Input path: /home/debian/html/nutritwin/output_llm/66afd88619a3e/input.json
Output path: /home/debian/html/nutritwin/output_llm/66afd88619a3e/output.json
Input text: Un portillon de jambon
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: Un portillon de jambon
==================================================================================================================================
==================================== 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: ###Un portillon de jambon###.
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 : """Un portillon de jambon""" 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 drink identifier, the name should not contain information related to quantity or container (like glass...). The cooking mode is not in the name. When the brand is very well-known (ex: Activia, Coca-Cola), the name is equal to the brand. 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. When the 'brand' is not specified and, the food or beverage is very well-known (like 'Coca-Cola'), provide the brand name in 'brand', otherwise set 'brand' to ''."@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": "jambon",
"quantity": "un portillon",
"type": "food",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "jambon",
"quantity": "un portillon",
"type": "food",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "jambon", "quantity": "un portillon", "type": "food", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'jambon', 'quantity': 'un portillon', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'jambon', 'quantity': 'un portillon', '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 '% jambon %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Jambon Cru - jambon cru - - - 9885 - - - CIQ#64b8482a5f9494f91650a6dfbb0cd41e
Jambon Sec - jambon sec - - - 0 - - - CIQ#96c8fe38103fc721a15cfe55d6e25c6f
Jambon Cru - jambon cru - fumé - - 268 - - - CIQ#5f3f73264b7c8e8500821bffaac09aee
Jambon Sec - jambon sec - découenné, dégraissé - - 293 - - - CIQ#25959c69f01c1f2120ccc677017fa727
Jambon Cru - jambon cru - fumé, allégé en matière grasse - - 0 - - - CIQ#f647a53f900ffb0f8b6bcc1b9daac3fd
Jambon Fumé - jambon fume - - - 1235 - - - KCA#b89a3b14af6277985c3d77e8a43fd3a7
Jambon Cuit - jambon cuit - fumé - - 130 - - - CIQ#17ca7e15b0319f1e287cbd0bcf02e149
Jambon Cuit - jambon cuit - choix - - 0 - - - CIQ#31a3ba17bd765304c35083900245a906
Jambon Cuit - jambon cuit - supérieur - - 879 - - - CIQ#62b09fb38df99e94d05d097272b0f943
Jambon Cuit - jambon cuit - choix, avec couenne - - 0 - - - CIQ#c197beb44fda0f03581cdd01ee751078
Jambon Cuit - jambon cuit - supérieur, découenné - - 0 - - - CIQ#a4feb0298e2ed9bf7086021f843d5542
Jambon Cuit - jambon cuit - supérieur, avec couenne - - 0 - - - CIQ#44f954aa2607fc98de99e42c7a2f34f0
Jambon Cuit - jambon cuit - choix, découenné dégraissé - - 0 - - - CIQ#1bdbfa77737e32f3afd8b85235c13da8
Jambon Cuit - jambon cuit - de Paris, découenné dégraissé - - 0 - - - CIQ#2204461860d60e77475581012d525590
Jambon Cuit - jambon cuit - supérieur, découenné dégraissé - - 0 - - - CIQ#7fe80de772280767444b552c0124ab0f
Jambon Cuit - jambon cuit - supérieur, à teneur réduite en sel - - 0 - - - CIQ#f6e3b7457066170ebc96fe96171fba23
Jambon Blanc - jambon blanc - - - 41088 - - - KCA#a2c3580fad4917288fe40406fb88cadb
Jambon Bayonne - jambon bayonne - - - 2108 - - - KCA#a7501ed926d61fc6282a9dc417593554
Jambon Persillé - jambon persille - - - 315 - - - KCA#a68e12a46f2795c6c267b411dd8111f4
Jambon de Poulet - jambon de poulet - - - 5421 - - - KCA#8a8c7fe60575ff37bd0a2f58c58a75a0
----------------------------------------------------
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': 'Un portillon de jambon', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Jambon Cru', 'normName': ' jambon cru ', 'comment': '', 'normComment': '', 'rank': 9885, 'id': 'CIQ#64b8482a5f9494f91650a6dfbb0cd41e', 'quantity': 'un portillon', 'quantityLem': '1 portillon', 'pack': ['TR3.w25'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.7927334308624268}
----------------------------------------------------------------------------------
LLM CPU Time: 1.7927334308624268