Input path: /home/debian/html/nutritwin/output_llm/660b081cc97d8/input.json
Output path: /home/debian/html/nutritwin/output_llm/660b081cc97d8/output.json
Input text: J'ai mangé deux portions de vache qui rit avec un morceau de baguette
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 avec un morceau de baguette
==================================================================================================================================
==================================== 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 avec un morceau de baguette###.
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 avec un morceau de baguette".
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 avec un morceau de baguette".
The sentence mentions two food items: "vache qui rit" and "baguette".
1. "Vache qui rit" is a well-known brand of cheese. The quantity is "deux portions" (two portions). The type is "cheese". The brand is "vache qui rit". The company is "Bel Group" (which is the company that owns the brand "vache qui rit"). The cooking mode is not specified. The time of day when the food was eaten is not specified. The event is a "declaration" because the consumption is in the past.
2. "Baguette" is a type of bread. The quantity is "un morceau" (one piece). The type is "bread". The brand is not specified, so it's "". The company is not specified. The cooking mode is not specified. The time of day when the food was eaten is not specified. The event is a "declaration" because the consumption is in the past.
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",
"time": "",
"event": "declaration"
},
{
"name": "baguette",
"quantity": "un morceau",
"cooking": "",
"brand": "",
"company": "",
"type": "bread",
"time": "",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "J'ai mangé deux portions de vache qui rit avec un morceau de baguette".
The sentence mentions two food items: "vache qui rit" and "baguette".
1. "Vache qui rit" is a well-known brand of cheese. The quantity is "deux portions" (two portions). The type is "cheese". The brand is "vache qui rit". The company is "Bel Group" (which is the company that owns the brand "vache qui rit"). The cooking mode is not specified. The time of day when the food was eaten is not specified. The event is a "declaration" because the consumption is in the past.
2. "Baguette" is a type of bread. The quantity is "un morceau" (one piece). The type is "bread". The brand is not specified, so it's "". The company is not specified. The cooking mode is not specified. The time of day when the food was eaten is not specified. The event is a "declaration" because the consumption is in the past.
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",
"time": "",
"event": "declaration"
},
{
"name": "baguette",
"quantity": "un morceau",
"cooking": "",
"brand": "",
"company": "",
"type": "bread",
"time": "",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "vache qui rit", "quantity": "deux portions", "cooking": "", "brand": "vache qui rit", "company": "Bel Group", "type": "cheese", "time": "", "event": "declaration" }, { "name": "baguette", "quantity": "un morceau", "cooking": "", "brand": "", "company": "", "type": "bread", "time": "", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'vache qui rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'vache qui rit', 'company': 'Bel Group', 'type': 'cheese', 'time': '', 'event': 'declaration'}, {'name': 'baguette', 'quantity': 'un morceau', 'cooking': '', 'brand': '', 'company': '', 'type': 'bread', 'time': '', 'event': 'declaration'}], 'cost': 0.08154}
--------------------------------------------------------------------------------
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
----------------------------------------------------
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 '% baguette %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Baguette - baguette - - - 13304 - - - KCA#4c58eb37d72a06c3eb6b4eca05a7eafc
Baguette Beurrée - baguette beurree - - - 6728 - - - KCA#81e38c2ee900b3581038d641b4a91c7f
Baguette Beurre Confiture - baguette beurre confiture - - - 5301 - - - KCA#d399b90a645a52039b2f409debeaa686
Pain, Baguette Courante - pain baguette courante - - - 1505 - - - KCA#b8216428789ad27dd11c8e9619cd3ad8
Pain, Baguette sans Sel - pain baguette san sel - - - 144 - - - KCA#9792a779f917b0a8bf1fc9c776628d0a
Sandwich Baguette - sandwich baguette - - - 0 - - - CIQ#ecdbce2254ce082246ccea95b54322d3
Sandwich Baguette - sandwich baguette - jambon, beurre - - 544 - - - CIQ#bd804df922badefbc8215232b9b741aa
Sandwich Baguette - sandwich baguette - salami, beurre - - 59 - - - CIQ#92b9f1c35fd21237d9716ba633faf6c3
Sandwich Baguette - sandwich baguette - jambon emmental - - 0 - - - CIQ#a3044be4730437e3137525aaa8469e38
Sandwich Baguette - sandwich baguette - pâté, cornichons - - 138 - - - CIQ#dffdf1e5117ae64f00c22627ab3670f2
Sandwich Baguette - sandwich baguette - camembert, beurre - - 23 - - - CIQ#0080a72a2d54a3ea5e04c0c631ac01fd
Sandwich Baguette - sandwich baguette - saucisson, beurre - - 0 - - - CIQ#64a51f36b8fcf7fb6aa69713d78a7477
Sandwich Baguette - sandwich baguette - saumon fumé, beurre - - 191 - - - CIQ#f319acba3059dd568c3ec0b09ffee8cd
Sandwich Baguette - sandwich baguette - thon, maïs, crudités - - 0 - - - CIQ#6bc3fa7c33408c471a521687ea57c022
Sandwich Baguette - sandwich baguette - jambon, emmental, beurre - - 5174 - - - CIQ#d4b56c51ab3ed4856726dc5540397da5
Sandwich Baguette - sandwich baguette - merguez, ketchup moutarde - - 71 - - - CIQ#5f4dda655b3b11f243dc5af84eb97c1f
Sandwich Baguette - sandwich baguette - crudités diverses, mayonnaise - - 17 - - - CIQ#80a019abb0e07979e1fadd760efb9be0
Sandwich Baguette - sandwich baguette - oeuf, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#ce1d206be86434bec3c26d455f9689e4
Sandwich Baguette - sandwich baguette - porc, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#8e8c84ec0b67513a1590bfedccec16eb
Sandwich Baguette - sandwich baguette - thon, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#2216cbeb818ce287e644567239e12d90
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "J'ai mangé deux portions de vache qui rit avec un morceau de baguette", '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', 'gtin': '3073768465500', 'gtinRef': '3073768465500', 'brand': 'group Bel', 'time': '', 'event': 'declaration', 'serving': 'VQR-200', 'posiNormName': 0}, {'name': 'Baguette', 'normName': ' baguette ', 'comment': '', 'normComment': '', 'rank': 13304, 'id': 'KCA#4c58eb37d72a06c3eb6b4eca05a7eafc', 'quantity': 'un morceau', 'quantityLem': '1 morceau', 'pack': ['BAG.w25', 'PAI.w30'], 'type': 'bread', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'BAG-100', 'posiNormName': 0}], 'activity': []}, 'cputime': 12.531003713607788}
----------------------------------------------------------------------------------
LLM CPU Time: 12.531003713607788