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Level: MA
Contact Person: Mostafa Chegeni
Keywords: Natural Language Processing (NLP), Large Language Models (LLMs), Blockchain Data Analysis, Prompt Engineering
This master's thesis introduces a Python-based application designed for streamlined data extraction and analysis from the Cardano blockchain. Leveraging advanced artificial intelligence, inspired by models like GitHub’s Copilot [1], CodexDB [2], and GPT-DB [3], the tool uses AI to interpret and execute data analysis tasks based on natural language prompts. It involves a customized GPT model, specifically tailored for the Cardano blockchain data structure in a PostgreSQL database [4], to accurately interpret user prompts and generate Python code and SQL queries.
The process starts with the user inputting analysis instructions as natural language prompts. These are processed by the customized GPT model through an OpenAI Assistant API [5], generating a Python script with SQL queries. This script, a crucial output of the model, extracts and analyzes the requested data according to user instructions.
The application then executes the Python script, facilitating data retrieval, analysis, and visualization based on user prompts. The result is a visual representation of the analyzed data, demonstrating a seamless integration of user input and AI-driven output.
A key aspect of the research is prompt engineering [6,7], which examines how different prompt styles and formats affect AI model performance. This exploration aims to optimize the model's accuracy and response usefulness, crucial for efficient data analysis.
In conclusion, the thesis presents an innovative approach to blockchain data analysis using AI and machine learning, contributing to both blockchain data analysis and the broader field of data science and analytics.
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