LLM_Engineering_OLD/week6/community-contributions/09_part3_e5embeddings_rag.ipynb

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   "source": [
    "# 🔍 Predicting Item Prices from Descriptions (Part 3)\n",
    "---\n",
    "-  Data Curation & Preprocessing\n",
    "- Model Benchmarking – Traditional ML vs LLMs\n",
    "- ➡️E5 Embeddings & RAG\n",
    "- Fine-Tuning GPT-4o Mini\n",
    "- Evaluating LLaMA 3.1 8B Quantized\n",
    "- Fine-Tuning LLaMA 3.1 with QLoRA\n",
    "- Evaluating Fine-Tuned LLaMA\n",
    "- Summary & Leaderboard\n",
    "\n",
    "---\n",
    "\n",
    "# 🧠 Part 3: E5 Embeddings & RAG\n",
    "\n",
    "- 🧑‍💻 Skill Level: Advanced\n",
    "- ⚙️ Hardware: ⚠️ GPU required for embeddings (400K items) - use Google Colab\n",
    "- 🛠️ Requirements: 🔑 HF Token, Open API Key\n",
    "- Tasks:\n",
    "    - Preprocessed item descriptions\n",
    "    - Generated and stored embeddings in ChromaDB\n",
    "    - Trained XGBoost on embeddings, pushed to HF Hub, and ran predictions\n",
    "    - Predicted prices with GPT-4o Mini using RAG\n",
    "\n",
    "Is Word2Vec enough for XGBoost, or do contextual E5 embeddings perform better?\n",
    "\n",
    "Does retrieval improve price prediction for GPT-4o Mini?\n",
    "\n",
    "Let’s find out.\n",
    "\n",
    "⚠️ This notebook assumes basic familiarity with RAG and contextual embeddings.\n",
    "We use the same E5 embedding space for both XGBoost and GPT-4o Mini with RAG, enabling a fair comparison.\n",
    "Embeddings are stored and queried via ChromaDB — no LangChain is used for creation or retrieval.\n",
    "\n",
    "---\n",
    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8e2af5e-03cc-46dc-8a8b-37cb102d0e92",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d8e2af5e-03cc-46dc-8a8b-37cb102d0e92",
    "outputId": "905907cc-81c5-4a3b-e7c8-9e237e594a09"
   },
   "outputs": [],
   "source": [
    "# Install required packages in Google Colab\n",
    "%pip install -q tqdm huggingface_hub numpy sentence-transformers datasets chromadb xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ce6a892-b357-4132-b9c0-a3142a0244c8",
   "metadata": {
    "id": "4ce6a892-b357-4132-b9c0-a3142a0244c8"
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   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import math\n",
    "import chromadb\n",
    "import re\n",
    "import joblib\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import gc\n",
    "from huggingface_hub import login, HfApi\n",
    "import numpy as np\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from datasets import load_dataset\n",
    "from google.colab import userdata\n",
    "from xgboost import XGBRegressor\n",
    "from openai import OpenAI\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yBH-mvV0QBiw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yBH-mvV0QBiw",
    "outputId": "b4b6df10-dc05-4dbe-dd8b-55bae5a2b7af"
   },
   "outputs": [],
   "source": [
    "# Mount Google Drive to access persistent storage\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3OUI1jQYyaeX",
   "metadata": {
    "id": "3OUI1jQYyaeX"
   },
   "outputs": [],
   "source": [
    "# Google Colab User Data\n",
    "# Ensure you have set the following in your Google Colab environment:\n",
    "openai_api_key = userdata.get(\"OPENAI_API_KEY\")\n",
    "hf_token = userdata.get('HF_TOKEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99f6f632",
   "metadata": {},
   "outputs": [],
   "source": [
    "openai = OpenAI(api_key=openai_api_key)\n",
    "login(hf_token, add_to_git_credential=True)\n",
    "\n",
    "# Configuration\n",
    "ROOT = \"/content/drive/MyDrive/deal_finder\"\n",
    "CHROMA_PATH = f\"{ROOT}/chroma\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "FF-HryRnDXm5",
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    "# Helper class for evaluating model predictions\n",
    "\n",
    "GREEN = \"\\033[92m\"\n",
    "YELLOW = \"\\033[93m\"\n",
    "RED = \"\\033[91m\"\n",
    "RESET = \"\\033[0m\"\n",
    "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}\n",
    "\n",
    "class Tester:\n",
    "\n",
    "    def __init__(self, predictor, data, title=None, size=250):\n",
    "        self.predictor = predictor\n",
    "        self.data = data\n",
    "        self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
    "        self.size = size\n",
    "        self.guesses = []\n",
    "        self.truths = []\n",
    "        self.errors = []\n",
