brad/LLM_Engineering_OLD
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Initial draft for AI Domain Name Generator, UI to be improved

Browse Source
This commit is contained in:
Nik
2025-10-23 12:22:35 +05:30
parent 3a10b6ea95
commit fb4012be8c
1 changed files with 310 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

310
week2/community-contributions/ai_domain_finder/ai_domain_finder.ipynb Normal file
View File

@@ -0,0 +1,310 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "1633a440",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nWeek 2 Assignment: LLM Engineering\\nAuthor: Nikhil Raut\\n\\nNotebook: ai_domain_finder.ipynb\\n\\nPurpose:\\nBuild an agentic AI Domain Finder that proposes short, brandable .com names, verifies availability via RDAP, \\nthen returns: \\n a list of available .coms, \\n one preferred pick, \\n and a brief audio rationale.\\n'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"Week 2 Assignment: LLM Engineering\n",
"Author: Nikhil Raut\n",
"\n",
"Notebook: ai_domain_finder.ipynb\n",
"\n",
"Purpose:\n",
"Build an agentic AI Domain Finder that proposes short, brandable .com names, verifies availability via RDAP, \n",
"then returns: \n",
" a list of available .coms, \n",
" one preferred pick, \n",
" and a brief audio rationale.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "da528fbe",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import requests\n",
"from typing import Dict, List, Tuple\n",
"\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"\n",
"load_dotenv(override=True)\n",
"\n",
"OPENAI_MODEL = \"gpt-5-nano-2025-08-07\"\n",
"TTS_MODEL = \"gpt-4o-mini-tts\"\n",
"\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "519674b2",
"metadata": {},
"outputs": [],
"source": [
"RDAP_URL = \"https://rdap.verisign.com/com/v1/domain/{}\"\n",
"\n",
"def _to_com(domain: str) -> str:\n",
" d = domain.strip().lower()\n",
" if d.endswith(\".com\"):\n",
" return d\n",
" return f\"{d}.com\"\n",
"\n",
"def check_com_availability(domain: str) -> Dict:\n",
" \"\"\"\n",
" Returns: {\"domain\": \"name.com\", \"available\": bool, \"status\": int}\n",
" Rule: HTTP 200 => already registered (NOT available); 404 => available.\n",
" \"\"\"\n",
" fqdn = _to_com(domain)\n",
" try:\n",
" r = requests.get(RDAP_URL.format(fqdn), timeout=6)\n",
" available = (r.status_code == 404)\n",
" return {\"domain\": fqdn, \"available\": available, \"status\": r.status_code}\n",
" except requests.RequestException:\n",
" return {\"domain\": fqdn, \"available\": False, \"status\": 0}\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cd20c262",
"metadata": {},
"outputs": [],
"source": [
"check_tool = {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"check_com_availability\",\n",
" \"description\": \"Check if a .com domain is available using RDAP. Accepts root or full domain.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"domain\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Domain root or FQDN to check (limited to .com).\"\n",
" }\n",
" },\n",
" \"required\": [\"domain\"],\n",
" \"additionalProperties\": False\n",
" }\n",
" }\n",
"}\n",
"\n",
"TOOLS = [check_tool]\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2a9138b6",
"metadata": {},
"outputs": [],
"source": [
"def handle_tool_calls(message) -> List[Dict]:\n",
" \"\"\"\n",
" Translates model tool_calls into tool results for follow-up completion.\n",
" \"\"\"\n",
" results = []\n",
" for call in (message.tool_calls or []):\n",
" if call.function.name == \"check_com_availability\":\n",
" args = json.loads(call.function.arguments or \"{}\")\n",
" payload = check_com_availability(args.get(\"domain\", \"\"))\n",
" results.append({\n",
" \"role\": \"tool\",\n",
" \"tool_call_id\": call.id,\n",
" \"content\": json.dumps(payload)\n",
" })\n",
" return results\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b80c860",
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"You are the Agent for project \"AI Domain Finder\".\n",
"Goal: suggest .com domains and verify availability using the tool ONLY (no guessing).\n",
"\n",
"Instructions:\n",
"- Always propose 5-12 brandable .com candidates based on:\n",
" (1) Industry, (2) Target Customers, (3) Description.\n",
"- For each candidate, CALL the tool check_com_availability.\n",
"- Respond ONLY after checking all candidates.\n",
"- Output Markdown with three sections and these exact headings:\n",
" 1) Available .com domains:\n",
