chore: import upstream snapshot with attribution

packages/graphrag-input/example_notebooks/input_example.ipynb

@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bfd1803d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) 2026 Microsoft Corporation.\n",
+    "# Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "077a563b",
+   "metadata": {},
+   "source": [
+    "## Markitdown support example\n",
+    "\n",
+    "Basic usage with the factory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d89952be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphrag_input import InputConfig, InputType, create_input_reader\n",
+    "from graphrag_storage import StorageConfig, create_storage\n",
+    "\n",
+    "config = InputConfig(\n",
+    "    type=InputType.Csv,\n",
+    "    text_column=\"content\",\n",
+    "    title_column=\"title\",\n",
+    ")\n",
+    "storage = create_storage(StorageConfig(base_dir=\"./input\"))\n",
+    "reader = create_input_reader(config, storage)\n",
+    "documents = await reader.read_files()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2e87b59d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphrag_input import InputConfig, InputType, create_input_reader\n",
+    "from graphrag_storage import StorageConfig, create_storage\n",
+    "\n",
+    "config = InputConfig(type=InputType.MarkItDown, file_pattern=\".*\\\\.pdf$\")\n",
+    "storage = create_storage(StorageConfig(base_dir=\"./input\"))\n",
+    "reader = create_input_reader(config, storage)\n",
+    "documents = await reader.read_files()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79fdf8cc",
+   "metadata": {},
+   "source": [
+    "Note that when specifying column names for data extraction, we can handle nested objects (e.g., in JSON) with dot notation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6c62fd82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alice\n"
+     ]
+    }
+   ],
+   "source": [
+    "from graphrag_input import get_property\n",
+    "\n",
+    "data = {\"user\": {\"profile\": {\"name\": \"Alice\"}}}\n",
+    "name = get_property(data, \"user.profile.name\")  # Returns \"Alice\"\n",
+    "\n",
+    "print(name)"
+   ]
+  }
+ ],
+ "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}