diff --git a/kairon/shared/channels/mail/processor.py b/kairon/shared/channels/mail/processor.py
index e17d33ded..09f4e1a7b 100644
--- a/kairon/shared/channels/mail/processor.py
+++ b/kairon/shared/channels/mail/processor.py
@@ -205,7 +205,6 @@ async def process_messages(bot: str, batch: [dict]):
                                                                 {
                                                                     'channel': ChannelTypes.MAIL.value
                                                                 })
-            # logger.info(chat_responses)
 
             for index, response in enumerate(chat_responses):
                 responses[index]['body'] = mp.process_mail(response, log_id=batch[index]['log_id'])
diff --git a/system.yaml b/system.yaml
index f4e7e4a60..3f4596832 100644
--- a/system.yaml
+++ b/system.yaml
@@ -127,7 +127,6 @@ events:
     type: ${EVENTS_QUEUE_TYPE:"mongo"}
     url: ${EVENTS_QUEUE_URL:"mongodb://localhost:27017/events"}
     name: ${EVENTS_DB_NAME:"kairon_events"}
-    mail_queue_name: ${EVENTS_MAIL_QUEUE_NAME:"mail_queue"}
   task_definition:
     model_training: ${MODEL_TRAINING_TASK_DEFINITION}
     model_testing: ${MODEL_TESTING_TASK_DEFINITION}
@@ -142,7 +141,6 @@ events:
     content_importer: ${DOC_CONTENT_IMPORTER_TASK_DEFINITION}
   scheduler:
     collection: ${EVENT_SCHEDULER_COLLECTION:"kscheduler"}
-    mail_scheduler_collection: ${MAIL_SCHEDULER_COLLECTION:"mail_scheduler"}
     type: ${EVENT_SCHEDULER_TYPE:"kscheduler"}
     min_trigger_interval: ${MIN_SCHDULER_TRIGGER_INTERVAL:86340}
   audit_logs:
diff --git a/tests/integration_test/action_service_test.py b/tests/integration_test/action_service_test.py
index 35f50340c..0de5a2fde 100644
--- a/tests/integration_test/action_service_test.py
+++ b/tests/integration_test/action_service_test.py
@@ -11756,6 +11756,122 @@ def test_prompt_action_response_action_with_prompt_question_from_slot(mock_embed
     ]
 
 
+@mock.patch.object(litellm, "aembedding", autospec=True)
+@mock.patch.object(ActionUtility, 'execute_request_async', autospec=True)
+def test_prompt_action_response_action_with_prompt_question_from_slot_perplexity(mock_execute_request_async, mock_embedding, aioresponses):
+    from uuid6 import uuid7
+    llm_type = "perplexity"
+    action_name = "test_prompt_action_response_action_with_prompt_question_from_slot"
+    bot = "5f50fd0a56b69s8ca10d35d2l"
+    user = "udit.pandey"
+    value = "keyvalue"
+    user_msg = "What kind of language is python?"
+    bot_content = "Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically typed and garbage-collected."
+    generated_text = "Python is dynamically typed, garbage-collected, high level, general purpose programming."
+    llm_prompts = [
+        {'name': 'System Prompt',
+         'data': 'You are a personal assistant. Answer question based on the context below.',
+         'type': 'system', 'source': 'static', 'is_enabled': True},
+        {'name': 'History Prompt', 'type': 'user', 'source': 'history', 'is_enabled': True},
+        {'name': 'Query Prompt', 'data': "What kind of language is python?", 'instructions': 'Rephrase the query.',
+         'type': 'query', 'source': 'static', 'is_enabled': False},
+        {'name': 'Similarity Prompt',
+         'instructions': 'Answer question based on the context above, if answer is not in the context go check previous logs.',
+         'type': 'user', 'source': 'bot_content', 'data': 'python',
+         'hyperparameters': {"top_results": 10, "similarity_threshold": 0.70},
+         'is_enabled': True}
+    ]
+    mock_execute_request_async.return_value = (
+        {
+            'formatted_response': 'Python is dynamically typed, garbage-collected, high level, general purpose programming.',
+            'response': 'Python is dynamically typed, garbage-collected, high level, general purpose programming.'},
+        200,
+        mock.ANY,
+        mock.ANY
+    )
+    embedding = list(np.random.random(OPENAI_EMBEDDING_OUTPUT))
+    mock_embedding.return_value = litellm.EmbeddingResponse(**{'data': [{'embedding': embedding}]})
+    expected_body = {'messages': [
+        {'role': 'system', 'content': 'You are a personal assistant. Answer question based on the context below.\n'},
+        {'role': 'user', 'content': 'hello'}, {'role': 'assistant', 'content': 'how are you'}, {'role': 'user',
+                                                                                                'content': "\nInstructions on how to use Similarity Prompt:\n['Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically typed and garbage-collected.']\nAnswer question based on the context above, if answer is not in the context go check previous logs.\n \nQ: What kind of language is python? \nA:"}],
