langchain/docs/modules/models/llms/integrations/predictionguard.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PredictionGuard\n",
    "\n",
    "How to use PredictionGuard wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3RqWPav7AtKL"
   },
   "outputs": [],
   "source": [
    "! pip install predictionguard langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "2xe8JEUwA7_y"
   },
   "outputs": [],
   "source": [
    "import predictionguard as pg\n",
    "from langchain.llms import PredictionGuard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mesCTyhnJkNS"
   },
   "source": [
    "## Basic LLM usage\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ua7Mw1N4HcER"
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   "outputs": [],
   "source": [
    "pgllm = PredictionGuard(name=\"default-text-gen\", token=\"<your access token>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Qo2p5flLHxrB"
   },
   "outputs": [],
   "source": [
    "pgllm(\"Tell me a joke\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v3MzIUItJ8kV"
   },
   "source": [
    "## Chaining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pPegEZExILrT"
   },
   "outputs": [],
   "source": [
    "from langchain import PromptTemplate, LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "suxw62y-J-bg"
   },
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
    "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
    "\n",
    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
    "\n",
    "llm_chain.predict(question=question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "l2bc26KHKr7n"
   },
   "outputs": [],
   "source": [
    "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
    "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
    "\n",
    "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I--eSa2PLGqq"
   },
   "outputs": [],
   "source": []
  }
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