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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DeepInfra\n",
"\n",
"`DeepInfra` provides [several LLMs](https://deepinfra.com/models).\n",
"\n",
"This notebook goes over how to use Langchain with [DeepInfra](https://deepinfra.com)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from langchain.llms import DeepInfra\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set the Environment API Key\n",
"Make sure to get your API key from DeepInfra. You have to [Login](https://deepinfra.com/login?from=%2Fdash) and get a new token.\n",
"\n",
"You are given a 1 hour free of serverless GPU compute to test different models. (see [here](https://github.com/deepinfra/deepctl#deepctl))\n",
"You can print your token with `deepctl auth token`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
" 路路路路路路路路\n"
]
}
],
"source": [
"# get a new token: https://deepinfra.com/login?from=%2Fdash\n",
"\n",
"from getpass import getpass\n",
"\n",
"DEEPINFRA_API_TOKEN = getpass()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the DeepInfra instance\n",
"Make sure to deploy your model first via `deepctl deploy create -m google/flat-t5-xl` (see [here](https://github.com/deepinfra/deepctl#deepctl))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = DeepInfra(model_id=\"DEPLOYED MODEL ID\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Prompt Template\n",
"We will create a prompt template for Question and Answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initiate the LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the LLMChain\n",
"Provide a question and run the LLMChain."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in 2015?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
"metadata": {
"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"
},
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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