Using agents to answer data science questions

Tools like LLMMathChain can be utilized to execute Python for answering computational queries. We’ve already seen different agents with tools before.

For instance, by chaining LLMs and tools, one can calculate mathematical powers and obtain results effortlessly:

from langchain import OpenAI, LLMMathChain
llm = OpenAI(temperature=0)
llm_math = LLMMathChain.from_llm(llm, verbose=True)
llm_math.run("What is 2 raised to the 10th power?")

We should see something like this:

> Entering new LLMMathChain chain...
What is 2 raised to the 10th power?
2**10
numexpr.evaluate("2**10")
Answer: 1024
> Finished chain.
[2]:'Answer: 1024'

Such capabilities, while adept at delivering straightforward numerical answers, are not as straightforward to integrate into conventional EDA workflows. Other chains, like CPAL (CPALChain) and PAL (PALChain), can tackle more complex reasoning challenges, mitigating...