Input path: /home/debian/html/nutritwin/output_llm/660b077757224/input.json Output path: /home/debian/html/nutritwin/output_llm/660b077757224/output.json Input text: Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit 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: Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit ================================================================================================================================== ==================================== Prompt ============================================= Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit###. Format the result in JSON format: {intents: []}. ========================================================================================= ------------------------------ LLM Raw response ----------------------------- { "intents": ["Capture the user physical activity", "Capture the user food consumption"] } ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ { "intents": ["Capture the user physical activity", "Capture the user food consumption"] } ------------------------------------------------------ ------------------------ After simplification ------------------------ {"intents": ["Capture the user physical activity", "Capture the user food consumption"]} ---------------------------------------------------------------------- ==================================== Prompt ============================================= I need to identify physical activities information from sentences. Analyze the following french sentence: "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit". Identify all sports or physical activities, its current name and its duration if it exists. If possible, map the name on one of the following activities in french: ["Basket-ball", "Cyclisme", "Boxe", "Danse", "Zumba", "Plong\u00e9e sous-marine", "Escrime", "Football", "Jardinage", "Golf", "Gymnastique", "Judo", "Karat\u00e9", "Corde \u00e0 sauter", "Rameur d'appartement", "Course \u00e0 pied", "Faire l'amour", "Ski de descente", "Natation", "Tennis", "Tapis de course", "A\u00e9robic", "Ski de fond", "danse", "V\u00e9lo d'appartement", "Aquabiking", "V\u00e9lo elliptique", "Exercice physique", "Equitation", "sports", "Escalade", "sports", "Patins en ligne", "sports", "Tennis de table", "sports", "Marche", "Activit\u00e9s nautiques et aquatiques", "Marche avec un chien", "T\u00e2ches m\u00e9nag\u00e8res", "Marche nordique"]. Ignore what it is not mapped to sport or physical activities. "Ménage" is similar to "Nettoyage". Cooking or eating are not a sport or a physical activities. Define "intensity" of the sport. Map for "intensity" on one of these values: ["unknown", "low", "moderate", "high", "intense", "very intense"]. Duration must be a number. Duration must be in minute. If a physical activity will occur in the future, the event is an "intent". If a physical activity has occured in the past, the event is a "declaration". Map this event of the physical activity on ["intent", "declaration", "unknown"]. Ignore the negative action . If the duration is unknown, set duration to 0. Format the result for sport in french in JSON in an array of tuples {"name":, "duration":, "intensity":, "event":}. When there is no solution, the result is: [] ========================================================================================= ------------------------------ LLM Raw response ----------------------------- Based on your instructions, I have analyzed the sentence and here is the information extracted: The sentence "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit" mentions one physical activity, which is "courir" (running). The duration of this activity is "une demi-heure" (half an hour or 30 minutes). The intensity of running can vary, but in general, it is considered a "moderate" to "high" intensity activity. The event is a "declaration" as it has occurred in the past. The activity "courir" can be mapped to "Course à pied" from the list you provided. The activity of having breakfast ("j'ai pris au petit déjeuner deux portions de vache qui rit") is not a physical activity, so it is ignored. Here is the result in the requested JSON format: ```json [ { "name": "Course à pied", "duration": 30, "intensity": "moderate", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Based on your instructions, I have analyzed the sentence and here is the information extracted: The sentence "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit" mentions one physical activity, which is "courir" (running). The duration of this activity is "une demi-heure" (half an hour or 30 minutes). The intensity of running can vary, but in general, it is considered a "moderate" to "high" intensity activity. The event is a "declaration" as it has occurred in the past. The activity "courir" can be mapped to "Course à pied" from the list you provided. The activity of having breakfast ("j'ai pris au petit déjeuner deux portions de vache qui rit") is not a physical activity, so it is ignored. Here is the result in the requested JSON format: ```json [ { "name": "Course à pied", "duration": 30, "intensity": "moderate", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Course à pied", "duration": 30, "intensity": "moderate", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Course à pied', 'duration': 30, 'intensity': 'moderate', 'event': 'declaration'}], 'cost': 0.07205999999999999} -------------------------------------------------------------------------------- ==================================== Prompt ============================================= I need to identify food information from sentences. Analyze the following french sentence: "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit". 