From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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397 lines
10 KiB
397 lines
10 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "2a0f44a9-37cd-4aa5-9b20-cfc0dc8dfc0a", |
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"metadata": {}, |
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"source": [ |
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"# The Price is Right\n", |
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"\n", |
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"Today we build a more complex solution for estimating prices of goods.\n", |
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"\n", |
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"1. Day 2.0 notebook: create a RAG database with our 400,000 training data\n", |
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"2. Day 2.1 notebook: visualize in 2D\n", |
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"3. Day 2.2 notebook: visualize in 3D\n", |
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"4. Day 2.3 notebook: build and test a RAG pipeline with GPT-4o-mini\n", |
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"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", |
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"\n", |
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"Phew! That's a lot to get through in one day!\n", |
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"\n", |
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"## PLEASE NOTE:\n", |
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"\n", |
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"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", |
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"\n", |
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"## We will go fast today! Hold on to your hat.." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "fbcdfea8-7241-46d7-a771-c0381a3e7063", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import re\n", |
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"import math\n", |
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"import json\n", |
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"from tqdm import tqdm\n", |
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"import random\n", |
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"from dotenv import load_dotenv\n", |
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"from huggingface_hub import login\n", |
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"import matplotlib.pyplot as plt\n", |
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"import numpy as np\n", |
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"import pickle\n", |
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"from openai import OpenAI\n", |
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"from sentence_transformers import SentenceTransformer\n", |
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"from datasets import load_dataset\n", |
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"import chromadb\n", |
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"from items import Item\n", |
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"from testing import Tester" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "98666e73-938e-469d-8987-e6e55ba5e034", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# environment\n", |
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"\n", |
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"load_dotenv()\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", |
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"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "9a25a5cf-8f6c-4b5d-ad98-fdd096f5adf8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"openai = OpenAI()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "dc696493-0b6f-48aa-9fa8-b1ae0ecaf3cd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load in the test pickle file:\n", |
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"\n", |
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"with open('test.pkl', 'rb') as file:\n", |
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" test = pickle.load(file)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "33d38a06-0c0d-4e96-94d1-35ee183416ce", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def make_context(similars, prices):\n", |
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" message = \"To provide some context, here are some other items that might be similar to the item you need to estimate.\\n\\n\"\n", |
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" for similar, price in zip(similars, prices):\n", |
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" message += f\"Potentially related product:\\n{similar}\\nPrice is ${price:.2f}\\n\\n\"\n", |
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" return message" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "61f203b7-63b6-48ed-869b-e393b5bfcad3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def messages_for(item, similars, prices):\n", |
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" system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", |
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" user_prompt = make_context(similars, prices)\n", |
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" user_prompt += \"And now the question for you:\\n\\n\"\n", |
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" user_prompt += item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", |
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" return [\n", |
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" {\"role\": \"system\", \"content\": system_message},\n", |
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" {\"role\": \"user\", \"content\": user_prompt},\n", |
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" {\"role\": \"assistant\", \"content\": \"Price is $\"}\n", |
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" ]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "b26f405d-6e1f-4caa-b97f-1f62cd9d1ebc", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"DB = \"products_vectorstore\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "d26a1104-cd11-4361-ab25-85fb576e0582", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"client = chromadb.PersistentClient(path=DB)\n", |
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"collection = client.get_or_create_collection('products')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "1e339760-96d8-4485-bec7-43fadcd30c4d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def description(item):\n", |
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" text = item.prompt.replace(\"How much does this cost to the nearest dollar?\\n\\n\", \"\")\n", |
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" return text.split(\"\\n\\nPrice is $\")[0]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a1bd0c87-8bad-43d9-9461-bb69a9e0e22c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"description(test[0])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "9f759bd2-7a7e-4c1a-80a0-e12470feca89", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "e44dbd25-fb95-4b6b-bbbb-8da5fc817105", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def vector(item):\n", |
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" return model.encode([description(item)])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ffd5ee47-db5d-4263-b0d9-80d568c91341", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def find_similars(item):\n", |
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" results = collection.query(query_embeddings=vector(item).astype(float).tolist(), n_results=5)\n", |
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" documents = results['documents'][0][:]\n", |
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" prices = [m['price'] for m in results['metadatas'][0][:]]\n", |
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" return documents, prices" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "6f7b9ff9-fd90-4627-bb17-7c2f7bbd21f3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"test[1].prompt" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ff1b2659-cc6b-47aa-a797-dd1cd3d1d6c3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"documents, prices = find_similars(test[1])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "24756d4d-edac-41ce-bb80-c3b6f1cea7ee", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"print(make_context(documents, prices))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "0b81eca2-0b58-4fe8-9dd6-47f13ba5f8ee", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"print(messages_for(test[1], documents, prices))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "d11f1c8d-7480-4d64-a274-b030d701f1b8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def get_price(s):\n", |
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" s = s.replace('$','').replace(',','')\n", |
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" match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n", |
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" return float(match.group()) if match else 0" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a919cf7d-b3d3-4968-8c96-54a0da0b0219", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# The function for gpt-4o-mini\n", |
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"\n", |
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"def gpt_4o_mini_rag(item):\n", |
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" documents, prices = find_similars(item)\n", |
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" response = openai.chat.completions.create(\n", |
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" model=\"gpt-4o-mini\", \n", |
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" messages=messages_for(item, documents, prices),\n", |
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" seed=42,\n", |
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" max_tokens=5\n", |
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" )\n", |
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" reply = response.choices[0].message.content\n", |
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" return get_price(reply)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "3e519e26-ff15-4425-90bb-bfbf55deb39b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"gpt_4o_mini_rag(test[1])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ce78741b-2966-41d2-9831-cbf8f8d176be", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"test[1].price" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "16d90455-ff7d-4f5f-8b8c-8e061263d1c7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"Tester.test(gpt_4o_mini_rag, test)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "e6d5deb3-6a2a-4484-872c-37176c5e1f07", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from agents.frontier_agent import FrontierAgent" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "56e8dd5d-ed36-49d8-95f7-dc82e548255b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"agent = FrontierAgent(collection)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "980dd126-f675-4499-8817-0cc0bb73e247", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"agent.price(\"Quadcast HyperX condenser mic for high quality podcasting\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "66c18a06-d0f1-4ec9-8aff-ec3ca294dd09", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from agents.specialist_agent import SpecialistAgent" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ba672fb4-2c3e-42ee-9ea0-21bfcfc5260c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"agent2 = SpecialistAgent()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a5a97004-95b4-46ea-b12d-a4ead22fcb2a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"agent2.price(\"Quadcast HyperX condenser mic for high quality podcasting\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "26d5ddc6-baa6-4760-a430-05671847ac47", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.10" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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}
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