Input path: /home/debian/html/nutritwin/output_llm/6824c9ecb3e1a/input.json Output path: /home/debian/html/nutritwin/output_llm/6824c9ecb3e1a/output.json Input text: J'ai mangé cinq carrés de chocolat noir. 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: J'ai mangé cinq carrés de chocolat noir. ================================================================================================================================== ==================================== 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: ###J'ai mangé cinq carrés de chocolat noir.###. 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 : """J'ai mangé cinq carrés de chocolat noir.""" 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": "chocolat noir", "quantity": "cinq carr\u00e9s", "timeOfTheDay": "unknown", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "chocolat noir", "quantity": "cinq carr\u00e9s", "timeOfTheDay": "unknown", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "chocolat noir", "quantity": "cinq carr\u00e9s", "timeOfTheDay": "unknown", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'chocolat noir', 'quantity': 'cinq carrés', 'timeOfTheDay': 'unknown', 'type': 'food', 'event': 'declaration'}], 'cost': 0.09953999999999999} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'chocolat noir', 'quantity': 'cinq carrés', 'timeOfTheDay': 'unknown', '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 '% chocolat noir %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Chocolat Noir à Croquer - chocolat noir croquer - - - 18834 - - - KCA#9b500c08695e76b67b18e2fa08773333 Chocolat Noir Noisettes - chocolat noir noisette - - - 1875 - - - KCA#49fc5b19d990e357f20d1160e7d62f54 Chocolat Noir Dégustation - chocolat noir degustation - 70% Cacao - - 7359 - - - KCA#0268c0bb7380b1456496d668269e3ff4 Chocolat Noir Dégustation - chocolat noir degustation - 70% Cacao sans sucre ajouté - - 1221 - - - KCA#34b04a8e141eece15dc24eb779ae71ef Chocolat Noir aux Fruits Secs - chocolat noir au fruit sec - noisettes, amandes, raisins, praline, tablette - - 0 - - - CIQ#2ebd03c0dd9aab3bfb07ac8958b5239c Chocolat Noir à 70% Cacao Minimum - chocolat noir 70% cacao minimum - extra, dégustation, tablette - - 0 - - - CIQ#fece0a5a54ed327de64a617f20b78b6c Chocolat Noir sans Sucres Ajoutés - chocolat noir san sucre ajoute - avec édulcorants, en tablette - - 0 - - - CIQ#3cece312c84cb7ddd4bcc80edf31a153 Mikado Chocolat Noir - mikado chocolat noir - - - 333 - - - KCA#280fc4b222ab1c646ddcd84d22f7ca90 Parfaits Chocolat Noir Noisettes - parfait chocolat noir noisette - - - 18 - - - KCA#205552965fb668730b8519c91500d669 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "J'ai mangé cinq carrés de chocolat noir.", 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Chocolat Noir à Croquer', 'normName': ' chocolat noir croquer ', 'comment': '', 'normComment': '', 'rank': 18834, 'id': 'KCA#9b500c08695e76b67b18e2fa08773333', 'quantity': 'cinq carrés', 'quantityLem': '5 carre', 'pack': ['CHO.w7'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'declaration', 'serving': 'CHO-500', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.3605411052703857} ---------------------------------------------------------------------------------- LLM CPU Time: 1.3605411052703857