Cognee simple document example (#521)

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## Description
Notebook and python example for cognee simple example

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Introduced an interactive demo showcasing asynchronous document
processing and querying for key insights from a sample text.
- **Documentation**
- Added an in-depth, step-by-step guide in a Jupyter Notebook that walks
users through setup, configuration, querying, and visualizing processed
data.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

examples/python/cognee_simple_document_demo.py

@@ -0,0 +1,34 @@
+import asyncio
+import cognee
+
+import os
+
+# By default cognee uses OpenAI's gpt-4o-mini LLM model
+# Provide your OpenAI LLM API KEY
+os.environ["LLM_API_KEY"] = ""
+
+
+async def cognee_demo():
+    # Get file path to document to process
+    from pathlib import Path
+
+    current_directory = Path(__file__).resolve().parent.parent
+    file_path = os.path.join(current_directory, "data", "alice_in_wonderland.txt")
+
+    # Call Cognee to process document
+    await cognee.add(file_path)
+    await cognee.cognify()
+
+    # Query Cognee for information from provided document
+    answer = await cognee.search("List me all the important characters in Alice in Wonderland.")
+    print(answer)
+
+    answer = await cognee.search("How did Alice end up in Wonderland?")
+    print(answer)
+
+    answer = await cognee.search("Tell me about Alice's personality.")
+    print(answer)
+
+
+# Cognee is an async library, it has to be called in an async context
+asyncio.run(cognee_demo())

notebooks/cognee_simple_demo.ipynb

@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "efb41c9e450b5a29",
+   "metadata": {},
+   "source": [
+    "# Cognee GraphRAG Simple Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "982b897a29a26f7d",
+   "metadata": {},
+   "source": [
+    "!pip install cognee==0.1.26"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51e92e9fdcf77b7",
+   "metadata": {},
+   "source": [
+    " By default cognee uses OpenAI's gpt-4o-mini LLM model.\n",
+    "\n",
+    " Provide your OpenAI LLM API KEY in the step bellow. Here's a guide on how to get your [OpenAI API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {},
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"LLM_API_KEY\"] = \"\""
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1fec64bc573bb",
+   "metadata": {},
+   "source": [
+    "In this step we'll get the location of the file to store and process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "5805c346f03d8070",
+   "metadata": {},
+   "source": [
+    "current_directory = os.getcwd()\n",
+    "file_path = os.path.join(current_directory, \"data\", \"alice_in_wonderland.txt\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2826a80ca1ad0438",
+   "metadata": {},
+   "source": [
+    "Give the file location to cognee to save it and process its contents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "875763366723ee48",
+   "metadata": {},
+   "source": [
+    "import cognee\n",
+    "await cognee.add(file_path)\n",
+    "await cognee.cognify()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4944567387ec5821",
+   "metadata": {},
+   "source": [
+    "Your data is ready to be queried:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "29b3a1e3279100d2",
+   "metadata": {},
+   "source": [
+    "await cognee.search(\"List me all the influential characters in Alice in Wonderland.\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "883ce50d2d9dc584",
+   "metadata": {},
+   "source": [
+    "await cognee.search(\"How did Alice end up in Wonderland?\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "677e1bc52aa078b6",
+   "metadata": {},
+   "source": [
+    "await cognee.search(\"Tell me about Alice's personality.\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd521e182fb66d49",
+   "metadata": {},
+   "source": [
+    "Bonus: See your processed data visualized in a knowledge graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "6effdae590b795d3",
+   "metadata": {},
+   "source": [
+    "import webbrowser\n",
+    "import os\n",
+    "from cognee.api.v1.visualize.visualize import visualize_graph\n",
+    "html = await visualize_graph()\n",
+    "home_dir = os.path.expanduser(\"~\")\n",
+    "html_file = os.path.join(home_dir, \"graph_visualization.html\")\n",
+    "display(html_file)\n",
+    "webbrowser.open(f\"file://{html_file}\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0945d6f1d962ab",
+   "metadata": {},
+   "source": [
+    "For more examples and information on how Cognee GraphRAG works checkout our other more detailed notebooks."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Cognee Env",
+   "language": "python",
+   "name": "venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}