langchain/docs/extras/guides/model_laboratory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "920a3c1a",
   "metadata": {},
   "source": [
    "# Model comparison\n",
    "\n",
    "Constructing your language model application will likely involved choosing between many different options of prompts, models, and even chains to use. When doing so, you will want to compare these different options on different inputs in an easy, flexible, and intuitive way. \n",
    "\n",
    "LangChain provides the concept of a ModelLaboratory to test out and try different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab9e95ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\nfrom langchain.llms import OpenAI, Cohere, HuggingFaceHub\nfrom langchain.prompts import 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(\n",
    "    template=\"What is the capital of {state}?\", input_variables=[\"state\"]\n",
    ")\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.chains import SelfAskWithSearchChain\nfrom langchain.utilities import SerpAPIWrapper\n",
    "\n",
    "open_ai_llm = OpenAI(temperature=0)\n",
    "search = SerpAPIWrapper()\n",
    "self_ask_with_search_openai = SelfAskWithSearchChain(\n",
    "    llm=open_ai_llm, search_chain=search, verbose=True\n",
    ")\n",
    "\n",
    "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
    "search = SerpAPIWrapper()\n",
    "self_ask_with_search_cohere = SelfAskWithSearchChain(\n",
    "    llm=cohere_llm, search_chain=search, verbose=True\n",
    ")"
   ]
  },
  {
   "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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