    "        self.sles = []\n",
    "        self.colors = []\n",
    "\n",
    "    def color_for(self, error, truth):\n",
    "        if error<40 or error/truth < 0.2:\n",
    "            return \"green\"\n",
    "        elif error<80 or error/truth < 0.4:\n",
    "            return \"orange\"\n",
    "        else:\n",
    "            return \"red\"\n",
    "\n",
    "    def run_datapoint(self, i):\n",
    "        datapoint = self.data[i]\n",
    "        guess = self.predictor(datapoint)\n",
    "        truth = datapoint[\"price\"]\n",
    "        error = abs(guess - truth)\n",
    "        log_error = math.log(truth+1) - math.log(guess+1)\n",
    "        sle = log_error ** 2\n",
    "        color = self.color_for(error, truth)\n",
    "        # title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n",
    "        self.guesses.append(guess)\n",
    "        self.truths.append(truth)\n",
    "        self.errors.append(error)\n",
    "        self.sles.append(sle)\n",
    "        self.colors.append(color)\n",
    "        # print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
    "\n",
    "    def chart(self, title):\n",
    "        # max_error = max(self.errors)\n",
    "        plt.figure(figsize=(12, 8))\n",
    "        max_val = max(max(self.truths), max(self.guesses))\n",
    "        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
    "        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
    "        plt.xlabel('Ground Truth')\n",
    "        plt.ylabel('Model Estimate')\n",
    "        plt.xlim(0, max_val)\n",
    "        plt.ylim(0, max_val)\n",
    "        plt.title(title)\n",
    "\n",
    "        # Add color legend\n",
    "        from matplotlib.lines import Line2D\n",
    "        legend_elements = [\n",
    "            Line2D([0], [0], marker='o', color='w', label='Accurate (green)', markerfacecolor='green', markersize=8),\n",
    "            Line2D([0], [0], marker='o', color='w', label='Medium error (orange)', markerfacecolor='orange', markersize=8),\n",
    "            Line2D([0], [0], marker='o', color='w', label='High error (red)', markerfacecolor='red', markersize=8)\n",
    "        ]\n",
    "        plt.legend(handles=legend_elements, loc='upper right')\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "    def report(self):\n",
    "        average_error = sum(self.errors) / self.size\n",
    "        rmsle = math.sqrt(sum(self.sles) / self.size)\n",
    "        hits = sum(1 for color in self.colors if color==\"green\")\n",
    "        title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
    "        self.chart(title)\n",
    "\n",
    "    def run(self):\n",
    "        self.error = 0\n",
    "        for i in range(self.size):\n",
    "            self.run_datapoint(i)\n",
    "        self.report()\n",
    "\n",
    "    @classmethod\n",
    "    def test(cls, function, data):\n",
    "        cls(function, data).run()\n"
   ]
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   "source": [
    "## 📥 Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ae00568",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #If you face NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported run:\n",
    "# %pip install -U datasets"
   ]
  },
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   "source": [
    "HF_USER = \"lisekarimi\"\n",
    "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
    "\n",
    "dataset = load_dataset(DATASET_NAME)\n",
    "train = dataset['train']\n",
    "test = dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85880d79-f1ba-4ee8-a039-b6acea84562c",
   "metadata": {
    "id": "85880d79-f1ba-4ee8-a039-b6acea84562c"
   },
   "outputs": [],
   "source": [
    "print(train[0][\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88842541-d73b-4fae-a550-6dedf8fab633",
   "metadata": {
    "id": "88842541-d73b-4fae-a550-6dedf8fab633"
   },
   "outputs": [],
   "source": [
    "print(train[0][\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8a9a5b-f74d-487d-a400-d157fea8c979",
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   "source": [
    "## 📦 Embed + Save Training Data to Chroma\n",
    "- No LangChain used.\n",
    "- We use `intfloat/e5-small-v2` for embeddings:\n",
    "    - Fast, high-quality, retrieval-tuned\n",
    "    - **Requires 'passage:' prefix**\n",
    "- We embed item descriptions and store them in ChromaDB, with price saved as metadata."
