diff --git a/.gitignore b/.gitignore
index 2521fac8..cd374f66 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,6 +15,8 @@ sbin/**
 **/*.sh
 **/etc/*.json
 **/optimizer_settings.json
+**/rag_base_optimizer_config.py
+**/rag_base_optimizer_config_mcp.py
 !opentofu/**/cloudinit-oke.sh
 !opentofu/examples/manual-test.sh
 !src/entrypoint.sh
@@ -82,4 +84,5 @@ spring_ai/target/**
 spring_ai/create_user.sql
 spring_ai/drop.sql
 src/client/spring_ai/target/classes/*
-api_server_key
\ No newline at end of file
+api_server_key
+DEVELOPMENT_NOTES.md
diff --git a/README.md b/README.md
index 4ab138bc..a1b1a518 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,7 @@ The **Oracle AI Optimizer and Toolkit** (the **AI Optimizer**) provides a stream
 
 - [Configuring Embedding and Chat Models](https://oracle.github.io/ai-optimizer/client/configuration/model_config)
 - [Splitting and Embedding Documentation](https://oracle.github.io/ai-optimizer/client/tools/split_embed)
+- [Auto-refresh Vector Store from OCI Object Storage buckets](https://oracle.github.io/ai-optimizer/server/api/v1/embed)
 - [Modifying System Prompts (Prompt Engineering)](https://oracle.github.io/ai-optimizer/client/tools/prompt_eng)
 - [Experimenting with **LLM** Parameters](https://oracle.github.io/ai-optimizer/client/chatbot)
 - [Testbed for auto-generated or existing Q&A datasets](https://oracle.github.io/ai-optimizer/client/testbed)
diff --git a/opentofu/cfgmgt/stage/.gitkeep b/opentofu/cfgmgt/stage/.gitkeep
deleted file mode 100644
index e69de29b..00000000
diff --git a/src/client/content/config/tabs/databases.py b/src/client/content/config/tabs/databases.py
index ddd34fe0..e4e20ed5 100644
--- a/src/client/content/config/tabs/databases.py
+++ b/src/client/content/config/tabs/databases.py
@@ -258,7 +258,7 @@ def display_databases() -> None:
     selected_database_alias = st.selectbox(
         "Current Database:",
         options=list(database_lookup.keys()),
-        index=list(database_lookup.keys()).index(state.client_settings["database"]["alias"]),
+        index=list(database_lookup.keys()).index(state.client_settings.get("database", {}).get("alias")),
         key="selected_database",
         on_change=st_common.update_client_settings("database"),
     )
diff --git a/src/client/content/config/tabs/settings.py b/src/client/content/config/tabs/settings.py
index 9dea1627..519c75fb 100644
--- a/src/client/content/config/tabs/settings.py
+++ b/src/client/content/config/tabs/settings.py
@@ -302,7 +302,7 @@ def spring_ai_obaas(src_dir, file_name, provider, ll_config, embed_config):
         sys_prompt=f"{sys_prompt}",
         ll_model=ll_config,
         vector_search=embed_config,
-        database_config=database_lookup[state.client_settings["database"]["alias"]],
+        database_config=database_lookup[state.client_settings.get("database", {}).get("alias")],
     )
 
     if file_name.endswith(".yaml"):
@@ -313,7 +313,7 @@ def spring_ai_obaas(src_dir, file_name, provider, ll_config, embed_config):
             sys_prompt=sys_prompt,
             ll_model=ll_config,
             vector_search=embed_config,
-            database_config=database_lookup[state.client_settings["database"]["alias"]],
+            database_config=database_lookup[state.client_settings.get("database", {}).get("alias")],
         )
 
         yaml_data = yaml.safe_load(formatted_content)
diff --git a/src/client/content/tools/tabs/split_embed.py b/src/client/content/tools/tabs/split_embed.py
index 547b5626..248d3055 100644
--- a/src/client/content/tools/tabs/split_embed.py
+++ b/src/client/content/tools/tabs/split_embed.py
@@ -8,6 +8,8 @@
 
 import math
 import re
+from dataclasses import dataclass
+from typing import Optional
 
 import pandas as pd
 
@@ -26,6 +28,47 @@
 logger = logging_config.logging.getLogger("client.tools.tabs.split_embed")
 
 
+#####################################################
+# Classes
+#####################################################
+@dataclass
+class FileSourceData:
+    """Data class to hold file source configuration and validation state"""
+
+    file_source: Optional[str] = None
+    # Web source
+    web_url: Optional[str] = None
+    # OCI source
+    oci_bucket: Optional[str] = None
+    oci_files_selected: Optional[pd.DataFrame] = None
+    # SQL source
+    sql_connection: Optional[str] = None
+    sql_query: Optional[str] = None
+
+    def is_valid(self) -> bool:
+        """Check if the current file source configuration is valid"""
+        if self.file_source == "Local":
+            return bool(state.get("local_file_uploader"))
+        if self.file_source == "Web":
+            return bool(self.web_url and functions.is_url_accessible(self.web_url)[0])
+        if self.file_source == "SQL":
+            return not functions.is_sql_accessible(self.sql_connection, self.sql_query)[0]
+        if self.file_source == "OCI":
+            return bool(self.oci_files_selected is not None and self.oci_files_selected["Process"].sum() > 0)
+        return False
+
+    def get_button_help(self) -> str:
+        """Get help text for the populate button based on file source"""
+        help_text_map = {
+            "Local": "This button is disabled if no local files have been provided.",
+            "Web": "This button is disabled if there the URL was unable to be validated.  Please check the URL.",
+            "SQL": "This button is disabled if there the SQL was unable to be validated.  Please check the SQL.",
+            "OCI": "This button is disabled if there are no documents from the source bucket split with "
+            "the current split and embed options.  Please Split and Embed to enable Vector Storage.",
+        }
+        return help_text_map.get(self.file_source, "")
+
+
 #####################################################
 # Functions
 #####################################################
@@ -56,15 +99,9 @@ def get_bucket_objects(bucket: str) -> list:
 @st.cache_resource
 def files_data_frame(objects, process=False):
     """Produce a data frame of files"""
-    files = pd.DataFrame({"File": [], "Process": []})
-    if len(objects) >= 1:
-        files = pd.DataFrame(
-            {"File": [objects[0]], "Process": [process]},
-        )
-        for file in objects[1:]:
-            new_record = pd.DataFrame([{"File": file, "Process": process}])
-            files = pd.concat([files, new_record], ignore_index=True)
-    return files
+    if not objects:
+        return pd.DataFrame({"File": [], "Process": []})
+    return pd.DataFrame({"File": objects, "Process": [process] * len(objects)})
 
 
 def files_data_editor(files, key):
@@ -108,8 +145,15 @@ def update_chunk_size_input() -> None:
 #############################################################################
 # Helper Functions
 #############################################################################
-def _render_embedding_configuration(embed_models_enabled: dict, embed_request: DatabaseVectorStorage) -> None:
-    """Render the embedding configuration section"""
+def _render_embedding_config_section(embed_models_enabled: dict, embed_request: DatabaseVectorStorage) -> None:
+    """Render the embedding configuration section
+
+    Args:
+        embed_models_enabled: Dictionary of enabled embedding models
+        embed_request: The database vector storage request object
+        show_vs_config: If True, show chunk size, overlap, distance metric, and index type options.
+                       If False, these are determined by the selected existing vector store.
+    """
     st.header("Embedding Configuration", divider="red")
     embed_request.model = st.selectbox(
         "Embedding models available: ",
@@ -184,60 +228,43 @@ def _render_embedding_configuration(embed_models_enabled: dict, embed_request: D
     )
 
