Input path: /home/debian/html/nutritwin/output_llm/671e750a3d549/input.json Output path: /home/debian/html/nutritwin/output_llm/671e750a3d549/output.json Input text: Aubergines parmigiano. 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: Aubergines parmigiano. ================================================================================================================================== ==================================== 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: ###Aubergines parmigiano.###. 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 : """Aubergines parmigiano.""" 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": "Aubergines", "cooking method": "parmigiano", "type of food": "food", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Aubergines", "cooking method": "parmigiano", "type of food": "food", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Aubergines", "cooking method": "parmigiano", "type of food": "food", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Aubergines', 'cooking method': 'parmigiano', 'type of food': 'food', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Aubergines', 'cooking method': 'parmigiano', 'type of food': 'food', 'event': 'unknown'} 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 '% aubergine %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Aubergine - aubergine - pulpe et peau, rôtie/cuite au four - - 0 - - - CIQ#e304e37e4ff4dfff70491879194127c4 Aubergines à l'Huile - aubergine huile - - - 1130 - - - KCA#a100d22000317cff05b7857aa0a6afab Aubergines à la Gitane - aubergine gitane - - - 107 - - - KCA#1fe513a4ae18a519cf8f0ffed1b82a9d Aubergines Sauce Piquante - aubergine sauce piquante - - - 29 - - - KCA#a00d9c974603ab4d672847703d768c96 Aubergines Alla Parmigiana - aubergine alla parmigiana - - - 263 - - - KCA#22ca0c2d067e019dbefeebb84171dc21 Aubergines et Tomates au Four - aubergine tomate four - - - 163 - - - KCA#1c97c6a0f288e2b9a688a321e53ea837 Gratin d'Aubergines au Four - gratin aubergine four - - - 89 - - - KCA#af8fb7b0c90be3b57dd66a6bf81fa87f Gratin d'Aubergines à l'Ail - gratin aubergine ail - - - 37 - - - KCA#00d3737b9737c824ff218c0df9d770bf Panaché d'Aubergine - panache aubergine - - - 11 - - - KCA#79b24eedcbd9f92b9efd838b3255911a Beignets d'Aubergine - beignet aubergine - et purée de Légumes rôtis - - 43 - - - KCA#b399054ed31ec00b6ad5d0626d338adb Porc Frit aux Aubergines - porc frit au aubergine - - - 13 - - - KCA#e9ccea2a5f707f0f60d5e158fc1bb717 Raviolis Frais Mozzarella, Aubergines, Tomates - ravioli frai mozzarella aubergine tomate - - - 106 - - - KCA#d4f4e3a8c39b3ea26608b7b1be1e7382 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Aubergines parmigiano.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Aubergine', 'normName': ' aubergine ', 'comment': 'pulpe et peau, rôtie/cuite au four', 'normComment': ' pulpe peau rotie/cuite four ', 'rank': 0, 'id': 'CIQ#e304e37e4ff4dfff70491879194127c4', 'quantity': '', 'quantityLem': '', 'pack': ['AUB.w300'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.6212754249572754} ---------------------------------------------------------------------------------- LLM CPU Time: 1.6212754249572754