diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
index 62d9ffd93bb6..11193a91e042 100644
--- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
@@ -125,7 +125,9 @@ def __init__(
                 - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
                 - customized_answer_prefix (Optional, str): the customized answer prefix for the retrieve chat. Default is "".
                     If not "" and the customized_answer_prefix is not in the answer, `Update Context` will be triggered.
-                - no_update_context (Optional, bool): if True, will not apply `Update Context` for interactive retrieval. Default is False.
+                - update_context (Optional, bool): if False, will not apply `Update Context` for interactive retrieval. Default is True.
+                - get_or_create (Optional, bool): if True, will create/recreate a collection for the retrieve chat.
+                    This is the same as that used in chromadb. Default is False.
             **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
         """
         super().__init__(
@@ -148,7 +150,8 @@ def __init__(
         self._embedding_model = self._retrieve_config.get("embedding_model", "all-MiniLM-L6-v2")
         self.customized_prompt = self._retrieve_config.get("customized_prompt", None)
         self.customized_answer_prefix = self._retrieve_config.get("customized_answer_prefix", "").upper()
-        self.no_update_context = self._retrieve_config.get("no_update_context", False)
+        self.update_context = self._retrieve_config.get("update_context", True)
+        self._get_or_create = self._retrieve_config.get("get_or_create", False)
         self._context_max_tokens = self._max_tokens * 0.8
         self._collection = False  # the collection is not created
         self._ipython = get_ipython()
@@ -231,7 +234,7 @@ def _generate_retrieve_user_reply(
         config: Optional[Any] = None,
     ) -> Tuple[bool, Union[str, Dict, None]]:
         """In this function, we will update the context and reset the conversation based on different conditions.
-        We'll update the context and reset the conversation if no_update_context is False and either of the following:
+        We'll update the context and reset the conversation if update_context is True and either of the following:
         (1) the last message contains "UPDATE CONTEXT",
         (2) the last message doesn't contain "UPDATE CONTEXT" and the customized_answer_prefix is not in the message.
         """
@@ -247,7 +250,7 @@ def _generate_retrieve_user_reply(
         update_context_case2 = (
             self.customized_answer_prefix and self.customized_answer_prefix not in message.get("content", "").upper()
         )
-        if (update_context_case1 or update_context_case2) and not self.no_update_context:
+        if (update_context_case1 or update_context_case2) and self.update_context:
             print(colored("Updating context and resetting conversation.", "green"), flush=True)
             # extract the first sentence in the response as the intermediate answer
             _message = message.get("content", "").split("\n")[0].strip()
@@ -286,7 +289,7 @@ def _generate_retrieve_user_reply(
             return False, None
 
     def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str = ""):
-        if not self._collection:
+        if not self._collection or self._get_or_create:
             print("Trying to create collection.")
             create_vector_db_from_dir(
                 dir_path=self._docs_path,
@@ -296,8 +299,10 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
                 chunk_mode=self._chunk_mode,
                 must_break_at_empty_line=self._must_break_at_empty_line,
                 embedding_model=self._embedding_model,
+                get_or_create=self._get_or_create,
             )
             self._collection = True
+            self._get_or_create = False
 
         results = query_vector_db(
             query_texts=[problem],
diff --git a/autogen/retrieve_utils.py b/autogen/retrieve_utils.py
index 5bb264612485..806834eb31ca 100644
--- a/autogen/retrieve_utils.py
+++ b/autogen/retrieve_utils.py
@@ -208,18 +208,13 @@ def create_vector_db_from_dir(
 
         chunks = split_files_to_chunks(get_files_from_dir(dir_path), max_tokens, chunk_mode, must_break_at_empty_line)
         print(f"Found {len(chunks)} chunks.")
-        # upsert in batch of 40000
-        for i in range(0, len(chunks), 40000):
+        # Upsert in batch of 40000 or less if the total number of chunks is less than 40000
+        for i in range(0, len(chunks), min(40000, len(chunks))):
+            end_idx = i + min(40000, len(chunks) - i)
             collection.upsert(
-                documents=chunks[
-                    i : i + 40000
-                ],  # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
-                ids=[f"doc_{i}" for i in range(i, i + 40000)],  # unique for each doc
+                documents=chunks[i:end_idx],
+                ids=[f"doc_{j}" for j in range(i, end_idx)],  # unique for each doc
             )
-        collection.upsert(
-            documents=chunks[i : len(chunks)],
-            ids=[f"doc_{i}" for i in range(i, len(chunks))],  # unique for each doc
-        )
     except ValueError as e:
         logger.warning(f"{e}")