diff --git a/autollm/__init__.py b/autollm/__init__.py
index 7c32b9d8..d7c61a62 100644
--- a/autollm/__init__.py
+++ b/autollm/__init__.py
@@ -4,7 +4,7 @@
 and vector databases, along with various utility functions.
 """
 
-__version__ = '0.1.3'
+__version__ = '0.1.4'
 __author__ = 'safevideo'
 __license__ = 'AGPL-3.0'
 
diff --git a/autollm/auto/query_engine.py b/autollm/auto/query_engine.py
index 959fed8e..964847b0 100644
--- a/autollm/auto/query_engine.py
+++ b/autollm/auto/query_engine.py
@@ -3,7 +3,7 @@
 from llama_index import Document, ServiceContext, VectorStoreIndex
 from llama_index.embeddings.utils import EmbedType
 from llama_index.indices.query.base import BaseQueryEngine
-from llama_index.prompts.base import PromptTemplate
+from llama_index.prompts.base import BasePromptTemplate, PromptTemplate
 from llama_index.prompts.prompt_type import PromptType
 from llama_index.response_synthesizers import get_response_synthesizer
 from llama_index.schema import BaseNode
@@ -24,11 +24,11 @@ def create_query_engine(
         llm_api_base: Optional[str] = None,
         # service_context_params
         system_prompt: str = None,
-        query_wrapper_prompt: str = None,
+        query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
         enable_cost_calculator: bool = True,
         embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
         chunk_size: Optional[int] = 512,
-        chunk_overlap: Optional[int] = 200,
+        chunk_overlap: Optional[int] = 100,
         context_window: Optional[int] = None,
         enable_title_extractor: bool = False,
         enable_summary_extractor: bool = False,
@@ -61,7 +61,7 @@ def create_query_engine(
         llm_temperature (float): The temperature to use for the LLM.
         llm_api_base (str): The API base to use for the LLM.
         system_prompt (str): The system prompt to use for the query engine.
-        query_wrapper_prompt (str): The query wrapper prompt to use for the query engine.
+        query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine.
         enable_cost_calculator (bool): Flag to enable cost calculator logging.
         embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings. "default" for OpenAI,
                                             "local" for HuggingFace or use full identifier (e.g., local:intfloat/multilingual-e5-large)
@@ -133,10 +133,15 @@ def create_query_engine(
         refine_prompt_template = PromptTemplate(refine_prompt, prompt_type=PromptType.REFINE)
     else:
         refine_prompt_template = None
+
+    # Convert query_wrapper_prompt to PromptTemplate if it is a string
+    if isinstance(query_wrapper_prompt, str):
+        query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt)
     response_synthesizer = get_response_synthesizer(
         service_context=service_context,
-        response_mode=response_mode,
+        text_qa_template=query_wrapper_prompt,
         refine_template=refine_prompt_template,
+        response_mode=response_mode,
         structured_answer_filtering=structured_answer_filtering)
 
     return vector_store_index.as_query_engine(
@@ -213,7 +218,7 @@ def from_defaults(
             llm_temperature: float = 0.1,
             # service_context_params
             system_prompt: str = None,
-            query_wrapper_prompt: str = None,
+            query_wrapper_prompt: Union[str, BasePromptTemplate] = None,
             enable_cost_calculator: bool = True,
             embed_model: Union[str, EmbedType] = "default",  # ["default", "local"]
             chunk_size: Optional[int] = 512,
@@ -246,7 +251,7 @@ def from_defaults(
             llm_temperature (float): The temperature to use for the LLM.
             llm_api_base (str): The API base to use for the LLM.
             system_prompt (str): The system prompt to use for the query engine.
-            query_wrapper_prompt (str): The query wrapper prompt to use for the query engine.
+            query_wrapper_prompt (Union[str, BasePromptTemplate]): The query wrapper prompt to use for the query engine.
             enable_cost_calculator (bool): Flag to enable cost calculator logging.
             embed_model (Union[str, EmbedType]): The embedding model to use for generating embeddings. "default" for OpenAI,
                                                 "local" for HuggingFace or use full identifier (e.g., local:intfloat/multilingual-e5-large)
diff --git a/autollm/auto/service_context.py b/autollm/auto/service_context.py
index ff45e794..9aad5294 100644
--- a/autollm/auto/service_context.py
+++ b/autollm/auto/service_context.py
@@ -65,11 +65,14 @@ def from_defaults(
         """
         if not system_prompt and not query_wrapper_prompt:
             system_prompt, query_wrapper_prompt = set_default_prompt_template()
-        # Convert system_prompt to ChatPromptTemplate if it is a string
+        # Convert query_wrapper_prompt to PromptTemplate if it is a string
         if isinstance(query_wrapper_prompt, str):
             query_wrapper_prompt = PromptTemplate(template=query_wrapper_prompt)
 
         callback_manager: CallbackManager = kwargs.get('callback_manager', CallbackManager())
+        kwargs.pop(
+            'callback_manager', None)  # Make sure callback_manager is not passed to ServiceContext twice
+
         if enable_cost_calculator:
             llm_model_name = llm.metadata.model_name if not "default" else "gpt-3.5-turbo"
             callback_manager.add_handler(CostCalculatingHandler(model_name=llm_model_name, verbose=True))