09_rag/04_langchain_llama2_calculate_with_pal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de6ff712-d1d2-4ef7-94b1-6ae54349a831",
   "metadata": {},
   "source": [
    "# Tested on ml.m5.2xlarge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "632a2aed-632c-429e-ac99-f422d46a1bc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch==2.0.1 in /opt/conda/lib/python3.10/site-packages (2.0.1)\n",
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      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.0.1) (1.3.0)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain==0.0.309) (0.4.3)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install \\\n",
    "    torch==2.0.1 \\\n",
    "    transformers==4.34.1 \\\n",
    "    langchain==0.0.309 \\\n",
    "    langchain_experimental \n",
    "#    iprogress==0.4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e9dba2-1703-43e1-ab23-e35e74c33add",
   "metadata": {},
   "source": [
    "Load libraries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68962043-4edf-485e-a628-eaec5c67382f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe08209b-75c8-4f55-9fb1-c1277b17f45c",
   "metadata": {},
   "source": [
    "Grab a LLM.  We use Hugging Face, and setup a simple pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc90c0e5-5c40-4039-bafa-6f8fbb30c26a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_checkpoint = \"NousResearch/Llama-2-7b-chat-hf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5df6adaa-e803-49b2-86f4-fd903f557252",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, model_max_length=2000)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19fa7cdb-d85c-4657-91dd-cf7c818792a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms.base import LLM\n",
    "from langchain.llms import HuggingFacePipeline\n",
    "\n",
    "pipe = pipeline(\n",
    "    \"text-generation\", model=model, tokenizer=tokenizer, temperature=0.8, max_new_tokens=2000\n",
    ")\n",
    "\n",
    "llm = HuggingFacePipeline(pipeline=pipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c2a7abc-add5-49db-ae17-1f9614f1f4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from langchain import PromptTemplate\n",
    "from langchain_experimental.pal_chain.base import PALChain\n",
    "\n",
    "pal_chain = PALChain.from_math_prompt(\n",
    "    llm, \n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "pal_prompt_template = \"\"\"Translate a math problem into a expression that can be executed using Python's numexpr library. \n",
    "Use the output of running this code to answer the question.\n",
    "\n",
    "Question: ${{Question with hard calculation.}}\n",
    "${{Code that prints what you need to know}}\n",
    "\n",
    "\n",
    "Question: I have three apples and get given two more, how many apples do I have?\n",
    "def solution():\n",
    "    initial_apples = 3\n",
    "    extra_apples = 2\n",
    "    return initial_apples + extra_apples\n",
    "\n",
    "\n",
    "Question: Jan has three times the number of pets as Marcia. Marcia has six pets, how many pets does jan have?\n",
    "def solution():\n",
    "    marcia_pets = 6\n",
    "    jan_pets = marcia_pets * 3\n",
    "    return jan_pets\n",
    "\n",
    "Question: Jane has 10 sweets. Peter has twice the number of sweets than Jane. How many sweets does Peter have?\"\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf8ca47a-0b4d-4b8c-a035-29a88f1d9750",
   "metadata": {},
   "outputs": [],
   "source": [
    "answer = pal_chain.run(pal_prompt_template)\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5565c4f-c9e1-499f-89e5-8e514b63a64a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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