{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DI5fr-UTfdzg"
      },
      "source": [
        "# Install Packages and Setup Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTQEnvU2c4lQ",
        "outputId": "6a52c70c-d375-4313-cd53-290299b37ec7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m455.6/455.6 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m35.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.7/55.7 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.2/196.2 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.5/118.5 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m106.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m459.8/459.8 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m304.2/304.2 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m108.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m453.1/453.1 kB\u001b[0m \u001b[31m25.1 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[31m2.7 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[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 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[32m129.3/129.3 kB\u001b[0m \u001b[31m9.3 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"
          ]
        }
      ],
      "source": [
        "!pip install -q llama-index==0.12.21 openai==1.59.8 tiktoken==0.8.0  huggingface-hub==0.30 chromadb==0.6.0 llama-index-vector-stores-chroma==0.4.1 llama-index-llms-gemini==0.4.1 llama-index-embeddings-adapter==0.3.0 llama-index-embeddings-huggingface==0.5.4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zREQvf1YeNra"
      },
      "outputs": [],
      "source": [
        "# set variables\n",
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('openai_api_key')\n",
        "\n",
        "os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN2')\n",
        "HF_TOKEN = userdata.get('HF_TOKEN2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q-E5-s41e785"
      },
      "outputs": [],
      "source": [
        "# Allows running asyncio in environments with an existing event loop, like Jupyter notebooks.\n",
        "\n",
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTPF89SkEXVM"
      },
      "source": [
        "# Load fine tuned embedding model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "c56d28b3e6314eb3ade9e007fda7824c",
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        },
        "id": "8VlKC4qyEIsf",
        "outputId": "83a0e984-26ff-4f9c-e1de-84612593bab8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c56d28b3e6314eb3ade9e007fda7824c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Fetching 3 files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8cc23cd30550448bb1462520d19d82f1",
              "version_major": 2,
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5b99b5e402bd49c299138db03259e7b2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/593k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b62c816958c94fffb0a4445965011826",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/55.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/finetuned_embed_model_full'"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# prompt: download a finetuned model from hugging face, repor vicpada/finetuned_embed_model\n",
        "from huggingface_hub import snapshot_download\n",
        "\n",
        "snapshot_download(repo_id=\"vicpada/finetuned_embed_model_full\", repo_type=\"model\", local_dir=\"./finetuned_embed_model_full\")"
      ]
    },
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        "id": "s_Lr9b-WEbO7",
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f5a59c84ed74889b6eb828c291d5b09",
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              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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        {
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          },
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              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f8c41ad2de934f8ba66e0499f9784a62",
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            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99adb09eebd74cc6a8ab4763dba9a921",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "267d568c20fd445aabb6023a8f201a4f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5b3ba799527401c98fc335b2f14f168",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from llama_index.core.embeddings import resolve_embed_model\n",
        "from llama_index.embeddings.adapter import AdapterEmbeddingModel\n",
        "\n",
        "# Load the Base model without fine-tuning\n",
        "base_embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")\n",
        "\n",
        "# Load the Fine-tuned model.\n",
        "embed_model = AdapterEmbeddingModel(base_embed_model, \"finetuned_embed_model_full\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vnai7KWNfB8k"
      },
      "outputs": [],
      "source": [
        "from llama_index.embeddings.openai import OpenAIEmbedding\n",
        "from llama_index.core import Settings\n",
        "\n",
        "Settings.embed_model = embed_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0z-X8oZ_fcvj"
      },
      "source": [
        "# Download nodes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99,
          "referenced_widgets": [
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          ]
        },
        "id": "wAR63onc3W_D",
        "outputId": "8145465e-dbb7-406c-b5ff-39bed1fd917c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54c8783a602f49729245ebba7d43f5c7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c4b4ac678244db3b51b771fe259c628",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "all_data_embedded.pkl:   0%|          | 0.00/1.26G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/nodes_full'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# prompt: download a file from hugging face\n",
        "\n",
        "snapshot_download(repo_id=\"vicpada/AzureResources\", repo_type=\"dataset\", local_dir=\"./nodes_full\", allow_patterns=['embedded/*'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3t-tuDy9fStt",
        "outputId": "d307663a-e83a-4e53-a1ed-bdbbf213ce98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10350\n"
          ]
        }
      ],
      "source": [
        "import chromadb\n",
        "from llama_index.vector_stores.chroma import ChromaVectorStore\n",
        "from llama_index.core import VectorStoreIndex\n",
        "\n",
        "\n",
        "\n",
        "# Load the vector store from the local storage.\n",
        "chroma_path = './azure-architect'\n",
        "db = chromadb.PersistentClient(path=chroma_path, settings=chromadb.config.Settings(allow_reset=True))\n",
        "db.list_collections()\n",
        "chroma_collection = db.get_collection(\"azure-architect\")\n",
        "print(chroma_collection.count())\n",
        "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
        "index = VectorStoreIndex.from_vector_store(vector_store)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qD7oQectg8AO"
      },
      "source": [
        "# Create questions and responses pairs"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "da91ca45-936b-4c79-ca4d-34e537b3abb4"
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      "outputs": [
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          "data": {
            "text/plain": [
              "195707"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pickle\n",
        "import os\n",
        "\n",
        "file_path = 'nodes_full/embedded/all_data_embedded.pkl'\n",
        "\n",
        "with open(file_path, 'rb') as f:\n",
        "   all_nodes = pickle.load(f)\n",
        "\n",
        "len(all_nodes)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MdFzoViF4v6E"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "random.shuffle(all_nodes)\n",
        "\n",
        "eval_nodes = all_nodes[:10000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "txuJ3FFTfWfh",
        "outputId": "ef58fa37-9c7b-49e9-821f-245f52db9be2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 10000/10000 [2:36:13<00:00,  1.07it/s]\n"
          ]
        }
      ],
      "source": [
        "from llama_index.core.evaluation import generate_question_context_pairs\n",
        "from llama_index.llms.gemini import Gemini\n",
        "from llama_index.llms.openai import OpenAI\n",
        "\n",
        "llm = Gemini(model=\"models/gemini-2.0-flash\", temperature=1, max_tokens=512)\n",
        "#llm = OpenAI(model=\"gpt-4o-mini\", temperature=1, max_tokens=512)\n",
        "rag_eval_dataset = generate_question_context_pairs(eval_nodes, llm=llm, num_questions_per_chunk=1)\n",
        "\n",
        "# We can save the dataset as a json file for later use.\n",
        "rag_eval_dataset.save_json(\"./rag_eval_dataset2.json\")\n"
      ]
    },
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          "text": [
            "Successfully uploaded rag_eval_dataset.json to Hugging Face repository: vicpada/AzureRAGEval\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import HfApi, HfFolder\n",
        "\n",
        "api = HfApi(token=HF_TOKEN)\n",
        "\n",
        "# Specify your Hugging Face repository details\n",
        "repo_id = \"vicpada/AzureRAGEval\" # Replace with your username and repository name\n",
        "\n",
        "# Create the repository if it doesn't exist\n",
        "api.create_repo(repo_id, repo_type=\"dataset\", exist_ok=True) # Uncomment if creating a new dataset repo\n",
        "\n",
        "# Upload the zipped file to the repository\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"rag_eval_dataset2.json\",\n",
        "    path_in_repo=\"rag_eval_dataset2.json\",\n",
        "    repo_id=repo_id,\n",
        "    repo_type=\"dataset\",\n",
        ")\n",
        "\n",
        "print(f\"Successfully uploaded rag_eval_dataset.json to Hugging Face repository: {repo_id}\")"
      ]
    }
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