Input path: /home/debian/html/nutritwin/output_llm/660edaef887bc/input.json
Output path: /home/debian/html/nutritwin/output_llm/660edaef887bc/output.json
Input text: Flocons d'avoine
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: Flocons d'avoine
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Flocons d'avoine###.
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"]}
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==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "Flocons d'avoine".
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, I have analyzed the sentence "Flocons d'avoine". Here is the information extracted:
[
{
"name": "Flocons d'avoine",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "céréale",
"time": "unknown",
"event": "unknown"
}
]
Explanation:
- The name of the food is "Flocons d'avoine" (Oat flakes).
- The quantity is not specified in the sentence.
- The cooking mode is not specified in the sentence.
- The brand is not specified and "Flocons d'avoine" is not a well-known branded product like "Coca-Cola".
- The company is not specified in the sentence.
- The type of food is "céréale" (cereal).
- The time of day when the food is eaten is not specified in the sentence.
- The event (intent, declaration, unknown) is unknown because the sentence does not indicate whether the consumption of the oat flakes is in the past, future, or present.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, I have analyzed the sentence "Flocons d'avoine". Here is the information extracted:
[
{
"name": "Flocons d'avoine",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "céréale",
"time": "unknown",
"event": "unknown"
}
]
Explanation:
- The name of the food is "Flocons d'avoine" (Oat flakes).
- The quantity is not specified in the sentence.
- The cooking mode is not specified in the sentence.
- The brand is not specified and "Flocons d'avoine" is not a well-known branded product like "Coca-Cola".
- The company is not specified in the sentence.
- The type of food is "céréale" (cereal).
- The time of day when the food is eaten is not specified in the sentence.
- The event (intent, declaration, unknown) is unknown because the sentence does not indicate whether the consumption of the oat flakes is in the past, future, or present.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Flocons d'avoine", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "céréale", "time": "unknown", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': "Flocons d'avoine", 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'céréale', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.06227999999999999}
--------------------------------------------------------------------------------
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 '% flocon avoine %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Flocons d'Avoine - flocon avoine - - - 7117 - - - KCA#829dd8b9f0b668d9f24a0c734e772f52
Flocons d'Avoine - flocon avoine - bouillis/cuits à l'eau - - 0 - - - CIQ#c4b942545129494ae5176d7dfc3b4423
Flocon d'Avoine Précuit - flocon avoine precuit - - - 0 - - - CIQ#d4abe710295ca9d12d154b734ea1d61d
Flocons d'Avoine, Cuits à l'Eau - flocon avoine cuit eau - - - 471 - - - KCA#f73f2fb7f8629269db6fa21615c6ad3a
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': "Flocons d'avoine", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': "Flocons d'Avoine", 'normName': ' flocon avoine ', 'comment': '', 'normComment': '', 'rank': 7117, 'id': 'KCA#829dd8b9f0b668d9f24a0c734e772f52', 'quantity': '', 'quantityLem': '', 'pack': ['CSS.w30'], 'type': 'céréale', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 7.487083196640015}
----------------------------------------------------------------------------------
LLM CPU Time: 7.487083196640015