Adding Domain Name Generator based on keyword ideas and target location using openai chat completion api
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Nik
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1633a440",
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"Week 1 Assignment: LLM Engineering\n",
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"Author: Nikhil Raut\n",
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"\n",
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"Notebook: domain_name_generator.ipynb\n",
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"\n",
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"Purpose:\n",
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"Generate short, memorable domain root ideas (no TLD) from keywords using an OpenAI Chat Completions system+user prompt.\n",
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"\n",
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"Quick setup:\n",
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"1) pip install openai python-dotenv ipython\n",
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"2) Add OPENAI_API_KEY to a .env file in the project root\n",
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"\n",
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"How to use (Python script):\n",
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"from domain_name_generator import generate_domain_ideas\n",
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"ideas = generate_domain_ideas([\"fitness\", \"coach\", \"wellness\"], target_country=\"India\", n=20)\n",
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"print(ideas)\n",
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"\n",
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"How to use (Notebook):\n",
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"# after running config/client cells\n",
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"generate_domain_ideas([\"fintech\", \"pay\"], target_country=\"US\", n=15)\n",
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"\n",
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"Notes:\n",
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"- n: 1-50 (returns list[str] of TLD-less roots)\n",
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"- Adjust MODEL and temperature in the config cell or function args\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": "da528fbe",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from dataclasses import dataclass, field\n",
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"from typing import List, Dict, Tuple\n",
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"from openai import OpenAI\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import Markdown, display\n",
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"import json\n",
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"import re\n",
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"from typing import Optional"
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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": "519674b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# --- Cell 2: Config & Client\n",
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"\n",
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"# Load environment (.env should contain OPENAI_API_KEY)\n",
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"load_dotenv()\n",
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"\n",
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"# Initialize OpenAI client (relies on OPENAI_API_KEY)\n",
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"openai = OpenAI()\n",
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"\n",
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"# Model constants (feel free to change to another chat model)\n",
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"MODEL = \"gpt-4o-mini\"\n",
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"\n",
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"# Deterministic-ish by default; raise temperature for wilder ideas.\n",
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"GENERATION_TEMPERATURE = 0.8\n",
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"SCORING_TEMPERATURE = 0.2\n"
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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": "cd20c262",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"SYSTEM_PROMPT = \"\"\"You are a helpful brand-naming assistant.\n",
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"Generate **domain ROOT** ideas (no TLD like .com) that are:\n",
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"- short (ideally 5–12 chars), pronounceable, and memorable\n",
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"- alphanumeric only (no spaces or hyphens), start with a letter\n",
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"- avoid famous trademarks and sensitive terms\n",
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"- diverse styles: blends, portmanteau, slight misspellings, synonyms\n",
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"Return ONLY valid JSON: {\"domains\": [\"idea1\", \"idea2\", ...]}\"\"\"\n",
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"\n",
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"def _build_user_prompt(keywords: list[str], target_country: Optional[str], n: int) -> str:\n",
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" kws = \", \".join(keywords)\n",
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" country_line = f\"Target country/market: {target_country}\" if target_country else \"Target country/market: (general/global)\"\n",
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" return (\n",
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" \"Given the keywords below, propose exactly \"\n",
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" f\"{n} short, brandable domain roots **without any TLD**.\\n\"\n",
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" f\"Keywords: {kws}\\n\"\n",
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" f\"{country_line}\\n\"\n",
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" \"Constraints:\\n\"\n",
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" \"- 1–2 syllables if possible\\n\"\n",
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" \"- No hyphens/underscores/spaces\\n\"\n",
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" \"- Avoid numbers unless they genuinely help memorability\\n\"\n",
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" \"Output format: a JSON object with a single key 'domains' whose value is an array of strings.\"\n",
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" )\n",
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"\n",
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"_valid_root = re.compile(r\"^[a-z][a-z0-9]{2,49}$\") # 3–50 chars, starts with letter\n",
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"\n",
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"def _sanitize_root(s: str) -> str:\n",
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" s = s.strip().lower()\n",
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" # remove anything after a dot (accidental TLDs)\n",
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" s = s.split(\".\", 1)[0]\n",
