Input path: /home/debian/html/nutritwin/output_llm/6681a6ef50860/input.json Output path: /home/debian/html/nutritwin/output_llm/6681a6ef50860/output.json Input text: Fromage de chèvre 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 de chèvre ================================================================================================================================== ==================================== 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 de chèvre###. 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 de chèvre""" 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 de chèvre", "type": "food", "event": "unknown" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Fromage de chèvre", "type": "food", "event": "unknown" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Fromage de chèvre", "type": "food", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Fromage de chèvre', 'type': 'food', 'event': 'unknown'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Fromage de chèvre', 'type': '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 '% fromage de chevre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Fromage de Chèvre - fromage de chevre - - - 4537 - - - KCA#87a40b8f006dcb11aafd4e97014ed3f4 Fromage de Chèvre - fromage de chevre - lactique affiné, au lait cru type Crottin - - 250 - - - KCA#2c01ba493c1fac82cabb393f8f3648a7 Fromage de Chèvre Bûche - fromage de chevre buche - - - 778 - - - KCA#d7a0efc82f778e3640fa8ad0797b2874 Fromage de Chèvre Frais - fromage de chevre frai - au lait pasteurisé ou cru - - 656 - - - KCA#ad75bd70c25eb9eff5b638ebd318d6bd Fromage de Chèvre Frais - fromage de chevre frai - au lait cru type Palet ou Crottin frais - - 143 - - - KCA#3a4d3c18874af62cad4141a952e53d2d Fromage de Chèvre Frais - fromage de chevre frai - au lait pasteurisé type Bûchette fraîche - - 0 - - - KCA#764594d012a5dd69bf4d4189cde200e8 Fromage de Chèvre Lactique - fromage de chevre lactique - affiné - - 24 - - - KCA#e150ab032eefca1b06afcd2518766c53 Fromage de Chèvre Lactique - fromage de chevre lactique - affiné, au lait pasteurisé type Bûchette ou Crottin - - 0 - - - KCA#720376e3f6b4648cec17a44b328507e3 Fromage de Chèvre à Tartiner - fromage de chevre tartiner - nature - - 374 - - - KCA#6bba0b44a26058518ba3b9ff622f101c Fromage de Chèvre Type 'Camembert', au Lait Pasteurisé ou Cru - fromage de chevre type camembert lait pasteurise ou cru - - - 66 - - - KCA#fe57c4f9bd9ea25db4dfb0464b021c9d Salade aux Fèves et au Fromage de Chèvre - salade au feve fromage de chevre - - - 207 - - - KCA#6e81ecb25023c8af886949335c52a64b Lasagnes ou Cannellonis aux Légumes et au Fromage de Chèvre - lasagne ou cannelloni au legume fromage de chevre - - - 0 - - - CIQ#5d42729119eba2b6dc2e0ff8d4ba716d ---------------------------------------------------- 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': 'Fromage de chèvre', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Fromage de Chèvre', 'normName': ' fromage de chevre ', 'comment': '', 'normComment': '', 'rank': 4537, 'id': 'KCA#87a40b8f006dcb11aafd4e97014ed3f4', 'quantity': '', 'quantityLem': '', 'pack': ['CH2.w20', 'CHE.w20'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.6450746059417725} ---------------------------------------------------------------------------------- LLM CPU Time: 1.6450746059417725