 
-def _render_file_source_section(file_sources: list, oci_setup: dict) -> tuple:
+def _render_load_kb_section(file_sources: list, oci_setup: dict) -> FileSourceData:
     """Render file source selection and return processing data"""
     st.header("Load Knowledge Base", divider="red")
-    file_source = st.radio("Knowledge Base Source:", file_sources, key="radio_file_source", horizontal=True)
-    button_help = None
-    populate_button_disabled = True
-    web_url = None
-    src_bucket = None
-    src_files_selected = None
-    db_connection = None
-    sql_query = None
+    data = FileSourceData()
+    data.file_source = st.radio("Knowledge Base Source:", file_sources, key="radio_file_source", horizontal=True)
 
     ######################################
     # SQL Source
     ######################################
-    if file_source == "SQL":
-        button_help = """
-            This button is disabled if there the SQL was unable to be validated.  Please check the SQL.
-        """
+    if data.file_source == "SQL":
         st.subheader("SQL query", divider=False)
-        db_connection = st.text_input("DB Connection:", key="db_connection_url")
-        sql_query = st.text_input("SQL:", key="sql_query")
+        data.sql_connection = st.text_input("DB Connection:", key="db_connection_url")
+        data.sql_query = st.text_input("SQL:", key="sql_query")
 
-        populate_button_disabled, msg = functions.is_sql_accessible(db_connection, sql_query)
-        if not populate_button_disabled or msg != "":
+        is_invalid, msg = functions.is_sql_accessible(data.sql_connection, data.sql_query)
+        if is_invalid or msg:
             st.error(f"Error: {msg}")
 
     ######################################
     # Local Source
     ######################################
-    if file_source == "Local":
-        button_help = "This button is disabled if no local files have been provided."
+    if data.file_source == "Local":
         st.subheader("Local Files", divider=False)
-        embed_files = st.file_uploader(
+        st.file_uploader(
             "Choose a file:",
             key="local_file_uploader",
             help="Large or many files?  Consider OCI Object Storage or invoking the API directly.",
             accept_multiple_files=True,
         )
-        populate_button_disabled = len(embed_files) == 0
 
-    elif file_source == "Web":
-        button_help = "This button is disabled if there the URL was unable to be validated.  Please check the URL."
+    elif data.file_source == "Web":
         st.subheader("Web Pages", divider=False)
-        web_url = st.text_input("URL:", key="selected_web_url")
-        is_web_accessible, _ = functions.is_url_accessible(web_url)
-        populate_button_disabled = not (web_url and is_web_accessible)
+        data.web_url = st.text_input("URL:", key="selected_web_url")
 
-    elif file_source == "OCI":
-        button_help = """This button is disabled if there are no documents from the source bucket split with
-            the current split and embed options.  Please Split and Embed to enable Vector Storage."""
+    elif data.file_source == "OCI":
         st.text(f"OCI namespace: {oci_setup['namespace']}")
         oci_compartments = get_compartments()
-        src_bucket_list = []
         col2_1, col2_2 = st.columns([0.5, 0.5])
         with col2_1:
             bucket_compartment = st.selectbox(
@@ -246,71 +273,144 @@ def _render_file_source_section(file_sources: list, oci_setup: dict) -> tuple:
                 index=None,
                 placeholder="Select bucket compartment...",
             )
-            if bucket_compartment:
-                src_bucket_list = get_buckets(oci_compartments[bucket_compartment])
+            src_bucket_list = get_buckets(oci_compartments[bucket_compartment]) if bucket_compartment else []
         with col2_2:
-            src_bucket = st.selectbox(
+            data.oci_bucket = st.selectbox(
                 "Source bucket:",
                 src_bucket_list,
                 index=None,
                 placeholder="Select source bucket...",
                 disabled=not bucket_compartment,
             )
-        if src_bucket:
-            src_objects = get_bucket_objects(src_bucket)
-            src_files = files_data_frame(src_objects)
+
+        src_objects = get_bucket_objects(data.oci_bucket) if data.oci_bucket else []
+        src_files = files_data_frame(src_objects)
+        data.oci_files_selected = files_data_editor(src_files, "source")
+
+    return data
+
+
+def _display_file_list_expander(file_list_response: dict) -> None:
+    """Display the file list expander with embedded files information"""
+    # Build expander title
+    total_files = file_list_response["total_files"]
+    total_chunks = file_list_response["total_chunks"]
+    expander_title = f"📁 Exiting Embeddings ({total_files} files, {total_chunks} chunks)"
+    orphaned = file_list_response.get("orphaned_chunks", 0)
+    if orphaned > 0:
+        expander_title += f" ⚠️ {orphaned} orphaned"
+
+    with st.expander(expander_title):
+        # Add refresh button
+        _, col2 = st.columns([0.9, 0.1])
+        with col2:
+            if st.button("🔄", key="refresh_file_list", help="Refresh file list"):
+                st.rerun()
+
+        # Show warning if there are orphaned chunks
+        if orphaned > 0:
+            st.warning(
+                f"**{orphaned} orphaned chunks found** - "
+                "These chunks have missing or invalid filename metadata "
+                "and won't be shown in search results properly."
+            )
+
+        if file_list_response["total_files"] > 0:
+            # Create DataFrame for better display
+            files_df = pd.DataFrame(file_list_response["files"])
+
+            # Select columns to display (always show filename and chunk_count)
+            display_cols = ["filename", "chunk_count"]
+            column_config = {
+                "filename": st.column_config.TextColumn("File Name", width="medium"),
+                "chunk_count": st.column_config.NumberColumn("Chunks", width="small"),
+            }
+
+            # Only include size column if at least one file has size data
+            if "size" in files_df.columns and files_df["size"].notna().any():
+                files_df["size"] = files_df["size"].apply(lambda x: f"{x / 1024:.1f} KB" if x else "N/A")
+                display_cols.append("size")
+                column_config["size"] = st.column_config.TextColumn("Size", width="small")
+
+            # Only include time_modified column if at least one file has timestamp data
+            if "time_modified" in files_df.columns and files_df["time_modified"].notna().any():
+                files_df["time_modified"] = files_df["time_modified"].apply(lambda x: x.split("T")[0] if x else "N/A")
+                display_cols.append("time_modified")
+                column_config["time_modified"] = st.column_config.TextColumn("Modified", width="small")
+
+            st.dataframe(files_df[display_cols], width="stretch", hide_index=True, column_config=column_config)
         else:
-            src_files = pd.DataFrame({"File": [], "Process": []})
-
-        src_files_selected = files_data_editor(src_files, "source")
-        populate_button_disabled = src_files_selected["Process"].sum() == 0
-
-    return (
-        file_source,
-        populate_button_disabled,
-        button_help,
-        web_url,
-        src_bucket,
-        src_files_selected,
-        db_connection,
-        sql_query,
-    )
+            st.info("No files found in this vector store.")
+
 
