Input path: /home/debian/html/nutritwin/output_llm/6762d7cf04438/input.json Output path: /home/debian/html/nutritwin/output_llm/6762d7cf04438/output.json Input text: Un grand verre d'eau gazeuse. 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: Un grand verre d'eau gazeuse. ================================================================================================================================== ==================================== 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: ###Un grand verre d'eau gazeuse.###. 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 : """Un grand verre d'eau gazeuse.""" 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": "eau gazeuse", "quantity": "un grand verre", "type": "beverage", "event": "unknown" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "eau gazeuse", "quantity": "un grand verre", "type": "beverage", "event": "unknown" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "eau gazeuse", "quantity": "un grand verre", "type": "beverage", "event": "unknown" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'eau gazeuse', 'quantity': 'un grand verre', 'type': 'beverage', 'event': 'unknown'}], 'cost': 0.09659999999999999} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'eau gazeuse', 'quantity': 'un grand verre', 'type': 'beverage', '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 '% eau gazeuse %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1212 seconds Word: Eau Gazeuse - dist: 0.3645181953907013 - row: 9738 Word: Eau Minérale Gazeuse - dist: 0.45353829860687256 - row: 61123 Word: Eau Minérale Naturelle Gazeuse - dist: 0.4703151285648346 - row: 9599 Word: Eau Minérale Naturellement Gazeuse - dist: 0.48046597838401794 - row: 45338 Word: Eau Minérale Naturelle Naturellement Gazeuse - dist: 0.4970378279685974 - row: 44713 Found embedding word: Eau Gazeuse Second try (embedded): 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_Name = 'Eau Gazeuse' ------------- Found solution (max 20) -------------- Eau Gazeuse - eau gazeuse - - Les Mousquetaires - 0 - 3250390748649 - 3250390748649 - OFF#0a0e5d64f956d7d7d4d90b0b30a9d090 Eau Gazeuse - eau gazeuse - - Nestlé - 0 - 8002270015786 - 8002270015786 - OFF#e2334f7b642d73eb2a679f3f50f30b6a Eau Gazeuse - eau gazeuse - - Nestlé - 0 - 8002270266560 - 8002270015786 - OFF#627d45a8f057ba7ad2fb5478285b8392 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "Un grand verre d'eau gazeuse.", 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Eau Gazeuse', 'normName': ' eau gazeuse ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#0a0e5d64f956d7d7d4d90b0b30a9d090', 'quantity': 'un grand verre', 'quantityLem': '1 grand verre', 'pack': ['VX1', 'BI4', 'VA2', 'VA3', 'GOB'], 'type': 'beverage', 'gtin': '3250390748649', 'gtinRef': '3250390748649', 'brand': 'Les Mousquetaires', 'time': '', 'event': 'unknown', 'serving': 'VX1-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.2246429920196533} ---------------------------------------------------------------------------------- LLM CPU Time: 2.2246429920196533