Input path: /home/debian/html/nutritwin/output_llm/66336eb51eb8a/input.json Output path: /home/debian/html/nutritwin/output_llm/66336eb51eb8a/output.json Input text: Ce matin j'ai bu du champagne dans un verre à dégustation 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: Ce matin j'ai bu du champagne dans un verre à dégustation ================================================================================================================================== ==================================== 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: ###Ce matin j'ai bu du champagne dans un verre à dégustation###. 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 : """Ce matin j'ai bu du champagne dans un verre à dégustation""" into an array in JSON of consumed foods and beverages. Provide a solution without explanation. Use only the ontology described in this 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. 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:event a owl:DatatypeProperty ; rdfs:label "event"@en; rdfs:comment "Event of eating or drinking. Each must have an event"@en; rdfs:range xsd:string. food:intent a food:event ; rdfs:label "intent" . rdfs:comment "When the event should happen"@en. food:declaration a food:event ; rdfs:label "declaration" . rdfs:comment "When the event has already occured"@en. food:unknownEvent a food:event ; rdfs:label "unknown" ; rdfs:comment "When the event is unknown in the day"@en. """ ========================================================================================= ------------------------------ LLM Raw response ----------------------------- ```json [ { "name": "champagne", "quantity": "un verre", "type": "beverage", "timeOfTheDay": "breakfast", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "champagne", "quantity": "un verre", "type": "beverage", "timeOfTheDay": "breakfast", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "champagne", "quantity": "un verre", "type": "beverage", "timeOfTheDay": "breakfast", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'champagne', 'quantity': 'un verre', 'type': 'beverage', 'timeOfTheDay': 'breakfast', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'champagne', 'quantity': 'un verre', 'type': 'beverage', 'timeOfTheDay': 'breakfast', '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 '% champagne %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Champagne - champagne - - - 12344 - - - KCA#9594d59f67802537367ff9f03d6efb38 Escargots au Champagne - escargot champagne - - - 9 - - - KCA#3a0938b543fd1edc84436a70228d099a Bécasses à la Fine Champagne - becasse fine champagne - - - 2 - - - KCA#1f07b7e17ab70fdaceb1874b30d0fa99 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "Ce matin j'ai bu du champagne dans un verre à dégustation", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4-0125-preview', 'solutions': {'nutrition': [{'name': 'Champagne', 'normName': ' champagne ', 'comment': '', 'normComment': '', 'rank': 12344, 'id': 'KCA#9594d59f67802537367ff9f03d6efb38', 'quantity': 'un verre', 'quantityLem': '1 verre', 'pack': ['FAC', 'VAD', 'COU'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'breakfast', 'event': 'declaration', 'serving': 'VAD-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.639528274536133} ---------------------------------------------------------------------------------- LLM CPU Time: 4.639528274536133