langchain/docs/modules/chains/examples/llm_math.ipynb
Harrison Chase 985496f4be
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 08:24:09 -08:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "e71e720f",
"metadata": {},
"source": [
"# LLM Math\n",
"\n",
"This notebook showcases using LLMs and Python REPLs to do complex word math problems."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "44e9ba31",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
"```python\n",
"import math\n",
"print(math.pow(13, .3432))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
"\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Answer: 2.4116004626599237\\n'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import OpenAI, LLMMathChain\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"llm_math = LLMMathChain(llm=llm, verbose=True)\n",
"\n",
"llm_math.run(\"What is 13 raised to the .3432 power?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f62f0c75",
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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