Input path: /home/debian/html/nutritwin/output_llm/6894d10d84ab1/input.json Output path: /home/debian/html/nutritwin/output_llm/6894d10d84ab1/output.json Input text: Deux cuillères à soupe de graines de chia. 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: Deux cuillères à soupe de graines de chia. ================================================================================================================================== ==================================== 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: ###Deux cuillères à soupe de graines de chia.###. 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 : """Deux cuillères à soupe de graines de chia.""" 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": "graines de chia", "quantity": "deux cuillères à soupe", "type": "food", "event": "unknown" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "graines de chia", "quantity": "deux cuillères à soupe", "type": "food", "event": "unknown" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "graines de chia", "quantity": "deux cuill\u00e8res \u00e0 soupe", "type": "food", "event": "unknown" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'graines de chia', 'quantity': 'deux cuillères à soupe', 'type': 'food', 'event': 'unknown'}], 'cost': 0.09762} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'graines de chia', 'quantity': 'deux cuillères à soupe', '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 '% graine de chia %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1365 seconds Word: Graines de Chia - dist: 0.3294573128223419 - row: 7641 Word: Graines de Chia BIO - dist: 0.38689494132995605 - row: 35454 Word: Graines de Chia Noir - dist: 0.45211777091026306 - row: 55681 Word: Graines et Fruits Framboises et Graines de Chia - dist: 0.4782252013683319 - row: 60214 Word: Baguette Avec Graines de Chia - dist: 0.5220747590065002 - row: 54916 Found embedding word: Graines de Chia 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 = 'Graines de Chia' ------------- Found solution (max 20) -------------- Graines de Chia - graine de chia - - Leclerc - 0 - 0230289016144 - 0230289016144 - OFF#8e669b52f41d454795d5619a46d8b828 Graines de Chia - graine de chia - - Vahiné - 0 - 3179142058396 - 3179142058396 - OFF#368681590ba4e2c7a1df91266a2ea228 Graines de Chia - graine de chia - - La Vie Claire - 0 - 3266191013312 - 3266191013312 - OFF#909a057ced0e4e67a025bae95e9bb8a3 Graines de Chia - graine de chia - - La Pâtelière - 0 - 3278584350133 - 3278584350133 - OFF#b46fb109d278cd5a865507a19149a3d9 Graines de Chia - graine de chia - - Moulin des Moines - 0 - 3347430024662 - 3347430024662 - OFF#dab36c871f4ef73c6e86f3367fd9b496 Graines de Chia - graine de chia - - Monoprix - 0 - 3350033500365 - 3350033500365 - OFF#a629fcb084804ba68c302001aa00d9f5 Graines de Chia - graine de chia - - Jardin Bio - 0 - 3456300004410 - 3456300004410 - OFF#210954fa513775eaa1ef95cbd33364f7 Graines de Chia - graine de chia - - Carrefour - 0 - 3560071270773 - 3560071270773 - OFF#d586ddd71a82558edd167575d97cba70 Graines de Chia - graine de chia - - Cora - 0 - 5400134407402 - 5400134407402 - OFF#ef8bc58655295e1329968129c0b85119 Graines de Chia - graine de chia - - Spar - 0 - 5400210188126 - 5400210188126 - OFF#fe14827055d480bab0497ce63fd151eb Graines de Chia - graine de chia - - La Vie Claire - 0 - 3266191035345 - 3266191013312 - OFF#70cd076ebbd2d2ad62feca25bf749b47 Graines de Chia - graine de chia - - Jardin Bio - 0 - 3456300004878 - 3456300004410 - OFF#c6144afc7dd08f08122f6c752f512225 Graines de Chia - graine de chia - - Carrefour - 0 - 5400101069053 - 3560071270773 - OFF#5bb4ac78f3c4dd94e32c0fb96258e43f ---------------------------------------------------- ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Deux cuillères à soupe de graines de chia.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Graines de Chia', 'normName': ' graine de chia ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#8e669b52f41d454795d5619a46d8b828', 'quantity': 'deux cuillères à soupe', 'quantityLem': '2 cuillere soupe', 'pack': ['BOB.w30', 'BOL.w50'], 'type': 'food', 'gtin': '0230289016144', 'gtinRef': '0230289016144', 'brand': 'Leclerc', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.117532253265381} ---------------------------------------------------------------------------------- LLM CPU Time: 3.117532253265381