Spaces:
Sleeping
Sleeping
File size: 9,114 Bytes
b38c914 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Welcome to the Second Lab - Week 1, Day 3\n",
"\n",
"Today we will work with lots of models! This is a way to get comfortable with APIs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Start with imports - ask ChatGPT to explain any package that you don't know\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI, AsyncOpenAI\n",
"from IPython.display import Markdown, display\n",
"import asyncio\n",
"from functools import partial"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Always remember to do this!\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print the key prefixes to help with any debugging\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"groq_api_key = os.getenv('GROQ_API_KEY')\n",
"\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
"\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n",
"else:\n",
" print(\"Google API Key not set (and this is optional)\")\n",
"\n",
"if groq_api_key:\n",
" print(f\"Groq API Key exists and begins {groq_api_key[:4]}\")\n",
"else:\n",
" print(\"Groq API Key not set (and this is optional)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"request = \"Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. \"\n",
"request += \"Answer only with the question, no explanation.\"\n",
"messages = [{\"role\": \"user\", \"content\": request}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"openai = AsyncOpenAI()\n",
"response = await openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=messages,\n",
")\n",
"question = response.choices[0].message.content\n",
"print(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"messages = [{\"role\": \"user\", \"content\": question}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"\n",
"@dataclass\n",
"class LLMResource:\n",
" api_key: str\n",
" model: str\n",
" url: str = None # optional otherwise NOone\n",
"\n",
"llm_resources = [\n",
" LLMResource(api_key=openai_api_key, model=\"gpt-4o-mini\"),\n",
" LLMResource(api_key=google_api_key, model=\"gemini-2.5-flash\", url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"),\n",
" LLMResource(api_key=groq_api_key, model=\"qwen/qwen3-32b\", url=\"https://api.groq.com/openai/v1\"),\n",
" LLMResource(api_key=\"ollama\", model=\"deepseek-r1:1.5b\", url=\"http://localhost:11434/v1\" )\n",
"]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"async def llm_call(key, model_name, url, messages) -> tuple:\n",
" if url is None:\n",
" llm = AsyncOpenAI(api_key=key)\n",
" else: \n",
" llm = AsyncOpenAI(base_url=url,api_key=key)\n",
" \n",
" response = await llm.chat.completions.create(\n",
" model=model_name, messages=messages)\n",
" \n",
" answer = (model_name, response.choices[0].message.content)\n",
"\n",
" return answer #returns tuple of modle and response from LLM\n",
"\n",
"llm_callable = partial(llm_call, messages=messages) #prefill with messages\n",
"# Always remember to do this!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#gather all responses concurrently\n",
"tasks = [llm_callable(res.api_key,res.model,res.url) for res in llm_resources]\n",
"results = await asyncio.gather(*tasks)\n",
"together = [f'Response from competitor {model}:{answer}' for model,answer in results]#gather results once all model finish running\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"judge = f\"\"\"You are judging a competition between {len(llm_resources)} competitors.\n",
"Each model has been given this question:\n",
"\n",
"{request}\n",
"\n",
"Your job is to evaluate each response for clarity and strength of argument, and rank them in order of best to worst.\n",
"Respond with JSON, and only JSON, with the following format:\n",
"{{\"results\": [\"best competitor number\", \"second best competitor number\", \"third best competitor number\", ...]}}\n",
"\n",
"Here are the responses from each competitor:\n",
"\n",
"{together} # all responses\n",
"\n",
"Now respond with the JSON with the ranked order of the competitors name, nothing else. Do not include markdown formatting or code blocks.\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(judge)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"judge_messages = [{\"role\": \"user\", \"content\": judge}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Judgement time!\n",
"\n",
"openai = OpenAI()\n",
"response = openai.chat.completions.create(\n",
" model=\"o3-mini\",\n",
" messages=judge_messages,\n",
")\n",
"results = response.choices[0].message.content\n",
"print(results)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# OK let's turn this into results!\n",
"\n",
"results_dict = json.loads(results)\n",
"\n",
"ranks = results_dict[\"results\"]\n",
"\n",
"for index, result in enumerate(ranks):\n",
" print(f\"Rank {index+1}: {result}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left; width:100%\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../assets/exercise.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
" <span style=\"color:#ff7800;\">Which pattern(s) did this use? Try updating this to add another Agentic design pattern.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left; width:100%\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../assets/business.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#00bfff;\">Commercial implications</h2>\n",
" <span style=\"color:#00bfff;\">These kinds of patterns - to send a task to multiple models, and evaluate results,\n",
" are common where you need to improve the quality of your LLM response. This approach can be universally applied\n",
" to business projects where accuracy is critical.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|