+def _render_populate_vs_section(
+    embed_request: DatabaseVectorStorage, create_new_vs: bool
+) -> tuple[DatabaseVectorStorage, int]:
+    """Render vector store configuration section and return validation status and rate limit
 
-def _render_vector_store_section(embed_request: DatabaseVectorStorage) -> tuple:
-    """Render vector store configuration section and return validation status and rate limit"""
+    Args:
+        embed_request: The database vector storage request object
+        create_new_vs: If True, allow creating new vector store. If False, select from existing only.
+
+    Returns:
+        Tuple of (embed_alias_invalid, rate_limit, vs_table)
+    """
     st.header("Populate Vector Store", divider="red")
-    database_lookup = st_common.state_configs_lookup("database_configs", "name")
-    existing_vs = database_lookup.get(state.client_settings.get("database", {}).get("alias"), {}).get(
-        "vector_stores", []
-    )
 
-    embed_alias_size, _ = st.columns([0.5, 0.5])
-    embed_alias_invalid = False
     embed_request.vector_store = None
-    embed_request.alias = embed_alias_size.text_input(
-        "Vector Store Alias:",
-        max_chars=20,
-        help=help_text.help_dict["embed_alias"],
-        key="selected_embed_alias",
-        placeholder="Press Enter to set.",
-    )
-    pattern = r"^[A-Za-z][A-Za-z0-9_]*$"
-    if embed_request.alias and not re.match(pattern, embed_request.alias):
-        st.error(
-            "Invalid Alias! It must start with a letter and only contain alphanumeric characters and underscores."
+    embed_alias_invalid = False
+    if create_new_vs:
+        # Creating new vector store: just show text input for new VS name
+        embed_request.alias = st.text_input(
+            "Vector Store Alias:",
+            max_chars=20,
+            help=help_text.help_dict["embed_alias"],
+            key="selected_embed_alias",
+            placeholder="Enter a name for the new vector store",
         )
-        embed_alias_invalid = True
-
-    if not embed_alias_invalid:
+        alias_pattern = r"^[A-Za-z][A-Za-z0-9_]*$"
+        if not embed_request.alias:
+            st.warning("Please enter a Vector Store Alias to continue.")
+            embed_alias_invalid = True
+        elif not re.match(alias_pattern, embed_request.alias):
+            st.error(
+                "Invalid Alias! It must start with a letter and only contain alphanumeric characters and underscores."
+            )
+            embed_alias_invalid = True
+    else:
+        # Using existing Vector Store
+        embed_request.alias = state.selected_vector_search_alias
+        embed_request.model = state.selected_vector_search_model
+        embed_request.chunk_size = state.selected_vector_search_chunk_size
+        embed_request.chunk_overlap = state.selected_vector_search_chunk_overlap
+        embed_request.distance_metric = state.selected_vector_search_distance_metric
+        embed_request.index_type = state.selected_vector_search_index_type
+
+    if not embed_alias_invalid and embed_request.alias:
         embed_request.vector_store, _ = functions.get_vs_table(
             **embed_request.model_dump(exclude={"database", "vector_store"})
         )
-    vs_msg = f"{embed_request.vector_store}, will be created."
-    if any(d.get("vector_store") == embed_request.vector_store for d in existing_vs):
-        vs_msg = f"{embed_request.vector_store} exists, new chunks will be added."
-    st.markdown(f"##### **Vector Store:** `{embed_request.vector_store}`")
-    st.caption(f"{vs_msg}")
+
+        # Show full vector store table name and check existence
+        st.markdown(f"##### **Vector Store:** `{embed_request.vector_store}`")
+        vs_exists = any(
+            store["vector_store"] == embed_request.vector_store
+            for db in state.database_configs
+            for store in db.get("vector_stores", [])
+        )
+        if vs_exists:
+            st.caption("Vector store already exists. New chunks will be added to existing Vector Store.")
+        else:
+            st.caption("New vector store will be created.")
+
+        # Display files in existing vector store
+        if not create_new_vs and embed_request.vector_store:
+            try:
+                file_list_response = api_call.get(endpoint=f"v1/embed/{embed_request.vector_store}/files")
+                if file_list_response and "files" in file_list_response:
+                    _display_file_list_expander(file_list_response)
+            except api_call.ApiError as e:
+                logger.warning("Could not retrieve file list for %s: %s", embed_request.vector_store, e)
 
     # Always render rate limit input to ensure session state is initialized
     rate_size, _ = st.columns([0.28, 0.72])
@@ -322,76 +422,131 @@ def _render_vector_store_section(embed_request: DatabaseVectorStorage) -> tuple:
         key="selected_rate_limit",
     )
 
-    return embed_alias_invalid, rate_limit
+    return embed_request, rate_limit
 
