diff --git a/cognee/modules/retrieval/brute_force_triplet_search.py b/cognee/modules/retrieval/brute_force_triplet_search.py
index 6fef6104f..ea7c2cb4d 100644
--- a/cognee/modules/retrieval/brute_force_triplet_search.py
+++ b/cognee/modules/retrieval/brute_force_triplet_search.py
@@ -1,5 +1,6 @@
 import asyncio
-from typing import Dict, List
+import logging
+from typing import List
 from cognee.modules.users.models import User
 from cognee.modules.users.methods import get_default_user
 from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
@@ -76,42 +77,74 @@ def delete_duplicated_vector_db_elements(collections, results): #:TODO: This is
     return results_dict
 
 
-async def brute_force_search(query: str, user: User, top_k: int,  collections: List[str] = None) -> list:
+async def brute_force_search(
+        query: str,
+        user: User,
+        top_k: int,
+        collections: List[str] = None
+) -> list:
+    """
+        Performs a brute force search to retrieve the top triplets from the graph.
+
+        Args:
+            query (str): The search query.
+            user (User): The user performing the search.
+            top_k (int): The number of top results to retrieve.
+            collections (Optional[List[str]]): List of collections to query. Defaults to predefined collections.
+
+        Returns:
+            list: The top triplet results.
+    """
+    if not query or not isinstance(query, str):
+        raise ValueError("The query must be a non-empty string.")
+    if top_k <= 0:
+        raise ValueError("top_k must be a positive integer.")
+
     if collections is None:
         collections = ["entity_name", "text_summary_text", "entity_type_name", "document_chunk_text"]
 
-    vector_engine = get_vector_engine()
-    graph_engine = await get_graph_engine()
+    try:
+        vector_engine = get_vector_engine()
+        graph_engine = await get_graph_engine()
+    except Exception as e:
+        logging.error("Failed to initialize engines: %s", e)
+        raise RuntimeError("Initialization error") from e
 
     send_telemetry("cognee.brute_force_triplet_search EXECUTION STARTED", user.id)
 
-    results = await asyncio.gather(
-        *[vector_engine.get_distances_of_collection(collection, query_text=query) for collection in collections]
-    )
+    try:
+        results = await asyncio.gather(
+            *[vector_engine.get_distances_of_collection(collection, query_text=query) for collection in collections]
+        )
 
-    ############################################# :TODO: Change when vector db does not contain duplicates
-    node_distances = delete_duplicated_vector_db_elements(collections, results)
-    # node_distances = {collection: result for collection, result in zip(collections, results)}
-    ##############################################
+        ############################################# :TODO: Change when vector db does not contain duplicates
+        node_distances = delete_duplicated_vector_db_elements(collections, results)
+        # node_distances = {collection: result for collection, result in zip(collections, results)}
+        ##############################################
 
-    memory_fragment = CogneeGraph()
+        memory_fragment = CogneeGraph()
 
-    await memory_fragment.project_graph_from_db(graph_engine,
-                                          node_properties_to_project=['id',
-                                                                      'description',
-                                                                      'name',
-                                                                      'type',
-                                                                      'text'],
-                                          edge_properties_to_project=['relationship_name'])
+        await memory_fragment.project_graph_from_db(graph_engine,
+                                              node_properties_to_project=['id',
+                                                                          'description',
+                                                                          'name',
+                                                                          'type',
+                                                                          'text'],
+                                              edge_properties_to_project=['relationship_name'])
 
-    await memory_fragment.map_vector_distances_to_graph_nodes(node_distances=node_distances)
+        await memory_fragment.map_vector_distances_to_graph_nodes(node_distances=node_distances)
 
-    #:TODO: Change when vectordb contains edge embeddings
-    await memory_fragment.map_vector_distances_to_graph_edges(vector_engine, query)
+        #:TODO: Change when vectordb contains edge embeddings
+        await memory_fragment.map_vector_distances_to_graph_edges(vector_engine, query)
 
-    results = await memory_fragment.calculate_top_triplet_importances(k=top_k)
+        results = await memory_fragment.calculate_top_triplet_importances(k=top_k)
 
-    send_telemetry("cognee.brute_force_triplet_search EXECUTION STARTED", user.id)
+        send_telemetry("cognee.brute_force_triplet_search EXECUTION STARTED", user.id)
 
-    #:TODO: Once we have Edge pydantic models we should retrieve the exact edge and node objects from graph db
-    return results
+        #:TODO: Once we have Edge pydantic models we should retrieve the exact edge and node objects from graph db
+        return results
+
+    except Exception as e:
+        logging.error("Error during brute force search for user: %s, query: %s. Error: %s", user.id, query, e)
+        send_telemetry("cognee.brute_force_triplet_search EXECUTION FAILED", user.id)
+        raise RuntimeError("An error occurred during brute force search") from e
diff --git a/examples/python/dynamic_steps_example.py b/examples/python/dynamic_steps_example.py
index 68bbb7bce..ed5c97561 100644
--- a/examples/python/dynamic_steps_example.py
+++ b/examples/python/dynamic_steps_example.py
@@ -1,6 +1,7 @@
 import cognee
 import asyncio
 from cognee.modules.retrieval.brute_force_triplet_search import brute_force_triplet_search
+from cognee.modules.retrieval.brute_force_triplet_search import format_triplets
 
 job_1 = """
 CV 1: Relevant
@@ -181,8 +182,8 @@ async def main(enable_steps):
 
     # Step 4: Query insights
     if enable_steps.get("retriever"):
-        await brute_force_triplet_search('Who has Phd?')
-
+        results = await brute_force_triplet_search('Who has the most experience with graphic design?')
+        print(format_triplets(results))
 
 if __name__ == '__main__':
     # Flags to enable/disable steps