Input path: /home/debian/html/nutritwin/output_llm/68bfff7feefd0/input.json Output path: /home/debian/html/nutritwin/output_llm/68bfff7feefd0/output.json Input text: Quatre tomates cerises. 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: Quatre tomates cerises. ================================================================================================================================== ==================================== 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: ###Quatre tomates cerises.###. 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 : """Quatre tomates cerises.""" 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": "tomates cerises", "quantity": "quatre", "type": "food", "event": "unknown" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "tomates cerises", "quantity": "quatre", "type": "food", "event": "unknown" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "tomates cerises", "quantity": "quatre", "type": "food", "event": "unknown" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'tomates cerises', 'quantity': 'quatre', 'type': 'food', 'event': 'unknown'}], 'cost': 0.0963} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'tomates cerises', 'quantity': 'quatre', '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 '% tomate cerise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Tomate Cerise - tomate cerise - crue - - 0 - - - CIQ#9f76e2172737f480f1c9b66f3627bfb0 Tomate Cerise - tomate cerise - tomate cerise - - 0 - - - KCA#fc7d1647e177b261c9a22262037f6216 Tomates Cerises Rôties aux Broccolinis - tomate cerise rotie au broccolini - et graines de citrouille - - 7 - - - KCA#b4e4bc20b89f5f2678a4843a5d0f40ea ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'Quatre tomates cerises.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Tomate Cerise', 'normName': ' tomate cerise ', 'comment': 'crue', 'normComment': ' crue ', 'rank': 0, 'id': 'CIQ#9f76e2172737f480f1c9b66f3627bfb0', 'quantity': 'quatre', 'quantityLem': '4', 'pack': ['CER.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': 'CER-40', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.7294068336486816} ---------------------------------------------------------------------------------- LLM CPU Time: 1.7294068336486816