 
-def _handle_vector_store_population(
+def _process_populate_request(
     embed_request: DatabaseVectorStorage,
-    file_source: str,
-    populate_button_disabled: bool,
-    button_help: str,
-    web_url: str,
-    src_bucket: str,
-    src_files_selected,
+    source_data: FileSourceData,
     rate_limit: int,
-    db_connection: str,
-    sql_query: str,
+) -> dict:
+    """Process the populate vector store request"""
+    if source_data.file_source == "Local":
+        endpoint = "v1/embed/local/store"
+        files = st_common.local_file_payload(state.local_file_uploader)
+        api_payload = {"files": files}
+    elif source_data.file_source == "Web":
+        endpoint = "v1/embed/web/store"
+        api_payload = {"json": [source_data.web_url]}
+    elif source_data.file_source == "SQL":
+        endpoint = "v1/embed/sql/store"
+        api_payload = {"json": [source_data.sql_connection, source_data.sql_query]}
+    else:  # OCI
+        endpoint = f"v1/oci/objects/download/{source_data.oci_bucket}/{state.client_settings['oci']['auth_profile']}"
+        process_list = source_data.oci_files_selected[source_data.oci_files_selected["Process"]].reset_index(drop=True)
+        api_payload = {"json": process_list["File"].tolist()}
+
+    api_call.post(endpoint=endpoint, payload=api_payload)
+
+    embed_params = {
+        "client": state.client_settings["client"],
+        "rate_limit": rate_limit,
+    }
+    response = api_call.post(
+        endpoint="v1/embed",
+        params=embed_params,
+        payload={"json": embed_request.model_dump()},
+        timeout=7200,
+    )
+    return response
+
+
+def _process_refresh_request(embed_request: DatabaseVectorStorage, src_bucket: str, rate_limit: int) -> dict:
+    """Process the refresh vector store request"""
+    refresh_request = {
+        "vector_store_alias": embed_request.alias,
+        "bucket_name": src_bucket,
+        "auth_profile": None,
+        "rate_limit": rate_limit if rate_limit else 0,
+    }
+
+    response = api_call.post(
+        endpoint="v1/embed/refresh",
+        params={"client": state.client_settings["client"]},
+        payload={"json": refresh_request},
+        timeout=7200,
+    )
+    return response
+
+
+def _handle_vector_store_population(
+    embed_request: DatabaseVectorStorage, source_data: FileSourceData, rate_limit: int, create_new_vs: bool
 ) -> None:
     """Handle vector store population button and processing"""
-    if not populate_button_disabled and embed_request.vector_store:
-        if "button_populate" in state and state.button_populate is True:
-            state.running = True
-        else:
-            state.running = False
-    else:
-        state.running = True
-    if not embed_request.alias:
+    is_source_valid = source_data.is_valid()
+
+    if not embed_request.alias and create_new_vs:
         st.info("Please provide a Vector Store Alias.")
-    elif st.button(
-        "Populate Vector Store",
-        type="primary",
-        key="button_populate",
-        disabled=state.running,
-        help=button_help,
-    ):
+
+    refresh_clicked = False
+    populate_clicked = False
+    if source_data.file_source == "OCI" and not create_new_vs:
+        state.running = (
+            not (is_source_valid and embed_request.vector_store) and state.get("button_refresh") is not True
+        )
+        refresh_clicked = st.button(
+            "Refresh from OCI",
+            type="primary",
+            key="button_refresh",
+            disabled=state.running,
+            help="Refresh vector store with new/modified files from OCI bucket",
+        )
+    else:
+        state.running = (
+            not (is_source_valid and embed_request.vector_store) and state.get("button_populate") is not True
+        )
+        populate_clicked = st.button(
+            "Populate Vector Store",
+            type="primary",
+            key="button_populate",
+            disabled=state.running,
+            help=source_data.get_button_help(),
+        )
+
+    if populate_clicked:
         try:
             with st.spinner("Populating Vector Store... please be patient.", show_time=True):
-                endpoint = None
-                api_payload = []
-                if file_source == "Local":
-                    endpoint = "v1/embed/local/store"
-                    files = st_common.local_file_payload(state.local_file_uploader)
-                    api_payload = {"files": files}
-
-                if file_source == "Web":
-                    endpoint = "v1/embed/web/store"
-                    api_payload = {"json": [web_url]}
-
-                if file_source == "SQL":
-                    endpoint = "v1/embed/sql/store"
-                    api_payload = {"json": [db_connection, sql_query]}
-
-                if file_source == "OCI":
-                    endpoint = f"v1/oci/objects/download/{src_bucket}/{state.client_settings['oci']['auth_profile']}"
-                    process_list = src_files_selected[src_files_selected["Process"]].reset_index(drop=True)
-                    api_payload = {"json": process_list["File"].tolist()}
-
-                response = api_call.post(endpoint=endpoint, payload=api_payload)
-
-                embed_params = {
-                    "client": state.client_settings["client"],
-                    "rate_limit": rate_limit,
-                }
-                response = api_call.post(
-                    endpoint="v1/embed",
-                    params=embed_params,
-                    payload={"json": embed_request.model_dump()},
-                    timeout=7200,
-                )
+                response = _process_populate_request(embed_request, source_data, rate_limit)
             st.success(f"Vector Store Populated: {response['message']}", icon="✅")
             get_databases(force=True)
         except api_call.ApiError as ex:
             st.error(ex, icon="🚨")
+    elif refresh_clicked:
+        state.running = True
+        try:
+            with st.spinner("Refreshing Vector Store... checking for new/modified files.", show_time=True):
+                response = _process_refresh_request(embed_request, source_data.oci_bucket, rate_limit)
+            # Display results
+            if response.get("new_files", 0) > 0 or response.get("updated_files", 0) > 0:
+                st.success(
+                    f"✅ Refresh Complete!\n\n"
+                    f"- New files: {response.get('new_files', 0)}\n"
+                    f"- Updated files: {response.get('updated_files', 0)}\n"
+                    f"- Chunks added: {response.get('total_chunks', 0)}\n"
+                    f"- Total chunks in store: {response.get('total_chunks_in_store', 0)}",
+                    icon="✅",
+                )
+            else:
+                st.info(
+                    f"No new or modified files found in the bucket.\n\n"
+                    f"Total chunks in store: {response.get('total_chunks_in_store', 0)}",
+                    icon="ℹ️",
+                )
+            get_databases(force=True)
+        except api_call.ApiError as ex:
+            st.error(f"Refresh failed: {ex}", icon="🚨")
+        finally:
+            state.running = False
 
 
 #############################################################################
@@ -419,6 +574,7 @@ def display_split_embed() -> None:
     if not db_avail or not embed_models_enabled:
         st.stop()
 
+    # Setup Corpus Sources
     file_sources = ["OCI", "Local", "Web", "SQL"]
     oci_lookup = st_common.state_configs_lookup("oci_configs", "auth_profile")
     oci_setup = oci_lookup.get(state.client_settings["oci"].get("auth_profile"))
@@ -426,36 +582,47 @@ def display_split_embed() -> None:
         st.warning("OCI is not fully configured, some functionality is disabled", icon="⚠️")
         file_sources.remove("OCI")
 
+    # Setup Model for Embedding Request
     embed_request = DatabaseVectorStorage()
 
