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{
"cells": [
{
"cell_type": "markdown",
"id": "046fd8f8-ad14-4c7f-b759-fec52f5b5306",
"metadata": {},
"source": [
"# The Price is Right\n",
"\n",
"Today we build a more complex solution for estimating prices of goods.\n",
"\n",
"1. This 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": "2359ccc0-dbf2-4b1e-9473-e472b32f548b",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv()\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')\n",
"DB = \"products_vectorstore\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "645167e6-cf0d-42d2-949f-1089a25a2841",
"metadata": {},
"outputs": [],
"source": [
"# Log in to HuggingFace\n",
"\n",
"hf_token = os.environ['HF_TOKEN']\n",
"login(hf_token, add_to_git_credential=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "688bd995-ec3e-43cd-8179-7fe14b275877",
"metadata": {},
"outputs": [],
"source": [
"# Let's avoid curating all our data again! Load in the pickle files:\n",
"\n",
"with open('train.pkl', 'rb') as file:\n",
" train = pickle.load(file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2817eaf5-4302-4a18-9148-d1062e3b3dbb",
"metadata": {},
"outputs": [],
"source": [
"train[0].prompt"
]
},
{
"cell_type": "markdown",
"id": "9ae1ba16-7e80-4096-ac88-64ef8edcc80c",
"metadata": {},
"source": [
"# Now create a Chroma Datastore\n",
"\n",
"In Week 5, we created a Chroma datastore with 123 documents representing chunks of objects from our fictional company Insurellm.\n",
"\n",
"Now we will create a Chroma datastore with 400,000 products from our training dataset! It's getting real!\n",
"\n",
"Note that we won't be using LangChain, but the API is very straightforward and consistent with before."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4aab95e-d719-4476-b6e7-e248120df25a",
"metadata": {},
"outputs": [],
"source": [
"client = chromadb.PersistentClient(path=DB)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f95dafd-ab80-464e-ba8a-dec7a2424780",
"metadata": {},
"outputs": [],
"source": [
"# Check if the collection exists and delete it if it does\n",
"collection_name = \"products\"\n",
"existing_collection_names = [collection.name for collection in client.list_collections()]\n",
"if collection_name in existing_collection_names:\n",
" client.delete_collection(collection_name)\n",
" print(f\"Deleted existing collection: {collection_name}\")\n",
"\n",
"collection = client.create_collection(collection_name)"
]
},
{
"cell_type": "markdown",
"id": "d392ed28-203d-4e73-be87-ac1390bdf722",
"metadata": {},
"source": [
"# Introducing the SentenceTransfomer\n",
"\n",
"The all-MiniLM is a very useful model from HuggingFace that maps sentences & paragraphs to a 384 dimensional dense vector space and is ideal for tasks like semantic search.\n",
"\n",
"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2\n",
"\n",
"It can run pretty quickly locally.\n",
"\n",
"Last time we used OpenAI embeddings to produce vector embeddings. Benefits compared to OpenAI embeddings:\n",
"1. It's free and fast!\n",
"3. We can run it locally, so the data never leaves our box - might be useful if you're building a personal RAG\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a87db200-d19d-44bf-acbd-15c45c70f5c9",
"metadata": {},
"outputs": [],
"source": [
"model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b23a025-4c35-4d3a-96ad-b956cad37b0a",
"metadata": {},
"outputs": [],
"source": [
"# Pass in a list of texts, get back a numpy array of vectors\n",
"\n",
"vector = model.encode([\"Well hi there\"])[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8adde63f-e732-4f7c-bba9-f8b2a469f14e",
"metadata": {},
"outputs": [],
"source": [
"vector"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38de1bf8-c9b5-45b4-9f4b-86af93b3f80d",
"metadata": {},
"outputs": [],
"source": [
"def description(item):\n",
" text = item.prompt.replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n",
" return text.split(\"\\n\\nPrice is $\")[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c1205bd-4692-44ef-8ea4-69f255354537",
"metadata": {},
"outputs": [],
"source": [
"description(train[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c79e2fe-1f50-4ebf-9a93-34f3088f2996",
"metadata": {},
"outputs": [],
"source": [
"for i in tqdm(range(0, len(train), 1000)):\n",
" documents = [description(item) for item in train[i: i+1000]]\n",
" vectors = model.encode(documents).astype(float).tolist()\n",
" metadatas = [{\"category\": item.category, \"price\": item.price} for item in train[i: i+1000]]\n",
" ids = [f\"doc_{j}\" for j in range(i, i+1000)]\n",
" collection.add(\n",
" ids=ids,\n",
" documents=documents,\n",
" embeddings=vectors,\n",
" metadatas=metadatas\n",
" )"
]
}
],
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