cognee/evals/eval_swe_bench.py

import argparse
import json
import subprocess
import sys
from pathlib import Path

from swebench.harness.utils import load_swebench_dataset
from swebench.inference.make_datasets.create_instance import PATCH_EXAMPLE

from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
from cognee.infrastructure.llm.get_llm_client import get_llm_client
from cognee.infrastructure.llm.prompts import read_query_prompt
from cognee.modules.retrieval.utils.description_to_codepart_search import (
    code_description_to_code_part_search,
)
from evals.eval_utils import download_github_repo


def check_install_package(package_name):
    """
    Check if a pip package is installed and install it if not.
    Returns True if package is/was installed successfully, False otherwise.
    """
    try:
        __import__(package_name)
        return True
    except ImportError:
        try:
            subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])
            return True
        except subprocess.CalledProcessError:
            return False


async def generate_patch_with_cognee(instance):
    import os
    from cognee import config

    file_path = Path(__file__).parent
    data_directory_path = str(Path(os.path.join(file_path, ".data_storage/code_graph")).resolve())

    config.data_root_directory(data_directory_path)
    config.system_root_directory(data_directory_path)

    repo_path = download_github_repo(instance, "../RAW_GIT_REPOS")
    include_docs = True
    problem_statement = instance["problem_statement"]
    instructions = read_query_prompt("patch_gen_kg_instructions.txt")

    async for result in run_code_graph_pipeline(repo_path, include_docs=include_docs):
        print(result)

    retrieved_codeparts, context_from_documents = await code_description_to_code_part_search(
        problem_statement, include_docs=include_docs
    )

    context = ""
    for code_piece in retrieved_codeparts:
        context = context + code_piece.get_attribute("source_code")

    if include_docs:
        context = context_from_documents + context

    prompt = "\n".join(
        [
            problem_statement,
            "<patch>",
            PATCH_EXAMPLE,
            "</patch>",
            "This is the additional context to solve the problem (description from documentation together with codeparts):",
            context,
        ]
    )

    llm_client = get_llm_client()
    answer_prediction = await llm_client.acreate_structured_output(
        text_input=prompt,
        system_prompt=instructions,
        response_model=str,
    )

    return answer_prediction


async def generate_patch_without_cognee(instance):
    instructions = read_query_prompt("patch_gen_instructions.txt")

    llm_client = get_llm_client()

    answer_prediction = await llm_client.acreate_structured_output(
        text_input=instance["text"],
        system_prompt=instructions,
        response_model=str,
    )
    return answer_prediction


async def get_preds(dataset, with_cognee=True):
    if with_cognee:
        model_name = "with_cognee"
        pred_func = generate_patch_with_cognee
    else:
        model_name = "without_cognee"
        pred_func = generate_patch_without_cognee

    preds = []

    for instance in dataset:
        instance_id = instance["instance_id"]
        model_patch = await pred_func(instance)  # Sequentially await the async function
        preds.append(
            {
                "instance_id": instance_id,
                "model_patch": model_patch,
                "model_name_or_path": model_name,
            }
        )

    return preds


async def main():
    parser = argparse.ArgumentParser(description="Run LLM predictions on SWE-bench dataset")
    parser.add_argument("--cognee_off", action="store_true")
    parser.add_argument("--max_workers", type=int, required=True)
    args = parser.parse_args()

    for dependency in ["transformers", "sentencepiece", "swebench"]:
        check_install_package(dependency)

    if args.cognee_off:
        dataset_name = "princeton-nlp/SWE-bench_Lite_bm25_13K"
        dataset = load_swebench_dataset(dataset_name, split="test")[:2]
        predictions_path = "preds_nocognee.json"
        if not Path(predictions_path).exists():
            preds = await get_preds(dataset, with_cognee=False)
            with open(predictions_path, "w") as file:
                json.dump(preds, file)
    else:
        dataset_name = "princeton-nlp/SWE-bench_Lite"
        swe_dataset = load_swebench_dataset(dataset_name, split="test")[:1]
        predictions_path = "preds.json"
        preds = await get_preds(swe_dataset, with_cognee=not args.cognee_off)
        with open(predictions_path, "w") as file:
            json.dump(preds, file)

    """ This part is for the evaluation
    subprocess.run(
        [
            "python",
            "-m",
            "swebench.harness.run_evaluation",
            "--dataset_name",
            dataset_name,
            "--split",
            "test",
            "--predictions_path",
            predictions_path,
            "--max_workers",
            str(args.max_workers),
            "--run_id",
            "test_run",
        ]
    )
    """


if __name__ == "__main__":
    import asyncio

    asyncio.run(main(), debug=True)