   ]
  },
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    },
    "id": "b95a87a8-2136-4e03-a36c-42e5d53a3e28",
    "outputId": "6094328e-8c33-4b40-80e9-08c5cfb3e277"
   },
   "outputs": [],
   "source": [
    "# Load embedding model\n",
    "model_embedding = SentenceTransformer(\"intfloat/e5-small-v2\", device='cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "733cf41d-e81e-4cfc-b597-67da02dbc3cf",
   "metadata": {
    "id": "733cf41d-e81e-4cfc-b597-67da02dbc3cf"
   },
   "outputs": [],
   "source": [
    "# Init Chroma\n",
    "client = chromadb.PersistentClient(path=CHROMA_PATH)\n",
    "collection = client.get_or_create_collection(name=\"price_items\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f493c7d-1c72-40f9-a5c6-63c7f6b1cf2c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 91
    },
    "id": "1f493c7d-1c72-40f9-a5c6-63c7f6b1cf2c",
    "outputId": "72627732-4eee-4d9a-c8cb-0c42e2541a80"
   },
   "outputs": [],
   "source": [
    "# Format description function (no price in text)\n",
    "def description(item):\n",
    "    text = item[\"text\"].replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n",
    "    text = text.split(\"\\n\\nPrice is $\")[0]\n",
    "    return f\"passage: {text}\"\n",
    "\n",
    "description(train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f44bf613-adf6-4993-bf7b-6aa9fad21a03",
   "metadata": {
    "id": "f44bf613-adf6-4993-bf7b-6aa9fad21a03"
   },
   "outputs": [],
   "source": [
    "batch_size = 300    # how many items to insert into Chroma at once\n",
    "encode_batch_size = 1024  # how many items to encode at once in GPU memory\n",
    "\n",
    "for i in tqdm(range(0, len(train), batch_size), desc=\"Processing batches\"):\n",
    "\n",
    "    end_idx = min(i + batch_size, len(train))\n",
    "\n",
    "    # Collect documents and metadata\n",
    "    documents = [description(train[j]) for j in range(i, end_idx)]\n",
    "    metadatas = [{\"price\": train[j][\"price\"]} for j in range(i, end_idx)]\n",
    "    ids = [f\"doc_{j}\" for j in range(i, end_idx)]\n",
    "\n",
    "    # GPU batch encoding\n",
    "    vectors = model_embedding.encode(\n",
    "        documents,\n",
    "        batch_size=encode_batch_size,\n",
    "        show_progress_bar=False,\n",
    "        normalize_embeddings=True\n",
    "    ).tolist()\n",
    "\n",
    "    # Insert into Chroma\n",
    "    collection.add(\n",
    "        ids=ids,\n",
    "        documents=documents,\n",
    "        embeddings=vectors,\n",
    "        metadatas=metadatas\n",
    "    )\n",
    "\n",
    "print(\"✅ Embedding and storage to ChromaDB completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e2ccc9-b772-45f7-8258-cbc4f9c3ed59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now flush and clean\n",
    "print(\"🧹 Cleaning up and saving ChromaDB...\")\n",
    "client = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35d2fab-583f-4527-a7cc-9d31214b2f35",
   "metadata": {},
   "source": [
    "Our ChromaDB is currently saved in a persistent Google Drive path; for a production-ready app, we recommend uploading it to AWS S3 for better reliability and scalability.\n",
    "\n",
    "🧩 Now that we've generated the E5 embeddings, let's use them for both **XGBoost regression** and **GPT-4o Mini with RAG** ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e4c587-211d-4bc0-91cf-6267f45405d6",
   "metadata": {
    "id": "40e4c587-211d-4bc0-91cf-6267f45405d6"
   },
   "source": [
    "## 📈 Embedding-Based Regression with XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f058ccac-3392-457d-b54c-6471960e9af3",
   "metadata": {
