{ "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" }, "outputs": [], "source": [ "pgllm = PredictionGuard(name=\"default-text-gen\", 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": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 1 }