Input path: /home/debian/html/nutritwin/output_llm/6612c8034d93c/input.json Output path: /home/debian/html/nutritwin/output_llm/6612c8034d93c/output.json Input text: Pommes de terre vapeur 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: Pommes de terre vapeur ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Pommes de terre vapeur###. 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: "Pommes de terre vapeur". 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 "Pommes de terre vapeur". The sentence translates to "Steamed potatoes" in English. Here's the formatted result: ```json [ { "name": "Pommes de terre", "quantity": "", "cooking": "vapeur", "brand": "", "company": "", "type": "légume", "time": "unknown", "event": "unknown" } ] ``` Explanation: - The name of the food is "Pommes de terre" (potatoes). - The quantity is not specified in the sentence. - The cooking mode is "vapeur" (steamed). - The brand and the company are not mentioned in the sentence. - The type of food is "légume" (vegetable). - The time of eating is not specified in the sentence, so it's "unknown". - The event (intent, declaration, unknown) is also "unknown" because the sentence does not indicate whether the potatoes were eaten, will be eaten, or are just being mentioned. ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Based on your instructions, here's the analysis of the sentence "Pommes de terre vapeur". The sentence translates to "Steamed potatoes" in English. Here's the formatted result: ```json [ { "name": "Pommes de terre", "quantity": "", "cooking": "vapeur", "brand": "", "company": "", "type": "légume", "time": "unknown", "event": "unknown" } ] ``` Explanation: - The name of the food is "Pommes de terre" (potatoes). - The quantity is not specified in the sentence. - The cooking mode is "vapeur" (steamed). - The brand and the company are not mentioned in the sentence. - The type of food is "légume" (vegetable). - The time of eating is not specified in the sentence, so it's "unknown". - The event (intent, declaration, unknown) is also "unknown" because the sentence does not indicate whether the potatoes were eaten, will be eaten, or are just being mentioned. ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Pommes de terre", "quantity": "", "cooking": "vapeur", "brand": "", "company": "", "type": "légume", "time": "unknown", "event": "unknown" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Pommes de terre', 'quantity': '', 'cooking': 'vapeur', 'brand': '', 'company': '', 'type': 'légume', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.06312} -------------------------------------------------------------------------------- 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 '% pomme de terre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Pomme de Terre - pomme de terre - égouttée - - 26541 - - - CIQ#bbc0fd1495ed69b7aadd91d1d9b9ae69 Pomme de Terre - pomme de terre - aliment moyen - - 0 - - - CIQ#15f690b8140afc79288abfb96a139095 Pomme de Terre - pomme de terre - sans peau, crue - - 0 - - - CIQ#9d1dc4d850cf0a126428e8235097b299 Pomme de Terre - pomme de terre - rôtie/cuite au four - - 0 - - - CIQ#73642ae51d1ceb413f96f404c2e8fcc5 Pomme de Terre - pomme de terre - purée, aliment moyen - - 0 - - - CIQ#20c56d85dc4d344fdfb3594d5e93f5ff Pomme de Terre - pomme de terre - bouillie/cuite à l'eau - - 0 - - - CIQ#6997e933cb8bbe4ad6fb62b2f04c05c2 Pomme de Terre - pomme de terre - sans peau, rôtie/cuite au four - - 0 - - - CIQ#7c973fe7644a5cc7a5e1ac7f7690f91c Pomme de Terre - pomme de terre - purée, avec lait et beurre, non salée - - 54 - - - CIQ#f6d85f887fb7a88d451e7d1390b123ee Pomme de Terre - pomme de terre - flocons déshydratés, au lait ou à la crème - - 0 - - - CIQ#1450a8209d87032018367a76931b19ad Pomme de Terre - pomme de terre - purée à base de flocons, reconstituée avec lait entier, matière grasse - - 0 - - - CIQ#e310092ee2308f72f5d4eb70daa82fbc Pomme de Terre - pomme de terre - purée à base de flocons, reconstituée avec lait demi-écrémé et eau, non salée - - 0 - - - CIQ#3b12d13dfd318911c754bcb37b7b05ab Pomme de Terre Anna - pomme de terre anna - - - 43 - - - KCA#96fe2fadd9f331eb4549227f2e4a6267 Pomme de Terre Chips - pomme de terre chip - - - 42 - - - KCA#1deb7b7eab80f8586099ee58a6db9ea2 Pomme de Terre Purée - pomme de terre puree - - - 40 - - - KCA#0d4cd5387a20885448dbbf1f634017b3 Pomme de Terre Byron - pomme de terre byron - - - 4 - - - KCA#244d59f3080438c8160682d32b6ff789 Pomme de Terre Rôties - pomme de terre rotie - - - 1077 - - - KCA#797b578eb598e7082faea0ae30d34021 Pomme de Terre Frites - pomme de terre frite - - - 178 - - - KCA#d9391c743d3aee9e28d0940b17624718 Pomme de Terre Vapeur - pomme de terre vapeur - sous vide - - 0 - - - CIQ#d52218f9e63c6cb0bf8151b244a71afd Pomme de Terre Poêlée - pomme de terre poelee - avec matière grasse - - 0 - - - CIQ#b717c125ad32aa35b8cd673ba48f8c60 Pomme de Terre Sautées - pomme de terre sautee - - - 5854 - - - KCA#7e685fe608808c6ddb2b7b1edab93c82 ---------------------------------------------------- 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 ERROR: Wrong quantity: '' ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': 'Pommes de terre vapeur', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Pomme de Terre', 'normName': ' pomme de terre ', 'comment': 'égouttée', 'normComment': ' egouttee ', 'rank': 26541, 'id': 'CIQ#bbc0fd1495ed69b7aadd91d1d9b9ae69', 'quantity': '', 'quantityLem': '', 'pack': ['PDT.w120'], 'type': 'légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 8.88080382347107} ---------------------------------------------------------------------------------- LLM CPU Time: 8.88080382347107