Input path: /home/debian/html/nutritwin/output_llm/660e620710d2e/input.json
Output path: /home/debian/html/nutritwin/output_llm/660e620710d2e/output.json
Input text: J'ai mangé 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: J'ai mangé 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: ###J'ai mangé 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"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "J'ai mangé 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 "J'ai mangé deux portions de Vache Qui Rit".
The sentence indicates that the person ate two portions of a product named "Vache Qui Rit". This product is a well-known brand of cheese spread.
Here's the information formatted in JSON:
```json
[
{
"name": "Vache Qui Rit",
"quantity": "deux portions",
"cooking": "",
"brand": "Vache Qui Rit",
"company": "Bel Group",
"type": "cheese spread",
"time": "unknown",
"event": "declaration"
}
]
```
Please note that the time of day when the food was eaten is not specified in the sentence, so "time" is set to "unknown". The event is a "declaration" because the person is stating that they have already eaten the food. The company name "Bel Group" is the company that owns the brand "Vache Qui Rit". The type of food is identified as a "cheese spread".
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "J'ai mangé deux portions de Vache Qui Rit".
The sentence indicates that the person ate two portions of a product named "Vache Qui Rit". This product is a well-known brand of cheese spread.
Here's the information formatted in JSON:
```json
[
{
"name": "Vache Qui Rit",
"quantity": "deux portions",
"cooking": "",
"brand": "Vache Qui Rit",
"company": "Bel Group",
"type": "cheese spread",
"time": "unknown",
"event": "declaration"
}
]
```
Please note that the time of day when the food was eaten is not specified in the sentence, so "time" is set to "unknown". The event is a "declaration" because the person is stating that they have already eaten the food. The company name "Bel Group" is the company that owns the brand "Vache Qui Rit". The type of food is identified as a "cheese spread".
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Vache Qui Rit", "quantity": "deux portions", "cooking": "", "brand": "Vache Qui Rit", "company": "Bel Group", "type": "cheese spread", "time": "unknown", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Vache Qui Rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'Vache Qui Rit', 'company': 'Bel Group', 'type': 'cheese spread', 'time': 'unknown', 'event': 'declaration'}], 'cost': 0.06155999999999999}
--------------------------------------------------------------------------------
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 '% vache qui rit %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second 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_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073768465500 - 3073768465500 - OFF#64a8dc23196895161ab1b53d6923bde4
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781192186 - 3073768465500 - OFF#da43cf7551a9e13c61a1ad694e521b4d
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781071559 - 3073768465500 - OFF#8c52efd954d14680fc301fe8e08e1c82
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073780886840 - 3073768465500 - OFF#e12170c7846fec182545d25fee1b2813
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073780574242 - 3073768465500 - OFF#5f4a5603fe8f62909abc2cc739c79aa5
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149586 - 3073768465500 - OFF#383167203c08d92736feccfd146b80de
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149838 - 3073768465500 - OFF#a450d0cc58d1fa1b104236b2a3b0fb1d
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149890 - 3073768465500 - OFF#1b0067194622ccea435fb351f5b4e9d4
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149876 - 3073768465500 - OFF#7e4fda2bf6eb008f72ad807808a6af64
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158533 - 3073768465500 - OFF#a6023c2768c642db84da32383a25ca81
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158830 - 3073768465500 - OFF#28d1e1d1aa56dae7fc32bcbf6859e135
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781194692 - 3073768465500 - OFF#e824de1734ce67d42f4333e7879c4545
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781192216 - 3073768465500 - OFF#9296abd5b4b24c23e27e9309776f6c1e
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781178913 - 3073768465500 - OFF#5c71f9d1919acec1ce090da7d83251f8
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781160420 - 3073768465500 - OFF#d105e86b448cf52866438cfa143f7aa9
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158854 - 3073768465500 - OFF#c2860291f39ec76a244f28873be7d183
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158847 - 3073768465500 - OFF#861fadf18788d9d0c2e6593e9378d4a3
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149937 - 3073768465500 - OFF#931815b46e255f01a8c4fc69aa6023d7
La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781070200 - 3073768465500 - OFF#712506d4ac1e1c5ca319750438d29a11
La Vache Qui Rit 16p - vache qui rit 16p - - group Bel - 0 - 3073781149852 - 3073781149852 - OFF#7ebe1de82703b44929fb2056a48c1e6d
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "J'ai mangé deux portions de Vache Qui Rit", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'La Vache Qui Rit', 'normName': ' vache qui rit ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#64a8dc23196895161ab1b53d6923bde4', 'quantity': 'deux portions', 'quantityLem': '2 portion', 'pack': ['VQR.w16'], 'type': 'cheese spread', 'gtin': '3073768465500', 'gtinRef': '3073768465500', 'brand': 'group Bel', 'time': 'unknown', 'event': 'declaration', 'serving': 'VQR-200', 'posiNormName': 0}], 'activity': []}, 'cputime': 8.093092441558838}
----------------------------------------------------------------------------------
LLM CPU Time: 8.093092441558838