{ "cells": [ { "cell_type": "markdown", "id": "b577c1be-f7a4-4549-8d27-30cb35407225", "metadata": {}, "source": [ "# The Price is Right\n", "\n", "Today we build a more complex solution for estimating prices of goods.\n", "\n", "1. Day 2.0 notebook: create a RAG database with our 400,000 training data\n", "2. Day 2.1 notebook: visualize in 2D\n", "3. Day 2.2 notebook: visualize in 3D\n", "4. Day 2.3 notebook: build and test a RAG pipeline with GPT-4o-mini\n", "5. Day 2.4 notebook: (a) bring back our Random Forest pricer (b) Create a Ensemble pricer that allows contributions from all the pricers\n", "\n", "Phew! That's a lot to get through in one day!\n", "\n", "## PLEASE NOTE:\n", "\n", "We already have a very powerful product estimator with our proprietary, fine-tuned LLM. Most people would be very satisfied with that! The main reason we're adding these extra steps is to deepen your expertise with RAG and with Agentic workflows.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "993a2a24-1a58-42be-8034-6d116fb8d786", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import re\n", "import math\n", "import json\n", "from tqdm import tqdm\n", "import random\n", "from dotenv import load_dotenv\n", "from huggingface_hub import login\n", "import numpy as np\n", "import pickle\n", "from sentence_transformers import SentenceTransformer\n", "from datasets import load_dataset\n", "import chromadb\n", "from items import Item\n", "from sklearn.manifold import TSNE\n", "import plotly.graph_objects as go" ] }, { "cell_type": "code", "execution_count": null, "id": "1cc1fe53-612f-4228-aa02-8758f4c2098f", "metadata": {}, "outputs": [], "source": [ "# It is very fun turning this up to 400_000 and seeing the full dataset visualized,\n", "# but it almost crashes my box every time so do that at your own risk!! 10_000 is safe!\n", "\n", "MAXIMUM_DATAPOINTS = 10_000" ] }, { "cell_type": "code", "execution_count": null, "id": "f4aab95e-d719-4476-b6e7-e248120df25a", "metadata": {}, "outputs": [], "source": [ "DB = \"products_vectorstore\"\n", "client = chromadb.PersistentClient(path=DB)" ] }, { "cell_type": "code", "execution_count": null, "id": "5f95dafd-ab80-464e-ba8a-dec7a2424780", "metadata": {}, "outputs": [], "source": [ "collection = client.get_or_create_collection('products')" ] }, { "cell_type": "code", "execution_count": null, "id": "525fc313-8a16-4ac0-8c42-6a6d1ba1c9b8", "metadata": {}, "outputs": [], "source": [ "CATEGORIES = ['Appliances', 'Automotive', 'Cell_Phones_and_Accessories', 'Electronics','Musical_Instruments', 'Office_Products', 'Tools_and_Home_Improvement', 'Toys_and_Games']\n", "COLORS = ['red', 'blue', 'brown', 'orange', 'yellow', 'green' , 'purple', 'cyan']" ] }, { "cell_type": "code", "execution_count": null, "id": "a4cf1c9a-1ced-48d4-974c-3c850905034e", "metadata": {}, "outputs": [], "source": [ "# Prework\n", "result = collection.get(include=['embeddings', 'documents', 'metadatas'], limit=MAXIMUM_DATAPOINTS)\n", "vectors = np.array(result['embeddings'])\n", "documents = result['documents']\n", "categories = [metadata['category'] for metadata in result['metadatas']]\n", "colors = [COLORS[CATEGORIES.index(c)] for c in categories]" ] }, { "cell_type": "code", "execution_count": null, "id": "c54df150-c8d8-4bc3-8877-6759691eeb42", "metadata": {}, "outputs": [], "source": [ "# Let's try a 2D chart\n", "\n", "tsne = TSNE(n_components=2, random_state=42, n_jobs=-1)\n", "reduced_vectors = tsne.fit_transform(vectors)" ] }, { "cell_type": "code", "execution_count": null, "id": "e8fb2a63-24c5-4dce-9e63-aa208272f82d", "metadata": {}, "outputs": [], "source": [ "\n", "# Create the 2D scatter plot\n", "fig = go.Figure(data=[go.Scatter(\n", " x=reduced_vectors[:, 0],\n", " y=reduced_vectors[:, 1],\n", " mode='markers',\n", " marker=dict(size=2, color=colors, opacity=0.7),\n", ")])\n", "\n", "fig.update_layout(\n", " title='2D Chroma Vectorstore Visualization',\n", " scene=dict(xaxis_title='x', yaxis_title='y'),\n", " width=1200,\n", " height=800,\n", " margin=dict(r=20, b=10, l=10, t=40)\n", ")\n", "\n", "fig.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }