diff --git a/backend/modules/brain/rags/quivr_rag.py b/backend/modules/brain/rags/quivr_rag.py
index 3a925a61d..8487bc33b 100644
--- a/backend/modules/brain/rags/quivr_rag.py
+++ b/backend/modules/brain/rags/quivr_rag.py
@@ -14,7 +14,7 @@ from langchain_cohere import CohereRerank
 from langchain_community.chat_models import ChatLiteLLM
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
-from langchain_core.runnables import RunnablePassthrough
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
 from langchain_openai import OpenAIEmbeddings
 from logger import get_logger
 from models import BrainSettings  # Importing settings related to the 'brain'
@@ -203,6 +203,40 @@ class QuivrRAG(BaseModel):
     def get_retriever(self):
         return self.vector_store.as_retriever()
 
+    def filter_history(
+        self, chat_history, max_history: int = 10, max_tokens: int = 2000
+    ):
+        """
+        Filter out the chat history to only include the messages that are relevant to the current question
+
+        Takes in a chat_history= [HumanMessage(content='Qui est Chloé ? '), AIMessage(content="Chloé est une salariée travaillant pour l'entreprise Quivr en tant qu'AI Engineer, sous la direction de son supérieur hiérarchique, Stanislas Girard."), HumanMessage(content='Dis moi en plus sur elle'), AIMessage(content=''), HumanMessage(content='Dis moi en plus sur elle'), AIMessage(content="Désolé, je n'ai pas d'autres informations sur Chloé à partir des fichiers fournis.")]
+        Returns a filtered chat_history with in priority: first max_tokens, then max_history where a Human message and an AI message count as one pair
+        a token is 4 characters
+        """
+        chat_history = chat_history[::-1]
+        total_tokens = 0
+        total_pairs = 0
+        filtered_chat_history = []
+        for i in range(0, len(chat_history), 2):
+            if i + 1 < len(chat_history):
+                human_message = chat_history[i]
+                ai_message = chat_history[i + 1]
+                message_tokens = (
+                    len(human_message.content) + len(ai_message.content)
+                ) // 4
+                if (
+                    total_tokens + message_tokens > max_tokens
+                    or total_pairs >= max_history
+                ):
+                    break
+                filtered_chat_history.append(human_message)
+                filtered_chat_history.append(ai_message)
+                total_tokens += message_tokens
+                total_pairs += 1
+        chat_history = filtered_chat_history[::-1]
+
+        return chat_history
+
     def get_chain(self):
         compressor = None
         if os.getenv("COHERE_API_KEY"):
@@ -216,7 +250,9 @@ class QuivrRAG(BaseModel):
         )
 
         loaded_memory = RunnablePassthrough.assign(
-            chat_history=lambda x: x["chat_history"],
+            chat_history=RunnableLambda(
+                lambda x: self.filter_history(x["chat_history"]),
+            ),
             question=lambda x: x["question"],
         )
 
@@ -227,7 +263,7 @@ class QuivrRAG(BaseModel):
         standalone_question = {
             "standalone_question": {
                 "question": lambda x: x["question"],
-                "chat_history": lambda x: x["chat_history"],
+                "chat_history": itemgetter("chat_history"),
             }
             | CONDENSE_QUESTION_PROMPT
             | ChatLiteLLM(temperature=0, model=self.model, api_base=api_base)