{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/07-Finetune_Embedding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcZqgUKBYQYn"
      },
      "source": [
        "# Install Packages and Setup Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "F0rUftGtpnZc"
      },
      "outputs": [],
      "source": [
        "!pip install -q llama-index==0.10.11 openai==1.12.0 chromadb==0.4.22 cohere==4.47 tiktoken==0.6.0 pandas==2.2.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hfZwQhohsb2M"
      },
      "outputs": [],
      "source": [
        "# Test with a few sample, processing dataset fully can be costly depanding on the size.\n",
        "# NOTE: Checkpoints are provided in the lesson, so no need to run the code on full dataset.\n",
        "testing = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RyE625lCtTsi"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWgHCmejcCFV"
      },
      "source": [
        "# Load the Dataset (Webpages)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3R7P7GUmemIL"
      },
      "source": [
        "## Download"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Gx4_5BF8B4tx"
      },
      "outputs": [],
      "source": [
        "TRAIN_URLs = [\n",
        "    \"https://towardsai.net/p/machine-learning/metas-llama-2-revolutionizing-open-source-language-models-for-commercial-use\",\n",
        "    \"https://towardsai.net/p/machine-learning/fine-tuning-a-llama-2-7b-model-for-python-code-generation\",\n",
        "    \"https://towardsai.net/p/machine-learning/how-to-create-llama-2-chatbot-with-gradio-and-hugging-face-in-free-colab\",\n",
        "    \"https://towardsai.net/p/machine-learning/meta-releases-llama-2-free-for-commercial-use\",\n",
        "    \"https://towardsai.net/p/machine-learning/gpt-4-llama-2-claude-how-different-language-models-react-to-prompts\",\n",
        "    \"https://towardsai.net/p/machine-learning/a-simple-hugging-face-guide-to-chatting-with-the-llama-2-7b-model-in-a-colab-notebook\",\n",
        "    \"https://towardsai.net/p/machine-learning/fine-tuning-a-llama-2-7b-model-for-python-code-generation\",\n",
        "    \"https://towardsai.net/p/machine-learning/llamaindex-last-version-from-basics-to-advanced-techniques-in-python-part-3\",\n",
        "    \"https://towardsai.net/p/machine-learning/meta-releases-llama-will-it-fail-too\",\n",
        "    \"https://towardsai.net/p/machine-learning/llama-by-meta-leaked-by-an-anonymous-forum-questions-arises-on-meta\"\n",
        "]\n",
        "VALIDATION_URLs = [\n",
        "    \"https://towardsai.net/p/machine-learning/deep-diving-into-llama-2-meta-ai-new-open-source-foundation-model\",\n",
        "    \"https://towardsai.net/p/machine-learning/gptq-quantization-on-a-llama-2-7b-fine-tuned-model-with-huggingface\",\n",
        "    \"https://towardsai.net/p/machine-learning/powerinfer-11x-speed-up-llama-ii-inference-on-a-local-gpu\",\n",
        "    \"https://towardsai.net/p/machine-learning/dense-x-retrieval-technique-in-langchain-and-llamaindex\",\n",
        "    \"https://towardsai.net/p/machine-learning/exploring-large-language-models-part-2\",\n",
        "    \"https://towardsai.net/p/machine-learning/inside-code-llama-meta-ais-entrance-in-the-code-llm-space\",\n",
        "    \"https://towardsai.net/p/machine-learning/llamaindex-use-the-power-of-llms-on-your-data\",\n",
        "    \"https://towardsai.net/p/l/inside-llama-meta-ai-new-large-language-model-that-outperforms-gpt-3-across-many-tasks\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Lua8G8seyEx"
      },
      "source": [
        "## Read the Page"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzzcVnWXB4rJ",
        "outputId": "03e17638-d4f8-4b8c-d5d0-e871120616fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10 8\n"
          ]
        }
      ],
      "source": [
        "from llama_index.readers.web import SimpleWebPageReader\n",
        "\n",
        "# Read the content of webpage into lists. We need two sets of documents for Training, and Validation.\n",
        "TRAIN_DOCs = SimpleWebPageReader(html_to_text=True).load_data(TRAIN_URLs)\n",
        "VALIDATION_DOCs = SimpleWebPageReader(html_to_text=True).load_data(VALIDATION_URLs)\n",
        "print( len(TRAIN_DOCs), len(VALIDATION_DOCs) )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGbmUhUVezdU"
      },
      "source": [
        "# Chunking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9r_UzQ5BB4kG",
        "outputId": "d440f72a-9fa2-4b8c-f320-ecc4615b0f2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "272 221\n"
          ]
        }
      ],
      "source": [
