diff --git a/scrapegraphai/nodes/generate_answer_node.py b/scrapegraphai/nodes/generate_answer_node.py
index eb440a75..9cd5dce5 100644
--- a/scrapegraphai/nodes/generate_answer_node.py
+++ b/scrapegraphai/nodes/generate_answer_node.py
@@ -1,13 +1,12 @@
 """
 GenerateAnswerNode Module
 """
-
+import asyncio
 from typing import List, Optional
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_core.runnables import RunnableParallel
 from tqdm import tqdm
-import asyncio
 from ..utils.merge_results import merge_results
 from ..utils.logging import get_logger
 from ..models import Ollama, OpenAI
@@ -136,21 +135,18 @@ class GenerateAnswerNode(BaseNode):
             chain_name = f"chunk{i+1}"
             chains_dict[chain_name] = prompt | self.llm_model | output_parser
 
+        async_runner = RunnableParallel(**chains_dict)
 
-        async def process_chains():
-            async_runner = RunnableParallel()
-            for chain_name, chain in chains_dict.items():
-                async_runner.add(chain.ainvoke([{"question": user_prompt}] * len(doc)))
-            
-            batch_results = await async_runner.run()
-            return batch_results
+        batch_results =  async_runner.invoke({"question": user_prompt})
 
-        loop = asyncio.get_event_loop()
-        batch_answers = loop.run_until_complete(process_chains())
+        merge_prompt = PromptTemplate(
+                template = template_merge_prompt,
+                input_variables=["context", "question"],
+                partial_variables={"format_instructions": format_instructions},
+            )
 
-        # Merge batch results (assuming same structure)
-        merged_answer = merge_results(batch_answers)
-        answers = merged_answer
+        merge_chain = merge_prompt | self.llm_model | output_parser
+        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
 
-        state.update({self.output[0]: answers})
+        state.update({self.output[0]: answer})
         return state