diff --git a/cognee/infrastructure/llm/llm_interface.py b/cognee/infrastructure/llm/llm_interface.py
index 069efb22d..4a5bd79b6 100644
--- a/cognee/infrastructure/llm/llm_interface.py
+++ b/cognee/infrastructure/llm/llm_interface.py
@@ -16,7 +16,6 @@ class LLMInterface(Protocol):
         """To get structured output, import/call this function"""
         raise NotImplementedError
 
-    @abstractmethod
     def show_prompt(self, text_input: str, system_prompt: str) -> str:
         """Format and display the prompt for a user query."""
         if not text_input:
diff --git a/profiling/util/DummyLLMAdapter.py b/profiling/util/DummyLLMAdapter.py
index 40698b938..df81ce123 100644
--- a/profiling/util/DummyLLMAdapter.py
+++ b/profiling/util/DummyLLMAdapter.py
@@ -1,26 +1,34 @@
-import spacy
-import textacy
 from typing import Type
 from uuid import uuid4
+
+import spacy
+import textacy
 from pydantic import BaseModel
+
 from cognee.infrastructure.llm.llm_interface import LLMInterface
-from cognee.shared.data_models import SummarizedContent
-from cognee.shared.data_models import KnowledgeGraph, Node, Edge
+from cognee.shared.data_models import Edge, KnowledgeGraph, Node, SummarizedContent
 
 
 class DummyLLMAdapter(LLMInterface):
-    nlp = spacy.load('en_core_web_sm')
-    async def acreate_structured_output(self,
-                                        text_input: str,
-                                        system_prompt: str,
-                                        response_model: Type[BaseModel]) -> BaseModel:
-        
-        if isinstance(response_model, SummarizedContent):
-            return(dummy_summarize_content(text_input))
-        elif isinstance(response_model, KnowledgeGraph):
-            return(dummy_extract_knowledge_graph(text_input, nlp))
+    nlp = spacy.load("en_core_web_sm")
+
+    async def acreate_structured_output(
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+    ) -> BaseModel:
+
+        if (
+            str(response_model)
+            == "<class 'cognee.shared.data_models.SummarizedContent'>"
+        ):
+            return dummy_summarize_content(text_input)
+        elif (
+            str(response_model) == "<class 'cognee.shared.data_models.KnowledgeGraph'>"
+        ):
+            return dummy_extract_knowledge_graph(text_input, self.nlp)
         else:
-            raise Exception("Currently dummy acreate_structured_input is only implemented for SummarizedContent and KnowledgeGraph")
+            raise Exception(
+                "Currently dummy acreate_structured_input is only implemented for SummarizedContent and KnowledgeGraph"
+            )
 
 
 def dummy_extract_knowledge_graph(text, nlp):
@@ -31,17 +39,27 @@ def dummy_extract_knowledge_graph(text, nlp):
     edges = []
     for triple in triples:
         source = "_".join([str(e) for e in triple.subject])
-        target  = "_".join([str(e) for e in triple.object])
-        nodes[source] = nodes.get(source, Node(id=str(uuid4()), name=source, type="object", description="")   )
-        nodes[target] = nodes.get(target,  Node(id=str(uuid4()), name=target, type="object", description="")  )
+        target = "_".join([str(e) for e in triple.object])
+        nodes[source] = nodes.get(
+            source, Node(id=str(uuid4()), name=source, type="object", description="")
+        )
+        nodes[target] = nodes.get(
+            target, Node(id=str(uuid4()), name=target, type="object", description="")
+        )
         edge_type = "_".join([str(e) for e in triple.verb])
-        edges.append(Edge(source_node_id=nodes[source].id, target_node_id=nodes[target].id, relationship_name=edge_type))
-    return(KnowledgeGraph(nodes=list(nodes.keys()), edges=edges))
-    
+        edges.append(
+            Edge(
+                source_node_id=nodes[source].id,
+                target_node_id=nodes[target].id,
+                relationship_name=edge_type,
+            )
+        )
+    return KnowledgeGraph(nodes=list(nodes.values()), edges=edges)
+
 
 def dummy_summarize_content(text):
     words = [(word, len(word)) for word in set(text.split(" "))]
     words = sorted(words, key=lambda x: x[1], reverse=True)
-    summary = " ".join([word for word, _ in words[:100]])
-    description =  " ".join([word for word, _ in words[:10]])
-    return(SummarizedContent(summary=summary, description=description))
\ No newline at end of file
+    summary = " ".join([word for word, _ in words[:50]])
+    description = " ".join([word for word, _ in words[:10]])
+    return SummarizedContent(summary=summary, description=description)