upgrade model from GPT-4o-mini to Gemini-1.5-flash

notebooks/04_RAG_with_VectorStore.ipynb

@@ -21,75 +21,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
-        "id": "QPJzr-I9XQ7l",
         "collapsed": true,
-        "outputId": "dad24c44-2f42-4c37-a597-232ccffb9861",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
+        "id": "QPJzr-I9XQ7l"
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/67.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.4/71.4 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m83.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m425.7/425.7 kB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.3/157.3 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "!pip install -q llama-index==0.10.57 llama-index-vector-stores-chroma llama-index-llms-gemini==0.1.11 langchain_google_genai google-generativeai==0.5.4 langchain==0.1.17 langchain-chroma langchain_openai==0.1.5 openai==1.37.0 chromadb"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 15,
       "metadata": {
         "id": "riuXwpSPcvWC"
       },
@@ -97,8 +41,12 @@
       "source": [
         "import os\n",
         "# Set the following API Keys in the Python environment. Will be used later.\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
-        "os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_API_KEY>\""
+        "# os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
+        "# os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_API_KEY>\"\n",
+        "\n",
+        "from google.colab import userdata\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('openai_api_key')\n",
+        "os.environ[\"GOOGLE_API_KEY\"] = userdata.get('Google_api_key')"
       ]
     },
     {
@@ -130,13 +78,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "-QTUkdfJjY4N",
-        "outputId": "b43abf38-f483-41b6-eb39-c21aa7eca276"
+        "outputId": "34becd46-808a-42ee-e620-3e6b18f79e1d"
       },
       "outputs": [
         {
@@ -145,7 +93,7 @@
           "text": [
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100  169k  100  169k    0     0   559k      0 --:--:-- --:--:-- --:--:--  557k\n"
+            "100  169k  100  169k    0     0   609k      0 --:--:-- --:--:-- --:--:--  612k\n"
           ]
         }
       ],
@@ -164,13 +112,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "7CYwRT6R0o0I",
-        "outputId": "bdcf783d-c75b-4650-dafe-70f99ddd7e76"
+        "outputId": "394603bd-6d33-40aa-8e06-6ef802879234"
       },
       "outputs": [
         {
@@ -210,13 +158,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "STACTMUR1z9N",
-        "outputId": "bc0c4808-f709-4eee-bb07-0993a2cb8f73"
+        "outputId": "d5360ce2-2c1e-459b-a3b3-e9899fe762b5"
       },
       "outputs": [
         {
@@ -249,7 +197,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "id": "CtdsIUQ81_hT"
       },
@@ -272,7 +220,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "id": "mXi56KTXk2sp"
       },
@@ -288,7 +236,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "metadata": {
         "id": "jKXURvLtkuTS"
       },
@@ -304,38 +252,38 @@
     },
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       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
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-        "outputId": "975aeeeb-df70-4946-d306-54aab19d3f09",
         "colab": {
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           "height": 81,
           "referenced_widgets": [
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-            "29bbfc318ffd4a8e9452960f0f2ccb8d",
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-            "77a5354e5209441bb6a69b71f96a2102",
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+            "d2df0b3067574d46b951890b996a5751",
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+            "74fe3cd416994417a2138d8a32beeed8",
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+            "23f0bdb422384eaf94a69d20502fdbe4",
+            "dfa74d96a1174e7ead6554b5c984f526"
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+        },
+        "id": "WsD52wtrlESi",
+        "outputId": "2d522a85-cdea-477b-a693-e26b404c8ed9"
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       "outputs": [
         {
@@ -347,7 +295,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "be409ad8ca7e42b2aac87e193d55d116"
+              "model_id": "1a0185d42be8489c87874049e5d78424"
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@@ -361,7 +309,7 @@
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-              "model_id": "6d93958f663f48b4922a9524efb70e91"
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           "metadata": {}
@@ -392,7 +340,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 17,
       "metadata": {
         "id": "mzS13x1ZlZ5X"
       },
@@ -410,14 +358,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 18,
       "metadata": {
+        "id": "AYsQ4uLN_Oxg",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 52
         },
-        "id": "AYsQ4uLN_Oxg",
