{ "query": "You are a super intelligent assistant. Please answer all my questions precisely and comprehensively.\n\nThrough our system KIOS you have a Knowledge Base named 10-7 with all the informations that the user requests. In this knowledge base are following Documents test.txt, test.csv, test.xlsx, test.docx, test.pdf, test_0.pdf, test_1.pdf, test_2.pdf, test.xls, test.doc, test.pptx, test.ppt, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf, Analytic%20Geometry%20%281922%29%20-%20Lewis%20Parker%20Siceloff%2C%20George%20Wentworth%2C%20David%20Eugene%20Smith%20%28PDF%29.pdf, test%281%29.pdf, test_0%281%29.pdf, test%281%29.txt, test%281%29.csv\n\nThis is the initial message to start the chat. Based on the following summary/context you should formulate an initial message greeting the user with the following user name [Gender] [Vorname] [Surname] tell them that you are the AI Chatbot Simon using the Large Language Model [Used Model] to answer all questions.\n\nFormulate the initial message in the Usersettings Language German\n\nPlease use the following context to suggest some questions or topics to chat about this knowledge base. List at least 3-10 possible topics or suggestions up and use emojis. The chat should be professional and in business terms. At the end ask an open question what the user would like to check on the list. Please keep the wildcards incased in brackets and make it easy to replace the wildcards. \n\n The provided context consists of various files, each containing information related to different topics. Here's a summary of each file:\n\n**File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf**\n\nThis file is a book about computer science, specifically focusing on the application of computer science to book production. It covers topics like letter forms, storing words, looking and finding, typing it in, saving space, doing sums, grey areas, our typeface, words to paragraphs, and solutions to problems presented throughout the book. The book aims to provide an understanding of why computer scientists are interested in computer science and how it can be used to solve real-world problems.\n\n**File: our-upcebu-edu-ph-58438.txt**\n\nThis file contains information about various academic procedures and policies at the University of the Philippines Cebu (UPC). It includes details on:\n\n- Scholastic delinquency: Defines different academic standing levels and their consequences.\n- Request for substitution of courses: Outlines the process for substituting courses.\n- Apply for graduation: Provides steps and deadlines for graduation applications.\n- Apply for the new UP RFID: Explains the process for obtaining a new RFID card.\n- Request to cross-register: Describes the process for cross-registration from other universities.\n- Removal of INC/4.0: Explains the process for removing incomplete grades or grades of 4.0.\n- Appeal for readmission/extension of residence: Outlines the process for appealing for readmission or extension of residence.\n- How to submit forms online: Provides instructions for submitting forms online.\n- Pre-enlistment: Explains the process for pre-enlisting in classes.\n- Advisement: Outlines the process for advisement.\n- Scholarships and/or tuition discount: Provides information about scholarships and tuition discounts.\n- Form 5A and UP Form 5 EOR: Explains the difference between these two forms.\n\n**File: test.ppt**\n\nThis file contains a simple code snippet:\n\n```\n. \n. \n.\n```\n\n**File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf**\n\nThis file is a book about machine learning, aiming to provide an intuitive understanding of various machine learning concepts and techniques. It covers topics like:\n\n- Data and information: Discusses data representation and preprocessing.\n- Data visualization: Explains the importance and types of data visualization.\n- Learning: Defines learning as generalization and abstraction.\n- Types of machine learning: Introduces supervised and unsupervised learning.\n- Nearest neighbors classification: Explains the concept of nearest neighbors classification.\n- The Naive Bayesian Classifier: Provides an overview of the Naive Bayesian Classifier.\n- The Perceptron: Introduces the perceptron model.\n- Support Vector Machines: Explains the concept of Support Vector Machines.\n- Support Vector Regression: Discusses Support Vector Regression.\n- Kernel Ridge Regression: Provides an overview of Kernel Ridge Regression.\n- Kernel K-means and Spectral Clustering: Explains these clustering techniques.\n- Kernel Principal Components Analysis: Discusses Kernel PCA.\n- Fisher Linear Discriminant Analysis: Introduces Fisher LDA.\n- Kernel Canonical Correlation Analysis: Explains Kernel CCA.\n- Essentials of Convex Optimization: Provides a brief overview of convex optimization.\n- Kernel Design: Discusses different kernel functions.\n\n**Overall, the context provides a comprehensive overview of computer science and machine learning, with a focus on practical applications and intuitive explanations.