Update app.py

app.py

@@ -203,7 +203,7 @@ def manage_conversation_history(question, answer, history, max_history=5):
         history.pop(0)
     return history
 
-def is_related_to_history(question, history, threshold=0.3):
+def is_related_to_history(question, history, threshold=0.5):  # Increased threshold from 0.3 to 0.5
     if not history:
         return False
     history_text = " ".join([f"{h['question']} {h['answer']}" for h in history])
@@ -632,7 +632,6 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search, g
     model = get_model(temperature, top_p, repetition_penalty)
     embed = get_embeddings()
 
-    # Check if the FAISS database exists
     if os.path.exists("faiss_database"):
         database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
     else:
@@ -686,12 +685,13 @@ def ask_question(question, temperature, top_p, repetition_penalty, web_search, g
 
         history_str = "\n".join([f"Q: {item['question']}\nA: {item['answer']}" for item in conversation_history])
 
-        if is_related_to_history(question, conversation_history):
-            context_str = "No additional context needed. Please refer to the conversation history."
-        else:
-            retriever = database.as_retriever()
-            relevant_docs = retriever.get_relevant_documents(question)
-            context_str = "\n".join([doc.page_content for doc in relevant_docs])
+        # Always retrieve relevant documents
+        retriever = database.as_retriever()
+        relevant_docs = retriever.get_relevant_documents(question)
+        doc_context = "\n".join([doc.page_content for doc in relevant_docs])
+
+        # Combine document context with conversation history
+        context_str = f"Document context:\n{doc_context}\n\nConversation history:\n{history_str}"
 
         prompt_val = ChatPromptTemplate.from_template(prompt)
         formatted_prompt = prompt_val.format(history=history_str, context=context_str, question=question)