Input path: /home/debian/html/nutritwin/output_llm/663a555f3c5fe/input.json
Output path: /home/debian/html/nutritwin/output_llm/663a555f3c5fe/output.json
Input text: J'ai mange une pizza, combien de cals?
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 mange une pizza, combien de cals?
==================================================================================================================================
==================================== 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: ###J'ai mange une pizza, combien de cals?###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
{
"intents": ["Identify food consumption or declaration", "Answer a nutrition question"]
}
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
{
"intents": ["Identify food consumption or declaration", "Answer a nutrition question"]
}
```
------------------------------------------------------
------------------------ After simplification ------------------------
{ "intents": ["Identify food consumption or declaration", "Answer a nutrition question"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
Convert this natural language query : """J'ai mange une pizza, combien de cals?""" into an array in JSON of consumed foods and beverages.
Provide a solution without explanation.
Use only the ontology described in this 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. 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:event a owl:DatatypeProperty ;
rdfs:label "event"@en;
rdfs:comment "Event of eating or drinking. Each must have an event"@en;
rdfs:range xsd:string.
food:intent a food:event ;
rdfs:label "intent" .
rdfs:comment "When the event should happen"@en.
food:declaration a food:event ;
rdfs:label "declaration" .
rdfs:comment "When the event has already occured"@en.
food:unknownEvent a food:event ;
rdfs:label "unknown" ;
rdfs:comment "When the event is unknown in the day"@en.
"""
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
[
{
"food:name": "pizza",
"food:quantity": "une",
"food:event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"food:name": "pizza",
"food:quantity": "une",
"food:event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "food:name": "pizza", "food:quantity": "une", "food:event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'food:name': 'pizza', 'food:quantity': 'une', 'food:event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'pizza', 'quantity': 'une', '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 '% pizza %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pizza - pizza - - - 10599 - - - CIQ#733e507c20c6036da06902e2929056db
Pizza - pizza - - - 0 - - - KCA#733e507c20c6036da06902e2929056db
Pizza - pizza - sauce garniture pour - - 0 - - - CIQ#a275181c086396e0bec873fcc94008cb
Pizza Kebab - pizza kebab - - - 6 - - - CIQ#6bbe41be8630f033bfe294b94bbf8d0c
Pizza Moyenne - pizza moyenne - - - 38 - - - KCA#9bdfcad1de65c2ebcc7384d0aa3fa55f
Pizza au Thon - pizza thon - - - 16 - - - CIQ#5f444b59309014aeab27095b6eb2d95b
Pizza Fromage - pizza fromage - - - 0 - - - KCA#5175d910a3bb5ffe553ada3ee1d50309
Pizza au Poulet - pizza poulet - - - 0 - - - CIQ#33e0a5ea4366eeb0aad919629cf8f008
Pizza au Saumon - pizza saumon - - - 0 - - - CIQ#531c0deee226a1ed25c6ad7e9344ecef
Pizza 4 Fromages - pizza fromage - - - 2361 - - - CIQ#5175d910a3bb5ffe553ada3ee1d50309
Pizza 'Spéciale' - pizza speciale - - - 146 - - - KCA#a6f6dd5434366be39fec21c560e1457e
Pizza à la Poêle - pizza poele - - - 64 - - - KCA#2cd730363965f0d5363b216aaaa75f26
Pizza Boulangerie - pizza boulangerie - - - 318 - - - KCA#291611656924ce924ca7d5200705c55e
Pizza à la Viande - pizza viande - type bolognaise - - 0 - - - CIQ#b17f77e6924678e84c353cde4ec8bdc4
Pizza aux Lardons - pizza au lardon - oignons et fromage - - 0 - - - CIQ#2ff2fb0af20f513208206f7883b4b537
Pizzas Végétariennes - pizza vegetarienne - - - 566 - - - KCA#9f884aabd1a0211b685859e3d93bb8c8
Pizza Jambon Fromage - pizza jambon fromage - - - 405 - - - CIQ#a5c5fe6f659b72fa37b3770428f770e9
Pizza Jambon Fromage - pizza jambon fromage - - - 0 - - - KCA#a5c5fe6f659b72fa37b3770428f770e9
Pizza Tomate et Fromage - pizza tomate fromage - - - 111 - - - KCA#0962f5517452bf8b32ecb09f5f3166da
Pizza aux Fruits de Mer - pizza au fruit de mer - - - 0 - - - CIQ#d9baa96a97f96e931fba42a44879122e
----------------------------------------------------
==================================== Prompt =============================================
Convert this natural language query : """J'ai mange une pizza, combien de cals?""" into an array in JSON of consumed foods and beverages.
Provide a solution without explanation.
Use only the ontology described in this 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. 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:event a owl:DatatypeProperty ;
rdfs:label "event"@en;
rdfs:comment "Event of eating or drinking. Each must have an event"@en;
rdfs:range xsd:string.
food:intent a food:event ;
rdfs:label "intent" .
rdfs:comment "When the event should happen"@en.
food:declaration a food:event ;
rdfs:label "declaration" .
rdfs:comment "When the event has already occured"@en.
food:unknownEvent a food:event ;
rdfs:label "unknown" ;
rdfs:comment "When the event is unknown in the day"@en.
