forked from Archives/langchain
Harrison/prediction guard update (#5404)
Co-authored-by: Daniel Whitenack <whitenack.daniel@gmail.com>searx_updates
Harrison Chase
12 months ago
committed by
GitHub
parent
416c8b1da3
commit
d6fb25c439
@ -1,155 +1,222 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# PredictionGuard\n",
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"\n",
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"How to use PredictionGuard wrapper"
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]
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "3RqWPav7AtKL"
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},
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"outputs": [],
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"source": [
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"! pip install predictionguard langchain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "2xe8JEUwA7_y"
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},
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"outputs": [],
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"source": [
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"import predictionguard as pg\n",
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"from langchain.llms import PredictionGuard"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "mesCTyhnJkNS"
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},
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"source": [
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"## Basic LLM usage\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "Ua7Mw1N4HcER"
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},
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"outputs": [],
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"source": [
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"pgllm = PredictionGuard(name=\"default-text-gen\", token=\"<your access token>\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "Qo2p5flLHxrB"
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},
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"outputs": [],
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"source": [
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"pgllm(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "v3MzIUItJ8kV"
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},
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"source": [
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"## Chaining"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "pPegEZExILrT"
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},
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"outputs": [],
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"source": [
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "suxw62y-J-bg"
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
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"\n",
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.predict(question=question)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "l2bc26KHKr7n"
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
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"\n",
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"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "I--eSa2PLGqq"
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "3RqWPav7AtKL"
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},
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"outputs": [],
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"source": [
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"! pip install predictionguard langchain"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import os\n",
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"\n",
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"import predictionguard as pg\n",
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"from langchain.llms import PredictionGuard\n",
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"from langchain import PromptTemplate, LLMChain"
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],
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"metadata": {
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"id": "2xe8JEUwA7_y"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Basic LLM usage\n",
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"\n"
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],
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"metadata": {
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"id": "mesCTyhnJkNS"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows\n",
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"# you to access all the latest open access models (see https://docs.predictionguard.com)\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI api key>\"\n",
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"\n",
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"# Your Prediction Guard API key. Get one at predictionguard.com\n",
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"os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"<your Prediction Guard access token>\""
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],
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"metadata": {
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"id": "kp_Ymnx1SnDG"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
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],
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"metadata": {
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"id": "Ua7Mw1N4HcER"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"pgllm(\"Tell me a joke\")"
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],
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"metadata": {
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"id": "Qo2p5flLHxrB"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Control the output structure/ type of LLMs"
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],
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"metadata": {
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"id": "EyBYaP_xTMXH"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"template = \"\"\"Respond to the following query based on the context.\n",
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"\n",
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"Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦\n",
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"Exclusive Candle Box - $80 \n",
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"Monthly Candle Box - $45 (NEW!)\n",
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"Scent of The Month Box - $28 (NEW!)\n",
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"Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉\n",
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"\n",
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"Query: {query}\n",
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"\n",
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"Result: \"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
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],
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"metadata": {
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"id": "55uxzhQSTPqF"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Without \"guarding\" or controlling the output of the LLM.\n",
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"pgllm(prompt.format(query=\"What kind of post is this?\"))"
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],
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"metadata": {
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"id": "yersskWbTaxU"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# With \"guarding\" or controlling the output of the LLM. See the \n",
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"# Prediction Guard docs (https://docs.predictionguard.com) to learn how to \n",
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"# control the output with integer, float, boolean, JSON, and other types and\n",
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"# structures.\n",
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\", \n",
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" output={\n",
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" \"type\": \"categorical\",\n",
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" \"categories\": [\n",
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" \"product announcement\", \n",
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" \"apology\", \n",
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" \"relational\"\n",
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" ]\n",
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" })\n",
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"pgllm(prompt.format(query=\"What kind of post is this?\"))"
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],
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"metadata": {
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"id": "PzxSbYwqTm2w"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Chaining"
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],
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"metadata": {
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"id": "v3MzIUItJ8kV"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
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],
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"metadata": {
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"id": "pPegEZExILrT"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
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"\n",
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.predict(question=question)"
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],
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"metadata": {
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"id": "suxw62y-J-bg"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
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"\n",
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"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
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],
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"metadata": {
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"id": "l2bc26KHKr7n"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "I--eSa2PLGqq"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}
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