**\n", "namespace": "f5e11584-d869-4baa-9ec3-5241e389cc7e", "messages": [], "stream": false, "language_level": "", "chat_channel": "", "language": "German", "tone": "neutral", "writing_style": "standard", "model": "gemini-1.5-flash", "knowledgebase": "ki-dev-large", "seed": 0, "client_id": 0, "all_context": true, "follow_up_for": null, "knowledgebase_files_count": 0, "override_command": "", "disable_clarity_check": true, "custom_primer": "", "logging": true, "query_route": "" } INITIALIZATION Knowledgebase: ki-dev-large Base Query: You are a super intelligent assistant. Please answer all my questions precisely and comprehensively. Through our system KIOS you have a Knowledge Base named 10-7 with all the informations that the user requests. In this knowledge base are following Documents test.txt, test.csv, test.xlsx, test.docx, test.pdf, test_0.pdf, test_1.pdf, test_2.pdf, test.xls, test.doc, test.pptx, test.ppt, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf, Analytic%20Geometry%20%281922%29%20-%20Lewis%20Parker%20Siceloff%2C%20George%20Wentworth%2C%20David%20Eugene%20Smith%20%28PDF%29.pdf, test%281%29.pdf, test_0%281%29.pdf, test%281%29.txt, test%281%29.csv This is the initial message to start the chat. Based on the following summary/context you should formulate an initial message greeting the user with the following user name [Gender] [Vorname] [Surname] tell them that you are the AI Chatbot Simon using the Large Language Model [Used Model] to answer all questions. Formulate the initial message in the Usersettings Language German Please use the following context to suggest some questions or topics to chat about this knowledge base. List at least 3-10 possible topics or suggestions up and use emojis. The chat should be professional and in business terms. At the end ask an open question what the user would like to check on the list. Please keep the wildcards incased in brackets and make it easy to replace the wildcards. The provided context consists of various files, each containing information related to different topics. Here's a summary of each file: **File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf** This file is a book about computer science, specifically focusing on the application of computer science to book production. It covers topics like letter forms, storing words, looking and finding, typing it in, saving space, doing sums, grey areas, our typeface, words to paragraphs, and solutions to problems presented throughout the book. The book aims to provide an understanding of why computer scientists are interested in computer science and how it can be used to solve real-world problems. **File: our-upcebu-edu-ph-58438.txt** This file contains information about various academic procedures and policies at the University of the Philippines Cebu (UPC). It includes details on: - Scholastic delinquency: Defines different academic standing levels and their consequences. - Request for substitution of courses: Outlines the process for substituting courses. - Apply for graduation: Provides steps and deadlines for graduation applications. - Apply for the new UP RFID: Explains the process for obtaining a new RFID card. - Request to cross-register: Describes the process for cross-registration from other universities. - Removal of INC/4.0: Explains the process for removing incomplete grades or grades of 4.0. - Appeal for readmission/extension of residence: Outlines the process for appealing for readmission or extension of residence. - How to submit forms online: Provides instructions for submitting forms online. - Pre-enlistment: Explains the process for pre-enlisting in classes. - Advisement: Outlines the process for advisement. - Scholarships and/or tuition discount: Provides information about scholarships and tuition discounts. - Form 5A and UP Form 5 EOR: Explains the difference between these two forms. **File: test.ppt** This file contains a simple code snippet: ``` . . . ``` **File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf** This file is a book about machine learning, aiming to provide an intuitive understanding of various machine learning concepts and techniques. It covers topics like: - Data and information: Discusses data representation and preprocessing. - Data visualization: Explains the importance and types of data visualization. - Learning: Defines learning as generalization and abstraction. - Types of machine learning: Introduces supervised and unsupervised learning. - Nearest neighbors classification: Explains the concept of nearest neighbors classification. - The Naive Bayesian Classifier: Provides an overview of the Naive Bayesian Classifier. - The Perceptron: Introduces the perceptron model. - Support Vector Machines: Explains the concept of Support Vector Machines. - Support Vector Regression: Discusses Support Vector Regression. - Kernel Ridge Regression: Provides an overview of Kernel Ridge Regression. - Kernel K-means and Spectral Clustering: Explains these clustering techniques. - Kernel Principal Components Analysis: Discusses Kernel PCA. - Fisher Linear Discriminant Analysis: Introduces Fisher LDA. - Kernel Canonical Correlation Analysis: Explains Kernel CCA. - Essentials of Convex Optimization: Provides a brief overview of convex optimization. - Kernel Design: Discusses different kernel functions. **Overall, the context provides a comprehensive overview of computer science and machine learning, with a focus on practical applications and intuitive explanations.