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 "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit". We can ignore the first part of the sentence as it is not related to food or beverage. The food information is in the second part: "j'ai pris au petit déjeuner deux portions de vache qui rit". The food product is "vache qui rit", which is a well-known brand of cheese. The quantity is "deux portions" (two portions). The time of eating is "petit déjeuner" (breakfast). The event is a "declaration" as the consumption is in the past. Here's the information formatted in JSON: ```json [ { "name": "vache qui rit", "quantity": "deux portions", "cooking": "", "brand": "vache qui rit", "company": "Bel Group", "type": "fromage", "time": "petit déjeuner", "event": "declaration" } ] ``` ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Based on your instructions, here's the analysis of the sentence "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit". We can ignore the first part of the sentence as it is not related to food or beverage. The food information is in the second part: "j'ai pris au petit déjeuner deux portions de vache qui rit". The food product is "vache qui rit", which is a well-known brand of cheese. The quantity is "deux portions" (two portions). The time of eating is "petit déjeuner" (breakfast). The event is a "declaration" as the consumption is in the past. Here's the information formatted in JSON: ```json [ { "name": "vache qui rit", "quantity": "deux portions", "cooking": "", "brand": "vache qui rit", "company": "Bel Group", "type": "fromage", "time": "petit déjeuner", "event": "declaration" } ] ``` ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "vache qui rit", "quantity": "deux portions", "cooking": "", "brand": "vache qui rit", "company": "Bel Group", "type": "fromage", "time": "petit déjeuner", "event": "declaration" }] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'vache qui rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'vache qui rit', 'company': 'Bel Group', 'type': 'fromage', 'time': 'petit déjeuner', 'event': 'declaration'}], 'cost': 0.06558} -------------------------------------------------------------------------------- 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 '% vache qui rit %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) Second 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_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%%' ------------- Found solution (max 20) -------------- La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073768465500 - 3073768465500 - OFF#64a8dc23196895161ab1b53d6923bde4 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781192186 - 3073768465500 - OFF#da43cf7551a9e13c61a1ad694e521b4d La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781071559 - 3073768465500 - OFF#8c52efd954d14680fc301fe8e08e1c82 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073780886840 - 3073768465500 - OFF#e12170c7846fec182545d25fee1b2813 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073780574242 - 3073768465500 - OFF#5f4a5603fe8f62909abc2cc739c79aa5 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149586 - 3073768465500 - OFF#383167203c08d92736feccfd146b80de La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149838 - 3073768465500 - OFF#a450d0cc58d1fa1b104236b2a3b0fb1d La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149890 - 3073768465500 - OFF#1b0067194622ccea435fb351f5b4e9d4 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149876 - 3073768465500 - OFF#7e4fda2bf6eb008f72ad807808a6af64 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158533 - 3073768465500 - OFF#a6023c2768c642db84da32383a25ca81 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158830 - 3073768465500 - OFF#28d1e1d1aa56dae7fc32bcbf6859e135 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781194692 - 3073768465500 - OFF#e824de1734ce67d42f4333e7879c4545 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781192216 - 3073768465500 - OFF#9296abd5b4b24c23e27e9309776f6c1e La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781178913 - 3073768465500 - OFF#5c71f9d1919acec1ce090da7d83251f8 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781160420 - 3073768465500 - OFF#d105e86b448cf52866438cfa143f7aa9 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158854 - 3073768465500 - OFF#c2860291f39ec76a244f28873be7d183 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781158847 - 3073768465500 - OFF#861fadf18788d9d0c2e6593e9378d4a3 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781149937 - 3073768465500 - OFF#931815b46e255f01a8c4fc69aa6023d7 La Vache Qui Rit - vache qui rit - - group Bel - 0 - 3073781070200 - 3073768465500 - OFF#712506d4ac1e1c5ca319750438d29a11 La Vache Qui Rit 16p - vache qui rit 16p - - group Bel - 0 - 3073781149852 - 3073781149852 - OFF#7ebe1de82703b44929fb2056a48c1e6d ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': "Je suis allé courir une demi-heure dans le parc d'à côté et j'ai pris au petit déjeuner deux portions de vache qui rit", 'intents': ['Capture the user physical activity', 'Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'La Vache Qui Rit', 'normName': ' vache qui rit ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#64a8dc23196895161ab1b53d6923bde4', 'quantity': 'deux portions', 'quantityLem': '2 portion', 'pack': ['VQR.w16'], 'type': 'fromage', 'gtin': '3073768465500', 'gtinRef': '3073768465500', 'brand': 'group Bel', 'time': 'petit déjeuner', 'event': 'declaration', 'serving': 'VQR-200', 'posiNormName': 0}], 'activity': [{'trigram': 'RUN', 'duration': 30, 'event': 'declaration', 'level': 'RUN06'}]}, 'cputime': 21.792745113372803} ---------------------------------------------------------------------------------- LLM CPU Time: 21.792745113372803