        "from llama_index.core.node_parser import SimpleNodeParser\n",
        "\n",
        "# Define a parser to perform the chunking process.\n",
        "parser = SimpleNodeParser.from_defaults(chunk_size=512, chunk_overlap=20)\n",
        "\n",
        "# Apply chunking on the training/validation sets.\n",
        "TRAIN_NODEs = parser.get_nodes_from_documents(TRAIN_DOCs)\n",
        "VALIDATION_NODEs = parser.get_nodes_from_documents(VALIDATION_DOCs)\n",
        "print( len( TRAIN_NODEs ), len( VALIDATION_NODEs ) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Y6iEHKfZsUiv"
      },
      "outputs": [],
      "source": [
        "# Use a subset of the dataset (5 samples) if testing.\n",
        "if testing:\n",
        "  TRAIN_NODEs = TRAIN_NODEs [0:5]\n",
        "  VALIDATION_NODEs = VALIDATION_NODEs[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvKf6i9Lg_2G"
      },
      "source": [
        "# Generate Question"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DMXD7nbhFsm"
      },
      "source": [
        "We use a Large Language Model (LLM) to produce questions for each chunk of the dataset. Then we can use these data to train the model to develop embeddings that more accurately represent the types of questions users may ask."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "geqDZn-pB4fI"
      },
      "outputs": [],
      "source": [
        "# Use this block of code if you don't want to generate the questions for the dataset. (Avoid API call charges!)\n",
        "# Uncomment the following code, and keep in mind to comment all the contents in the next coding block.\n",
        "\n",
        "# from llama_index.finetuning import EmbeddingQAFinetuneDataset\n",
        "\n",
        "# # Load the pre-generated questions json files.\n",
        "# TRAIN_DATASET = EmbeddingQAFinetuneDataset.from_json(\"train_dataset.json\")\n",
        "# VALIDATION_DATASET = EmbeddingQAFinetuneDataset.from_json(\"val_dataset.json\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Es3rbmO-B4hv",
        "outputId": "7cb141ff-3ac2-47ac-8e05-6fe7720da5bf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5/5 [00:06<00:00,  1.29s/it]\n",
            "100%|██████████| 5/5 [00:07<00:00,  1.60s/it]\n"
          ]
        }
      ],
      "source": [
        "from llama_index.finetuning import generate_qa_embedding_pairs\n",
        "from llama_index.llms.openai import OpenAI\n",
        "\n",
        "# Load the OpenAI API with the \"gpt-3.5-turbo\" model\n",
        "llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
        "\n",
        "# Generate questions for each chunk.\n",
        "TRAIN_DATASET = generate_qa_embedding_pairs(TRAIN_NODEs, llm=llm)\n",
        "VALIDATION_DATASET = generate_qa_embedding_pairs(VALIDATION_NODEs, llm=llm)\n",
        "\n",
        "TRAIN_DATASET.save_json(\"train_dataset.json\")\n",
        "VALIDATION_DATASET.save_json(\"val_dataset.json\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hthjh_SjlB-C"
      },
      "source": [
        "# Load an Embedding Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "ZHWPMZHUupNW",
        "outputId": "9f731c0a-6406-48fd-c68c-5d8df8f12d64"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/louis/Documents/GitHub/ai-tutor-rag-system/.conda/lib/python3.11/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",
            "config.json: 100%|██████████| 743/743 [00:00<00:00, 4.08MB/s]\n",
            "model.safetensors: 100%|██████████| 133M/133M [00:02<00:00, 53.6MB/s] \n",
            "tokenizer_config.json: 100%|██████████| 366/366 [00:00<00:00, 2.01MB/s]\n",
            "vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 5.38MB/s]\n",
            "tokenizer.json: 100%|██████████| 711k/711k [00:00<00:00, 16.4MB/s]\n",
            "special_tokens_map.json: 100%|██████████| 125/125 [00:00<00:00, 781kB/s]\n"
          ]
        }
      ],
      "source": [
        "from llama_index.core.embeddings import resolve_embed_model\n",
        "\n",
        "# Load an existing embedding model with a linear layer adopter on top.\n",
        "base_embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "evLkr4EZB4cJ"
      },
      "outputs": [],
      "source": [
        "from llama_index.finetuning import EmbeddingAdapterFinetuneEngine\n",
        "import torch\n",
        "\n",
        "# Finetune the adapter\n",
        "finetune_engine = EmbeddingAdapterFinetuneEngine(\n",
        "    TRAIN_DATASET,\n",
        "    base_embed_model,\n",
        "    model_output_path=\"model_output_test\",\n",
        "    epochs=4,\n",
        "    verbose=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
