It seems like you’re working on a project related to anomaly detection in e-commerce order data using OpenAI’s o3 model. This involves a practical implementation of a stack that combines multimodal input, reasoning, and structured output.

Here’s a summary of the main points and steps from your overview:

Objective

Build a time-series anomaly detection system to identify unusual patterns in e-commerce order data.

Structure of the Solution

1. Case Study:

   • Generate synthetic daily order data representing different profiles over a month.
   • Each dataset includes specific types of anomalies.
2. Solution Overview:
   • Prepare visualizations of time series data.
   • Utilize the o3 model to analyze these visualizations and identify anomalies.
   • Output findings in a structured JSON format.

Implementation Steps

1. Setup the o3 Model:

   • Use the Azure OpenAI endpoint to connect and authenticate.
   • Define a role, task, and rules for the model to follow when analyzing the data.
2. Image Preparation:
   • Convert visualizations (figures) into a base64 data URL format for the model to process.
   • Utilize a function to create plots and encode them properly.

Example Code

Here’s a segment of your code that highlights setting up the o3 model and preparing the image:

python
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import io
import base64
from openai import AzureOpenAI
from dotenv import load_dotenv

load_dotenv()

Setup LLM client

endpoint = os.getenv("api_base")
api_key = os.getenv("o3_API_KEY")
api_version = "2025-04-01-preview"
model_name = "o3"
deployment = os.getenv("deployment_name")

LLM_client = AzureOpenAI(
api_key=api_key,
api_version=api_version,
azure_endpoint=endpoint
)

Function to convert figure to a base64 data URL

def fig_to_data_url(fig, fmt="png"):
buf = io.BytesIO()
fig.savefig(buf, format=fmt, bbox_inches="tight")
buf.seek(0)

base64_encoded_data = base64.b64encode(buf.read()).decode("utf-8")
mime_type = f"image/{fmt.lower()}"

return f"data:{mime_type};base64,{base64_encoded_data}"

Create figure function can be added here…

Next Steps

• Implement the logic to generate the time series plot and invoke the model with the figure.
• Implement the create_fig function to plot your order data, shade weekends, and convert that plot into a base64 URL.
• Test the model’s performance by validating its anomaly detection capabilities against known anomalies.

This framework provides a hands-on approach to leveraging LLM capabilities for analyzing time-series data, making it applicable beyond just e-commerce anomaly detection.

If you need help with specific parts of the implementation or have questions, feel free to ask!