Challenge 7- AI-Generated Football Players Scouting Report

Beginner-friendly objective(s): 1. Set up the initial data reading process by configuring the CSV Reader node to import the dataset. 2. Filter the dataset based on specific criteria using the Row Filter node.

Intermediate-friendly objective(s): 3. Convert the filtered data into JSON format and manage flow variables for dynamic workflow control. 4. Create and configure prompts for the language model using Variable Expression nodes.

Advanced objective(s): 5. Integrate the language model interaction by setting up the LLM Prompter nodes to generate responses based on the prompts. 6. Compile the final report by configuring the Report PDF Writer node to output the results.

Solution Summary: The solution involves a multi-step workflow that begins with reading and filtering a dataset of player statistics. The filtered data is then converted into JSON format, and flow variables are managed to dynamically control the workflow. Prompts are created for a language model, which generates responses that are compiled into a final report. This comprehensive approach combines data preprocessing, JSON conversion, and language model interaction to deliver insightful analysis.

Solution Details: The workflow starts with a CSV Reader node configured to import a dataset from a local file. The data is then filtered using a Row Filter node, which selects rows based on specific conditions such as competition, age, and nationality. The filtered data is converted into JSON format using the Table to JSON node, which is configured to handle a wide range of columns and omit missing values. Flow variables are managed using the Table Row to Variable node, which converts table rows into variables for dynamic workflow control. Next, Variable Expression nodes are used to create prompts for the language model. These nodes concatenate strings and flow variables to generate specific prompts for analysis. The OpenAI Authenticator node is configured to authenticate with the OpenAI API, ensuring secure access to the language model. The LLM Prompter nodes are then set up to interact with the language model, using the generated prompts to obtain responses. These nodes are configured to handle system messages and store responses in a specified column. Finally, the Report PDF Writer node is configured to compile the results into a PDF report. This node is set to save the report locally, with specific handling for existing files and a defined timeout period. The workflow effectively combines data preprocessing, JSON conversion, and language model interaction to deliver a comprehensive solution.

External resources

• Dataset