 'comment': "son d'avoine", 'normComment': ' son avoine ', 'rank': 0, 'id': 'KCA#ee0cc8aa1cfae65d8d93aa0a3d94e0ad', 'quantity': '', 'quantityLem': '', 'pack': ['CSS.w20'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}, {'name': 'Graines de Sesame', 'normName': ' graine de sesame ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#4a5c0282e8807f3877298d86f55b156b', 'quantity': '', 'quantityLem': '', 'pack': ['BOB.w30', 'BOL.w50'], 'type': 'food', 'gtin': '5400101025240', 'gtinRef': '5400101025240', 'brand': 'Carrefour', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 2.898533344268799} ---------------------------------------------------------------------------------- LLM CPU Time: 2.898533344268799