feat/connect code graph pipeline to benchmarking

evals/eval_swe_bench.py

@@ -8,26 +8,35 @@ from swebench.harness.utils import load_swebench_dataset
 from swebench.inference.make_datasets.create_instance import PATCH_EXAMPLE
 
 import cognee
-
-from cognee.shared.data_models import SummarizedContent
-from cognee.shared.utils import render_graph
-from cognee.tasks.repo_processor import (
-    enrich_dependency_graph,
-    expand_dependency_graph,
-    get_repo_file_dependencies,
-)
-from cognee.tasks.storage import add_data_points
-from cognee.tasks.summarization import summarize_code
-from cognee.modules.pipelines import Task, run_tasks
 from cognee.api.v1.cognify.code_graph_pipeline import code_graph_pipeline
 from cognee.api.v1.search import SearchType
 from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.infrastructure.llm.prompts import read_query_prompt
-from evals.eval_utils import download_instances
-from evals.eval_utils import ingest_repos
-from evals.eval_utils import download_github_repo
-from evals.eval_utils import delete_repo
+from cognee.modules.pipelines import Task, run_tasks
+from cognee.modules.retrieval.brute_force_triplet_search import \
+    brute_force_triplet_search
+from cognee.shared.data_models import SummarizedContent
+from cognee.shared.utils import render_graph
+from cognee.tasks.repo_processor import (enrich_dependency_graph,
+                                         expand_dependency_graph,
+                                         get_repo_file_dependencies)
+from cognee.tasks.storage import add_data_points
+from cognee.tasks.summarization import summarize_code
+from evals.eval_utils import (delete_repo, download_github_repo,
+                              download_instances, ingest_repos)
+
+
+def node_to_string(node):
+    text = node.attributes["text"]
+    return f"Node({node.id}, {text})"
+def retrieved_edges_to_string(retrieved_edges):
+    edge_strings = []
+    for edge in retrieved_edges:
+        relationship_type = edge.attributes["relationship_type"]
+        edge_str = f"{node_to_string(edge.node1)} {relationship_type} {node_to_string(edge.node2)}"
+        edge_strings.append(edge_str)
+    return "\n".join(edge_strings)  
 
 async def generate_patch_with_cognee(instance):
     await cognee.prune.prune_data()
@@ -39,19 +48,18 @@ async def generate_patch_with_cognee(instance):
 
     # repo_path = download_github_repo(instance, '../RAW_GIT_REPOS')
 
-    repo_path = '/Users/borisarzentar/Projects/graphrag'
-
+    repo_path = '../minimal_repo'
     tasks = [
         Task(get_repo_file_dependencies),
         Task(add_data_points, task_config = { "batch_size": 50 }),
         Task(enrich_dependency_graph, task_config = { "batch_size": 50 }),
         Task(expand_dependency_graph, task_config = { "batch_size": 50 }),
         Task(add_data_points, task_config = { "batch_size": 50 }),
-        # Task(summarize_code, summarization_model = SummarizedContent),
+        Task(summarize_code, summarization_model = SummarizedContent),
     ]
 
     pipeline = run_tasks(tasks, repo_path, "cognify_code_pipeline")
-
+        
     async for result in pipeline:
         print(result)
 
@@ -62,29 +70,27 @@ async def generate_patch_with_cognee(instance):
     problem_statement = instance['problem_statement']
     instructions = read_query_prompt("patch_gen_instructions.txt")
 
-    graph_str = 'HERE WE SHOULD PASS THE TRIPLETS FROM GRAPHRAG'
+    retrieved_edges = await brute_force_triplet_search(problem_statement, top_k = 3)
+    
+    retrieved_edges_str = retrieved_edges_to_string(retrieved_edges)
 
     prompt = "\n".join([
-        instructions,
         "<patch>",
         PATCH_EXAMPLE,
         "</patch>",
-        "This is the knowledge graph:",
-        graph_str
+        "These are the retrieved edges:",
+        retrieved_edges_str
     ])
 
-    return 0
-
-    ''' :TODO: We have to find out how do we do the generation
     llm_client = get_llm_client()
     answer_prediction = await llm_client.acreate_structured_output(
-        text_input=problem_statement,
-        system_prompt=prompt,
+        text_input=prompt,
+        system_prompt=instructions,
         response_model=str,
     )
 
     return answer_prediction
-    '''
+
 
 async def generate_patch_without_cognee(instance):
     problem_statement = instance['problem_statement']
@@ -111,12 +117,11 @@ async def get_preds(dataset, with_cognee=True):
     for instance in dataset:
         await pred_func(instance)
 
-    '''
     preds = [{"instance_id": instance["instance_id"],
               "model_patch": await pred_func(instance),
               "model_name_or_path": model_name} for instance in dataset]
-    '''
-    return 0
+  
+    return preds
 
 
 async def main():