" - itemized list (root + .com)\n",
" 2) Preferred domain:\n",
" - a single best pick\n",
" 3) Audio explanation:\n",
" - 1-2 concise sentences explaining the preference\n",
"\n",
"Constraints:\n",
"- Use customer-familiar words where helpful.\n",
"- Keep names short, simple, pronounceable; avoid hyphens/numbers unless meaningful.\n",
"- Never include TLDs other than .com.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b45c6382",
"metadata": {},
"outputs": [],
"source": [
"def run_agent_with_tools(history: List[Dict]) -> str:\n",
" \"\"\"\n",
" history: list of {\"role\": \"...\", \"content\": \"...\"} messages\n",
" returns assistant markdown string (includes sections required by SYSTEM_PROMPT)\n",
" \"\"\"\n",
" messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}] + history\n",
" resp = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages, tools=TOOLS)\n",
"\n",
" while resp.choices[0].finish_reason == \"tool_calls\":\n",
" tool_msg = resp.choices[0].message\n",
" tool_results = handle_tool_calls(tool_msg)\n",
" messages.append(tool_msg)\n",
" messages.extend(tool_results)\n",
" resp = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages, tools=TOOLS)\n",
"\n",
" return resp.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92306515",
"metadata": {},
"outputs": [],
"source": [
"def extract_audio_text(markdown_reply: str) -> str:\n",
" \"\"\"\n",
" Pulls the 'Audio explanation:' section; falls back to first sentence.\n",
" \"\"\"\n",
" marker = \"Audio explanation:\"\n",
" lower = markdown_reply.lower()\n",
" idx = lower.find(marker.lower())\n",
" if idx != -1:\n",
" segment = markdown_reply[idx + len(marker):].strip()\n",
" parts = segment.split(\".\")\n",
" return (\". \".join([p.strip() for p in parts if p.strip()][:2]) + \".\").strip()\n",
" return \"This domain is the clearest, most memorable fit for the audience and brand goals.\"\n",
"\n",
"def synth_audio(text: str) -> bytes:\n",
" audio = openai.audio.speech.create(\n",
" model=TTS_MODEL,\n",
" voice=\"alloy\",\n",
" input=text\n",
" )\n",
" return audio.content\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7bdf7c67",
"metadata": {},
"outputs": [],
"source": [
"def chat(message: str, history_ui: List[Dict]) -> Tuple[List[Dict], bytes]:\n",
" \"\"\"\n",
" Gradio ChatInterface callback.\n",
" - message: latest user text (free-form)\n",
" - history_ui: [{\"role\": \"user\"/\"assistant\", \"content\": \"...\"}]\n",
" Returns: updated history, audio bytes for the 'Audio explanation'.\n",
" \"\"\"\n",
" # Convert Gradio UI history to OpenAI-format history\n",
" history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history_ui]\n",
" history.append({\"role\": \"user\", \"content\": message})\n",
"\n",
" reply_md = run_agent_with_tools(history)\n",
" history.append({\"role\": \"assistant\", \"content\": reply_md})\n",
"\n",
" audio_text = extract_audio_text(reply_md)\n",
" audio_bytes = synth_audio(audio_text)\n",
"\n",
" return history, audio_bytes\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc6c0650",
"metadata": {},
"outputs": [],
"source": [
"INTRO = (\n",
" \"Please provide details as text (three lines or paragraphs):\\n\"\n",
" \"Industry: ...\\n\"\n",
" \"Target Customers: ...\\n\"\n",
" \"Description: ...\\n\\n\"\n",
" \"You can refine in follow-ups (e.g., tone, shorter names, avoid words, etc.).\"\n",
")\n",
"\n",
"with gr.Blocks(title=\"AI Domain Finder (.com only)\") as ui:\n",
" gr.Markdown(\"# AI Domain Finder (.com only)\")\n",
" gr.Markdown(\"Provide your business details. The Agent will suggest .com options, verify availability, pick a preferred domain, and speak a short rationale.\")\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(type=\"messages\", height=460)\n",
" with gr.Row():\n",
" audio_out = gr.Audio(label=\"Audio explanation\", autoplay=True)\n",
" with gr.Row():\n",
" msg = gr.Textbox(label=\"Your input\", placeholder=INTRO, lines=6)\n",
"\n",
" def _append_user(m, hist):\n",
" return \"\", hist + [{\"role\": \"user\", \"content\": m}]\n",
"\n",
" msg.submit(_append_user, inputs=[msg, chatbot], outputs=[msg, chatbot]).then(\n",
" chat, inputs=[msg, chatbot], outputs=[chatbot, audio_out]\n",
" )\n",
"\n",
"ui.launch(inbrowser=True, auth=None, show_error=True)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llm-engineering",
"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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}