From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
566 lines
14 KiB
566 lines
14 KiB
{ |
|
"cells": [ |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 1, |
|
"id": "fbcdfea8-7241-46d7-a771-c0381a3e7063", |
|
"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 openai import OpenAI\n", |
|
"from sentence_transformers import SentenceTransformer\n", |
|
"from datasets import load_dataset\n", |
|
"import chromadb\n", |
|
"from items import Item\n", |
|
"from testing import Tester\n", |
|
"from agents.pricer_agent import price\n", |
|
"import pandas as pd\n", |
|
"import numpy as np\n", |
|
"from sklearn.linear_model import LinearRegression\n", |
|
"from sklearn.metrics import mean_squared_error, r2_score" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 2, |
|
"id": "e6e88bd1-f89c-4b98-92fa-aa4bc1575bca", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# CONSTANTS\n", |
|
"\n", |
|
"QUESTION = \"How much does this cost to the nearest dollar?\\n\\n\"\n", |
|
"DB = \"products_vectorstore\"" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 3, |
|
"id": "98666e73-938e-469d-8987-e6e55ba5e034", |
|
"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')" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 4, |
|
"id": "9a25a5cf-8f6c-4b5d-ad98-fdd096f5adf8", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"openai = OpenAI()" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 5, |
|
"id": "dc696493-0b6f-48aa-9fa8-b1ae0ecaf3cd", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Load in the test pickle file:\n", |
|
"\n", |
|
"with open('test.pkl', 'rb') as file:\n", |
|
" test = pickle.load(file)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": 6, |
|
"id": "33d38a06-0c0d-4e96-94d1-35ee183416ce", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def make_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": "61f203b7-63b6-48ed-869b-e393b5bfcad3", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def messages_for(item, similars, prices):\n", |
|
" system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", |
|
" user_prompt = make_context(similars, prices)\n", |
|
" user_prompt += \"And now the question for you:\\n\\n\"\n", |
|
" user_prompt += item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\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": "b26f405d-6e1f-4caa-b97f-1f62cd9d1ebc", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "d26a1104-cd11-4361-ab25-85fb576e0582", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"client = chromadb.PersistentClient(path=DB)\n", |
|
"collection = client.get_or_create_collection('products')" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "1e339760-96d8-4485-bec7-43fadcd30c4d", |
|
"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 $\")[0]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "9f759bd2-7a7e-4c1a-80a0-e12470feca89", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "e44dbd25-fb95-4b6b-bbbb-8da5fc817105", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def vector(item):\n", |
|
" return model.encode([description(item)])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "ffd5ee47-db5d-4263-b0d9-80d568c91341", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def find_similars(item):\n", |
|
" results = collection.query(query_embeddings=vector(item).astype(float).tolist(), n_results=5)\n", |
|
" documents = results['documents'][0][:]\n", |
|
" prices = [m['price'] for m in results['metadatas'][0][:]]\n", |
|
" return documents, prices" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "d11f1c8d-7480-4d64-a274-b030d701f1b8", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def get_price(s):\n", |
|
" s = s.replace('$','').replace(',','')\n", |
|
" match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n", |
|
" return float(match.group()) if match else 0" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "a919cf7d-b3d3-4968-8c96-54a0da0b0219", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# The function for gpt-4o-mini\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=messages_for(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": "8b918cfc-76c1-442a-8caa-bec500cd504b", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"gpt_4o_mini_rag(test[1000])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "c92cfc0b-b36d-456f-94cc-fe3f315cc25e", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"test[1000]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "e6d5deb3-6a2a-4484-872c-37176c5e1f07", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def proprietary(item):\n", |
|
" text = item.prompt.split(\"to the nearest dollar?\\n\\n\")[1].split(\"\\n\\nPrice is $\")[0]\n", |
|
" return price(text)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "bacdf607-37b9-4997-adb1-d63abfb645b1", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"print(proprietary(test[1]))\n", |
|
"print(gpt_4o_mini_rag(test[1]))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "b35532e7-098a-4ab9-a8f7-8f101b437181", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"truths = []\n", |
|
"proprietaries = []\n", |
|
"rags = []\n", |
|
"for i in tqdm(range(1000,1250)):\n", |
|
" item = test[i]\n", |
|
" truths.append(item.price)\n", |
|
" proprietaries.append(proprietary(item))\n", |
|
" rags.append(gpt_4o_mini_rag(item))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "e6ae54c7-6e8e-4333-b075-b59978fed560", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"mins = [min(p,r) for p,r in zip(proprietaries, rags)]\n", |
|
"maxes = [max(p,r) for p,r in zip(proprietaries, rags)]\n", |
|
"\n", |
|
"X = pd.DataFrame({\n", |
|
" 'Proprietary': proprietaries,\n", |
|
" 'RAG': rags,\n", |
