Input path: /home/debian/html/nutritwin/output_llm/68d6585350bf7/input.json Output path: /home/debian/html/nutritwin/output_llm/68d6585350bf7/output.json Input text: Deux cafés au lait avec de sucre. 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 cafés au lait avec de sucre. ================================================================================================================================== ==================================== 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 cafés au lait avec de sucre.###. 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 cafés au lait avec de sucre.""" 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": "caf\u000e9 au lait", "quantity": "deux", "timeOfTheDay": "unknown", "type": "beverage", "event": "intent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "caf\u000e9 au lait", "quantity": "deux", "timeOfTheDay": "unknown", "type": "beverage", "event": "intent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "caf\u000e9 au lait", "quantity": "deux", "timeOfTheDay": "unknown", "type": "beverage", "event": "intent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'caf\x0e9 au lait', 'quantity': 'deux', 'timeOfTheDay': 'unknown', 'type': 'beverage', 'event': 'intent'}], 'cost': 0.09899999999999998} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'caf\x0e9 au lait', 'quantity': 'deux', 'timeOfTheDay': 'unknown', 'type': 'beverage', 'event': 'intent'} 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 '% caf9 lait %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1429 seconds Word: Lait pour Café - dist: 0.5742781758308411 - row: 55130 Word: Lactel Café au Lait - dist: 0.5969517827033997 - row: 43044 Word: Café au Lait - dist: 0.6195234060287476 - row: 3947 Word: Lait au Chocolat - dist: 0.6434111595153809 - row: 31522 Word: Top Cao Fourré au Lait - dist: 0.6451378464698792 - row: 33228 Found embedding word: Lait pour Café 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 = 'Lait pour Café' ------------- Found solution (max 20) -------------- Lait pour Café - lait pour cafe - - Carrefour - 0 - 5400101252820 - 5400101252820 - OFF#ac57d797e5cc103ed0ebaefe8deff864 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': 'Deux cafés au lait avec de sucre.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Lait pour Café', 'normName': ' lait pour cafe ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#ac57d797e5cc103ed0ebaefe8deff864', 'quantity': 'deux', 'quantityLem': '2', 'pack': ['VX1', 'BI4', 'VA2', 'VA3', 'GOB'], 'type': 'beverage', 'gtin': '5400101252820', 'gtinRef': '5400101252820', 'brand': 'Carrefour', 'time': 'unknown', 'event': 'intent', 'serving': 'VX1-200', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 2.8921327590942383} ---------------------------------------------------------------------------------- LLM CPU Time: 2.8921327590942383 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.