"""
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
[
{
"food:name": "pizza",
"food:quantity": "une",
"food:event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"food:name": "pizza",
"food:quantity": "une",
"food:event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "food:name": "pizza", "food:quantity": "une", "food:event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'food:name': 'pizza', 'food:quantity': 'une', 'food:event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'pizza', 'quantity': 'une', '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 '% pizza %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pizza - pizza - - - 10599 - - - CIQ#733e507c20c6036da06902e2929056db
Pizza - pizza - - - 0 - - - KCA#733e507c20c6036da06902e2929056db
Pizza - pizza - sauce garniture pour - - 0 - - - CIQ#a275181c086396e0bec873fcc94008cb
Pizza Kebab - pizza kebab - - - 6 - - - CIQ#6bbe41be8630f033bfe294b94bbf8d0c
Pizza Moyenne - pizza moyenne - - - 38 - - - KCA#9bdfcad1de65c2ebcc7384d0aa3fa55f
Pizza au Thon - pizza thon - - - 16 - - - CIQ#5f444b59309014aeab27095b6eb2d95b
Pizza Fromage - pizza fromage - - - 0 - - - KCA#5175d910a3bb5ffe553ada3ee1d50309
Pizza au Poulet - pizza poulet - - - 0 - - - CIQ#33e0a5ea4366eeb0aad919629cf8f008
Pizza au Saumon - pizza saumon - - - 0 - - - CIQ#531c0deee226a1ed25c6ad7e9344ecef
Pizza 4 Fromages - pizza fromage - - - 2361 - - - CIQ#5175d910a3bb5ffe553ada3ee1d50309
Pizza 'Spéciale' - pizza speciale - - - 146 - - - KCA#a6f6dd5434366be39fec21c560e1457e
Pizza à la Poêle - pizza poele - - - 64 - - - KCA#2cd730363965f0d5363b216aaaa75f26
Pizza Boulangerie - pizza boulangerie - - - 318 - - - KCA#291611656924ce924ca7d5200705c55e
Pizza à la Viande - pizza viande - type bolognaise - - 0 - - - CIQ#b17f77e6924678e84c353cde4ec8bdc4
Pizza aux Lardons - pizza au lardon - oignons et fromage - - 0 - - - CIQ#2ff2fb0af20f513208206f7883b4b537
Pizzas Végétariennes - pizza vegetarienne - - - 566 - - - KCA#9f884aabd1a0211b685859e3d93bb8c8
Pizza Jambon Fromage - pizza jambon fromage - - - 405 - - - CIQ#a5c5fe6f659b72fa37b3770428f770e9
Pizza Jambon Fromage - pizza jambon fromage - - - 0 - - - KCA#a5c5fe6f659b72fa37b3770428f770e9
Pizza Tomate et Fromage - pizza tomate fromage - - - 111 - - - KCA#0962f5517452bf8b32ecb09f5f3166da
Pizza aux Fruits de Mer - pizza au fruit de mer - - - 0 - - - CIQ#d9baa96a97f96e931fba42a44879122e
----------------------------------------------------
PIZ.w200.p2
ERROR with converion of PIZ.w200.p2
==================================== Prompt =============================================
Here is all known information:
For "Pizza", here are the nutrition values:
name: Pizza
GTIN: none
brand: none
calorie: 233.0Kcal per 100g
salt: 1.27g per 100g
sugar: 2.93g per 100g
NutriScore: none
EcoScore: none
allergens: en:gluten
allergen traces: none
data source: Ciqual
Answer in less than 50 words to this question with a short explanation if needed: "J'ai mange une pizza, combien de cals?"
" + "Mention the data source in the response if it exists. The answer must be in the same language than the question
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Une pizza contient 233,0 Kcal pour 100g. La quantité de calories dépend du poids de la pizza consommée. Source: Ciqual.
-----------------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': 'Une pizza contient 233,0 Kcal pour 100g. La quantité de calories dépend du poids de la pizza consommée. Source: Ciqual.', 'cost': 0.0}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "J'ai mange une pizza, combien de cals?", 'intents': ['Identify food consumption or declaration', 'Answer a nutrition question'], 'model': 'gpt-4-0125-preview', 'solutions': {'nutrition': [{'name': 'Pizza', 'normName': ' pizza ', 'comment': '', 'normComment': '', 'rank': 10599, 'id': 'CIQ#733e507c20c6036da06902e2929056db', 'quantity': 'une', 'quantityLem': '1', 'pack': ['PIZ.w200.p2'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'PIZ-100', 'posiNormName': 0}], 'activity': [], 'response': {'type': 'text', 'data': 'Une pizza contient 233,0 Kcal pour 100g. La quantité de calories dépend du poids de la pizza consommée. Source: Ciqual.'}}, 'cputime': 11.363526105880737}
----------------------------------------------------------------------------------
LLM CPU Time: 11.363526105880737