    "id": "f058ccac-3392-457d-b54c-6471960e9af3"
   },
   "outputs": [],
   "source": [
    "# Step 1: Load vectors and prices from Chroma\n",
    "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
    "vectors = np.array(result['embeddings'])\n",
    "documents = result['documents']\n",
    "prices = [meta['price'] for meta in result['metadatas']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "JYQo0RaMb8Ql",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 254
    },
    "id": "JYQo0RaMb8Ql",
    "outputId": "c1641347-1fd4-41bb-e060-147224fc6bed"
   },
   "outputs": [],
   "source": [
    "# Step 2: Train XGBoost model\n",
    "xgb_model = XGBRegressor(n_estimators=100, random_state=42, n_jobs=-1, verbosity=0)\n",
    "xgb_model.fit(vectors, prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yaqG0z7jb919",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yaqG0z7jb919",
    "outputId": "6a2f9120-97e0-4436-aa12-40d94fbc5c64"
   },
   "outputs": [],
   "source": [
    "# Step 3: Serialize XGBoost model locally for Hugging Face upload\n",
    "MODEL_DIR = os.path.join(ROOT, \"models\")\n",
    "MODEL_FILENAME = \"xgboost_model.pkl\"\n",
    "LOCAL_MODEL = os.path.join(MODEL_DIR, MODEL_FILENAME)\n",
    "\n",
    "os.makedirs(MODEL_DIR, exist_ok=True)\n",
    "joblib.dump(xgb_model, LOCAL_MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Z_17sQUdxIr3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
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      "57bf3388622241869a5e9dab558dca72",
      "aa87f4feddd6409fbfb81f417e5d6662",
      "973a83ca118e4ed1b5a51821034ecc31",
      "d5a3c955aba14b3ea8e9b5c90a3bf20a",
      "daaa4f26bad545a394685e266f85a6ae"
     ]
    },
    "id": "Z_17sQUdxIr3",
    "outputId": "68ebdbdb-d42e-4bc8-addc-85b42d418d1d"
   },
   "outputs": [],
   "source": [
    "# Step 4: Push serialized XGBoost model to Hugging Face Hub\n",
    "api = HfApi(token=hf_token)\n",
    "REPO_NAME = \"smart-deal-finder-models\"\n",
    "REPO_ID = f\"{HF_USER}/{REPO_NAME}\"\n",
    "\n",
    "# Create the model repo if it doesn't exist\n",
    "api.create_repo(repo_id=REPO_ID, repo_type=\"model\", private=True, exist_ok=True)\n",
    "\n",
    "# Upload the saved model\n",
    "api.upload_file(\n",
    "    path_or_fileobj=LOCAL_MODEL,\n",
    "    path_in_repo=MODEL_FILENAME,\n",
    "    repo_id=REPO_ID,\n",
    "    repo_type=\"model\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f59125d-9fa6-483b-957f-4423a9b2c900",
   "metadata": {
    "id": "3f59125d-9fa6-483b-957f-4423a9b2c900"
   },
   "outputs": [],
   "source": [
    "# Step 5: Define the predictor\n",
    "def xgb_predictor(datapoint):\n",
    "    doc = description(datapoint)\n",
    "    vector = model_embedding.encode([doc], normalize_embeddings=True)[0]\n",
    "    return max(0, xgb_model.predict([vector])[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a890f1f0-d827-472f-a7a9-6c2cbe3d8341",
   "metadata": {
    "id": "a890f1f0-d827-472f-a7a9-6c2cbe3d8341"
   },
   "source": [
    "🔔 Reminder: In Part 2, XGBoost with Word2Vec (non-contextual embeddings) achieved:\n",
    "- Avg. Error: ~$107\n",
    "- RMSLE: 0.83\n",
    "- Accuracy: 29.20%\n",
    "\n",
    "🧪 Now, let’s see if contextual embeddings improve XGBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "q-tIbVilTPxP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 718
    },
    "id": "q-tIbVilTPxP",
    "outputId": "7c9043ef-a2c4-4933-b334-18d99690ba0f"
   },
   "outputs": [],
   "source": [
    "# Step 4: Run the Tester on a subset of test data\n",
    "tester = Tester(xgb_predictor, test)\n",
    "tester.run()"
