Running inference with and without cognee

evals/eval_swe_bench.py

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+from swebench.harness.utils import load_swebench_dataset
+from swebench.inference.make_datasets.create_instance import PATCH_EXAMPLE
+from evals.eval_utils import download_instances
+import cognee
+from cognee.api.v1.cognify.code_graph_pipeline import code_graph_pipeline
+from cognee.api.v1.search import SearchType
+import os
+from pathlib import Path
+from cognee.infrastructure.databases.graph import get_graph_engine
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+from cognee.shared.data_models import Answer
+
+async def cognee_and_llm(dataset, search_type = SearchType.CHUNKS):
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata = True)
+
+    dataset_name = "SWE_test_data"
+    code_text = dataset[0]["text"][:100000]
+    await cognee.add([code_text], dataset_name)
+    await cognee.cognify([dataset_name])
+    graph_engine = await get_graph_engine()
+    with open(graph_engine.filename, "r") as f:
+        graph_str  = f.read()
+    
+    problem_statement = dataset[0]['problem_statement']
+    instructions = (
+        f"I need you to solve this issue by looking at the provided knowledge graph and by "
+        + f"generating a single patch file that I can apply directly to this repository "
+        + f"using git apply. Please respond with a single patch "
+        + f"file in the following format."  
+    )
+    
+    prompt = "\n".join([
+        instructions,
+        "<patch>",
+        PATCH_EXAMPLE,
+        "</patch>",
+        "This is the knowledge graph:",
+        graph_str
+    ])
+    
+    llm_client = get_llm_client()
+    answer_prediction = llm_client.create_structured_output(
+                        text_input = problem_statement,
+                        system_prompt = prompt,
+                        response_model = str,
+                        )
+    return answer_prediction
+
+def llm_on_preprocessed_data(dataset):
+    problem_statement = dataset[0]['problem_statement']
+    prompt = dataset[0]["text"]
+    
+    llm_client = get_llm_client()
+    answer_prediction = llm_client.create_structured_output(
+                        text_input = problem_statement,
+                        system_prompt = prompt, # TODO check if this is correct
+                        response_model = str,
+                        )
+    return answer_prediction
+
+
+async def main():
+    swe_dataset = load_swebench_dataset('princeton-nlp/SWE-bench', split='test')
+    swe_dataset_preprocessed = load_swebench_dataset('princeton-nlp/SWE-bench_bm25_13K', split='test')
+    test_data = swe_dataset[:1] 
+    test_data_preprocessed = swe_dataset_preprocessed[:1] 
+    assert test_data[0]["instance_id"] == test_data_preprocessed[0]["instance_id"]
+    filepath = Path("SWE-bench_testsample")
+    if filepath.exists():
+        from datasets import Dataset
+        dataset = Dataset.load_from_disk(filepath)
+    else:
+        dataset = download_instances(test_data, filepath)
+    
+    llm_output_with_cognee = await cognee_and_llm(dataset)
+    llm_output_without_cognee = llm_on_preprocessed_data(test_data_preprocessed)
+
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main(), debug=True)