example: fix notebook for docs site loader (#294)

notebooks/embedchain-docs-site-example.ipynb

@@ -1,28 +1,100 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e9a9dc6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from embedchain import App\n",
+    "\n",
+    "embedchain_docs_bot = App()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9743e7ee",
+   "id": "c1c24d68",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "embedchain not found\r\n"
+      "All data from https://docs.embedchain.ai/ already exists in the database.\n"
      ]
     }
    ],
-   "source": []
+   "source": [
+    "embedchain_docs_bot.add(\"docs_site\", \"https://docs.embedchain.ai/\")"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e3b55735",
+   "execution_count": 3,
+   "id": "48cdaecf",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "answer = embedchain_docs_bot.query(\"Write a flask API for embedchain bot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0fe18085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "To write a Flask API for the embedchain bot, you can use the following code snippet:\n",
+       "\n",
+       "```python\n",
+       "from flask import Flask, request, jsonify\n",
+       "from embedchain import App\n",
+       "\n",
+       "app = Flask(__name__)\n",
+       "bot = App()\n",
+       "\n",
+       "# Add datasets to the bot\n",
+       "bot.add(\"youtube_video\", \"https://www.youtube.com/watch?v=3qHkcs3kG44\")\n",
+       "bot.add(\"pdf_file\", \"https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf\")\n",
+       "\n",
+       "@app.route('/query', methods=['POST'])\n",
+       "def query():\n",
+       "    data = request.get_json()\n",
+       "    question = data['question']\n",
+       "    response = bot.query(question)\n",
+       "    return jsonify({'response': response})\n",
+       "\n",
+       "if __name__ == '__main__':\n",
+       "    app.run()\n",
+       "```\n",
+       "\n",
+       "In this code, we create a Flask app and initialize an instance of the embedchain bot. We then add the desired datasets to the bot using the `add()` function.\n",
+       "\n",
+       "Next, we define a route `/query` that accepts POST requests. The request body should contain a JSON object with a `question` field. The bot's `query()` function is called with the provided question, and the response is returned as a JSON object.\n",
+       "\n",
+       "Finally, we run the Flask app using `app.run()`.\n",
+       "\n",
+       "Note: Make sure to install Flask and embedchain packages before running this code."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Markdown\n",
+    "# Create a Markdown object and display it\n",
+    "markdown_answer = Markdown(answer)\n",
+    "display(markdown_answer)"
+   ]
   }
  ],
  "metadata": {
@@ -41,7 +113,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,