Input path: /home/debian/html/nutritwin/output_llm/669fe4e0b0b51/input.json Output path: /home/debian/html/nutritwin/output_llm/669fe4e0b0b51/output.json Input text: J'ai mangé du pâté 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 du pâté ================================================================================================================================== ==================================== 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: ###J'ai mangé du pâté###. 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 : """J'ai mangé du pâté""" 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 drink identifier, the name should not contain information related to quantity or container (like glass...). The cooking mode is not in the name. When the brand is very well-known (ex: Activia, Coca-Cola), the name is equal to the brand. 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. When the 'brand' is not specified and, the food or beverage is very well-known (like 'Coca-Cola'), provide the brand name in 'brand', otherwise set 'brand' to ''."@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": "pâté", "type": "food", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "pâté", "type": "food", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "pâté", "type": "food", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'pâté', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'pâté', '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 '% pate %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Pâté - pate - - - 35 - - - CIQ#afa9f7f047da1f15de2883f037186a92 Pâtes - pate - sans gluten, à base de riz et maïs, à l'eau, non salées - - 0 - - - CIQ#fbb4c57fdca55e795247628ccb5aecdd Pâtes - pate - sans gluten, à base de lentilles corail, à l'eau, non salées - - 0 - - - CIQ#b616881505a8dc3bb22f36ba73c591e5 Pâté Breton - pate breton - - - 0 - - - CIQ#7bf7cf124b0a4bd2e2ef3a9a0a499589 Pâtes Cuites - pate cuite - - - 40303 - - - KCA#5f79f58611165eed8a9639bfa123a9ca Pâté de Foie - pate de foie - - - 754 - - - KCA#a5e2912dd9f9cde202e6768375fa2481 Pâté de Tête - pate de tete - - - 191 - - - KCA#f90aa2ff530cc5bc04459e1ca2ba4490 Pâtes Sèches - pate seche - aux oeufs, crues - - 0 - - - CIQ#52cf76f71ceae840a6e8cfb7bb87401e Pâtes Sèches - pate seche - sans gluten, crues - - 0 - - - CIQ#a6df809c43c5e8ea99c2290e16e50a23 Pâtes Sèches - pate seche - au blé complet, crues - - 0 - - - CIQ#2cd29b7b7d0a8beffb2a20bdcd5b67d9 Pâtes Sèches - pate seche - aux oeufs, non salées - - 0 - - - CIQ#475f5a3e0ebed8ce058915c8c0e2488a Pâtes Sèches - pate seche - sans gluten, non salées - - 0 - - - CIQ#a83a046d5cb792a1634de34a8b103f8c Pâtes Sèches - pate seche - au blé complet, non salées - - 0 - - - CIQ#086a2b5c3417a99bed48fb94c6f8e347 Pâte d'Amande - pate amande - - - 753 - - - CIQ#7c0811ad432704e3560ead7d11dcc54b Pâté de Lapin - pate de lapin - - - 228 - - - CIQ#cd9ac9416e8376ef0d33dc474b22d8d1 Pâte de Fruits - pate de fruit - - - 904 - - - CIQ#ddc417db85ad45f7b63c72987afd1efd Pâté en Croûte - pate en croute - - - 69 - - - CIQ#e2118c3e025007fd1644c613af45b0cf Pâté de Gibier - pate de gibier - - - 62 - - - CIQ#68811d74011dd1931c6725029c3ec0d8 Pâté Ardennais - pate ardennai - - - 33 - - - KCA#1c1510a6deb74a99fe2687d0ba87d678 Pâtes Fraîches - pate fraiche - aux oeufs, crues - - 0 - - - CIQ#9afbc65919a12bd31e467b9e01a43777 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution 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': "J'ai mangé du pâté", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Pâté', 'normName': ' pate ', 'comment': '', 'normComment': '', 'rank': 35, 'id': 'CIQ#afa9f7f047da1f15de2883f037186a92', 'quantity': '', 'quantityLem': '', 'pack': ['TR5.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.1465158462524414} ---------------------------------------------------------------------------------- LLM CPU Time: 2.1465158462524414