Input path: /home/debian/html/nutritwin/output_llm/66e6a338e82d9/input.json Output path: /home/debian/html/nutritwin/output_llm/66e6a338e82d9/output.json Input text: J'ai mangé deux Vache Qui Rit 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: J'ai mangé deux Vache Qui Rit ================================================================================================================================== ==================================== 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: ###J'ai mangé deux Vache Qui Rit###. 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 : """J'ai mangé deux Vache Qui Rit""" 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": "Vache Qui Rit", "quantity": "deux", "type": "food", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Vache Qui Rit", "quantity": "deux", "type": "food", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Vache Qui Rit", "quantity": "deux", "type": "food", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Vache Qui Rit', 'quantity': 'deux', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Vache Qui Rit', 'quantity': 'deux', 'type': '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 '% vache qui rit %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) Second 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_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%%' ------------- Found solution (max 20) -------------- La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073768465500 - 3073768465500 - OFF#64a8dc23196895161ab1b53d6923bde4 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781192186 - 3073768465500 - OFF#da43cf7551a9e13c61a1ad694e521b4d La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781071559 - 3073768465500 - OFF#8c52efd954d14680fc301fe8e08e1c82 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073780886840 - 3073768465500 - OFF#e12170c7846fec182545d25fee1b2813 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073780574242 - 3073768465500 - OFF#5f4a5603fe8f62909abc2cc739c79aa5 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149586 - 3073768465500 - OFF#383167203c08d92736feccfd146b80de La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149838 - 3073768465500 - OFF#a450d0cc58d1fa1b104236b2a3b0fb1d La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149890 - 3073768465500 - OFF#1b0067194622ccea435fb351f5b4e9d4 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149876 - 3073768465500 - OFF#7e4fda2bf6eb008f72ad807808a6af64 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158533 - 3073768465500 - OFF#a6023c2768c642db84da32383a25ca81 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158830 - 3073768465500 - OFF#28d1e1d1aa56dae7fc32bcbf6859e135 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781194692 - 3073768465500 - OFF#e824de1734ce67d42f4333e7879c4545 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781192216 - 3073768465500 - OFF#9296abd5b4b24c23e27e9309776f6c1e La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781178913 - 3073768465500 - OFF#5c71f9d1919acec1ce090da7d83251f8 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781160420 - 3073768465500 - OFF#d105e86b448cf52866438cfa143f7aa9 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158854 - 3073768465500 - OFF#c2860291f39ec76a244f28873be7d183 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158847 - 3073768465500 - OFF#861fadf18788d9d0c2e6593e9378d4a3 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149937 - 3073768465500 - OFF#931815b46e255f01a8c4fc69aa6023d7 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781070200 - 3073768465500 - OFF#712506d4ac1e1c5ca319750438d29a11 La Vache Qui Rit 16p - vache qui rit 16p - - group Bel - 0 - 3073781149852 - 3073781149852 - OFF#7ebe1de82703b44929fb2056a48c1e6d ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "J'ai mangé deux Vache Qui Rit", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'La Vache Qui Rit', 'normName': ' vache qui rit ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#64a8dc23196895161ab1b53d6923bde4', 'quantity': 'deux', 'quantityLem': '2', 'pack': ['VQR.w16'], 'type': 'food', 'gtin': '3073768465500', 'gtinRef': '3073768465500', 'brand': 'group Bel', 'time': '', 'event': 'declaration', 'serving': 'VQR-200', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.643665075302124} ---------------------------------------------------------------------------------- LLM CPU Time: 1.643665075302124