Input path: /home/debian/html/nutritwin/output_llm/6893b3d109aac/input.json
Output path: /home/debian/html/nutritwin/output_llm/6893b3d109aac/output.json
Input text: J'ai vu j'ai bu j'ai bu deux verres de vin rouge.
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 vu j'ai bu j'ai bu deux verres de vin rouge.
==================================================================================================================================

==================================== 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 vu j'ai bu j'ai bu deux verres de vin rouge.###. 
 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 vu j'ai bu j'ai bu deux verres de vin rouge.""" 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: <http://www.3dminded.com/RDF/ontology/food#> .

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . 
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix prov: <http://www.w3.org/ns/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": "vin rouge",
        "quantity": "deux verres",
        "timeOfTheDay": "unknown",
        "type": "beverage",
        "event": "declaration"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "vin rouge",
        "quantity": "deux verres",
        "timeOfTheDay": "unknown",
        "type": "beverage",
        "event": "declaration"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "vin rouge",
        "quantity": "deux verres",
        "timeOfTheDay": "unknown",
        "type": "beverage",
        "event": "declaration"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'vin rouge', 'quantity': 'deux verres', 'timeOfTheDay': 'unknown', 'type': 'beverage', 'event': 'declaration'}], 'cost': 0.09875999999999999}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'vin rouge', 'quantity': 'deux verres', 'timeOfTheDay': 'unknown', 'type': 'beverage', '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 '% vin rouge %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Vin Rouge -  vin rouge  -  -  - 0 -  -  - CIQ#0247898eabeefe3884ee430550359cfb
Vin Rouge 9° -  vin rouge 9°  -  -  - 235 -  -  - KCA#9db74ea9610e574a4b5fd169739808d7
Vin Rouge 13° -  vin rouge 13°  -  -  - 27665 -  -  - KCA#f965995ec93171a22515f7141f3fcaec
Vin Rouge 12° -  vin rouge 12°  -  -  - 13758 -  -  - KCA#6576b07568c57c226d3a8a15baa81be6
Vin Rouge 10° -  vin rouge 10°  -  -  - 1065 -  -  - KCA#8c95a77df14a31cf02c05e7b2258cdf9
Vin Rouge 11° -  vin rouge 11°  -  -  - 996 -  -  - KCA#4defe56e99d409c448e479743de50aad
Vin Rouge 14° -  vin rouge 14°  -  -  - 891 -  -  - KCA#7b7cb654b939b936970e863f0cf9a707
Vin Rouge 15° -  vin rouge 15°  -  -  - 285 -  -  - KCA#522430b440ab36f2b30e37915271d575
Poule au Vin Rouge -  poule vin rouge  -  -  - 0 -  -  - KCA#16152425348f25d2abe48e2d55c22eca
Vinaigre de Vin Rouge -  vinaigre de vin rouge  -  -  - 0 -  -  - CIQ#0e65f9a58f80513c4123cfe859bb81f5
Filets de Sole au Vin Rouge -  filet de sole vin rouge  -  -  - 3 -  -  - KCA#623ccf9a58c32a1884f4e7799961e816
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': "J'ai vu j'ai bu j'ai bu deux verres de vin rouge.", 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Vin Rouge', 'normName': ' vin rouge ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'CIQ#0247898eabeefe3884ee430550359cfb', 'quantity': 'deux verres', 'quantityLem': '2 verre', 'pack': ['VAV'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'declaration', 'serving': 'VAV-200', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.8159101009368896}
----------------------------------------------------------------------------------
LLM CPU Time: 2.8159101009368896