# Interpreters

Executing code for more accurate computation
Sometimes the limitation isn’t factual knowledge, but ability to do computation.
For example, if we ask the basic question-answerer “What is 578921 days * 12312 miles/day?”:
python qa_simple.py --question "What is 578921 days * 12312 miles/day?"
we get:
7223849252 miles
This is similar to the correct answer `7127675352 miles`, but not the same.

## Evaluating Python expressions

Let’s add a method for evaluating Python expressions:
eval_direct.py
from fvalues import F
from ice.recipe import recipe
def eval_python(expression: str) -> str:
try:
result = eval(expression)
except Exception as e:
result = F(f"Error: {e}")
return str(result)
return eval_python(question)
This works as expected for expressions that are literally Python code:
python eval_direct.py --question "1 + 1"
2
Of course, it doesn’t work for natural language questions that benefit from compute:
python eval_direct.py --question "What is 578921 days * 12312 miles/day?"
Error: invalid syntax (<string>, line 1)
So, we need to choose what to evaluate.
Evaluating arbitrary expressions is dangerous. Don’t use this approach outside of highly experimental code.

## Choosing what to evaluate

We make a prompt that asks the model what expression to enter into a Python interpreter to answer the question. We’ll also print out the result of evaluating this expression:
eval_selective.py
from fvalues import F
from ice.recipe import recipe
def make_computation_choice_prompt(question: str) -> str:
return F(
>>>"""
)
def eval_python(expression: str) -> str:
try:
result = eval(expression)
except Exception as e:
result = F(f"Error: {e}")
return str(result)
async def choose_computation(question: str) -> str:
prompt = make_computation_choice_prompt(question)
async def eval_selective(question: str):
expression = await choose_computation(question)
result = eval_python(expression)
return (expression, result)
recipe.main(eval_selective)
If we run this on our example…
python eval_selective.py --question "What is 578921 days * 12312 miles/day?"
…we get:
('578921 * 12312', '7127675352')
This is a helpful expression and result! Execution trace (view online)

## Using the results of evaluation

Now all we need to do this provide this expression and result as additional context for the basic question-answerer.
from fvalues import F
from ice.recipe import recipe
def make_computation_choice_prompt(question: str) -> str:
return F(
>>>"""
)
def make_compute_qa_prompt(question: str, expression: str, result: str) -> str:
return F(
f"""A recording of a Python interpreter session:
>>> {expression}: {result}
Question: "{question}"
"""
).strip()
def eval_python(expression: str) -> str:
try:
result = eval(expression)
except Exception as e:
result = F(f"Error: {e}")
return str(result)
async def choose_computation(question: str) -> str:
prompt = make_computation_choice_prompt(question)
expression = await choose_computation(question)
result = eval_python(expression)
prompt = make_compute_qa_prompt(question, expression, result)
Rerunning our test case…
python answer_by_computation.py --question "What is 578921 days * 12312 miles/day?"
7127675352 miles
Another example:
If I have \$500 and get 3.7% interest over 16 years, what do I have at the end?
Running this:
python answer_by_computation.py --question "If I have \\$500 and get 3.7% interest over 16 years, what do I have at the end?"
We get:
If you have \$500 and get 3.7% interest over 16 years, you will have \$894.19 at the end.
In contrast, the basic question-answerer says “You would have \$1,034,957.29 at the end.” Execution trace (view online)

## Exercises

1. 1.
Many questions can only be answered using longer algorithms in Python. Extend the code above to support multi-line Python programs (example).
2. 2.
Another approach to (1) is to let the model “enter” multiple expressions into the interpreter. Extend the recipe to support this.
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