load .env variables for vscode debugging

notebooks/03-RAG_with_LlamaIndex.ipynb

@@ -18,7 +18,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -33,19 +33,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 1,
       "metadata": {
         "id": "CWholrWlt2OQ"
       },
       "outputs": [],
       "source": [
         "import os\n",
+        "from dotenv import load_dotenv\n",
         "\n",
-        "# Set your \"OPENAI_API_KEY\" environment variable\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\"\n",
+        "load_dotenv(\".env\")\n",
+        "\n",
+        "# Here we look for the OPENAI_API_KEY in the environment variables\n",
+        "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
+        "if not OPENAI_API_KEY:\n",
+        "    # If it's not found, you can set it manually\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\"\n",
         "\n",
         "# Get your GOOGLE_API_KEY from https://aistudio.google.com/app/apikey\n",
-        "os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_GOOGLE_KEY>\""
+        "GOOGLE_API_KEY = os.getenv(\"GOOGLE_API_KEY\")\n",
+        "if not GOOGLE_API_KEY:\n",
+        "    os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_GOOGLE_KEY>\""
       ]
     },
     {
@@ -77,7 +85,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -92,7 +100,7 @@
           "text": [
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100  169k  100  169k    0     0  1817k      0 --:--:-- --:--:-- --:--:-- 1823k\n"
+            "100  169k  100  169k    0     0   772k      0 --:--:-- --:--:-- --:--:--  774k\n"
           ]
         }
       ],
@@ -111,7 +119,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -135,14 +143,16 @@
         "\n",
         "# Load the CSV file\n",
         "with open(\"./mini-dataset.csv\", mode=\"r\", encoding=\"utf-8\") as file:\n",
-        "  csv_reader = csv.reader(file)\n",
+        "    csv_reader = csv.reader(file)\n",
         "\n",
-        "  for idx, row in enumerate( csv_reader ):\n",
-        "    if idx == 0: continue; # Skip header row\n",
-        "    rows.append( row )\n",
+        "    for idx, row in enumerate(csv_reader):\n",
+        "        if idx == 0:\n",
+        "            continue\n",
+        "            # Skip header row\n",
+        "        rows.append(row)\n",
         "\n",
         "# The number of characters in the dataset.\n",
-        "print( \"number of articles:\", len( rows ) )"
+        "print(\"number of articles:\", len(rows))"
       ]
     },
     {
@@ -156,7 +166,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 4,
       "metadata": {
         "id": "iXrr5-tnEfm9"
       },
@@ -170,7 +180,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -210,8 +220,10 @@
           "text": [
             "/Users/omar/Documents/ai_repos/ai-tutor-rag-system/env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
             "  from .autonotebook import tqdm as notebook_tqdm\n",
-            "Parsing nodes: 100%|██████████| 14/14 [00:00<00:00, 252.38it/s]\n",
-            "Generating embeddings: 100%|██████████| 56/56 [00:01<00:00, 41.05it/s]\n"
+            "Parsing nodes: 100%|██████████| 14/14 [00:00<00:00, 247.39it/s]\n",
+            "/Users/omar/Documents/ai_repos/ai-tutor-rag-system/env/lib/python3.12/site-packages/langchain/agents/json_chat/base.py:22: SyntaxWarning: invalid escape sequence '\\ '\n",
+            "  \"\"\"Create an agent that uses JSON to format its logic, build for Chat Models.\n",
+            "Generating embeddings: 100%|██████████| 56/56 [00:01<00:00, 43.08it/s]\n"
           ]
         }
       ],
@@ -241,7 +253,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 6,
       "metadata": {
         "id": "bUaNH97dEfh9"
       },
@@ -259,7 +271,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -272,21 +284,19 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "LLaMA 2 is available in four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
+            "LLaMA 2 comes in four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
             "\n"
           ]
         }
       ],
       "source": [
-        "response = query_engine.query(\n",
-        "    \"How many parameters LLaMA2 model has?\"\n",
-        ")\n",
+        "response = query_engine.query(\"How many parameters LLaMA2 model has?\")\n",
         "print(response)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -299,15 +309,13 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "The context does not provide information about the release date of Llama 3. \n",
+            "The context does not provide information about the release of Llama 3. \n",
             "\n"
           ]
         }
       ],
       "source": [
-        "response = query_engine.query(\n",
-        "    \"When will Llama3 will be released?\"\n",
-        ")\n",
+        "response = query_engine.query(\"When will Llama3 will be released?\")\n",
         "print(response)"
       ]
     }