Input path: /home/debian/html/nutritwin/output_llm/661018a5b5482/input.json Output path: /home/debian/html/nutritwin/output_llm/661018a5b5482/output.json Input text: Champignons 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: Champignons ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Champignons###. 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: "Champignons". 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've analyzed the sentence "Champignons". However, the sentence is quite simple and does not provide much context. Here's the information I could extract: ```json [ { "name": "Champignons", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "Légume", "time": "", "event": "unknown" } ] ``` The sentence does not provide information about the quantity, brand, company, cooking method, time of consumption, or whether this is a past, future, or present action. The type of food is a vegetable, as "champignons" refers to mushrooms. The event is unknown because the sentence does not indicate when or if the mushrooms were or will be consumed. ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Based on your instructions, I've analyzed the sentence "Champignons". However, the sentence is quite simple and does not provide much context. Here's the information I could extract: ```json [ { "name": "Champignons", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "Légume", "time": "", "event": "unknown" } ] ``` The sentence does not provide information about the quantity, brand, company, cooking method, time of consumption, or whether this is a past, future, or present action. The type of food is a vegetable, as "champignons" refers to mushrooms. The event is unknown because the sentence does not indicate when or if the mushrooms were or will be consumed. ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Champignons", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "Légume", "time": "", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Champignons', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'Légume', 'time': '', 'event': 'unknown'}], 'cost': 0.05321999999999999} -------------------------------------------------------------------------------- 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 '% champignon %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Champignon - champignon - cèpe, cru - - 0 - - - CIQ#507bf7eedd01023a656de6a680e5253b Champignon - champignon - morille, crue - - 0 - - - CIQ#5a42db3f720c459ca8a664618d25cb75 Champignon - champignon - pleurote, crue - - 0 - - - CIQ#8a27db7118edd4e9f4660a26806fc021 Champignon - champignon - tout type, cru - - 0 - - - CIQ#5a45b2147e895e9c204f1ec73d856944 Champignon - champignon - oronge vraie, crue - - 0 - - - CIQ#dae7b304a96a7fcf5a6a266a6d84aad8 Champignon - champignon - rosé des prés, cru - - 0 - - - CIQ#f324b9c23b69a938642850ec277feabe Champignon - champignon - tout type, égoutté - - 0 - - - CIQ#334a7f823845a3a699895c405348f517 Champignon - champignon - chanterelle ou girolle, crue - - 0 - - - CIQ#92c83cfc5f670913dcac08ecee3da035 Champignon - champignon - lentin comestible ou shiitaké - - 0 - - - CIQ#3c4b31a66351114e03870c4dd8b9ae1b Champignon - champignon - lentin comestible ou shiitaké, séché - - 0 - - - CIQ#fca11dc03464769331766aed3628d0ba Champignons Crus - champignon cru - - - 3579 - - - KCA#3cc80555ef5ce4a78c202b8ab14d5e06 Champignons Sautés - champignon saute - - - 956 - - - KCA#f1a890378210c207314dbfecc84cff43 Champignons Grillés - champignon grille - aux Tomates et au Basilic - - 14 - - - KCA#9e228d5fbbc34e30d173cb3d39d14353 Champignons Gratinés - champignon gratine - - - 37 - - - KCA#928331d41ce0d6041dddaed4bf258197 Champignons en Salade - champignon en salade - - - 160 - - - KCA#4b803014b0707eb6119d6f8b5cf1091a Champignons Apertisés - champignon apertise - - - 68 - - - KCA#ffb947a6246047e223a055c43af04259 Champignons à la Crème - champignon creme - - - 654 - - - KCA#8d6f5386b19b03577f6a1e2cc4483343 Champignons Vinaigrette - champignon vinaigrette - - - 24 - - - KCA#2b370fdead13ae1b072147efe2939971 Champignons Crus en Salade - champignon cru en salade - - - 59 - - - KCA#2f96039f4b2b2afe907b0ec3ef0e8a64 Champignon de Paris ou Champignon de Couche - champignon de pari ou champignon de couche - cru - - 0 - - - CIQ#3d3674c5fd5a40d6343a2a115a1b0ead ---------------------------------------------------- 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 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': 'Champignons', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Champignon', 'normName': ' champignon ', 'comment': 'cèpe, cru', 'normComment': ' cepe cru ', 'rank': 0, 'id': 'CIQ#507bf7eedd01023a656de6a680e5253b', 'quantity': '', 'quantityLem': '', 'pack': ['LEG.w150'], 'type': 'Légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 5.8325114250183105} ---------------------------------------------------------------------------------- LLM CPU Time: 5.8325114250183105