-    _render_embedding_configuration(embed_models_enabled, embed_request)
-
-    (
-        file_source,
-        populate_button_disabled,
-        button_help,
-        web_url,
-        src_bucket,
-        src_files_selected,
-        db_connection,
-        sql_query,
-    ) = _render_file_source_section(file_sources, oci_setup)
-
-    embed_alias_invalid, rate_limit = _render_vector_store_section(embed_request)
-
-    if not embed_alias_invalid:
-        _handle_vector_store_population(
-            embed_request,
-            file_source,
-            populate_button_disabled,
-            button_help,
-            web_url,
-            src_bucket,
-            src_files_selected,
-            rate_limit,
-            db_connection,
-            sql_query,
+    # Check for existing Vector Stores with corresponding enabled embedding models
+    create_new_vs = True
+    db_alias = state.client_settings.get("database", {}).get("alias")
+    database_lookup = st_common.state_configs_lookup("database_configs", "name")
+    vs_df = pd.DataFrame(database_lookup.get(db_alias, {}).get("vector_stores", []))
+    if not vs_df.empty:
+        # Toggle between creating new vector store or using existing
+        create_new_vs = st.toggle(
+            "Create New Vector Store",
+            key="selected_create_new_vs",
+            value=True,
+            help="Toggle between creating a new vector store or adding to an existing one. "
+            "When using an existing vector store, chunk size, overlap, distance metric, "
+            "and index type are already defined and cannot be changed.",
         )
+        if not create_new_vs:
+            # Render vector store selection controls
+            st_common.render_vector_store_selection(vs_df)
+
+    # Render embedding configuration for new VS
+    if create_new_vs:
+        _render_embedding_config_section(embed_models_enabled, embed_request)
+    else:
+        vs_fields = ["alias", "model", "chunk_size", "chunk_overlap", "distance_metric", "index_type"]
+        vs_missing = [
+            f"selected_vector_search_{field}"
+            for field in vs_fields
+            if not getattr(state, f"selected_vector_search_{field}", None)
+        ]
+        if vs_missing:
+            st.stop()
+
+    source_data = _render_load_kb_section(file_sources, oci_setup)
+
+    embed_request, rate_limit = _render_populate_vs_section(embed_request, create_new_vs)
+
+    if embed_request:
+        _handle_vector_store_population(embed_request, source_data, rate_limit, create_new_vs)
 
 
 if __name__ == "__main__":
diff --git a/src/client/mcp/rag/.python-version b/src/client/mcp/rag/.python-version
new file mode 100644
index 00000000..2c073331
--- /dev/null
+++ b/src/client/mcp/rag/.python-version
@@ -0,0 +1 @@
+3.11
diff --git a/src/client/mcp/rag/main.py b/src/client/mcp/rag/main.py
new file mode 100644
index 00000000..899a0529
--- /dev/null
+++ b/src/client/mcp/rag/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from rag!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/client/mcp/rag/pyproject.toml b/src/client/mcp/rag/pyproject.toml
new file mode 100644
index 00000000..73c75998
--- /dev/null
+++ b/src/client/mcp/rag/pyproject.toml
@@ -0,0 +1,15 @@
+[project]
+name = "rag"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "langchain-community==0.3.21",
+    "langchain-core==0.3.52",
+    "langchain-huggingface==0.1.2",
+    "langchain-ollama==0.3.2",
+    "langchain-openai==0.3.13",
+    "mcp>=1.19.0",
+    "oracledb~=3.1",
+]
diff --git a/src/client/mcp/rag/rag_base_optimizer_config.py b/src/client/mcp/rag/rag_base_optimizer_config.py
deleted file mode 100644
index ae9a6cae..00000000
--- a/src/client/mcp/rag/rag_base_optimizer_config.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""
-Copyright (c) 2024, 2025, Oracle and/or its affiliates.
-Licensed under the Universal Permissive License v1.0 as shown at http://oss.oracle.com/licenses/upl.
-"""
-import sys
-from typing import List
-import os
-from dotenv import load_dotenv
-import logging
-logger = logging.getLogger(__name__)
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(name)s - %(levelname)s - %(message)s"
-)
-
-
-logger.info("Successfully imported libraries and modules")
-
-from optimizer_utils import config
-
-from optimizer_utils import rag
-
-CHUNKS_DIR = "chunks_temp"
-data = {}
-
-def similarity_search(question: str, max_results: int = 5) -> List[str]:
-    """
-    Use this tool to get the top similar information to any question that may benefit from up-to-date or domain-specific information.
-    
-    Args:
-        question: The topic to search for
-        max_results: Maximum number of results to retrieve (default: 5)
-        
-    Returns:
-        List of information related to the question
-    """
-    
-    logger.info(f"Results provided for question: {question} with top {max_results}")
-    chunks=["first chunk", "second chunk"]
-    
-    return chunks
-
-if __name__ == "__main__":
-    # Initialize and run the server
-    # Load JSON file
-    file_path = os.path.join(os.getcwd(), "optimizer_settings.json")
-    logger.info(file_path)
-    rag.set_optimizer_settings_path(file_path)
-
-    if len(sys.argv) > 1:
-        question = sys.argv[1]
-        print(question)
-        logger.info(f"Question: {sys.argv[1]}")
-        logger.info(f"\n\nAnswer: {rag.rag_tool_base(question)}")
-    else:
-        logger.info("No question provided.")
\ No newline at end of file
diff --git a/src/client/mcp/rag/rag_base_optimizer_config_mcp.py b/src/client/mcp/rag/rag_base_optimizer_config_mcp.py
deleted file mode 100644
index c16d11f7..00000000
--- a/src/client/mcp/rag/rag_base_optimizer_config_mcp.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-Copyright (c) 2024, 2025, Oracle and/or its affiliates.
-Licensed under the Universal Permissive License v1.0 as shown at http://oss.oracle.com/licenses/upl.
-"""
-from typing import List
-from mcp.server.fastmcp import FastMCP
-import os
-from dotenv import load_dotenv
-#from sentence_transformers import CrossEncoder
-#from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_core.prompts import PromptTemplate
-from langchain_core.runnables import RunnablePassthrough
-from langchain_core.output_parsers import StrOutputParser
-import json
-import logging
-logger = logging.getLogger(__name__)
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(name)s - %(levelname)s - %(message)s"
-)
-
-from optimizer_utils import rag
-
-
-logging.info("Successfully imported libraries and modules")
-
-CHUNKS_DIR = "chunks_temp"
-data = {}
-
-# Initialize FastMCP server
-mcp = FastMCP("rag", port=9090) #Remote client
-#mcp = FastMCP("rag") #Local
-
-
-@mcp.tool()
-def rag_tool(question: str) -> str:
-    """
-    Use this tool to answer any question that may benefit from up-to-date or domain-specific information.
-    
-    Args:
-        question: the question for which are you looking for an answer
-        
-    Returns:
-        JSON string with answer
-    """
-    
-    answer = rag.rag_tool_base(question)
-
-    return f"{answer}"
-
-if __name__ == "__main__":
-
-    # To dinamically change Tool description: not used but in future maybe
-    rag_tool_desc=[
-    f"""
-    Use this tool to answer any question that may benefit from up-to-date or domain-specific information.
-    
-    Args:
-        question: the question for which are you looking for an answer
-        
-    Returns:
-        JSON string with answer
-    """
-    ]
-
-
-    # Initialize and run the server
-    
-    # Set optimizer_settings.json file ABSOLUTE path
-    rag.set_optimizer_settings_path("optimizer_settings.json")
-    
-    #mcp.run(transport='stdio')
-    mcp.run(transport='sse')
\ No newline at end of file
diff --git a/src/client/mcp/rag/uv.lock b/src/client/mcp/rag/uv.lock
new file mode 100644
index 00000000..c1cf4b34
--- /dev/null
+++ b/src/client/mcp/rag/uv.lock
@@ -0,0 +1,2925 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.12.4'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
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+]
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+[[package]]
+name = "aiohttp"
+version = "3.13.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
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diff --git a/src/client/mcp/todo_list.md b/src/client/mcp/todo_list.md
new file mode 100644
index 00000000..80e32d1e
--- /dev/null
+++ b/src/client/mcp/todo_list.md
@@ -0,0 +1,6 @@
+- [ ] Analyze requirements
+- [ ] Set up necessary files
+- [ ] Implement main functionality
+- [ ] Handle edge cases
+- [ ] Test the implementation
+- [ ] Verify results
diff --git a/src/client/spring_ai/templates/start.sh b/src/client/spring_ai/templates/start.sh
deleted file mode 100644
index 98c6231d..00000000
--- a/src/client/spring_ai/templates/start.sh
+++ /dev/null
@@ -1,45 +0,0 @@
-# Set Values
-export PROVIDER="{provider}"
-
-if [[ "{provider}" == "ollama" ]]; then
-    PREFIX="OL"; UNSET_PREFIX="OP"
-    export OPENAI_CHAT_MODEL=""
-    unset OPENAI_EMBEDDING_MODEL
-    unset OPENAI_URL
-    export OLLAMA_BASE_URL="{ll_model[api_base]}"
-    export OLLAMA_CHAT_MODEL="{ll_model[id]}"
-    export OLLAMA_EMBEDDING_MODEL="{vector_search[id]}"
-else
-    PREFIX="OP"; UNSET_PREFIX="OL"
-    export OPENAI_CHAT_MODEL="{ll_model[id]}"
-    export OPENAI_EMBEDDING_MODEL="{vector_search[id]}"
-    export OPENAI_URL="{ll_model[api_base]}"
-    export OLLAMA_CHAT_MODEL=""
-    unset OLLAMA_EMBEDDING_MODEL
-fi
-
-TEMPERATURE="{ll_model[temperature]}"
-FREQUENCY_PENALTY="{ll_model[frequency_penalty]}"
-PRESENCE_PENALTY="{ll_model[presence_penalty]}"
-MAX_TOKENS="{ll_model[max_tokens]}"
-TOP_P="{ll_model[top_p]}"
-COMMON_VARS=("TEMPERATURE" "FREQUENCY_PENALTY" "PRESENCE_PENALTY" "MAX_TOKENS" "TOP_P")
-
-# Loop through the common variables and export them
-for var in "${{COMMON_VARS[@]}}"; do
-    export ${{PREFIX}}_${{var}}="${{!var}}"
-    unset ${{UNSET_PREFIX}}_${{var}}
-done
-
-# env_vars
-export SPRING_AI_OPENAI_API_KEY=${{OPENAI_API_KEY}}
-export DB_DSN="jdbc:oracle:thin:@{database_config[dsn]}"
-export DB_USERNAME="{database_config[user]}"
-export DB_PASSWORD="{database_config[password]}"
-export DISTANCE_TYPE="{vector_search[distance_metric]}"
-export INDEX_TYPE="{vector_search[index_type]}"
-export SYS_INSTR="{sys_prompt}"
-export TOP_K="{vector_search[top_k]}"
-
-export VECTOR_STORE="{vector_search[vector_store]}"
-mvn spring-boot:run -P {provider}
\ No newline at end of file
diff --git a/src/client/utils/st_common.py b/src/client/utils/st_common.py
index 0e4eca05..0743918f 100644
--- a/src/client/utils/st_common.py
+++ b/src/client/utils/st_common.py
@@ -117,7 +117,7 @@ def is_db_configured() -> bool:
         (
             config.get("connected")
             for config in state.database_configs
-            if config.get("name") == state.client_settings["database"]["alias"]
+            if config.get("name") == state.client_settings.get("database", {}).get("alias")
         ),
         False,
     )
@@ -274,7 +274,7 @@ def _update_set_tool():
         switch_prompt("sys", "Basic Example")
     else:
         # Client Settings
-        db_alias = state.client_settings["database"]["alias"]
+        db_alias = state.client_settings.get("database", {}).get("alias")
         oci_auth_profile = state.client_settings["oci"]["auth_profile"]
 