   ]
  },
  {
   "attachments": {
    "fcef5d55-0911-4925-a67d-13d47f28bf73.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "dcb09db0-7d69-40e1-a6e3-b92263e38f1e",
   "metadata": {
    "id": "dcb09db0-7d69-40e1-a6e3-b92263e38f1e"
   },
   "source": [
    "![image.png](attachment:fcef5d55-0911-4925-a67d-13d47f28bf73.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccd5d3f-98cd-45a8-951f-d6446062addc",
   "metadata": {
    "id": "1ccd5d3f-98cd-45a8-951f-d6446062addc"
   },
   "source": [
    "Results are nearly the same. In this setup, switching to contextual embeddings didn’t yield performance gains for XGBoost."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db1051d-9a7e-4cec-87fc-0d77fd858ced",
   "metadata": {
    "id": "4db1051d-9a7e-4cec-87fc-0d77fd858ced"
   },
   "source": [
    "## 🚰 Retrieval-Augmented Pipeline – GPT-4o Mini\n",
    "\n",
    "- Preprocess: clean the input text (description(item))\n",
    "- Embed: generate embedding vector (get_embedding(item))\n",
    "- Retrieve: find similar items from ChromaDB (find_similar_items)\n",
    "- Build Prompt: create the LLM prompt using context and masked target (build_messages)\n",
    "- Predict: get price estimate from LLM (estimate_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YPLxSn7eHp9N",
   "metadata": {
    "id": "YPLxSn7eHp9N"
   },
   "outputs": [],
   "source": [
    "test[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eFxFKNroNiyD",
   "metadata": {
    "id": "eFxFKNroNiyD"
   },
   "outputs": [],
   "source": [
    "# Step 1: Preprocess test item text\n",
    "# (uses the same `description(item)` function as during training)\n",
    "description(test[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lxIEtSWYHqCT",
   "metadata": {
    "id": "lxIEtSWYHqCT"
   },
   "outputs": [],
   "source": [
    "# Step 2: Embed a test item\n",
    "def get_embedding(item):\n",
    "    return model_embedding.encode([description(item)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "y43prQsuHp_w",
   "metadata": {
    "id": "y43prQsuHp_w"
   },
   "outputs": [],
   "source": [
    "# Step 3: Query Chroma for similar items\n",
    "def find_similars(item):\n",
    "    results = collection.query(query_embeddings=get_embedding(item).astype(float).tolist(), n_results=5)\n",
    "    documents = results['documents'][0][:]\n",
    "    prices = [m['price'] for m in results['metadatas'][0][:]]\n",
    "    return documents, prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nxAOUFRkHp6v",
   "metadata": {
    "id": "nxAOUFRkHp6v"
   },
   "outputs": [],
   "source": [
    "documents, prices = find_similars(test[1])\n",
    "documents, prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "djPoSk6sHo84",
   "metadata": {
    "id": "djPoSk6sHo84"
   },
   "outputs": [],
   "source": [
    "# Step 4: Format similar items as context\n",
    "def format_context(similars, prices):\n",
    "    message = \"To provide some context, here are some other items that might be similar to the item you need to estimate.\\n\\n\"\n",
    "    for similar, price in zip(similars, prices):\n",
    "        message += f\"Potentially related product:\\n{similar}\\nPrice is ${price:.2f}\\n\\n\"\n",
    "    return message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "F3yxhnqSHp4C",
   "metadata": {
    "id": "F3yxhnqSHp4C"
   },
   "outputs": [],
   "source": [
    "print(format_context(documents, prices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pEJobsKNHqE8",
   "metadata": {
    "id": "pEJobsKNHqE8"
   },
   "outputs": [],
   "source": [
    "# Step 5: Mask the price in the test item\n",