-        "outputId": "1acd38f6-d083-4d4a-aff2-a0063561adc1"
+        "outputId": "f56876cc-f7bc-4e63-bb7b-9515e9b404cc"
       },
       "outputs": [
         {
@@ -445,7 +393,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 19,
       "metadata": {
         "id": "SMPAniL2e4NP"
       },
@@ -468,7 +416,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 20,
       "metadata": {
         "id": "2xas7HkuhJ8A"
       },
@@ -499,7 +447,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 21,
       "metadata": {
         "id": "-H64YLxshM2b"
       },
@@ -519,20 +467,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 25,
       "metadata": {
+        "id": "AxBqPNtthPaa",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "AxBqPNtthPaa",
-        "outputId": "138a1238-97b8-41e1-9fd7-d655997d0743"
+        "outputId": "607b082a-66c2-49a2-ca59-b3cb85bf6067"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "I'm sorry, but the provided context doesn't mention the number of parameters for the LLaMA2 model. \n",
+            "The LLaMA 2 model comes in four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n",
             "\n"
           ]
         }
@@ -540,8 +488,8 @@
       "source": [
         "from langchain.chains import RetrievalQA\n",
         "\n",
-        "query = \"How many parameters LLaMA2 model has?\"\n",
-        "retriever = chroma_db.as_retriever(search_kwargs={\"k\": 2})\n",
+        "query = \"How many parameters LLaMA 2 model has?\"\n",
+        "retriever = chroma_db.as_retriever(search_kwargs={\"k\": 4})\n",
         "# Define a RetrievalQA chain that is responsible for retrieving related pieces of text,\n",
         "# and using a LLM to formulate the final answer.\n",
         "chain = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=retriever)\n",
@@ -552,12 +500,12 @@
     },
     {
       "cell_type": "code",
-      "source": [],
+      "execution_count": null,
       "metadata": {
         "id": "AKr16L_kwyYX"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     }
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@@ -583,7 +531,7 @@
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@@ -598,14 +546,14 @@
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             "children": [
-              "IPY_MODEL_2985c54fc3834d8599323f52075a01a6",
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-              "IPY_MODEL_29bbfc318ffd4a8e9452960f0f2ccb8d"
+              "IPY_MODEL_e91d7972a14b4444808d649d5db15b7a",
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+            "layout": "IPY_MODEL_eb7ce1338f6a4c9a8fb9a92e8de78821"
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@@ -620,13 +568,13 @@
             "_view_name": "HTMLView",
             "description": "",
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-            "layout": "IPY_MODEL_77a5354e5209441bb6a69b71f96a2102",
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-            "style": "IPY_MODEL_4d682a386d1146cf828470083fba1fe6",
+            "style": "IPY_MODEL_2a99f51fa5c24ce09f629d9d5322879d",
             "value": "Parsing nodes: 100%"
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@@ -642,15 +590,15 @@
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-            "layout": "IPY_MODEL_555508eb2f8c4caf81b623a8c157e742",
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-            "value": " 335/335 [00:00&lt;00:00, 599.37it/s]"
+            "style": "IPY_MODEL_3d78bf2951e04c21bd89c1d944412e5a",
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@@ -723,7 +671,7 @@
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@@ -775,7 +723,7 @@
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@@ -790,7 +738,7 @@
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@@ -842,7 +790,7 @@
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@@ -910,7 +858,7 @@
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@@ -925,7 +873,7 @@
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@@ -940,14 +888,14 @@
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@@ -962,13 +910,13 @@
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-            "layout": "IPY_MODEL_eecd42e03f4d484c87032c25df7570b3",
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@@ -984,15 +932,15 @@
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@@ -1065,7 +1013,7 @@
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@@ -1117,7 +1065,7 @@
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@@ -1132,7 +1080,7 @@
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@@ -1184,7 +1132,7 @@
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@@ -1200,7 +1148,7 @@
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-        "91ca1a302884473f8314c097c41d03fd": {
+        "23f0bdb422384eaf94a69d20502fdbe4": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -1252,7 +1200,7 @@
             "width": null
           }
         },
-        "b71c84b4ca3443d29d650bb8ea0f5458": {
+        "dfa74d96a1174e7ead6554b5c984f526": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",