Input path: /home/debian/html/nutritwin/output_llm/68fa3bd23c00e/input.json Output path: /home/debian/html/nutritwin/output_llm/68fa3bd23c00e/output.json Input text: J'ai mangé du pain. 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 du pain. ================================================================================================================================== ==================================== 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: ###J'ai mangé du pain.###. 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 : """J'ai mangé du pain.""" 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": "pain", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "pain", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "pain", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'pain', 'type': 'food', 'event': 'declaration'}], 'cost': 0.09509999999999999} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'pain', '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 '% pain %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Pain - pain - - - 261532 - - - CIQ#78316c0b820d8f80c640c9d0bc741c50 Pain - pain - sans gluten - - 29 - - - CIQ#9d6a800b4a9dbe9504fb68b26057ad7b Pain - pain - baguette, courante - - 0 - - - CIQ#c92016dc98d790db0bc7c949d601f5c2 Pain - pain - baguette ou boule, au levain - - 0 - - - CIQ#4b65f0348cbdd1f29daadea789369616 Pain - pain - baguette ou boule, de campagne - - 0 - - - CIQ#665da1982ec8e7e74501d57dc7e111b8 Pain - pain - baguette, de tradition française - - 0 - - - CIQ#e5e8a2a86b1a95d66e26a64c18c0b520 Pain - pain - baguette ou boule, bis, à la farine T80 ou T110 - - 0 - - - CIQ#233b9a74f0cc423be7b3fe6fa040567b Pain - pain - baguette ou boule, bio, à la farine T55 jusqu'à T110 - - 0 - - - CIQ#91fae3ae1c9b87dd0039d7caa03a7d72 Pain - pain - baguette ou boule, aux céréales et graines, artisanal - - 0 - - - CIQ#5fed24621fe6dde995398f020bf84d7d Pain Bis - pain bi - - - 77 - - - KCA#0d04d397f5620b8618c8972be2ce29a7 Pain Pita - pain pita - - - 951 - - - KCA#0a6b29619370c1e5c09e5ec16992feed Pain Azyme - pain azyme - - - 1038 - - - KCA#90d292248257ebd4aba91b7e0f6f67d7 Pain Perdu - pain perdu - - - 783 - - - CIQ#67427fe34e70bfc99fd131b16908c1ee Pain de Son - pain de son - - - 302 - - - KCA#3ccdb3c87985b4f83e1354ee3a2cebfd Pain au Son - pain son - - - 0 - - - CIQ#825cc00fe7ac81ed34e142fde0f6ddf4 Pain de Mie - pain de mie - au son - - 0 - - - CIQ#1f8d06921f1e892824b0f8cef870e840 Pain de Mie - pain de mie - complet - - 7211 - - - CIQ#d93405497d2314d29dbd770c5b956eeb Pain de Mie - pain de mie - courant - - 0 - - - CIQ#667832b5357e637fdb28760b7d6c2d8d Pain Grillé - pain grille - domestique - - 0 - - - CIQ#f4bc68c618fb825e526db4034e88b66a Pain de Mie - pain de mie - sans croûte - - 32 - - - CIQ#be3f663945b51703d39413cadc3becab ---------------------------------------------------- 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': "J'ai mangé du pain.", 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Pain', 'normName': ' pain ', 'comment': '', 'normComment': '', 'rank': 261532, 'id': 'CIQ#78316c0b820d8f80c640c9d0bc741c50', 'quantity': '', 'quantityLem': '', 'pack': ['PAI.w60', 'BAG.w60', 'TPA.w30'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.4999232292175293} ---------------------------------------------------------------------------------- LLM CPU Time: 1.4999232292175293 Traceback (most recent call last): File "/home/debian/html/nutritwin/resources/KCALLMMainService.py", line 71, in omess = KCALLMMain.runEvent(event) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/html/nutritwin/resources/KCALLMMain.py", line 132, in runEvent resp = KCALLMMainSpeechToData.execute(speech, imagePath, image64, comment, appId, device, version, age, gender, longitude, latitude, test) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/html/nutritwin/resources/KCALLMMainSpeechToData.py", line 111, in execute response = table.put_item( ^^^^^^^^^^^^^^^ File "/home/debian/myVirtualPythonEnvV2/lib/python3.11/site-packages/boto3/resources/factory.py", line 581, in do_action response = action(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/myVirtualPythonEnvV2/lib/python3.11/site-packages/boto3/resources/action.py", line 88, in __call__ response = getattr(parent.meta.client, operation_name)(*args, **params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/myVirtualPythonEnvV2/lib/python3.11/site-packages/botocore/client.py", line 565, in _api_call return self._make_api_call(operation_name, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/myVirtualPythonEnvV2/lib/python3.11/site-packages/botocore/client.py", line 1021, in _make_api_call raise error_class(parsed_response, operation_name) botocore.exceptions.ClientError: An error occurred (UnrecognizedClientException) when calling the PutItem operation: The security token included in the request is invalid.