Input path: /home/debian/html/nutritwin/output_llm/66e56225622c3/input.json
Output path: /home/debian/html/nutritwin/output_llm/66e56225622c3/output.json
Input text: Et j'ai bu trois verres de lait
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: Et j'ai bu trois verres de lait
==================================================================================================================================
==================================== 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: ###Et j'ai bu trois verres de lait###.
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 : """Et j'ai bu trois verres de lait""" 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": "lait",
"quantity": "trois verres",
"type": "beverage",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "lait",
"quantity": "trois verres",
"type": "beverage",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "lait", "quantity": "trois verres", "type": "beverage", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'lait', 'quantity': 'trois verres', 'type': 'beverage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'lait', 'quantity': 'trois verres', '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 '% lait %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Lait - lait - teneur en matière grasse inconnue, UHT, aliment moyen - - 0 - - - CIQ#ebdfafe0fce6b513193ae9c0855b4094
Lait à 1 - lait - 2% de matière grasse, UHT, enrichi en plusieurs vitamines - - 0 - - - CIQ#825f8bcb068ecde315938147ed819623
Lait Entier - lait entier - - - 1435 - - - KCA#c131edf4d3c1e17da0b0a54b5ed8bbb6
Lait Écrémé - lait ecreme - UHT - - 9353 - - - CIQ#27de8d007093ae392f4b782851e7fd9c
Lait Entier - lait entier - UHT - - 0 - - - CIQ#5118aac9b89cceae9a62423175de70eb
Lait Écrémé - lait ecreme - pasteurisé - - 0 - - - CIQ#1622e54576ffea9bca81697cacb48d94
Lait Entier - lait entier - pasteurisé - - 0 - - - CIQ#d5881852b522b09ee02aa0fe46885b00
Lait de Soja - lait de soja - - - 3001 - - - KCA#7484ab8a01f886bca7607cf06a579a2c
Lait d'Avoine - lait avoine - - - 837 - - - KCA#54605e0becbb04ace3db6bf78748c15f
Lait de Poule - lait de poule - sans alcool - - 0 - - - CIQ#f6756ecdc46ec65e5972c6aaf481f4a2
Lait en Poudre - lait en poudre - écrémé - - 117 - - - CIQ#1d9ba583216533c41321ffd9ea51b327
Lait en Poudre - lait en poudre - entier - - 25 - - - CIQ#be7d16f0a05422e5eb1d5ff077dee20c
Lait de Brebis - lait de brebi - entier - - 0 - - - CIQ#b54f3b8a48f8d3e0ba7a0228c8adca4f
Lait de Jument - lait de jument - entier - - 0 - - - CIQ#05ea74b811b1a15ad91876c22391f13a
Lait en Poudre - lait en poudre - demi-écrémé - - 0 - - - CIQ#ee03115de1c18f635dbb62d80d6f9715
Lait de Chèvre - lait de chevre - entier, cru - - 0 - - - CIQ#8fb6afe4302a0073de91d274e3722c3e
Lait de Chèvre - lait de chevre - entier, UHT - - 0 - - - CIQ#9d462cfc80afac9cf259f0f2f305db74
Lait de Chèvre - lait de chevre - demi-écrémé, UHT - - 0 - - - CIQ#a497c21ecfbd7c2930cb99326897a779
Lait 1/2 Écrémé - lait 1/2 ecreme - - - 23220 - - - KCA#d5b12fbedab6d0f0a741feeaa8e92b35
Lait Entier UHT - lait entier uht - - - 25 - - - KCA#aeb66cc691b5e08f15b01dc094a51d18
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Et j'ai bu trois verres de lait", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Lait', 'normName': ' lait ', 'comment': 'teneur en matière grasse inconnue, UHT, aliment moyen', 'normComment': ' teneur en matiere grasse inconnue uht aliment moyen ', 'rank': 0, 'id': 'CIQ#ebdfafe0fce6b513193ae9c0855b4094', 'quantity': 'trois verres', 'quantityLem': '3 verre', 'pack': ['VX1', 'VA2', 'VA3', 'BI4', 'VA4'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'VA3-300', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.64599609375}
----------------------------------------------------------------------------------
LLM CPU Time: 1.64599609375