Input path: /home/debian/html/nutritwin/output_llm/689f5634adce9/input.json Output path: /home/debian/html/nutritwin/output_llm/689f5634adce9/output.json Input text: Les quelques légumes. 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: Les quelques légumes. ================================================================================================================================== ==================================== 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: ###Les quelques légumes.###. 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 : """Les quelques légumes.""" 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": "légumes", "quantity": "quelques", "type": "food", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "légumes", "quantity": "quelques", "type": "food", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "l\u00e9gumes", "quantity": "quelques", "type": "food", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'légumes', 'quantity': 'quelques', 'type': 'food', 'event': 'unknownEvent'}], 'cost': 0.09617999999999999} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'légumes', 'quantity': 'quelques', 'type': 'food', 'event': 'unknownEvent'} 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 '% legume %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Légumes - legume - mélange surgelé, crus - - 0 - - - CIQ#cdac90625481044846cb7fc75e6b8fc4 Légumes - legume - 3-4 sortes en mélange, purée - - 0 - - - CIQ#49d3727220538d9432ea7f00ea88a120 Légume Cuit - legume cuit - - - 0 - - - CIQ#9e3ab76bd76adf48369352042a898f0c Légumes Secs - legume sec - - - 26 - - - KCA#11ff259e13559012b3a61e08404ecc8d Légumes, Jus - legume ju - aliment moyen - - 62 - - - KCA#ec8e8059fe82f748a29ed4668b08c870 Légumes Verts - legume vert - - - 3680 - - - KCA#3fbcb3c978a56a43b3a94faa14a7d372 Légumes Farcis - legume farci - sauf tomate - - 0 - - - CIQ#be71e1477212f59facda71eec7f5995a Légumes Grillés - legume grille - - - 3351 - - - KCA#71cbd3bc5f5e4bf3933a63272dac0d1e Légumes pour Potages - legume pour potage - surgelés, crus - - 0 - - - CIQ#e9c7ec608d8fb09e0f9289669b20b604 Légumes pour Couscous - legume pour couscou - surgelés, crus - - 0 - - - CIQ#75869428923d4c90f5d609bad2192b4f Légumes, Mélange Surgelé - legume melange surgele - - - 2346 - - - KCA#e39891e7f0758373a0b3b4e8bb9b9a1c Légumes Farcis (sauf Tomate) - legume farci - - - 304 - - - KCA#e512cf542a49b999b54099799c521384 Légumes Cuits (aliment Moyen) - legume cuit - - - 860 - - - KCA#9e3ab76bd76adf48369352042a898f0c Légumes Braisés à la Gremolata - legume braise gremolata - - - 49 - - - KCA#e143157033c13edf1668aaed06032a65 Légume en Purée, sans Sel (aliment Moyen) - legume en puree san sel - - - 44 - - - KCA#d9ac9a98a2c3f56f729608c377b7745f Jus de Légumes - ju de legume - pur jus, aliment moyen - - 0 - - - CIQ#bebc98ac1c870759f243d591f960a525 Pain de Légumes - pain de legume - - - 30 - - - KCA#5f911853df6018fce97dd843c894a42d Chips de Légumes - chip de legume - - - 246 - - - KCA#5a6ce99a16d6417aceae1a542b182077 Pâtes aux Légumes Crues - pate au legume crue - - - 98 - - - KCA#7bdf5788c7783bf48c7cad4f21e4a20c Pâté de Légumes en Croûte - pate de legume en croute - - - 23 - - - KCA#d57fad2b2ea5ec850d1d61950e018dae ---------------------------------------------------- ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Les quelques légumes.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Légumes', 'normName': ' legume ', 'comment': 'mélange surgelé, crus', 'normComment': ' melange surgele cru ', 'rank': 0, 'id': 'CIQ#cdac90625481044846cb7fc75e6b8fc4', 'quantity': 'quelques', 'quantityLem': 'quelque', 'pack': ['LEG.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.9782745838165283} ---------------------------------------------------------------------------------- LLM CPU Time: 3.9782745838165283