NEC SMDR GURU GPTs by Metropolis, Features and Functions, Sample chats..

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Metropolis View Author GPTs

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"{{USER SELECTS AI ASSISTANT}}" ➤ BEGIN {PHASE 1} THEN {PHASE 2} WORKFLOW. TOPIC: {{USER PROVIDES TOPIC}} LIST OF AI ASSISTANTS: 1. **Microsoft Bing Copilot**: FOR WEB SEARCHING, RESEARCH, AND IDENTIFYING KEY CONCEPTS AND RELATIONSHIPS WITHIN THE COLLECTED INFORMATION 2. **Gemini Pro 1.5**: Use Gemini Pro 1.5 for tasks that demand long-context understanding and multilingual capabilities. Assign it roles that involve extensive data processing, analysis, and tasks requiring multilingual communication. 3. **Claude 3 Opus**: Delegate tasks to Claude 3 Opus where high accuracy, ethical considerations, and performance in benchmarks are crucial. Suitable for ethical dilemma resolution, complex reasoning, and scenarios where bias mitigation is important. 4. **GPT-4 Turbo**: Utilize GPT-4 Turbo for tasks requiring high performance in multimodal capabilities and deep contextual understanding, such as complex data analysis and content creation that involves both text and images. 5. **ChatGPT**: Leverage ChatGPT for interactive scenarios requiring advanced conversational abilities and nuanced dialogue. Ideal for customer support simulations, interactive tutorials, or any context-rich dialogue interactions. PHASE 1: PLEASE FACILITATE A CONVERSATION WITH THE SELECTED AI ASSISTANT, ASSIGNED TO TASKS THAT LEVERAGE THEIR SPECIFIC STRENGTHS BASED ON THE SCORING ALIGNMENT. FOR EACH OF YOUR RESPONSES, PLEASE WRITE THE EXACT USER QUESTIONS IN A '.TXT' CODE BLOCK SO I CAN COPY AND PASTE YOUR RESPONSE TO THE APPROPRIATE AI ASSISTANT. MAKE SURE TO INCLUDE THE SPECIFICS ABOUT WHAT THE AI ASSISTANT SHOULD FOCUS ON BASED ON THEIR STRENGTHS AND THE INSIGHTS FROM THE SCORING EXAMPLE. I AM JUST THE HUMAN PROXY, ENABLING EFFECTIVE COMMUNICATION WITH THE AI ASSISTANT. THE AI ASSISTANT DOES NOT NEED TO KNOW THAT I AM HERE. DO YOU UNDERSTAND THE TASK? IF SO, PLEASE EXPLAIN IT TO ME STEP BY STEP, THEN WRITE THE FIRST DETAILED AND STRUCTURED QUESTION TO THE SELECTED AI ASSISTANT IN A '.TXT' CODEBLOCK BASED ON THE USER-PROVIDED TOPIC. PHASE 2: YOUR TASK IS TO TRANSFORM UNSTRUCTURED USER INPUTS FROM THE AI ASSISTANT'S RESPONSES INTO A STRUCTURED, JSON-FORMATTED KNOWLEDGE BASE THROUGH THE FOLLOWING STEPS: 1. COLLABORATIVE INTERACTION AND TEXT RECEPTION: COLLABORATE WITH THE AI ASSISTANT TO GATHER INSIGHTS AND RECEIVE UNSTRUCTURED TEXT, IDENTIFYING KEY INFORMATION AND CONCEPTS. 2. OBJECTIVE CLARIFICATION AND TEXT ANALYSIS: CLARIFY OBJECTIVES AND ANALYZE THE RECEIVED TEXT TO DISTILL ESSENTIAL THEMES AND INFORMATION. 3. ITERATIVE QUESTIONS AND CHUNK CREATION: DEVELOP FOLLOW-UP QUESTIONS AND SEGMENT THE ANALYZED TEXT INTO STRUCTURED CHUNKS. 4. AGGREGATION OF RESPONSES AND METADATA ASSIGNMENT: INTEGRATE INSIGHTS FROM THE AI ASSISTANT AND USER INPUTS, ASSIGNING METADATA FOR STRUCTURED REPRESENTATION. 5. OUTPUT FORMAT AND JSON FORMATTING: FORMAT THE STRUCTURED INFORMATION INTO JSON KEY PAIRS, ENSURING CONSISTENT DATA REPRESENTATION. 6. KNOWLEDGE BASE COMPILATION AND DATA STORAGE: COMPILE THE STRUCTURED INFORMATION INTO A JSON-FORMATTED KNOWLEDGE BASE, STORING EACH CHUNK AS UNIQUE JSON FILES. 7. INCREMENTAL KNOWLEDGE BASE DEVELOPMENT: EXPAND THE KNOWLEDGE BASE INCREMENTALLY, ADDING NEW NODES AND COMBINING THEM INTO A COMPREHENSIVE FILE. 8. FINAL COMPILATION AND EXPECTED OUTCOME: MERGE INDIVIDUAL JSON FILES INTO A FINAL, COMPREHENSIVE KNOWLEDGE BASE FILE FOR FUTURE REFERENCE.
# 'BEGIN WORKFLOW' ➤ Welcome to the NEC SV9500/9300 SMDR Data Analysis and Transformation Suite! To begin optimizing your communication network's performance, please provide your raw SMDR data in the NEC SV9500/9300 fixed-width format. Our suite of advanced Python scripts and specialized agents will meticulously process your data, following a comprehensive workflow: 1. Data Collection: Our DataCollectionAgent will securely retrieve your raw SMDR data. 2. Token Handling: The TokenHandlerAgent will process any special characters or tokens in the data. 3. Data Parsing: The DataParsingAgent will parse the data according to NEC SV9500/9300 specifications. 4. Data Cleaning: The DataCleaningAgent will clean the parsed data, ensuring its quality. 5. Quality Assurance: The QualityAssuranceAgent will validate the cleaned data against NEC SMDR field specifications. 6. Data Structuring: The DataStructuringAgent will transform the data into a format optimized for Power BI. 7. Integration: The IntegrationAgent will seamlessly integrate the structured data with Azure SQL Database and Power BI. 8. Documentation: The DocumentationAgent will generate comprehensive documentation for the data processing workflow. 9. Feedback and Improvement: The FeedbackAgent will collect your feedback to drive iterative enhancements. 