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        },
        "id": "qwi98s-RjXDA",
        "outputId": "e541c667-d2fa-4074-fa9e-e0f377aafb6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34m> Prepared optimizer, scheduler, and loss model.\n",
            "\u001b[0m"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch:   0%|          | 0/4 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34m> [Epoch 0] Current loss: 1.579697608947754\n",
            "\u001b[0m"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Iteration: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]\n",
            "Iteration: 100%|██████████| 1/1 [00:00<00:00,  1.04it/s]\n",
            "Epoch:  50%|█████     | 2/4 [00:06<00:05,  2.69s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34m> [Epoch 1] Current loss: 1.5728983879089355\n",
            "\u001b[0m"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Iteration: 100%|██████████| 1/1 [00:00<00:00,  1.01it/s]\n",
            "Epoch:  75%|███████▌  | 3/4 [00:07<00:01,  1.92s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34m> [Epoch 2] Current loss: 1.5678406953811646\n",
            "\u001b[0m"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Iteration: 100%|██████████| 1/1 [00:00<00:00,  1.10it/s]\n",
            "Epoch: 100%|██████████| 4/4 [00:08<00:00,  2.01s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;3;34m> [Epoch 3] Current loss: 1.5644880533218384\n",
            "\u001b[0m\u001b[1;3;34m> Finished training, saving to model_output_test\n",
            "\u001b[0m"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Initiate the Finetuning process\n",
        "finetune_engine.finetune()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "JJOhEdlawqL7"
      },
      "outputs": [],
      "source": [
        "embed_model = finetune_engine.get_finetuned_model()\n",
        "\n",
        "# Or, import model from the directory whenever required.\n",
        "# from llama_index.core.embeddings import LinearAdapterEmbeddingModel\n",
        "# embed_model = LinearAdapterEmbeddingModel(base_embed_model, \"model_output_test\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hjc_5c5w9xm",
        "outputId": "dacc9d8d-baa1-468a-b457-b39baabae681"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AdapterEmbeddingModel(model_name='Adapter for BAAI/bge-small-en-v1.5', embed_batch_size=10, callback_manager=<llama_index.core.callbacks.base.CallbackManager object at 0x15f4b6950>)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embed_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrj0FrzhygGD"
      },
      "source": [
        "# Evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yEj5ZAEioCss"
      },
      "source": [
        "## Define the Evaluation Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0g_0yQT0HlR"
      },
      "source": [
        "Hit-rate metric: For each (query, context) pair, we retrieve the top-k documents with the query. It’s a hit if the results contain the ground-truth context."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "sbfgQIyIyhCZ"
      },
      "outputs": [],
      "source": [
        "from llama_index.core import ServiceContext, VectorStoreIndex\n",
        "from llama_index.core.schema import TextNode\n",
        "from tqdm import tqdm\n",
        "\n",
        "def evaluate( dataset, embed_model, top_k=5, verbose=False):\n",
        "    corpus = dataset.corpus\n",
        "    queries = dataset.queries\n",
        "    relevant_docs = dataset.relevant_docs\n",
        "\n",
        "    # Chunking the documents and generating embeddings\n",
        "    service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
        "    nodes = [TextNode(id_=id_, text=text) for id_, text in corpus.items()]\n",
        "    index = VectorStoreIndex(\n",
        "        nodes, service_context=service_context, show_progress=True\n",
        "    )\n",
        "\n",
        "    # Define a retriever to answer the questions\n",
        "    retriever = index.as_retriever(similarity_top_k=top_k)\n",
        "\n",
        "    eval_results = []\n",
        "\n",
        "    # Look into each response sources to see if the chunk that contains the answer is retrieved.\n",
        "    for query_id, query in tqdm(queries.items()):\n",
        "        retrieved_nodes = retriever.retrieve(query)\n",
        "        retrieved_ids = [node.node.node_id for node in retrieved_nodes]\n",
        "        expected_id = relevant_docs[query_id][0]\n",
        "        is_hit = expected_id in retrieved_ids  # assume 1 relevant doc\n",
        "\n",
        "        eval_result = {\n",
        "            \"is_hit\": is_hit,\n",