|
" 'Min': mins,\n", |
|
" 'Max': maxes,\n", |
|
"})\n", |
|
"\n", |
|
"# Convert y to a Series\n", |
|
"y = pd.Series(truths)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "e68684ed-d029-4d95-bb13-eead19b20e49", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Train a Linear Regression\n", |
|
"np.random.seed(42)\n", |
|
"\n", |
|
"lr = LinearRegression()\n", |
|
"lr.fit(X, y)\n", |
|
"\n", |
|
"feature_columns = [\"Proprietary\", \"RAG\", \"Min\", \"Max\"]\n", |
|
"\n", |
|
"for feature, coef in zip(feature_columns, lr.coef_):\n", |
|
" print(f\"{feature}: {coef:.2f}\")\n", |
|
"print(f\"Intercept={lr.intercept_:.2f}\")" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "28530362-97b8-42a0-bf89-967539b6f170", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def ensemble(item):\n", |
|
" prop = proprietary(item)\n", |
|
" rag = gpt_4o_mini_rag(item)\n", |
|
" Xt = pd.DataFrame({\n", |
|
" 'Proprietary': [prop],\n", |
|
" 'RAG': [rag],\n", |
|
" 'Min': [min(prop,rag)],\n", |
|
" 'Max': [max(prop,rag)],\n", |
|
" })\n", |
|
" yt = lr.predict(Xt)\n", |
|
" return yt[0]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "08021c05-340b-4ee2-9d11-4b280766976f", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"ensemble(test[0])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "d8308c74-546f-4fc0-ada4-1974addacfd1", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"test[0].price" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "80792910-c59f-4d96-aa53-683464a8e60c", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"Tester.test(ensemble, test)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "d0c41043-2049-4883-947f-2aad2f6954c2", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"from sklearn.ensemble import RandomForestRegressor\n", |
|
"\n", |
|
"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", |
|
"vectors = np.array(result['embeddings'])\n", |
|
"documents = result['documents']\n", |
|
"prices = [metadata['price'] for metadata in result['metadatas']]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "e9c3276f-ae01-478d-bb27-dc73b567b41a", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=8)\n", |
|
"rf_model.fit(vectors, prices)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "3e8f70cd-4147-40c6-9861-a3513b7e5499", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def new_rf(item):\n", |
|
" text = item.prompt.split(\"to the nearest dollar?\\n\\n\")[1].split(\"\\n\\nPrice is $\")[0]\n", |
|
" vector = model.encode([text])\n", |
|
" return max(0, rf_model.predict(vector)[0])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "a2e3340f-7ed4-47eb-a5a9-dff4c0353f58", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"new_rf(test[0])" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "f91c903b-8db1-4374-807e-3a8ce282ef30", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"Tester.test(new_rf, test)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "3c8e23c5-1ed3-4bd1-a3c0-129d4712c93a", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"forests = []\n", |
|
"for i in tqdm(range(1000,1250)):\n", |
|
" item = test[i]\n", |
|
" forests.append(new_rf(item))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "8e2eca63-8230-4904-9a79-7e779747479e", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"truths2 = []\n", |
|
"proprietaries2 = []\n", |
|
"rags2 = []\n", |
|
"forests2 = []\n", |
|
"for i in tqdm(range(1000,2000)):\n", |
|
" item = test[i]\n", |
|
" truths2.append(item.price)\n", |
|
" proprietaries2.append(proprietary(item))\n", |
|
" rags2.append(gpt_4o_mini_rag(item))\n", |
|
" forests2.append(new_rf(item))" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "0a3e057f-05c5-4f8f-8b3b-0afdfccc1412", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"mins2 = [min(p,r,f) for p,r,f in zip(proprietaries2, rags2, forests2)]\n", |
|
"maxes2 = [max(p,r,f) for p,r,f in zip(proprietaries2, rags2, forests2)]\n", |
|
"\n", |
|
"\n", |
|
"\n", |
|
"X2 = pd.DataFrame({\n", |
|
" 'Proprietary': proprietaries2,\n", |
|
" 'RAG': rags2,\n", |
|
" 'Forest': forests2,\n", |
|
" 'Min': mins2,\n", |
|
" 'Max': maxes2,\n", |
|
"})\n", |
|
"\n", |
|
"# Convert y to a Series\n", |
|
"y2 = pd.Series(truths2)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "1ae62175-b955-428e-b077-705c49ee71bd", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"# Train a Linear Regression\n", |
|
"np.random.seed(42)\n", |
|
"\n", |
|
"lr2 = LinearRegression()\n", |
|
"lr2.fit(X2, y2)\n", |
|
"\n", |
|
"feature_columns = X2.columns.tolist()\n", |
|
"\n", |
|
"for feature, coef in zip(feature_columns, lr2.coef_):\n", |
|
" print(f\"{feature}: {coef:.2f}\")\n", |
|
"print(f\"Intercept={lr.intercept_:.2f}\")" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "214a3831-c464-4218-a349-534b6bda7f12", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"def ensemble2(item):\n", |
|
" prop = proprietary(item)\n", |
|
" rag = gpt_4o_mini_rag(item)\n", |
|
" r_f = new_rf(item)\n", |
|
" Xt2 = pd.DataFrame({\n", |
|
" 'Proprietary': [prop],\n", |
|
" 'RAG': [rag],\n", |
|
" 'Forest': [r_f],\n", |
|
" 'Min': [min(prop,rag, r_f)],\n", |
|
" 'Max': [max(prop,rag, r_f)],\n", |
|
" })\n", |
|
" yt2 = lr.predict(Xt2)\n", |
|
" return yt2[0]" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "b234cb68-af68-4475-ae18-8892aac6b74e", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [ |
|
"Tester.test(ensemble2, test)" |
|
] |
|
}, |
|
{ |
|
"cell_type": "code", |
|
"execution_count": null, |
|
"id": "10a7275f-1aa9-4446-9100-a7a0ba0215f2", |
|
"metadata": {}, |
|
"outputs": [], |
|
"source": [] |
|
} |
|
], |
|
"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 |
|
}
|
|
|