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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "fc00c2fa",
"metadata": {},
"outputs": [],
"source": [
"import dspy\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a1670ba",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null
"id": "194bf39b",
"metadata": {},
"outputs": [],
"source": [
"gpt_5_mini = dspy.LM(\n",
" model=\"openai/gpt-5-mini\",\n",
" api_key=os.getenv(\"OPENAI_API_KEY\"),\n",
" temperature=float(os.getenv(\"TEMPERATURE\", 1.0)),\n",
" max_tokens= None,\n",
" # max_completion_tokens=3000,\n",
" cache=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "011b0ca4",
"metadata": {},
"outputs": [],
"source": [
"dspy.configure(lm= gpt_5_mini)\n",
"\n",
"allocator = dspy.Predict(\"goal,planner_desc,dataset->exact_word_complexity:Literal['unrelated','basic', 'intermediate', 'advanced'],reasoning\")\n",
"\n",
"\n",
"session = allocator(goal=\"build me a regression model and then visualize the residuals\", planner_desc='{data_viz_agent:\"I love visualizaing\"}', dataset='housing dataset, can only answer housing')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0217d501",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Prediction(\n",
" exact_word_complexity='intermediate',\n",
" reasoning='Building a regression model and visualizing residuals requires moderate statistical and coding knowledge: selecting and preprocessing features from the housing dataset, splitting data, fitting a model (e.g., linear or regularized regression), computing evaluation metrics (MSE, R²), and creating diagnostic plots (residuals vs. fitted, histogram or KDE of residuals, Q–Q plot, and scale-location). It’s more than a basic copy-paste task because it involves assumption checks and exploratory/feature work, but it doesn’t require the advanced theory or highly specialized modeling techniques that would push it into “advanced.” The work is therefore best categorized as intermediate.'\n",
")"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"session"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2bf84f7e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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