        "            \"retrieved\": retrieved_ids,\n",
        "            \"expected\": expected_id,\n",
        "            \"query\": query_id,\n",
        "        }\n",
        "        eval_results.append(eval_result)\n",
        "    return eval_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKsxY6Vvy7M3"
      },
      "source": [
        "## OpenAI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "63a6bfeb1090449db39485b55d8746c8",
            "b7b72e0bbbc944a8a0e13760c3201ed5",
            "015e870c26544188913643377bb6ddff",
            "f22e4441e60243d9b88657784824476d"
          ]
        },
        "id": "QpEDBv4cylM1",
        "outputId": "a93621d8-028c-47cd-e23a-4eb918172a85"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/l7/9qcp7g5x5rl9x8ltw0t85qym0000gn/T/ipykernel_36797/1235362969.py:11: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
            "  service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
            "Generating embeddings: 100%|██████████| 5/5 [00:00<00:00, 11.71it/s]\n",
            "100%|██████████| 10/10 [00:01<00:00,  5.41it/s]\n"
          ]
        }
      ],
      "source": [
        "from llama_index.embeddings.openai import OpenAIEmbedding\n",
        "\n",
        "# Load the OpenAI Ada model and evaluate it.\n",
        "ada = OpenAIEmbedding()\n",
        "ada_val_results = evaluate(VALIDATION_DATASET, ada)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQOTBi58yxp_",
        "outputId": "4b58bd41-ad54-4505-b9f8-f8209b830d38"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df_ada = pd.DataFrame(ada_val_results)\n",
        "hit_rate_ada = df_ada[\"is_hit\"].mean()\n",
        "hit_rate_ada"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBVSCKGQy81W"
      },
      "source": [
        "## BAAI Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208,
          "referenced_widgets": [
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        },
        "id": "oXMS43FHy4V5",
        "outputId": "2db942fb-0bc8-473e-d7f2-08409da823c1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/l7/9qcp7g5x5rl9x8ltw0t85qym0000gn/T/ipykernel_36797/1235362969.py:11: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
            "  service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
            "Generating embeddings: 100%|██████████| 5/5 [00:01<00:00,  4.27it/s]\n",
            "100%|██████████| 10/10 [00:01<00:00,  8.29it/s]\n"
          ]
        }
      ],
      "source": [
        "# Load the Base model without fine-tuning\n",
        "base_embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")\n",
        "bge_val_results = evaluate(VALIDATION_DATASET, base_embed_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtq0_tSHzFCR",
        "outputId": "ec67f93f-f0ab-4f4a-82e2-07fe47cf26b1"
      },
      "outputs": [
        {
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              "1.0"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_bge = pd.DataFrame(bge_val_results)\n",
        "hit_rate_bge = df_bge[\"is_hit\"].mean()\n",
        "hit_rate_bge"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1awOq9cLzo7M"
      },
      "source": [
        "## FineTuned"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        },
        "id": "qKB9HEFozoW7",
        "outputId": "311cfbff-a8e4-4840-ffe7-c45e92500f4c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/l7/9qcp7g5x5rl9x8ltw0t85qym0000gn/T/ipykernel_36797/1235362969.py:11: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
            "  service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
            "Generating embeddings: 100%|██████████| 5/5 [00:01<00:00,  4.69it/s]\n",
            "100%|██████████| 10/10 [00:00<00:00, 14.90it/s]\n"
          ]
        }
      ],
      "source": [
        "from llama_index.embeddings.adapter import LinearAdapterEmbeddingModel\n",
        "\n",
        "# Load the Fine-tuned model.\n",
        "embed_model = LinearAdapterEmbeddingModel(base_embed_model, \"model_output_test\")\n",
        "\n",
        "val_results_finetuned = evaluate(VALIDATION_DATASET, embed_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRtUHjuszuWE",
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      "outputs": [
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              "1.0"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_finetuned = pd.DataFrame(val_results_finetuned)\n",
        "hit_rate_finetuned = df_finetuned[\"is_hit\"].mean()\n",
        "hit_rate_finetuned"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
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