96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
|
import json
|
||
|
|
import pandas as pd
|
||
|
|
|
||
|
|
from rag.query_vector_db import RAGFormat, get_rag_from_objective, get_rag_from_problem_categories, \
|
||
|
|
get_rag_from_problem_description
|
||
|
|
from rag.rag_utils import RAGMode, constraint_path
|
||
|
|
from utils import get_response, extract_equal_sign_closed
|
||
|
|
|
||
|
|
prompt_objective_model = """
|
||
|
|
You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
|
||
|
|
|
||
|
|
-----
|
||
|
|
{description}
|
||
|
|
-----
|
||
|
|
|
||
|
|
And here's a list of parameters that we have extracted from the description:
|
||
|
|
|
||
|
|
{params}
|
||
|
|
|
||
|
|
And here's a list of all variables that we have defined so far to model the problem as an (MI)LP:
|
||
|
|
|
||
|
|
{vars}
|
||
|
|
|
||
|
|
Your task is to model the following objective mathematically in LaTeX for the MILP formulation:
|
||
|
|
|
||
|
|
{objective}
|
||
|
|
|
||
|
|
The objective is the goal that the optimization model is trying to achieve (e.g. maximize profit, minimize cost). Please generate the output in the following format:
|
||
|
|
|
||
|
|
=====
|
||
|
|
objective formulation in LaTeX, between $...$,
|
||
|
|
=====
|
||
|
|
|
||
|
|
Here's an example output:
|
||
|
|
|
||
|
|
=====
|
||
|
|
$\max \sum_{{i=1}}^{{N}} price_i x_i$
|
||
|
|
=====
|
||
|
|
|
||
|
|
- You can only use existing parameters and variables in the formulation.
|
||
|
|
- Do not generate anything after the objective!
|
||
|
|
|
||
|
|
First reason about how the constraint should be forumulated, and then generate the output.
|
||
|
|
Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
def get_objective_formulation(desc, params, vars, objective, model,
|
||
|
|
check=False, rag_mode: RAGMode | None = None, labels: dict | None = None):
|
||
|
|
if isinstance(rag_mode, RAGMode):
|
||
|
|
match rag_mode:
|
||
|
|
case RAGMode.PROBLEM_DESCRIPTION:
|
||
|
|
rag = get_rag_from_problem_description(desc, RAGFormat.OBJECTIVE_FORMULATION, top_k=10)
|
||
|
|
case RAGMode.CONSTRAINT_OR_OBJECTIVE:
|
||
|
|
rag = ""
|
||
|
|
case RAGMode.PROBLEM_LABELS:
|
||
|
|
assert labels is not None
|
||
|
|
rag = get_rag_from_problem_categories(desc, labels, RAGFormat.OBJECTIVE_FORMULATION, top_k=10)
|
||
|
|
rag = f"-----\n{rag}-----\n\n"
|
||
|
|
else:
|
||
|
|
rag = ""
|
||
|
|
|
||
|
|
k = 1
|
||
|
|
while k > 0:
|
||
|
|
try:
|
||
|
|
if rag_mode == RAGMode.CONSTRAINT_OR_OBJECTIVE:
|
||
|
|
constraint_df = pd.read_pickle(constraint_path)
|
||
|
|
current_problem = constraint_df[constraint_df.description == desc]
|
||
|
|
if not current_problem.empty:
|
||
|
|
problem_name = current_problem.iloc[0].problem_name
|
||
|
|
else:
|
||
|
|
problem_name = None
|
||
|
|
rag = get_rag_from_objective(desc, RAGFormat.OBJECTIVE_FORMULATION, top_k=10,
|
||
|
|
current_problem_name=problem_name)
|
||
|
|
rag = f"-----\n{rag}-----\n\n"
|
||
|
|
res = get_response(
|
||
|
|
prompt_objective_model.format(
|
||
|
|
description=desc,
|
||
|
|
params=json.dumps(params, indent=4),
|
||
|
|
vars=json.dumps(vars, indent=4),
|
||
|
|
objective=objective["description"],
|
||
|
|
),
|
||
|
|
model=model,
|
||
|
|
)
|
||
|
|
formulation = extract_equal_sign_closed(res)
|
||
|
|
break
|
||
|
|
except Exception as e:
|
||
|
|
k -= 1
|
||
|
|
if k == 0:
|
||
|
|
raise e
|
||
|
|
|
||
|
|
return {
|
||
|
|
"description": objective["description"],
|
||
|
|
"formulation": formulation,
|
||
|
|
}
|