Input path: /home/debian/html/nutritwin/output_llm/6611361ce4194/input.json Output path: /home/debian/html/nutritwin/output_llm/6611361ce4194/output.json Input text: Café au lait 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: Café au lait ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Café au lait###. 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: "Café au lait". 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 "Café au lait". [ { "name": "Café au lait", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "boisson", "time": "unknown", "event": "unknown" } ] Explanation: - The name of the beverage is "Café au lait". - The type of the beverage is "boisson" (drink). - The quantity, cooking mode, brand, company, time of consumption, and event (intent, declaration, unknown) are not specified in the sentence. ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Based on your instructions, here's the analysis of the sentence "Café au lait". [ { "name": "Café au lait", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "boisson", "time": "unknown", "event": "unknown" } ] Explanation: - The name of the beverage is "Café au lait". - The type of the beverage is "boisson" (drink). - The quantity, cooking mode, brand, company, time of consumption, and event (intent, declaration, unknown) are not specified in the sentence. ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Café au lait", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "boisson", "time": "unknown", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Café au lait', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'boisson', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.04973999999999999} -------------------------------------------------------------------------------- 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 '% cafe lait %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Café au Lait - cafe lait - entier sucré - - 686 - - - KCA#79a7269ac953a86d5d8964ee0f4152db Café au Lait - cafe lait - écrémé sucré - - 653 - - - KCA#cea770a189e838bbc39e36cf537abb5a Café au Lait - cafe lait - 1/2 écrémé sucré - - 15199 - - - KCA#138ec7dba7fa585306b852c3f7e0a463 Café au Lait - cafe lait - écrémé non sucré - - 6369 - - - KCA#eefa4e0f868d9c342316060e62f23159 Café au Lait - cafe lait - entier non sucré - - 1063 - - - KCA#766d75aba9738d735cfb5303e24e0712 Café au Lait - cafe lait - 1/2 écrémé non sucré - - 21616 - - - KCA#e8f1a390014f879ed671041ebfeb6366 Café au Lait - cafe lait - café crème ou cappuccino, instantané ou non, non sucré, prêt à boire - - 0 - - - CIQ#61667259d09a30eac4d1919dafb0f043 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution 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': 'Café au lait', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Café au Lait', 'normName': ' cafe lait ', 'comment': 'entier sucré', 'normComment': ' entier sucre ', 'rank': 686, 'id': 'KCA#79a7269ac953a86d5d8964ee0f4152db', 'quantity': '', 'quantityLem': '', 'pack': ['TA2', 'TAS', 'TAC', 'TA3', 'MUG', 'BOL'], 'type': 'boisson', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 4.623575448989868} ---------------------------------------------------------------------------------- LLM CPU Time: 4.623575448989868