+        'metadata': {'user': 'udit.pandey', 'bot': '5f50fd0a56b698ca10d35d2l', 'invocation': 'prompt_action'},
+        'api_key': 'keyvalue',
+        'num_retries': 3, 'temperature': 0.0, 'max_tokens': 300, 'model': 'gpt-4o-mini', 'top_p': 0.0, 'n': 1,
+        'stop': None, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'logit_bias': {}}
+    aioresponses.add(
+        url=urljoin(Utility.environment['llm']['url'],
+                    f"/{bot}/completion/{llm_type}"),
+        method="POST",
+        status=200,
+        payload={'formatted_response': generated_text, 'response': generated_text},
+        body=json.dumps(expected_body)
+    )
+    aioresponses.add(
+        url=f"{Utility.environment['vector']['db']}/collections/{bot}_python_faq_embd/points/search",
+        body={'vector': embedding},
+        payload={'result': [{'id': uuid7().__str__(), 'score': 0.80, 'payload': {'content': bot_content}}]},
+        method="POST",
+        status=200
+    )
+    hyperparameters = Utility.get_llm_hyperparameters("perplexity")
+    hyperparameters['search_domain_filter'] = ["domain1.com", "domain2.com"]
+    Actions(name=action_name, type=ActionType.prompt_action.value, bot=bot, user=user).save()
+    BotSettings(llm_settings=LLMSettings(enable_faq=True), bot=bot, user=user).save()
+    PromptAction(name=action_name, bot=bot, user=user, num_bot_responses=2, llm_prompts=llm_prompts, llm_type="perplexity", hyperparameters = hyperparameters,
+                 user_question=UserQuestion(type="from_slot", value="prompt_question")).save()
+    llm_secret = LLMSecret(
+        llm_type=llm_type,
+        api_key=value,
+        models=["perplexity/llama-3.1-sonar-small-128k-online", "perplexity/llama-3.1-sonar-large-128k-online", "perplexity/llama-3.1-sonar-huge-128k-online"],
+        bot=bot,
+        user=user
+    )
+    llm_secret.save()
+    llm_secret = LLMSecret(
+        llm_type="openai",
+        api_key="api_key",
+        models=["gpt-3.5-turbo", "gpt-4o-mini"],
+        bot=bot,
+        user=user
+    )
+    llm_secret.save()
+    request_object = json.load(open("tests/testing_data/actions/action-request.json"))
+    request_object["tracker"]["slots"] = {"bot": bot, "prompt_question": user_msg}
+    request_object["next_action"] = action_name
+    request_object["tracker"]["sender_id"] = user
+    request_object['tracker']['events'] = [{"event": "user", 'text': 'hello',
+                                            "data": {"elements": '', "quick_replies": '', "buttons": '',
+                                                     "attachment": '', "image": '', "custom": ''}},
+                                           {'event': 'bot', "text": "how are you",
+                                            "data": {"elements": '', "quick_replies": '', "buttons": '',
+                                                     "attachment": '', "image": '', "custom": ''}}]
+    response = client.post("/webhook", json=request_object)
+    response_json = response.json()
+    mock_execute_request_async.assert_called_once_with(
+        http_url=f"{Utility.environment['llm']['url']}/{urllib.parse.quote(bot)}/completion/{llm_type}",
+        request_method="POST",
+        request_body={
+            'messages': [{'role': 'system', 'content': 'You are a personal assistant. Answer question based on the context below.\n'},
+                         {'role': 'user', 'content': 'hello'},
+                         {'role': 'assistant', 'content': 'how are you'},
+                         {'role': 'user', 'content': "\nInstructions on how to use Similarity Prompt:\n['Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically typed and garbage-collected.']\nAnswer question based on the context above, if answer is not in the context go check previous logs.\n \nQ: What kind of language is python? inurl:domain1.com|domain2.com \nA:"}],
+            'hyperparameters': hyperparameters,
+            'user': user,
+            'invocation': "prompt_action"
+        },
+        timeout=Utility.environment['llm'].get('request_timeout', 30)
+    )
+    called_args = mock_execute_request_async.call_args
+    user_message = called_args.kwargs['request_body']['messages'][-1]['content']
+    assert "inurl:domain1.com|domain2.com" in user_message
+    assert response_json['events'] == [
+        {'event': 'slot', 'timestamp': None, 'name': 'kairon_action_response', 'value': generated_text}]
+    assert response_json['responses'] == [
+        {'text': generated_text, 'buttons': [], 'elements': [], 'custom': {}, 'template': None,
+         'response': None, 'image': None, 'attachment': None}
+    ]
+
 @mock.patch.object(litellm, "aembedding", autospec=True)
 def test_prompt_action_response_action_with_prompt_question_from_slot_different_embedding_completion(mock_embedding, aioresponses):
     from uuid6 import uuid7