diff --git a/community/log_analysis_multi_agent_rag/binary_score_models.py b/community/log_analysis_multi_agent_rag/binary_score_models.py
index d894541a5..f6227e29a 100644
--- a/community/log_analysis_multi_agent_rag/binary_score_models.py
+++ b/community/log_analysis_multi_agent_rag/binary_score_models.py
@@ -1,4 +1,4 @@
-from langchain_core.pydantic_v1 import BaseModel,Field
+from pydantic import BaseModel, Field
 # Data models
 class GradeDocuments(BaseModel):
     """Binary score for relevance check on retrieved documents."""
diff --git a/community/log_analysis_multi_agent_rag/graphedges.py b/community/log_analysis_multi_agent_rag/graphedges.py
index c56a2d276..859a6c864 100644
--- a/community/log_analysis_multi_agent_rag/graphedges.py
+++ b/community/log_analysis_multi_agent_rag/graphedges.py
@@ -40,8 +40,8 @@ def grade_generation_vs_documents_and_question(state):
 
         print("GRADE GENERATED vs QUESTION")
         try:
-            score_text = automation.answer_grader.invoke({"question": question, "generation": generation})
-            if "yes" in score_text.lower():
+            score = automation.answer_grader.invoke({"question": question, "generation": generation})
+            if score and score.get("binary_score") == "yes":
                 print("DECISION: GENERATION ADDRESSES QUESTION")
                 return "useful"
             else:
diff --git a/community/log_analysis_multi_agent_rag/graphnodes.py b/community/log_analysis_multi_agent_rag/graphnodes.py
index 9b257d44f..96d2dec75 100644
--- a/community/log_analysis_multi_agent_rag/graphnodes.py
+++ b/community/log_analysis_multi_agent_rag/graphnodes.py
@@ -50,7 +50,7 @@ def grade_documents(state):
             score = automation.retrieval_grader.invoke(
                 {"question": question, "document": doc.page_content}
             )
-            grade = score.binary_score
+            grade = score.get("binary_score") if score else "no"
             if grade == "yes":
                 print("---GRADE: DOCUMENT RELEVANT---")
                 filtered_docs.append(doc)
diff --git a/community/log_analysis_multi_agent_rag/utils.py b/community/log_analysis_multi_agent_rag/utils.py
index 6a787e55e..a23398bcf 100644
--- a/community/log_analysis_multi_agent_rag/utils.py
+++ b/community/log_analysis_multi_agent_rag/utils.py
@@ -1,12 +1,20 @@
 from langchain_nvidia_ai_endpoints import ChatNVIDIA
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
+from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
 from binary_score_models import GradeAnswer,GradeDocuments,GradeHallucinations
 import os
 from dotenv import load_dotenv
 load_dotenv()
+import re
 import json
 
+def clean_text(text):
+    # Remove <think> blocks (including content)
+    text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
+    # Remove any standalone <think> tags just in case
+    text = text.replace('<think>', '').replace('</think>', '')
+    return text
+
 class Nodeoutputs:
     def __init__(self, api_key, model, prompts_file):
         os.environ["NVIDIA_API_KEY"] = api_key
@@ -41,7 +49,7 @@ def setup_prompts(self):
                 ("human", self.prompts["grade_human"]),
             ]
         )
-        self.retrieval_grader = grade_prompt | self.llm.with_structured_output(GradeDocuments)
+        self.retrieval_grader = grade_prompt | self.llm | StrOutputParser() | clean_text | JsonOutputParser()
 
         hallucination_prompt = ChatPromptTemplate.from_messages(
             [
@@ -49,7 +57,7 @@ def setup_prompts(self):
                 ("human", self.prompts["hallucination_human"]),
             ]
         )
-        self.hallucination_grader = hallucination_prompt | self.llm.with_structured_output(GradeHallucinations)
+        self.hallucination_grader = hallucination_prompt | self.llm | StrOutputParser() | clean_text | JsonOutputParser()
 
         answer_prompt = ChatPromptTemplate.from_messages(
             [
@@ -57,7 +65,7 @@ def setup_prompts(self):
                 ("human", self.prompts["answer_human"]),
             ]
         )
-        self.answer_grader = answer_prompt | self.llm.with_structured_output(GradeAnswer)
+        self.answer_grader = answer_prompt | self.llm | StrOutputParser() | clean_text | JsonOutputParser()
 
     def format_docs(self, docs):
         return "\n\n".join(doc.page_content for doc in docs)