{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "920a3c1a",
   "metadata": {},
   "source": [
    "# Model Laboratory\n",
    "\n",
    "This example goes over basic functionality of how to use the ModelLaboratory to test out and try different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab9e95ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import LLMChain, OpenAI, Cohere, HuggingFaceHub, PromptTemplate\n",
    "from langchain.model_laboratory import ModelLaboratory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32cb94e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "llms = [\n",
    "    OpenAI(temperature=0), \n",
    "    Cohere(model=\"command-xlarge-20221108\", max_tokens=20, temperature=0), \n",
    "    HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1})\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14cde09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lab = ModelLaboratory.from_llms(llms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f186c741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mInput:\u001b[0m\n",
      "What color is a flamingo?\n",
      "\n",
      "\u001b[1mOpenAI\u001b[0m\n",
      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
      "\u001b[36;1m\u001b[1;3m\n",
      "\n",
      "Flamingos are pink.\u001b[0m\n",
      "\n",
      "\u001b[1mCohere\u001b[0m\n",
      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 20, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
      "\u001b[33;1m\u001b[1;3m\n",
      "\n",
      "Pink\u001b[0m\n",
      "\n",
      "\u001b[1mHuggingFaceHub\u001b[0m\n",
      "Params: {'repo_id': 'google/flan-t5-xl', 'temperature': 1}\n",
      "\u001b[38;5;200m\u001b[1;3mpink\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_lab.compare(\"What color is a flamingo?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "248b652a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = PromptTemplate(template=\"What is the capital of {state}?\", input_variables=[\"state\"])\n",
    "model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f64377ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mInput:\u001b[0m\n",
      "New York\n",
      "\n",
      "\u001b[1mOpenAI\u001b[0m\n",
      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
      "\u001b[36;1m\u001b[1;3m\n",
      "\n",
      "The capital of New York is Albany.\u001b[0m\n",
      "\n",
      "\u001b[1mCohere\u001b[0m\n",
      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 20, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
      "\u001b[33;1m\u001b[1;3m\n",
      "\n",
      "The capital of New York is Albany.\u001b[0m\n",
      "\n",
      "\u001b[1mHuggingFaceHub\u001b[0m\n",
      "Params: {'repo_id': 'google/flan-t5-xl', 'temperature': 1}\n",
      "\u001b[38;5;200m\u001b[1;3mst john s\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_lab_with_prompt.compare(\"New York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54336dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import SelfAskWithSearchChain, SerpAPIWrapper\n",
    "\n",
    "open_ai_llm = OpenAI(temperature=0)\n",
    "search = SerpAPIWrapper()\n",
    "self_ask_with_search_openai = SelfAskWithSearchChain(llm=open_ai_llm, search_chain=search, verbose=True)\n",
    "\n",
    "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
    "search = SerpAPIWrapper()\n",
    "self_ask_with_search_cohere = SelfAskWithSearchChain(llm=cohere_llm, search_chain=search, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a50a9f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "chains = [self_ask_with_search_openai, self_ask_with_search_cohere]\n",
    "names = [str(open_ai_llm), str(cohere_llm)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3549e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lab = ModelLaboratory(chains, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "362f7f57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mInput:\u001b[0m\n",
      "What is the hometown of the reigning men's U.S. Open champion?\n",
      "\n",
      "\u001b[1mOpenAI\u001b[0m\n",
      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "What is the hometown of the reigning men's U.S. Open champion?\n",
      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
      "Intermediate answer: \u001b[33;1m\u001b[1;3mCarlos Alcaraz.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Follow up: Where is Carlos Alcaraz from?\u001b[0m\n",
      "Intermediate answer: \u001b[33;1m\u001b[1;3mEl Palmar, Spain.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "So the final answer is: El Palmar, Spain\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\u001b[36;1m\u001b[1;3m\n",
      "So the final answer is: El Palmar, Spain\u001b[0m\n",
      "\n",
      "\u001b[1mCohere\u001b[0m\n",
      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 256, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "What is the hometown of the reigning men's U.S. Open champion?\n",
      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
      "Intermediate answer: \u001b[33;1m\u001b[1;3mCarlos Alcaraz.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "So the final answer is:\n",
      "\n",
      "Carlos Alcaraz\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\u001b[33;1m\u001b[1;3m\n",
      "So the final answer is:\n",
      "\n",
      "Carlos Alcaraz\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_lab.compare(\"What is the hometown of the reigning men's U.S. Open champion?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94159131",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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