Input path: /home/debian/html/nutritwin/output_llm/66093ba2edbb7/input.json
Output path: /home/debian/html/nutritwin/output_llm/66093ba2edbb7/output.json
Input text: Je veux deux portions de vache qui rit
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: Je veux deux portions de vache qui rit
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Je veux deux portions de vache qui rit###. 
 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"]}
----------------------------------------------------------------------
=====> KO

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

Analyze the following french sentence: "Je veux deux portions de vache qui rit".

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 "Je veux deux portions de vache qui rit".

The sentence indicates a desire for a certain amount of a specific food product. The product is "La vache qui rit", which is a well-known brand of cheese. The quantity is "deux portions" or "two portions".

The sentence does not provide information about the company, the cooking mode, the time of day, or whether this is a past or future event. However, we can infer that the event is an "intent" because the verb "veux" (want) indicates a future or intended action.

Here's the information formatted in JSON:

```json
[
  {
    "name": "vache qui rit",
    "quantity": "deux portions",
    "cooking": "",
    "brand": "La vache qui rit",
    "company": "",
    "type": "fromage",
    "time": "",
    "event": "intent"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Je veux deux portions de vache qui rit".

The sentence indicates a desire for a certain amount of a specific food product. The product is "La vache qui rit", which is a well-known brand of cheese. The quantity is "deux portions" or "two portions".

The sentence does not provide information about the company, the cooking mode, the time of day, or whether this is a past or future event. However, we can infer that the event is an "intent" because the verb "veux" (want) indicates a future or intended action.

Here's the information formatted in JSON:

```json
[
  {
    "name": "vache qui rit",
    "quantity": "deux portions",
    "cooking": "",
    "brand": "La vache qui rit",
    "company": "",
    "type": "fromage",
    "time": "",
    "event": "intent"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "vache qui rit",    "quantity": "deux portions",    "cooking": "",    "brand": "La vache qui rit",    "company": "",    "type": "fromage",    "time": "",    "event": "intent"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'vache qui rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'La vache qui rit', 'company': '', 'type': 'fromage', 'time': '', 'event': 'intent'}], 'cost': 0.05814}
--------------------------------------------------------------------------------

First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%' 


Second try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%' 

-------------------------------------------
------ERROR--------------------------------
No solution for query: SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Je veux deux portions de vache qui rit', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 6.554217338562012}
----------------------------------------------------------------------------------
LLM CPU Time: 6.554217338562012