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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# xVector Analysis\n",
"\n",
"This notebook is a template for visualizing the results of the dashVector / xVector engine.\n",
"It connects to the generated logs and the Qdrant instance to provide insights."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import json\n",
"import os\n",
"\n",
"# Path to logs\n",
"LOG_FILE = \"../logs/active_learning_queue.jsonl\"\n",
"\n",
"def load_logs():\n",
" data = []\n",
" if os.path.exists(LOG_FILE):\n",
" with open(LOG_FILE, 'r') as f:\n",
" for line in f:\n",
" data.append(json.loads(line))\n",
" return pd.DataFrame(data)\n",
"\n",
"df = load_logs()\n",
"if not df.empty:\n",
" print(f\"Loaded {len(df)} log entries.\")\n",
" display(df.head())\n",
"else:\n",
" print(\"No logs found yet. Run main.py first.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confidence Distribution\n",
"Analyze the confidence scores of queries that triggered active learning."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not df.empty:\n",
" plt.figure(figsize=(10, 6))\n",
" plt.hist(df['confidence'], bins=20, color='skyblue', edgecolor='black')\n",
" plt.title('Distribution of Confidence Scores (Hard Negatives)')\n",
" plt.xlabel('Confidence')\n",
" plt.ylabel('Count')\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.10"
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"nbformat_minor": 5
} |