** Model: gemini-1.5-flash **Elapsed Time: 0.00 seconds** ROUTING Query type: simple_conversation **Elapsed Time: 1.78 seconds** PRIMER Primer: You are Simon, a highly intelligent personal assistant in a system called KIOS. You are a chatbot that can read knowledgebases through the "CONTEXT" that is included in the user's chat message. In the "CONTEXT", prioritize information from data tagged "FEEDBACK:". You are a conversational bot, an intelligent system designed to interact with the user. Respond courteously to the message of the user. To keep the conversation going, ask the user if there is any specific information they would like to know at the end of your message. **Elapsed Time: 0.00 seconds** FINAL QUERY Final Query: You are a super intelligent assistant. Please answer all my questions precisely and comprehensively. Through our system KIOS you have a Knowledge Base named 10-7 with all the informations that the user requests. In this knowledge base are following Documents test.txt, test.csv, test.xlsx, test.docx, test.pdf, test_0.pdf, test_1.pdf, test_2.pdf, test.xls, test.doc, test.pptx, test.ppt, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf, Advanced%20Algebra%20-%20Anthony%20W.%20Knapp%20%28PDF%29.pdf, Analytic%20Geometry%20%281922%29%20-%20Lewis%20Parker%20Siceloff%2C%20George%20Wentworth%2C%20David%20Eugene%20Smith%20%28PDF%29.pdf, test%281%29.pdf, test_0%281%29.pdf, test%281%29.txt, test%281%29.csv This is the initial message to start the chat. Based on the following summary/context you should formulate an initial message greeting the user with the following user name [Gender] [Vorname] [Surname] tell them that you are the AI Chatbot Simon using the Large Language Model [Used Model] to answer all questions. Formulate the initial message in the Usersettings Language German Please use the following context to suggest some questions or topics to chat about this knowledge base. List at least 3-10 possible topics or suggestions up and use emojis. The chat should be professional and in business terms. At the end ask an open question what the user would like to check on the list. Please keep the wildcards incased in brackets and make it easy to replace the wildcards. The provided context consists of various files, each containing information related to different topics. Here's a summary of each file: **File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf** This file is a book about computer science, specifically focusing on the application of computer science to book production. It covers topics like letter forms, storing words, looking and finding, typing it in, saving space, doing sums, grey areas, our typeface, words to paragraphs, and solutions to problems presented throughout the book. The book aims to provide an understanding of why computer scientists are interested in computer science and how it can be used to solve real-world problems. **File: our-upcebu-edu-ph-58438.txt** This file contains information about various academic procedures and policies at the University of the Philippines Cebu (UPC). It includes details on: - Scholastic delinquency: Defines different academic standing levels and their consequences. - Request for substitution of courses: Outlines the process for substituting courses. - Apply for graduation: Provides steps and deadlines for graduation applications. - Apply for the new UP RFID: Explains the process for obtaining a new RFID card. - Request to cross-register: Describes the process for cross-registration from other universities. - Removal of INC/4.0: Explains the process for removing incomplete grades or grades of 4.0. - Appeal for readmission/extension of residence: Outlines the process for appealing for readmission or extension of residence. - How to submit forms online: Provides instructions for submitting forms online. - Pre-enlistment: Explains the process for pre-enlisting in classes. - Advisement: Outlines the process for advisement. - Scholarships and/or tuition discount: Provides information about scholarships and tuition discounts. - Form 5A and UP Form 5 EOR: Explains the difference between these two forms. **File: test.ppt** This file contains a simple code snippet: ``` . . . ``` **File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf** This file is a book about machine learning, aiming to provide an intuitive understanding of various machine learning concepts and techniques. It covers topics like: - Data and information: Discusses data representation and preprocessing. - Data visualization: Explains the importance and types of data visualization. - Learning: Defines learning as generalization and abstraction. - Types of machine learning: Introduces supervised and unsupervised learning. - Nearest neighbors classification: Explains the concept of nearest neighbors classification. - The Naive Bayesian Classifier: Provides an overview of the Naive Bayesian Classifier. - The Perceptron: Introduces the perceptron model. - Support Vector Machines: Explains the concept of Support Vector Machines. - Support Vector Regression: Discusses Support Vector Regression. - Kernel Ridge Regression: Provides an overview of Kernel Ridge Regression. - Kernel K-means and Spectral Clustering: Explains these clustering techniques. - Kernel Principal Components Analysis: Discusses Kernel PCA. - Fisher Linear Discriminant Analysis: Introduces Fisher LDA. - Kernel Canonical Correlation Analysis: Explains Kernel CCA. - Essentials of Convex Optimization: Provides a brief overview of convex optimization. - Kernel Design: Discusses different kernel functions. **Overall, the context provides a comprehensive overview of computer science and machine learning, with a focus on practical applications and intuitive explanations.** Final Files Sources: ================================================== **Elapsed Time: 2.16 seconds** ================================================== FINAL ANSWER Answer: ================================================== **Elapsed Time: 0.00 seconds** ==================================================