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" # drop spaces and hyphens just in case\n",
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" s = s.replace(\" \", \"\").replace(\"-\", \"\")\n",
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" # keep only a–z0–9\n",
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" s = re.sub(r\"[^a-z0-9]\", \"\", s)\n",
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" # ensure starts with letter\n",
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" if s and not s[0].isalpha():\n",
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" s = re.sub(r\"^[^a-z]+\", \"\", s)\n",
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" return s\n",
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"\n",
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"def _unique_preserve_order(items: list[str]) -> list[str]:\n",
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" seen = set()\n",
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" out = []\n",
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" for it in items:\n",
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" if it not in seen:\n",
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" seen.add(it)\n",
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" out.append(it)\n",
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" return out"
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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": "2a9138b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# --- Cell 4: Core generator function (Chat Completions)\n",
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"\n",
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"def generate_domain_ideas(\n",
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" keywords: list[str],\n",
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" target_country: Optional[str] = None,\n",
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" n: int = 20,\n",
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" *,\n",
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" model: str = MODEL,\n",
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" temperature: float = GENERATION_TEMPERATURE,\n",
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") -> list[str]:\n",
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" \"\"\"\n",
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" Generate up to `n` domain ROOT ideas (no TLD).\n",
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" - keywords: list of seed terms\n",
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" - target_country: optional market hint (e.g., 'India', 'US', 'DE')\n",
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" - n: number of ideas to return (1–50)\n",
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" \"\"\"\n",
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" if not keywords or not any(k.strip() for k in keywords):\n",
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" raise ValueError(\"Provide at least one non-empty keyword.\")\n",
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" if not (1 <= int(n) <= 50):\n",
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" raise ValueError(\"`n` must be between 1 and 50.\")\n",
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"\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
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" {\"role\": \"user\", \"content\": _build_user_prompt([k.strip() for k in keywords if k.strip()], target_country, int(n))},\n",
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" ]\n",
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"\n",
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" resp = openai.chat.completions.create(\n",
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" model=model,\n",
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" messages=messages,\n",
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" temperature=temperature,\n",
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" response_format={\"type\": \"json_object\"}, # ask for strict JSON\n",
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" )\n",
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"\n",
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" content = resp.choices[0].message.content\n",
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"\n",
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" # Try to parse JSON; if it fails, fall back to naive extraction.\n",
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" ideas: list[str] = []\n",
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" try:\n",
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" data = json.loads(content)\n",
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" if isinstance(data, dict) and isinstance(data.get(\"domains\"), list):\n",
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" ideas = [str(x) for x in data[\"domains\"]]\n",
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" except Exception:\n",
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" # Fallback: split lines / commas\n",
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" raw = re.split(r\"[\\n,]+\", content)\n",
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" ideas = [r for r in raw if r.strip()]\n",
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"\n",
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" # Sanitize, validate, dedupe, and enforce count\n",
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" ideas = [_sanitize_root(x) for x in ideas]\n",
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" ideas = [x for x in ideas if _valid_root.match(x)]\n",
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" ideas = _unique_preserve_order(ideas)[: int(n)]\n",
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"\n",
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" return ideas\n"
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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": "0b80c860",
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"metadata": {},
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"outputs": [],
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"source": [
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"example_keywords = [\"law\", \"gpt\", \"chatbot\", \"lawyer helper\"]\n",
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"ideas = generate_domain_ideas(example_keywords, target_country=\"India\", n=20)\n"
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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": "b45c6382",
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"metadata": {},
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"outputs": [],
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"source": [
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"display(Markdown(\"## Domain ideas (no TLD)\\n\" + \"\\n\".join(f\"{i+1}. `{d}`\" for i, d in enumerate(ideas))))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llm-engineering",
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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": "3.12.10"
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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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