         # Lookups
@@ -346,7 +346,7 @@ def _disable_vector_search(reason):
 #####################################################
 def selectai_sidebar() -> None:
     """SelectAI Sidebar Settings, conditional if Database/SelectAI are configured"""
-    db_alias = state.client_settings["database"]["alias"]
+    db_alias = state.client_settings.get("database", {}).get("alias")
     database_lookup = state_configs_lookup("database_configs", "name")
     if state.client_settings["selectai"]["enabled"]:
         st.sidebar.subheader("SelectAI", divider="red")
@@ -418,22 +418,6 @@ def _render_vector_search_options(vector_search_type: str) -> None:
         )
 
 
-def _vs_reset() -> None:
-    """Reset Vector Store Selections"""
-    for key in state.client_settings["vector_search"]:
-        if key in (
-            "model",
-            "chunk_size",
-            "chunk_overlap",
-            "distance_metric",
-            "vector_store",
-            "alias",
-            "index_type",
-        ):
-            clear_state_key(f"selected_vector_search_{key}")
-            state.client_settings["vector_search"][key] = ""
-
-
 def _vs_gen_selectbox(label: str, options: list, key: str):
     """Handle selectbox with auto-setting for a single unique value"""
     valid_options = [option for option in options if option != ""]
@@ -447,6 +431,10 @@ def _vs_gen_selectbox(label: str, options: list, key: str):
             logger.debug("Defaulting %s to %s", key, selected_value)
         else:
             selected_value = state.client_settings["vector_search"][key.removeprefix("selected_vector_search_")] or ""
+            # Check if selected_value is actually in valid_options, otherwise reset to empty
+            if selected_value and selected_value not in valid_options:
+                logger.debug("Previously selected %s '%s' no longer available, resetting", key, selected_value)
+                selected_value = ""
             logger.debug("User selected %s to %s", key, selected_value)
     return st.sidebar.selectbox(
         label,
@@ -457,7 +445,7 @@ def _vs_gen_selectbox(label: str, options: list, key: str):
     )
 
 
-def _update_filtered_df(vs_df: pd.DataFrame) -> pd.DataFrame:
+def update_filtered_vector_store(vs_df: pd.DataFrame) -> pd.DataFrame:
     """Dynamically update filtered_df based on selected filters"""
     embed_models_enabled = enabled_models_lookup("embed")
     filtered = vs_df.copy()
@@ -478,9 +466,26 @@ def _update_filtered_df(vs_df: pd.DataFrame) -> pd.DataFrame:
     return filtered
 
