A package for translating natural-language descriptions of optimization problems into Constraint Programming models using Large Language Models (LLMs). For this pre-stable version stage, our target is to have models solved via CBLS.jl. Eventually, we expect this library to work for most of Julia CP ecosystem, alongside other CP modeling languages such as MiniZinc, OR-Tools, etc.
This package acts as a light wrapper around common LLM API endpoints, supplying appropriate system prompts and context informations to the LLMs to generate CP models. Specifically, we first prompt the model for generating an high-level representation of the problem in editable Markdown format, and then we prompt the model to generate Julia code.
We currently support the following LLM APIs:
- Groq (https://groq.com)
- Google Gemini (https://ai.google.dev)
- llama.cpp (https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md)
Groq and Gemini are currently offering rate-limited free access to their APIs, and llama.cpp is free and open-source. We are still actively experimenting with this package, and we are not in a position to pay for API access. We might consider adding support for other APIs in the future.
Before playing with the package, we need to set up two environment variables:
- The EDITOR variable for specifying a text editor (such as
vim,nano,emacs, ...). This will be used during interactive execution. - An API key. This is necessary only for interacting with proprietary LLMs.
We can configure those variables by, e.g., appending the following to your .bashrc or equivalent:
export EDITOR="vim"
export GOOGLE_API_KEY="42"Or we can configure them in Julia:
ENV["EDITOR"] = "vim"
ENV["GOOGLE_API_KEY"] = "42"Finally, we can start playing with the package. Below, an example for translating a natural-language description of the Traveling Salesman Problem:
using ConstraintsTranslator
llm = GoogleLLM("gemini-1.5-pro-latest")
description = """
We need to determine the shortest possible route for a salesman who must visit a set of cities exactly once and return to the starting city.
The objective is to minimize the total travel distance while ensuring that each city is visited exactly once.
Example input data:
1. distances.csv
from,to,distance
CityA,CityB,10
CityA,CityC,8
"""
response = translate(llm, description, interactive=true)The translate function will first produce a Markdown representation of the problem, and then return the generated Julia code for parsing the input data and building the model.
The flag interactive=true will enable a simple interactive command-line application, where you will be able to inspect, edit and regenerate each intermediate output.
At each generation step, it will prompt the user in an interactive menu to accept the answer, edit the prompt and/or the generated text, or generate another answer with the same prompt.
The LLM expects the user to provide examples of the input data format. If no examples are present, the LLM will make assumptions about the data format based on the problem description.
This example uses Google Gemini as an LLM. You will need an API key and a model id to access proprietary API endpoints. Use help?> in the Julia REPL to learn more about the available models.