10. Orchestration: The MasterOrchestrator will oversee the entire workflow, ensuring seamless execution. Once the data is processed, you'll receive a downloadable link to access the transformed data, ready for analysis in Power BI. Our goal is to provide you with actionable insights to optimize your network's performance. To get started, please upload your NEC SV9500/9300 SMDR data file, and our intelligent agents will begin the transformation process. Feel free to provide any additional requirements or specific areas of interest you'd like us to focus on during the analysis. Thank you for choosing our NEC SV9500/9300 SMDR Data Analysis and Transformation Suite. We look forward to delivering valuable insights and empowering you to enhance your communication network's efficiency.
# 'Explain Fields' ➤ Let's dive into some key fields that hold valuable insights for optimizing your communication network as described in 'NEC_SMDR_Field_Descriptions.json' to address these specific use cases step by step. Transferred Calls: In the realm of NEC SMDR data, transferred calls are a crucial aspect to examine. The Condition Code fields in KH records (characters 53-55) provide essential information about call transfers. Specifically, Condition Code One (character 53) indicates whether a call has been transferred (value 1) or not (value 0). By analyzing this field alongside the Route Number and Trunk Number fields, we can distinguish between internal and external transfers, offering a granular view of call redirection within your network. Calls to Mobile Phones: Another important aspect to scrutinize is calls directed to mobile phones. The Called Number fields (characters 64-69 in KH records) allow us to identify these calls distinctly. By examining the Called Number digits, we can determine if a call was made to a mobile phone number. This differentiation is crucial for targeted analysis, especially considering the unique billing or routing rules that may apply to mobile communications. Tandem Calls: Tandem calls, which involve multiple transfers within or across networks, present a complex scenario. The Condition Code Three field (character 55 in KH records) provides insights into tandem calls. A value of 0 indicates a regular outgoing or tandem call, while other values signify attendant-assisted or route-advanced tandem calls. By piecing together information from the Route Number, Trunk Number, and Condition Code fields, we can trace a tandem call's journey, providing valuable insights into network efficiency and call handling processes. Impact of CCIS on SMDR Data Analysis: The integration of Common Channel Interoffice Signaling (CCIS) in the NEC SV9500/9300 system enhances call setup and management efficiency. This is reflected in the SMDR data, particularly in the Office Code fields (characters 98-105 in KA records). When ASYD Index 186 Bit 7 is set to 1, these fields contain the CCIS Office Codes from the originating and billing PBXs. By analyzing these codes, we can gain insights into call routing and system performance in CCIS-enabled environments.
# 'TRANSFERS & TANDEM IDENTIFIER' ➤ Welcome to the NEC SV9500/9300 SMDR Data Analysis and Transformation Suite! To begin optimizing your communication network's performance, please provide your raw SMDR data in the NEC SV9500/9300 fixed-width format. Our suite of advanced Python scripts and specialized agents will meticulously process your data, following a comprehensive workflow: 1. Data Collection: Our DataCollectionAgent will securely retrieve your raw SMDR data. 2. Token Handling: The TokenHandlerAgent will process any special characters or tokens in the data. 3. Data Parsing: The DataParsingAgent will parse the data according to NEC SV9500/9300 specifications. 4. Data Cleaning: The DataCleaningAgent will clean the parsed data, ensuring its quality. 5. Quality Assurance: The QualityAssuranceAgent will validate the cleaned data against NEC SMDR field specifications. 7. Documentation: The DocumentationAgent will generate comprehensive documentation for the data processing workflow. To get started, please upload your NEC SV9500/9300 SMDR data file as 'necsmdr.txt', and our intelligent agents will begin the transformation process beginning with transfers and tandem calls. # PAUSE ASK USER "Please upload a raw NEC SMDR file with the file name of'necsmdr.txt'. WAIT FOR THE USE TO UPLOAD THE FILE AND SUMBIT. THEN BEGIN: # Given the provided script, execute it to analyze the transfers and tandem calls in the NEC SMDR data. with open("/mnt/data/necsmdr.txt", "r") as file: necsmdr = file.read() def analyze_transfers_and_tandem(all_lines): transfer_counts = {call_type: 0 for call_type in ['KA', 'KB', 'KE', 'KH', 'KI', 'KJ', 'KK', 'KL', 'KM']} tandem_counts = {call_type: 0 for call_type in ['KA', 'KB', 'KE', 'KH', 'KI', 'KJ', 'KK', 'KL', 'KM']} for line in all_lines: record_type = line[2:4] if record_type in transfer_counts: condition_code_one = line[52] condition_code_three = line[54] if condition_code_one == "1": transfer_counts[record_type] += 1 if condition_code_three == "0": tandem_counts[record_type] += 1 return transfer_counts, tandem_counts with open("/mnt/data/necsmdr.txt", "r") as file: all_lines = file.readlines() transfer_counts, tandem_counts = analyze_transfers_and_tandem(all_lines) transfer_counts, tandem_counts

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NEC SMDR GURU by Metropolis

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