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Harrison ChaseandShreya Rajpal 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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LLM Math

This notebook showcases using LLMs and Python REPLs to do complex word math problems.

In [5]:

from langchain import OpenAI, LLMMathChain

llm = OpenAI(temperature=0)
llm_math = LLMMathChain(llm=llm, verbose=True)

llm_math.run("What is 13 raised to the .3432 power?")

Out [5]:


[1m> Entering new LLMMathChain chain...[0m
What is 13 raised to the .3432 power?[32;1m[1;3m
```python
import math
print(math.pow(13, .3432))
```
[0m
Answer: [33;1m[1;3m2.4116004626599237
[0m
[1m> Finished LLMMathChain chain.[0m

'Answer: 2.4116004626599237\n'

In [ ]:

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LLM Math

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