    "def mask_price_value(text):\n",
    "    return re.sub(r\"(\\n\\nPrice is \\$).*\", r\"\\1\", text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "vLhBNVBNQAHS",
   "metadata": {
    "id": "vLhBNVBNQAHS"
   },
   "outputs": [],
   "source": [
    "# Step 6: Build LLM messages\n",
    "def build_messages(datapoint, similars, prices):\n",
    "\n",
    "    system_message = \"You estimate prices of items. Reply only with the price, no explanation.\"\n",
    "\n",
    "    context = format_context(similars, prices)\n",
    "\n",
    "    prompt = mask_price_value(datapoint[\"text\"])\n",
    "    prompt = prompt.replace(\" to the nearest dollar\", \"\").replace(\"\\n\\nPrice is $\", \"\")\n",
    "\n",
    "    user_prompt = context + \"And now the question for you:\\n\\n\" + prompt\n",
    "\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": user_prompt},\n",
    "        {\"role\": \"assistant\", \"content\": \"Price is $\"}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "I94fNHfBHp1a",
   "metadata": {
    "id": "I94fNHfBHp1a"
   },
   "outputs": [],
   "source": [
    "build_messages(test[1], documents, prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5NfY_GAVHpy4",
   "metadata": {
    "id": "5NfY_GAVHpy4"
   },
   "outputs": [],
   "source": [
    "# Step 7: Run prediction\n",
    "def get_price(s):\n",
    "    s = s.replace('$','').replace(',','')\n",
    "    match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n",
    "    return float(match.group()) if match else 0\n",
    "\n",
    "def gpt_4o_mini_rag(item):\n",
    "    documents, prices = find_similars(item)\n",
    "    response = openai.chat.completions.create(\n",
    "        model=\"gpt-4o-mini\",\n",
    "        messages=build_messages(item, documents, prices),\n",
    "        seed=42,\n",
    "        max_tokens=5\n",
    "    )\n",
    "    reply = response.choices[0].message.content\n",
    "    return get_price(reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Pg-GJTT0HpwV",
   "metadata": {
    "id": "Pg-GJTT0HpwV"
   },
   "outputs": [],
   "source": [
    "print(test[1][\"price\"])\n",
    "print(gpt_4o_mini_rag(test[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54103ab4-d6dd-4c0b-add5-5d9741e934b4",
   "metadata": {
    "id": "54103ab4-d6dd-4c0b-add5-5d9741e934b4"
   },
   "source": [
    "🔔 Reminder: In Part 2, GPT-4o Mini (without RAG) achieved:\n",
    "- Avg. Error: ~$99\n",
    "- RMSLE: 0.75\n",
    "- Accuracy: 44.8%\n",
    "\n",
    "🧪 Let’s find out if RAG can boost GPT-4o Mini’s price prediction capabilities.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "r0NGJupwHppF",
   "metadata": {
    "id": "r0NGJupwHppF"
   },
   "outputs": [],
   "source": [
    "Tester.test(gpt_4o_mini_rag, test)"
   ]
  },
  {
   "attachments": {
    "42320914-5487-4af2-bd29-56267e986925.png": {
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   },
   "source": [
    "![image.png](attachment:42320914-5487-4af2-bd29-56267e986925.png)"
   ]
  },
  {
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   "source": [
    "🎉 **GPT-4o Mini + RAG shows clear gains:**  \n",
    "Average error dropped from **$99 → $59.54**, RMSLE from **0.75 → 0.42**, and accuracy rose from **48.8% → 69.2%**.  \n",
    "\n",
    "Adding retrieval-based context led to a strong performance boost for GPT-4o Mini.\n",
    "\n",
    "Now the question is — can fine-tuning push it even further, surpass RAG, and challenge larger models?\n",
    "\n",
    "🔜 See you in the [next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part4_ft_gpt4omini.ipynb)"
   ]
  }
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