Input path: /home/debian/html/nutritwin/output_llm/6681a75ac2941/input.json Output path: /home/debian/html/nutritwin/output_llm/6681a75ac2941/output.json Input text: Fromage pané 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: Fromage pané ================================================================================================================================== ==================================== 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: ###Fromage pané###. 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 : """Fromage pané""" 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": "Fromage", "cooking method": "pané", "type of food": "food", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Fromage", "cooking method": "pané", "type of food": "food", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Fromage", "cooking method": "pané", "type of food": "food", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Fromage', 'cooking method': 'pané', 'type of food': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Fromage', 'cooking method': 'pané', 'type of food': '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 '% fromage %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Fromage - fromage - - - 23096 - - - KCA#e2646df35885ba5fc75c406a551c9fbc Fromage 45% MG - fromage 45% mg - - - 6874 - - - KCA#14ed2b0745972d44df97c5d52a44ac69 Fromage 20% MG - fromage 20% mg - - - 1124 - - - KCA#e32d6c98bf1d5f0a3c853a8f6bb7c3b3 Fromage 70% MG - fromage 70% mg - - - 494 - - - KCA#351b50fec02ae7c43d964985ac9086c6 Fromage de Tête - fromage de tete - - - 258 - - - CIQ#a80997979cdf84066ed5ed98f0291aef Fromage de Chèvre - fromage de chevre - - - 4537 - - - KCA#87a40b8f006dcb11aafd4e97014ed3f4 Fromage de Brebis - fromage de brebi - pâte pressée - - 0 - - - KCA#58787aec327646598cc7785b49eea77a Fromage de Brebis - fromage de brebi - pâte molle à croûte fleurie - - 0 - - - KCA#a463c1fc485a4f9d296ce6817ce2c361 Fromage de Chèvre - fromage de chevre - lactique affiné, au lait cru type Crottin - - 250 - - - KCA#2c01ba493c1fac82cabb393f8f3648a7 Fromage Frais 0% MG - fromage frai 0% mg - - - 519 - - - KCA#88f1992eded597fa4d19465f74683774 Fromage Fondu 25% MG - fromage fondu 25% mg - - - 3246 - - - KCA#d149670a9548a1b193a2c41eca41b75f Fromage Frais 30% MG - fromage frai 30% mg - - - 145 - - - KCA#7925728898a08e85f13745b60bc71320 Fromage Fondu 45% MG - fromage fondu 45% mg - - - 95 - - - KCA#6d8e1e183c61d211654c306cf3835256 Fromage Frais 20% MG - fromage frai 20% mg - - - 81 - - - KCA#0c277d2e26315ef0b610a1ac6f0b2c8f Fromage Fondu 70% MG - fromage fondu 70% mg - - - 55 - - - KCA#4310db392dfdcff70718326fee922034 Fromage Fondu 65% MG - fromage fondu 65% mg - - - 52 - - - KCA#df8a055eb661bce01be58e63581e3ace Fromage Blanc Nature - fromage blanc nature - 0% MG - - 24178 - - - CIQ#36c17f9437be97fba469ea7cd5441d75 Fromage Blanc Nature - fromage blanc nature - 3% MG environ - - 10606 - - - CIQ#4a1c07f162d63ff83801c1fb767aafcf Fromage Blanc Nature - fromage blanc nature - gourmand, 8% MG environ - - 0 - - - CIQ#4ec95c0d5d5444677063a6486af1e1c9 Fromage Fondu aux Noix - fromage fondu au noix - - - 23 - - - KCA#849bff96c14abb755613ff11508fe7c9 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Fromage pané', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Fromage', 'normName': ' fromage ', 'comment': '', 'normComment': '', 'rank': 23096, 'id': 'KCA#e2646df35885ba5fc75c406a551c9fbc', 'quantity': '', 'quantityLem': '', 'pack': ['CAM.w20', 'GRU.w20', 'MIM.w20', 'ROC.w20', 'CH2.w20'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 23.316839933395386} ---------------------------------------------------------------------------------- LLM CPU Time: 23.316839933395386