{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d581e8a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n",
      "Imagine a dish that captures the very essence of rustic French comfort: **Boeuf Bourguignon**. This iconic stew features tender chunks of beef slow-\n",
      "simmered until they are melt-in-your-mouth soft, bathed in a rich, velvet sauce made from full-bodied red wine and savory stock. Sautéed with earthy\n",
      "mushrooms, buttery pearl onions, and smoky lardons, every spoonful offers a complex symphony of flavors that deepens and intensifies with each hour on\n",
      "the stove. It is more than just a meal; it is a masterpiece of patience and flavor that transforms simple ingredients into a soulful, sophisticated\n",
      "feast.\n"
     ]
    }
   ],
   "source": [
    "from langchain_ollama import ChatOllama\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.caches import InMemoryCache \n",
    "from langchain_core.globals import set_llm_cache\n",
    "import textwrap\n",
    "\n",
    "set_llm_cache(InMemoryCache())\n",
    "\n",
    "#Initialize the language model\n",
    "llm = ChatOllama(\n",
    "    model=\"gemma4:12b-mlx\",\n",
    "    base_url=\"http://mini2.dorceus.local:11434\"\n",
    ")\n",
    "llm2 = ChatOllama(\n",
    "    model=\"qwen3.5:35b-mlx\",\n",
    "    base_url=\"http://studio1.dorceus.local:11434\"\n",
    ")\n",
    "\n",
    "llm_with_retry = llm.with_retry(\n",
    "    stop_after_attempt= 3\n",
    ")\n",
    "llm2_with_retry = llm2.with_retry(stop_after_attempt=3)\n",
    "\n",
    "runner = llm_with_retry.with_fallbacks([llm2_with_retry])\n",
    "\n",
    "#Create a prompt template\n",
    "prompt = ChatPromptTemplate.from_template(\n",
    "    \"Write a brief, engaging paragraph about {topic}\"\n",
    ")\n",
    "\n",
    "prompt2  = ChatPromptTemplate.from_template(\n",
    "    \"Summarize the following in one sentence : {paragraph}\"\n",
    ")\n",
    "\n",
    "prompt3 = ChatPromptTemplate.from_template(\n",
    "    \"Suggest some side dished that goes with the following food description : {food_description}\"\n",
    ")\n",
    "\n",
    "#Create the chain \n",
    "# chain = prompt | llm\n",
    "# chain2 = prompt2 | llm2\n",
    "# chain3 = prompt3 | llm2 \n",
    "\n",
    "#sequential chain\n",
    "# seq_chain = (\n",
    "#     {\n",
    "#         \"paragraph\" : prompt,\n",
    "#         \"food_description\" : prompt2\n",
    "#     }\n",
    "#     | prompt2 \n",
    "#     | prompt3 \n",
    "#     | llm2\n",
    "# )\n",
    "\n",
    "seq_chain = (\n",
    "    prompt\n",
    "    | (lambda message : {\"paragraph\" : message.content})\n",
    "    | prompt2 \n",
    "    | (lambda message : {\"food_description\" : message.content})\n",
    "    | prompt3 \n",
    "    | runner\n",
    ")\n",
    "\n",
    "response = None\n",
    "for i in range(1,5) :\n",
    "    response = chain.invoke(\n",
    "        {\"topic\": \"Boeuf bourguignon\"}\n",
    "    )\n",
    "    #print(response.content)\n",
    "\n",
    "print(\"DONE\")\n",
    "print(textwrap.fill(response.content, 150) )\n",
    "\n",
    "#Run the chain\n",
    "# result = chain.invoke({\"topic\": \"adobong baboy\"})\n",
    "# result2 = chain2.invoke({\"topic\": result.content})\n",
    "# result3 = chain3.invoke({\"food_description\" : result2.content})\n",
    "# print(result.content)\n",
    "# print(result2.content)\n",
    "# print(result3.content)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "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": "3.14.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}