mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
182 lines
3.7 KiB
Plaintext
182 lines
3.7 KiB
Plaintext
{
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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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"# StochasticAI\n",
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"\n",
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">[Stochastic Acceleration Platform](https://docs.stochastic.ai/docs/introduction/) aims to simplify the life cycle of a Deep Learning model. From uploading and versioning the model, through training, compression and acceleration to putting it into production.\n",
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"\n",
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"This example goes over how to use LangChain to interact with `StochasticAI` models."
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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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"source": [
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"You have to get the API_KEY and the API_URL [here](https://app.stochastic.ai/workspace/profile/settings?tab=profile)."
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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": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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" ········\n"
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]
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}
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],
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"source": [
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"from getpass import getpass\n",
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"\n",
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"STOCHASTICAI_API_KEY = getpass()"
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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": 4,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"STOCHASTICAI_API_KEY\"] = STOCHASTICAI_API_KEY"
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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": 10,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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" ········\n"
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]
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}
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],
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"source": [
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"YOUR_API_URL = getpass()"
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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": 5,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.llms import StochasticAI\n",
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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": 6,
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"metadata": {
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"tags": []
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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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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"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": 11,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"llm = StochasticAI(api_url=YOUR_API_URL)"
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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": 12,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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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": 13,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\"\\n\\nStep 1: In 1999, the St. Louis Rams won the Super Bowl.\\n\\nStep 2: In 1999, Beiber was born.\\n\\nStep 3: The Rams were in Los Angeles at the time.\\n\\nStep 4: So they didn't play in the Super Bowl that year.\\n\""
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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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.run(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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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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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": "3.10.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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