Input path: /home/debian/html/nutritwin/output_llm/66161b66c695a/input.json
Output path: /home/debian/html/nutritwin/output_llm/66161b66c695a/output.json
Input text: Un demi de verre 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: Un demi de verre de vin rouge
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Un demi de verre de vin rouge###. 
 Format the result in JSON format: {intents: []}.
=========================================================================================


------------------------------ LLM Raw response -----------------------------
{
"intents": ["Capture the user food consumption"]
}
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
{
"intents": ["Capture the user food consumption"]
}
------------------------------------------------------

------------------------ After simplification ------------------------
{"intents": ["Capture the user food consumption"]}
----------------------------------------------------------------------

==================================== Prompt =============================================
I need to identify food information from sentences.

Analyze the following french sentence: "Un demi de verre de vin rouge".

I want to identify for the food or beverage: the name, the type, the quantity for each ingredient and, if it exists, identify the brand, the cooking mode and the company name.
Containers, like "canette" or "verre", are quantities and not ingredients or food product.
"Portions", like "tranche", are quantities. 
"Quantity" is in french.
"Company" is the company of the brand.
"Quignon" is a quantity. 
Ignore what it is not connected to nutrition, beverage or food. 
Music and is not nutrition. 
Extract how the product is consumed. 
In the name, ignore the level of cooking mode. 
When brand is not specified and the product is very well-known (like "Coca-Cola"), provide the brand name in "brand", otherwise set "brand" to "". 
Ignore the actions. 
The restaurants are not brand. 
Identify what type of food. 
Ignore food with a negative verb, ex "Je n'ai pas pris de viande".
Do not extract ingredients for product with a brand. 
If the food or beverage consumption is in the past, the event is a "declaration", for example: "J'ai mangé du pain", the event is a declaration. 
If the food or beverage consumption will be in the future or even soon, the event is an "intent", for example: "Je vais manger du pain", the event is an intent. 
Otherwise the event is unknown. 
Map the event of eating on ["intent", "declaration", "unknown"]. 
Identify the time of day when the foods were eaten and map it on "petit-déjeuner", "déjeuner", "grignotage" or "dîner". 

Format the result in french in JSON in an array of tuples {"name":, "quantity":, "cooking":, "brand":, "company":, "type":, "time":, "event":}.


=========================================================================================


------------------------------ LLM Raw response -----------------------------
Based on your instructions, here's the analysis of the sentence "Un demi de verre de vin rouge".

The sentence mentions a beverage, "vin rouge" (red wine), with a quantity of "un demi de verre" (half a glass). There's no information about the brand, the company, the cooking mode, or the time of day when the wine was consumed. The event is unknown because the sentence does not indicate whether the consumption of wine is in the past, future, or present.

Here's the information formatted in JSON:

```json
[
  {
    "name": "vin rouge",
    "quantity": "un demi de verre",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "boisson",
    "time": "",
    "event": "inconnu"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Un demi de verre de vin rouge".

The sentence mentions a beverage, "vin rouge" (red wine), with a quantity of "un demi de verre" (half a glass). There's no information about the brand, the company, the cooking mode, or the time of day when the wine was consumed. The event is unknown because the sentence does not indicate whether the consumption of wine is in the past, future, or present.

Here's the information formatted in JSON:

```json
[
  {
    "name": "vin rouge",
    "quantity": "un demi de verre",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "boisson",
    "time": "",
    "event": "inconnu"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "vin rouge",    "quantity": "un demi de verre",    "cooking": "",    "brand": "",    "company": "",    "type": "boisson",    "time": "",    "event": "inconnu"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'vin rouge', 'quantity': 'un demi de verre', 'cooking': '', 'brand': '', 'company': '', 'type': 'boisson', 'time': '', 'event': 'inconnu'}], 'cost': 0.055139999999999995}
--------------------------------------------------------------------------------

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': 'Un demi de verre de vin rouge', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Vin Rouge', 'normName': ' vin rouge ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'CIQ#0247898eabeefe3884ee430550359cfb', 'quantity': 'un demi de verre', 'quantityLem': '1/2 verre', 'pack': ['VAV'], 'type': 'boisson', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'inconnu', 'serving': 'VAV-50', 'posiNormName': 0}], 'activity': []}, 'cputime': 9.328620672225952}
----------------------------------------------------------------------------------
LLM CPU Time: 9.328620672225952