langchain/docs/extras/integrations/llms/predictionguard.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction Guard"
   ],
   "id": "3f0a201c"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3RqWPav7AtKL"
   },
   "outputs": [],
   "source": [
    "! pip install predictionguard langchain"
   ],
   "id": "4f810331"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2xe8JEUwA7_y"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import predictionguard as pg\n",
    "from langchain.llms import PredictionGuard\n",
    "from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain"
   ],
   "id": "7191a5ce"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mesCTyhnJkNS"
   },
   "source": [
    "## Basic LLM usage\n",
    "\n"
   ],
   "id": "a8d356d3"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows\n",
    "# you to access all the latest open access models (see https://docs.predictionguard.com)\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI api key>\"\n",
    "\n",
    "# Your Prediction Guard API key. Get one at predictionguard.com\n",
    "os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"<your Prediction Guard access token>\""
   ],
   "id": "158b109a"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ua7Mw1N4HcER"
   },
   "outputs": [],
   "source": [
    "pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
   ],
   "id": "140717c9"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Qo2p5flLHxrB"
   },
   "outputs": [],
   "source": [
    "pgllm(\"Tell me a joke\")"
   ],
   "id": "605f7ab6"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EyBYaP_xTMXH"
   },
   "source": [
    "## Control the output structure/ type of LLMs"
   ],
   "id": "99de09f9"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "55uxzhQSTPqF"
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   "outputs": [],
   "source": [
    "template = \"\"\"Respond to the following query based on the context.\n",
    "\n",
    "Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦\n",
    "Exclusive Candle Box - $80 \n",
    "Monthly Candle Box - $45 (NEW!)\n",
    "Scent of The Month Box - $28 (NEW!)\n",
    "Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉\n",
    "\n",
    "Query: {query}\n",
    "\n",
    "Result: \"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
   ],
   "id": "ae6bd8a1"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yersskWbTaxU"
   },
   "outputs": [],
   "source": [
    "# Without \"guarding\" or controlling the output of the LLM.\n",
    "pgllm(prompt.format(query=\"What kind of post is this?\"))"
   ],
   "id": "f81be0fb"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PzxSbYwqTm2w"
   },
   "outputs": [],
   "source": [
    "# With \"guarding\" or controlling the output of the LLM. See the\n",
    "# Prediction Guard docs (https://docs.predictionguard.com) to learn how to\n",
    "# control the output with integer, float, boolean, JSON, and other types and\n",
    "# structures.\n",
    "pgllm = PredictionGuard(\n",
    "    model=\"OpenAI-text-davinci-003\",\n",
    "    output={\n",
    "        \"type\": \"categorical\",\n",
    "        \"categories\": [\"product announcement\", \"apology\", \"relational\"],\n",
    "    },\n",
    ")\n",
    "pgllm(prompt.format(query=\"What kind of post is this?\"))"
   ],
   "id": "0cb3b91f"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v3MzIUItJ8kV"
   },
   "source": [
    "## Chaining"
   ],
   "id": "c3b6211f"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pPegEZExILrT"
   },
   "outputs": [],
   "source": [
    "pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
   ],
   "id": "8d57d1b5"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "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)"
   ],
   "id": "7915b7fa"
  },
  {
   "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\")"
   ],
   "id": "32ffd783"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I--eSa2PLGqq"
   },
   "outputs": [],
   "source": [],
   "id": "408ad1e1"
  }
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