Input path: /home/debian/html/nutritwin/output_llm/671fecc01cb2a/input.json Output path: /home/debian/html/nutritwin/output_llm/671fecc01cb2a/output.json Input text: Babybel. 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: Babybel. ================================================================================================================================== ==================================== 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: ###Babybel.###. 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 : """Babybel.""" 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 beverage identifier, the name should not contain information related to quantity or container (like glass...)."@en; rdfs:comment "Ignore food or beverage when it is not consumed in the past, now or in the future."@en; rdfs:comment "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."@en; rdfs:comment "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. """ ========================================================================================= ------------------------------ LLM Raw response ----------------------------- ```json [ { "name": "Babybel", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ ```json [ { "name": "Babybel", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Babybel", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Babybel', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Babybel', '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 '% babybel %' 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 '% babybel %' AND V_NormTrademark LIKE '%%' ------------- Found solution (max 20) -------------- Babybel - babybel - - group Bel - 0 - 3073780460262 - 3073780460262 - OFF#ab5692c8a74a7bb6128db8fe9f7947f2 Babybel - babybel - - group Bel - 0 - 3073780975063 - 3073780460262 - OFF#5003711f2f2e372cc24eebf535c00b7c Babybel - babybel - - group Bel - 0 - 3073780918763 - 3073780460262 - OFF#39d6e30d44a3d2271716d0c701c43ebb Babybel - babybel - - group Bel - 0 - 3073780968515 - 3073780460262 - OFF#cacef27920ea49182e1ba2d69d51afcf Babybel - babybel - - group Bel - 0 - 3073781091533 - 3073780460262 - OFF#9a62971731070ae14a622a4f52dc36b2 Babybel - babybel - - group Bel - 0 - 3073781094497 - 3073780460262 - OFF#7f25361b90854edf9900d1c27021a138 Babybel - babybel - - group Bel - 0 - 3073781106374 - 3073780460262 - OFF#ab66adf8ddb737cbe7dcaa73d6bc9932 Babybel - babybel - - group Bel - 0 - 3073781079272 - 3073780460262 - OFF#4a30a558957d0bfcb54e0b9b3a01ffe2 Babybel - babybel - - group Bel - 0 - 3073781106435 - 3073780460262 - OFF#9af70bb2e2a75fc46bb4247488338eb0 Babybel - babybel - - group Bel - 0 - 3073781178005 - 3073780460262 - OFF#06f38a1485a2240c4e966708bfae800e Babybel - babybel - - group Bel - 0 - 3073781069716 - 3073780460262 - OFF#70e32e2dad98168f63c7ef06780119b0 Babybel BIO - babybel bio - - group Bel - 0 - 3073781108842 - 3073781108842 - OFF#01eecd9f0ffc34903325eacdec23b1f3 Babybel Light - babybel light - - group Bel - 0 - 3073781106398 - 3073781106398 - OFF#dd7f97d138a0adceae191d200759a064 Babybel Protein - babybel protein - - group Bel - 0 - 3073781136814 - 3073781136814 - OFF#fb8e06962da28872ad4eb74c5273dc4d Babybel Tranches - babybel tranche - - group Bel - 0 - 3073780874687 - 3073780874687 - OFF#6b9aa75f0dfe2ea3773eb06f9c2a699a Babybel Moelleux Généreux - babybel moelleu genereu - - group Bel - 0 - 3073783318195 - 3073783318195 - OFF#b08496017502daebd522e1e08c93b1cf Mini Babybel - mini babybel - - group Bel - 0 - 26045306 - 26045306 - OFF#299471cfbd11a1784f549d877808f074 Mini Babybel - mini babybel - - group Bel - 0 - 3073781071955 - 26045306 - OFF#b22ea355af5887bb44f5994d83ffa1ca Mini Babybel - mini babybel - - group Bel - 0 - 8132874887318 - 26045306 - OFF#6adb42d347e29677a1a740fe3d6bc98c Mini Babybel - mini babybel - - group Bel - 0 - 3073781088915 - 26045306 - OFF#f39f9520710719516fcb96c17da7f5dd ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Babybel.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Babybel', 'normName': ' babybel ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#ab5692c8a74a7bb6128db8fe9f7947f2', 'quantity': '', 'quantityLem': '', 'pack': ['UN2.w20'], 'type': '', 'gtin': '3073780460262', 'gtinRef': '3073780460262', 'brand': 'group Bel', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.0400536060333252} ---------------------------------------------------------------------------------- LLM CPU Time: 1.0400536060333252