 
-def _render_vector_store_selection(vs_df: pd.DataFrame) -> None:
+def render_vector_store_selection(vs_df: pd.DataFrame) -> None:
     """Render vector store selection controls and handle state updates."""
-    filtered_df = _update_filtered_df(vs_df)
+    st.sidebar.subheader("Vector Store", divider="red")
+
+    def reset() -> None:
+        """Reset Vector Store Selections"""
+        for key in state.client_settings["vector_search"]:
+            if key in (
+                "model",
+                "chunk_size",
+                "chunk_overlap",
+                "distance_metric",
+                "vector_store",
+                "alias",
+                "index_type",
+            ):
+                clear_state_key(f"selected_vector_search_{key}")
+                state.client_settings["vector_search"][key] = ""
+
+    filtered_df = update_filtered_vector_store(vs_df)
 
     # Render selectbox with updated options
     alias = _vs_gen_selectbox("Select Alias:", filtered_df["alias"].unique().tolist(), "selected_vector_search_alias")
@@ -488,7 +493,9 @@ def _render_vector_store_selection(vs_df: pd.DataFrame) -> None:
         "Select Model:", filtered_df["model"].unique().tolist(), "selected_vector_search_model"
     )
     chunk_size = _vs_gen_selectbox(
-        "Select Chunk Size:", filtered_df["chunk_size"].unique().tolist(), "selected_vector_search_chunk_size"
+        "Select Chunk Size:",
+        filtered_df["chunk_size"].unique().tolist(),
+        "selected_vector_search_chunk_size",
     )
     chunk_overlap = _vs_gen_selectbox(
         "Select Chunk Overlap:",
@@ -501,7 +508,9 @@ def _render_vector_store_selection(vs_df: pd.DataFrame) -> None:
         "selected_vector_search_distance_metric",
     )
     index_type = _vs_gen_selectbox(
-        "Select Index Type:", filtered_df["index_type"].unique().tolist(), "selected_vector_search_index_type"
+        "Select Index Type:",
+        filtered_df["index_type"].unique().tolist(),
+        "selected_vector_search_index_type",
     )
 
     if all([alias, embed_model, chunk_size, chunk_overlap, distance_metric, index_type]):
@@ -514,11 +523,11 @@ def _render_vector_store_selection(vs_df: pd.DataFrame) -> None:
         state.client_settings["vector_search"]["distance_metric"] = distance_metric
         state.client_settings["vector_search"]["index_type"] = index_type
     else:
-        st.error("Please select Vector Store options or disable Vector Search to continue.", icon="❌")
+        st.info("Please select existing Vector Store options to continue.", icon="⬅️")
         state.enable_client = False
 
     # Reset button
-    st.sidebar.button("Reset", type="primary", on_click=_vs_reset)
+    st.sidebar.button("Reset", type="primary", on_click=reset)
 
 
 def vector_search_sidebar() -> None:
@@ -541,10 +550,9 @@ def vector_search_sidebar() -> None:
         _render_vector_search_options(vector_search_type)
 
         # Vector Store Section
-        st.sidebar.subheader("Vector Store", divider="red")
-        db_alias = state.client_settings["database"]["alias"]
+        db_alias = state.client_settings.get("database", {}).get("alias")
         database_lookup = state_configs_lookup("database_configs", "name")
-        vs_df = pd.DataFrame(database_lookup[db_alias].get("vector_stores"))
+        vs_df = pd.DataFrame(database_lookup.get(db_alias, {}).get("vector_stores", []))
 
         # Render vector store selection controls
-        _render_vector_store_selection(vs_df)
+        render_vector_store_selection(vs_df)
diff --git a/src/common/schema.py b/src/common/schema.py
index 52ee5e1a..fdc7af72 100644
--- a/src/common/schema.py
+++ b/src/common/schema.py
@@ -38,6 +38,28 @@ class DatabaseVectorStorage(BaseModel):
     index_type: Optional[IndexTypes] = Field(default=None, description="Vector Index")
 
 
+class VectorStoreRefreshRequest(BaseModel):
+    """Request for refreshing vector store from OCI bucket"""
+
+    vector_store_alias: str = Field(..., description="Alias of the existing vector store to refresh")
+    bucket_name: str = Field(..., description="OCI bucket name containing documents")
+    auth_profile: Optional[str] = Field(default="DEFAULT", description="OCI auth profile to use")
+    rate_limit: Optional[int] = Field(default=0, description="Rate limit in requests per minute")
+
+
+class VectorStoreRefreshStatus(BaseModel):
+    """Status response for vector store refresh operation"""
+
+    status: Literal["processing", "completed", "failed"] = Field(..., description="Current status")
+    message: str = Field(..., description="Status message")
+    processed_files: int = Field(default=0, description="Number of files processed")
+    new_files: int = Field(default=0, description="Number of new files found")
+    updated_files: int = Field(default=0, description="Number of updated files found")
+    total_chunks: int = Field(default=0, description="Total number of chunks processed")
+    total_chunks_in_store: int = Field(default=0, description="Total number of chunks in vector store after refresh")
+    errors: Optional[list[str]] = Field(default=[], description="Any errors encountered")
+
+
 class DatabaseSelectAIObjects(BaseModel):
     """Database SelectAI Objects"""
 
diff --git a/src/server/api/utils/embed.py b/src/server/api/utils/embed.py
index 24bd9709..f097294f 100644
--- a/src/server/api/utils/embed.py
+++ b/src/server/api/utils/embed.py
@@ -4,11 +4,13 @@
 """
 # spell-checker:ignore langchain docstore docos vectorstores oraclevs genai hnsw
 
-import json
 import copy
+import datetime
+import json
 import math
 import os
 from pathlib import Path
+import shutil
 import time
 from typing import Union
 
@@ -26,6 +28,7 @@
 from langchain_text_splitters import HTMLHeaderTextSplitter, CharacterTextSplitter
 
 import server.api.utils.databases as utils_databases
+import server.api.utils.oci as utils_oci
 
 from common import schema, functions
 
@@ -54,15 +57,15 @@ def doc_to_json(document: LangchainDocument, file: str, output_dir: str = None)
     json_data = json.dumps(docs_dict, indent=4)
 
     dst_file_path = os.path.join(output_dir, dst_file_name)
-    with open(dst_file_path, "w", encoding="utf-8") as file:
-        file.write(json_data)
+    with open(dst_file_path, "w", encoding="utf-8") as f:
+        f.write(json_data)
     file_size = os.path.getsize(dst_file_path)
     logger.info("Wrote split JSON file: %s (%i bytes)", dst_file_path, file_size)
 
     return dst_file_path
 
 
-def process_metadata(idx: int, chunk: str) -> str:
+def process_metadata(idx: int, chunk: str, file_metadata: dict = None) -> str:
     """Add Metadata to Split Document"""
     filename = os.path.basename(chunk.metadata["source"])
     file = os.path.splitext(filename)[0]
@@ -72,6 +75,13 @@ def process_metadata(idx: int, chunk: str) -> str:
     # Add More Metadata as Required
     chunk_metadata["id"] = f"{file}_{idx}"
     chunk_metadata["filename"] = filename
+
+    # Add file size and timestamp if available
+    if file_metadata and filename in file_metadata:
+        chunk_metadata["size"] = file_metadata[filename].get("size")
+        chunk_metadata["time_modified"] = file_metadata[filename].get("time_modified")
+        chunk_metadata["etag"] = file_metadata[filename].get("etag")
+
     split_doc_with_mdata.append(LangchainDocument(page_content=str(chunk.page_content), metadata=chunk_metadata))
     return split_doc_with_mdata
 
