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The content describes the process of setting up a language model for data processing and visualization using a wine reviews dataset as an example. The setup involves installing necessary packages and defining the language model to be used. Context is created around the dataset by extracting metadata like the number of columns, schema, data types, and a sample row. The metadata extraction code is provided.
To help the language model understand the dataset, the extracted metadata is augmented into prompt templates. An example prompt template is shown, which includes schema, data types, and a sample from the dataset. The language model is then prompted to suggest charts based on the metadata.
The process involves creating tools for the language model, creating prompts for the agent, and defining the agent with a model, tools, and base prompt. The agent is invoked to generate code for data aggregation, which is then executed using a REPL tool. The final data structure for the top 10 wineries by average points is shown.
The article covers an end-to-end flow for processing and visualizing data using an agent, including data ingestion, visualization suggestion, code generation, and data aggregation. The agent successfully generates the required data structure for plotting a bar chart based on the wine reviews dataset. The content concludes by inviting questions and promoting curiosity.
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Source link: https://medium.com/@cubode/comprehensive-guide-using-ai-agents-to-analyze-and-process-csv-data-a0259e2af761?source=rss——ai-5
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