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View All on GitHubAI Summary: The task involves analyzing API call response times from the Africa's Talking API integration within the provided Python project. This includes calculating response times, identifying outliers using visualization techniques (like boxplots or histograms), and summarizing the findings to explain potential causes of latency variations. The goal is to improve data quality and detect anomalies in API performance.
This project demonstrates function-calling with Python and Ollama, utilizing the Africa's Talking API to send airtime and messages to phone numbers using natural language prompts. Ollama + LLM w/ functions + Natural language = User Interface for non-coders.
AI Summary: Analyze API response data from the Africa's Talking integration within the provided Python project to identify, categorize, and count common error patterns (4xx and 5xx HTTP status codes). The goal is to create a script or notebook that summarizes these errors, provides examples, and suggests potential handling or visualization methods for future improvements.
This project demonstrates function-calling with Python and Ollama, utilizing the Africa's Talking API to send airtime and messages to phone numbers using natural language prompts. Ollama + LLM w/ functions + Natural language = User Interface for non-coders.
AI Summary: The task involves analyzing sample API response data from a Python project that uses the Africa's Talking API to send messages and airtime. The goal is to write a script (or notebook) to load, inspect, and summarize the data, calculating basic statistics and noting any patterns or anomalies. The output should be a summary in markdown or notebook comments.
This project demonstrates function-calling with Python and Ollama, utilizing the Africa's Talking API to send airtime and messages to phone numbers using natural language prompts. Ollama + LLM w/ functions + Natural language = User Interface for non-coders.