geoevolve 0.2.4

A Geographical Knowledge Informed Code Evolver

from unicodedata import category

GeoEvolve

GeoEvolve aims to accelerate geospatial model discovery by the power of large language models.

CLI Usage

pip install geoevolve

export OPENAI_API_KEY='your-openai-api-key' && source ~/.zshrc

# Optional If you already have a GeoKnowRAG
# Build GeoKnowRAG
python ./build_geo_knowledge_db.py --geo_knowledge_dir ./geo_knowledge --working_dir ../geoevolve_storage --chunk_size 300 --chunk_overlap 50 --topic_file ./topics.json --add_knowledge True --collect_knowledge True --github_token your-github-token --embedding_model text-embedding-3-large --llm_model gpt-4.1

# Run GeoEvolve
python ./run_geoevolve.py --initial_program_path path-to-initial_program --evaluator_path path-to-evaluator --config_path path-to-config --total_rounds 10 --num_iterations_per_round 10 --output path-to-output --log_name your_program_name --embedding_model text-embedding-3-large --llm_model gpt-4.1;

Library Usage

import os
from geoevolve import save_wiki_pages, save_arxiv_papers, save_github_codes, initialize_or_get_geo_know_db, import_knowledge_into_geo_know_db, run_geo_evolution

os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'
# Initialize an Empty GeoKnowRAG with Chroma
geokg_rag = initialize_or_get_geo_know_db(persist_dir='your_geoevolve_storage',
                                   embedding_model_name='text-embedding-3-large',
                                   llm_model_name='gpt-4.1')

# Run GeoEvolve
run_geo_evolution(initial_program_file='your-initial-program-path',
                  evaluator_file='your-evaluator-file',
                  config_path='your-config-path',
                  rounds=15,
                  iterations_per_round=15,
                  output_path='your-output-path',
                  log_name='your-program-name',
                  embedding_model_name='text-embedding-3-large',
                  llm_model_name='gpt-4.1')

Import Knowledge into GeoKnowRAG

from geoevolve import initialize_or_get_geo_know_db, import_knowledge_into_geo_know_db
from langchain_core.documents import Document

# You can import knowledge from a well-structured geographical knowledge directory
# The structure like geo_knowledge/{category}/{knowledge}.txt

import_knowledge_into_geo_know_db(geo_knowledge_dir='your_geo_knowledge_dir',
                                  persist_dir='your_geoevolve_storage',
                                  collection_name='your_collection_name',
                                  embedding_model_name='text-embedding-3-large',
                                  llm_model_name='gpt-4.1')

# You can also just import a document into the GeoKnowRAG
rag = initialize_or_get_geo_know_db(persist_dir='your_geoevolve_storage',
                                   embedding_model_name='text-embedding-3-large',
                                   llm_model_name='gpt-4.1')

knowledge = '...'
category = '...'
title = '...'

max_length = 1000
docs = []
if knowledge != '':
    if len(knowledge) > max_length:
        chunks = [knowledge[i:i + max_length] for i in
                  range(0, len(knowledge), max_length)]
        chunked_docs = [Document(page_content=chunk,
                                 metadata={'category': category, 'name': title})
                        for chunk in chunks]
        docs.extend(chunked_docs)
    else:
        doc = Document(page_content=knowledge,
                       metadata={'category': category, 'name': title})
        docs.append(doc)
rag.add_document_to_db(docs)

GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models
Peng Luo, Xiayin Lou, Yu Zheng, Zhuo Zheng, Stefano Ermon