Input path: /home/debian/html/nutritwin/output_llm/6793837e0c35a/input.json Output path: /home/debian/html/nutritwin/output_llm/6793837e0c35a/output.json Input text: À midi je mange une salade verte avec la vinaigrette. 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: À midi je mange une salade verte avec la vinaigrette. ================================================================================================================================== ==================================== 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: ###À midi je mange une salade verte avec la vinaigrette.###. 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 : """À midi je mange une salade verte avec la vinaigrette.""" 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": "salade verte", "quantity": "une", "timeOfTheDay": "lunch", "type": "food", "event": "declaration" }, { "name": "vinaigrette", "quantity": "une", "timeOfTheDay": "lunch", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "salade verte", "quantity": "une", "timeOfTheDay": "lunch", "type": "food", "event": "declaration" }, { "name": "vinaigrette", "quantity": "une", "timeOfTheDay": "lunch", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "salade verte", "quantity": "une", "timeOfTheDay": "lunch", "type": "food", "event": "declaration" }, { "name": "vinaigrette", "quantity": "une", "timeOfTheDay": "lunch", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'salade verte', 'quantity': 'une', 'timeOfTheDay': 'lunch', 'type': 'food', 'event': 'declaration'}, {'name': 'vinaigrette', 'quantity': 'une', 'timeOfTheDay': 'lunch', 'type': 'food', 'event': 'declaration'}], 'cost': 0.10505999999999999} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'salade verte', 'quantity': 'une', 'timeOfTheDay': 'lunch', '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 '% salade verte %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Salade Verte - salade verte - avec vinaigrette - - 61586 - - - KCA#4ab5b82c387924b3ce222cda9fdf1f3e Salade Verte - salade verte - sans vinaigrette - - 15302 - - - KCA#ffaecdf2b67b1ae9d2055afa2173c027 ---------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'vinaigrette', 'quantity': 'une', 'timeOfTheDay': 'lunch', '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 '% vinaigrette %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Vinaigrette - vinaigrette - - - 16273 - - - KCA#e4a8d271a13253a076ecb84c590edc45 Vinaigrette au Sésame - vinaigrette sesame - - - 78 - - - KCA#42042e19b8d3b31a68f6ebd0a3670cac Raie à la Vinaigrette - raie vinaigrette - - - 16 - - - KCA#4ca1fb45b968beb65422f8e85881ac50 Crudité Vinaigrette - crudite vinaigrette - aliment moyen - - 3404 - - - KCA#70807618ddd3f6b30e585b929c5f006a Poireaux Vinaigrette - poireau vinaigrette - - - 1011 - - - KCA#7079a39528c94a392c83315b250f3c29 Artichauts à la Vinaigrette - artichaut vinaigrette - - - 84 - - - KCA#6821b9ef7fd9c2b91e6fada84027cbb2 Champignons Vinaigrette - champignon vinaigrette - - - 24 - - - KCA#2b370fdead13ae1b072147efe2939971 Carotte Râpée à la Vinaigrette - carotte rapee vinaigrette - - - 1371 - - - KCA#550aab930f59f61f6d4b015c1f19f2a7 Museau de Porc Vinaigrette - museau de porc vinaigrette - - - 0 - - - CIQ#6d45d5637bfe086a63507f6ce6b9b653 Salade de Tomates Vinaigrette - salade de tomate vinaigrette - - - 6673 - - - KCA#554aa2ce201cd0052f644a5f6dd86568 Museau de Boeuf en Vinaigrette - museau de boeuf en vinaigrette - - - 0 - - - CIQ#8ffeecb67101e712102cde30549c10cd ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'À midi je mange une salade verte avec la vinaigrette.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Salade Verte', 'normName': ' salade verte ', 'comment': 'avec vinaigrette', 'normComment': ' avec vinaigrette ', 'rank': 61586, 'id': 'KCA#4ab5b82c387924b3ce222cda9fdf1f3e', 'quantity': 'une', 'quantityLem': '1', 'pack': ['SAL.w170'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'lunch', 'event': 'declaration', 'serving': 'SAL-100', 'posiNormName': 0}, {'name': 'Vinaigrette', 'normName': ' vinaigrette ', 'comment': '', 'normComment': '', 'rank': 16273, 'id': 'KCA#e4a8d271a13253a076ecb84c590edc45', 'quantity': 'une', 'quantityLem': '1', 'pack': ['CSL', 'CCL'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'lunch', 'event': 'declaration', 'serving': 'CSL-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.924126625061035} ---------------------------------------------------------------------------------- LLM CPU Time: 2.924126625061035