Input path: /home/debian/html/nutritwin/output_llm/660805a0dc820/input.json Output path: /home/debian/html/nutritwin/output_llm/660805a0dc820/output.json Input text: Betteraves champignons chorizo 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: Betteraves champignons chorizo ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Betteraves champignons chorizo###. 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"]} ---------------------------------------------------------------------- =====> KO ==================================== Prompt ============================================= I need to identify food information from sentences. Analyze the following french sentence: "Betteraves champignons chorizo". 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 the instructions provided, here's the analysis of the sentence "Betteraves champignons chorizo". The sentence does not provide information about the quantity, brand, company, cooking mode, time of consumption, or whether it's a declaration, intent, or unknown event. However, we can identify the names and types of the food items. Here's the formatted result: ```json [ { "name": "Betteraves", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "", "event": "" }, { "name": "Champignons", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "", "event": "" }, { "name": "Chorizo", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "viande", "time": "", "event": "" } ] ``` Each tuple in the array represents a food item. The "name" field is the name of the food item, the "type" field is the type of the food item, and the rest of the fields are left empty as the sentence does not provide that information. ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Based on the instructions provided, here's the analysis of the sentence "Betteraves champignons chorizo". The sentence does not provide information about the quantity, brand, company, cooking mode, time of consumption, or whether it's a declaration, intent, or unknown event. However, we can identify the names and types of the food items. Here's the formatted result: ```json [ { "name": "Betteraves", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "", "event": "" }, { "name": "Champignons", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "", "event": "" }, { "name": "Chorizo", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "viande", "time": "", "event": "" } ] ``` Each tuple in the array represents a food item. The "name" field is the name of the food item, the "type" field is the type of the food item, and the rest of the fields are left empty as the sentence does not provide that information. ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Betteraves", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "", "event": "" }, { "name": "Champignons", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "", "event": "" }, { "name": "Chorizo", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "viande", "time": "", "event": "" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Betteraves', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'légume', 'time': '', 'event': 'unknown'}, {'name': 'Champignons', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'légume', 'time': '', 'event': ''}, {'name': 'Chorizo', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'viande', 'time': '', 'event': ''}], 'cost': 0.07128} -------------------------------------------------------------------------------- First try: SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% betterave %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 10) -------------- Betterave Rouge - - - 8160 - - CIQ#19e3af05ec2db8b4603c4be2bc446a39 Betterave Ménagère - - - 196 - - KCA#cf59645b55ec29f3def37e35399eb3d0 Jus de Betterave, Carotte et Epinard - - - 190 - - KCA#bc44fc6902bae2f6850e3afe6f063d2d Salade Betteraves et Agneau au Miel - - - 24 - - KCA#2166cb4870932bad02161df026c04633 Salade Betterave, Fenouil et Saumon au Carvi - - - 31 - - KCA#7c82baca18b6e6cbeeeec05c39082e8f Salade de Betterave, Haricots, Feta et Menthe - - - 106 - - KCA#f31a5e8ed43442368982779c1513d16f Risotto aux Betteraves - et à la roquette - - 9 - - KCA#76a7da44ad3d01e54c8d3d7ebe35ed32 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution First try: SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,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 10) -------------- Champignon - cèpe, cru - - 0 - - CIQ#507bf7eedd01023a656de6a680e5253b Champignon - morille, crue - - 0 - - CIQ#5a42db3f720c459ca8a664618d25cb75 Champignon - tout type, cru - - 0 - - CIQ#5a45b2147e895e9c204f1ec73d856944 Champignon - pleurote, crue - - 0 - - CIQ#8a27db7118edd4e9f4660a26806fc021 Champignon - tout type, égoutté - - 0 - - CIQ#334a7f823845a3a699895c405348f517 Champignon - rosé des prés, cru - - 0 - - CIQ#f324b9c23b69a938642850ec277feabe Champignon - oronge vraie, crue - - 0 - - CIQ#dae7b304a96a7fcf5a6a266a6d84aad8 Champignon - chanterelle ou girolle, crue - - 0 - - CIQ#92c83cfc5f670913dcac08ecee3da035 Champignon - lentin comestible ou shiitaké - - 0 - - CIQ#3c4b31a66351114e03870c4dd8b9ae1b Champignon - lentin comestible ou shiitaké, séché - - 0 - - CIQ#fca11dc03464769331766aed3628d0ba ---------------------------------------------------- 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 First try: SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% chorizo %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 10) -------------- Chorizo - - - 14 - - CIQ#a46af02da5ec7aa0a9fa5961dcb42209 Chorizo Sec - - - 3084 - - KCA#d4d56798a68171de83a1251ddbf828fb Chorizo Supérieur - doux ou fort, type saucisse sèche - - 0 - - CIQ#6c2bfc7543a083f0a2c79697d7a95821 Chorizo Supérieur - doux ou fort, type charcuterie en tranches - - 0 - - CIQ#889704b5222cf94f8d8f0d2196cf793b Pizza au Chorizo ou Salami - - - 0 - - CIQ#3ea9e0270d39c3f2672f4fb4edf0fe77 Lentilles au Chorizo - - - 37 - - KCA#861eb5d32ed6b0481dec0520a24603f9 Salade de Chorizo - aux Artichauts et aux Poivrons rouges - - 18 - - KCA#a7ca4bd710a44c707d2bfba71c92422e Lentilles Brunes à la Courgette et au Chorizo - - - 6 - - KCA#488b3d0b5e6b068cb458940010656fd5 ---------------------------------------------------- ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Betteraves champignons chorizo', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Betterave Rouge', 'normName': ' betterave rouge ', 'comment': '', 'normComment': '', 'rank': 8160, 'id': 'CIQ#19e3af05ec2db8b4603c4be2bc446a39', 'quantity': '', 'quantityLem': '', 'pack': ['LEG.w150'], 'type': 'légume', 'gtin': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}, {'name': 'Champignon', 'normName': ' champignon ', 'comment': 'cèpe, cru', 'normComment': ' cepe cru ', 'rank': 0, 'id': 'CIQ#507bf7eedd01023a656de6a680e5253b', 'quantity': '', 'quantityLem': '', 'pack': ['LEG.w150'], 'type': 'légume', 'gtin': '', 'brand': '', 'time': '', 'event': '', 'serving': '', 'posiNormName': 0}, {'name': 'Chorizo', 'normName': ' chorizo ', 'comment': '', 'normComment': '', 'rank': 14, 'id': 'CIQ#a46af02da5ec7aa0a9fa5961dcb42209', 'quantity': '', 'quantityLem': '', 'pack': ['TR2.w15'], 'type': 'viande', 'gtin': '', 'brand': '', 'time': '', 'event': '', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 8.244468450546265} ---------------------------------------------------------------------------------- LLM CPU Time: 8.244468450546265