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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "mesCTyhnJkNS"
},
"source": [
"# Prediction Guard\n",
"\n",
">[Prediction Guard](https://docs.predictionguard.com/) gives a quick and easy access to state-of-the-art open and closed access LLMs, without needing to spend days and weeks figuring out all of the implementation details, managing a bunch of different API specs, and setting up the infrastructure for model deployments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3RqWPav7AtKL"
},
"outputs": [],
"source": [
"! pip install predictionguard langchain"
]
},
{
"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 import PromptTemplate, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kp_Ymnx1SnDG"
},
"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>\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ua7Mw1N4HcER"
},
"outputs": [],
"source": [
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Qo2p5flLHxrB"
},
"outputs": [],
"source": [
"pgllm(\"Tell me a joke\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EyBYaP_xTMXH"
},
"source": [
"# Control the output structure/ type of LLMs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "55uxzhQSTPqF"
},
"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\"])"
]
},
{
"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?\"))"
]
},
{
"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(model=\"OpenAI-text-davinci-003\", \n",
" output={\n",
" \"type\": \"categorical\",\n",
" \"categories\": [\n",
" \"product announcement\", \n",
" \"apology\", \n",
" \"relational\"\n",
" ]\n",
" })\n",
"pgllm(prompt.format(query=\"What kind of post is this?\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v3MzIUItJ8kV"
},
"source": [
"# Chaining"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pPegEZExILrT"
},
"outputs": [],
"source": [
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
]
},
{
"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": "3.10.6"
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"nbformat": 4,
"nbformat_minor": 4
}