@@ -149,6 +159,7 @@ def load_and_split_documents(
     chunk_overlap: int,
     write_json: bool = False,
     output_dir: str = None,
+    file_metadata: dict = None,
 ) -> list[LangchainDocument]:
     """
     Loads file into a Langchain Document.  Calls the Splitter (split_document) function
@@ -157,11 +168,24 @@ def load_and_split_documents(
     """
     split_files = []
     all_split_docos = []
+
+    # If no metadata provided, create from file system
+    if file_metadata is None:
+        file_metadata = {}
+
     for file in src_files:
         name = os.path.basename(file)
         stat = os.stat(file)
         extension = os.path.splitext(file)[1][1:]
         logger.info("Loading %s (%i bytes)", name, stat.st_size)
+
+        # Capture file metadata if not already provided (from upload)
+        if name not in file_metadata:
+            file_metadata[name] = {
+                "size": stat.st_size,
+                "time_modified": datetime.datetime.fromtimestamp(stat.st_mtime, datetime.timezone.utc).isoformat(),
+            }
+
         split = True
         match extension.lower():
             case "pdf":
@@ -190,7 +214,7 @@ def load_and_split_documents(
             # Add IDs to metadata
             split_docos = []
             for idx, chunk in enumerate(split_doc, start=1):
-                split_doc_with_mdata = process_metadata(idx, chunk)
+                split_doc_with_mdata = process_metadata(idx, chunk, file_metadata)
                 split_docos += split_doc_with_mdata
         else:
             split_files = file
@@ -369,3 +393,321 @@ def json_to_doc(file: str):
     comment = f"COMMENT ON TABLE {vector_store.vector_store} IS 'GENAI: {store_comment}'"
     utils_databases.execute_sql(db_conn, comment)
     utils_databases.disconnect(db_conn)
+
+
+##########################################
+# Vector Store Refresh
+##########################################
+def get_vector_store_by_alias(db_details: schema.Database, alias: str) -> schema.DatabaseVectorStorage:
+    """Retrieve vector store configuration by alias"""
+    db_conn = utils_databases.connect(db_details)
+
+    try:
+        # Query for vector store with the given alias - using all_tab_comments like _get_vs does
+        query = """
+            SELECT ut.table_name,
+                   REPLACE(utc.comments, 'GENAI: ', '') AS comments
+            FROM all_tab_comments utc, all_tables ut
+            WHERE utc.table_name = ut.table_name
+            AND utc.comments LIKE 'GENAI:%'
+        """
+
+        cursor = db_conn.cursor()
+        cursor.execute(query)
+        results = cursor.fetchall()
+
+        # Find the vector store with matching alias
+        for table_name, comments in results:
+            try:
+                comments_dict = json.loads(comments)
+                if comments_dict.get("alias") == alias:
+                    vs_config = schema.DatabaseVectorStorage(vector_store=table_name, **comments_dict)
+                    return vs_config
+            except (json.JSONDecodeError, KeyError):
+                logger.warning("Failed to parse comments for table %s", table_name)
+                continue
+
+        raise ValueError(f"Vector store with alias '{alias}' not found")
+
+    finally:
+        utils_databases.disconnect(db_conn)
+
+
+def get_total_chunks_count(db_details: schema.Database, vector_store_name: str) -> int:
+    """Get total number of chunks in the vector store"""
+    db_conn = utils_databases.connect(db_details)
+
+    try:
+        query = f'SELECT COUNT(*) FROM "{vector_store_name}"'
+        cursor = db_conn.cursor()
+        cursor.execute(query)
+        result = cursor.fetchone()
+        return result[0] if result else 0
+    except Exception as ex:
+        logger.warning("Could not count chunks in %s: %s", vector_store_name, ex)
+        return 0
+    finally:
+        utils_databases.disconnect(db_conn)
+
+
+def get_processed_objects_metadata(db_details: schema.Database, vector_store_name: str) -> dict:
+    """Get metadata of previously processed objects for a vector store"""
+    db_conn = utils_databases.connect(db_details)
+
+    try:
+        # Retrieve all metadata and parse in Python since JSON_VALUE doesn't work
+        logger.info("Retrieving metadata from %s", vector_store_name)
+        cursor = db_conn.cursor()
+
+        # Get all unique metadata entries - metadata is automatically converted to dict by Oracle driver
+        query = f'SELECT DISTINCT metadata FROM "{vector_store_name}"'
+        logger.info("SQL Query: %s", query)
+        cursor.execute(query)
+        results = cursor.fetchall()
+        logger.info("Query returned %s metadata entries", len(results))
+
+        # Parse metadata in Python to extract filename, etag, etc.
+        processed_objects = {}
+        for row in results:
+            metadata = row[0]
+            # metadata is already a dict thanks to Oracle driver
+            if isinstance(metadata, dict) and "filename" in metadata:
+                filename = metadata.get("filename")
+                if filename:
+                    processed_objects[filename] = {
+                        "etag": metadata.get("etag"),
+                        "time_modified": metadata.get("time_modified"),
+                        "size": metadata.get("size"),
+                    }
+
+        if processed_objects:
+            logger.info(
+                "Found %i previously processed objects (new format) in %s", len(processed_objects), vector_store_name
+            )
+            return processed_objects
+
+        # Try old format - check for 'source' field in metadata
+        logger.info("No filename field found, trying old format with 'source' field")
+        for row in results:
+            metadata = row[0]
+            if isinstance(metadata, dict) and "source" in metadata:
+                source_path = metadata.get("source")
+                if source_path:
+                    filename = os.path.basename(source_path)
+                    # For old format, we don't have etag/time_modified, so just mark as existing
+                    processed_objects[filename] = {"etag": None, "time_modified": None, "size": None}
+
+        if processed_objects:
+            logger.info(
+                "Found %s previously processed objects (old format) in %s",
+                len(processed_objects),
+                vector_store_name,
+            )
+            logger.info(
+                "Note: Old metadata format detected. Files will be re-processed with new metadata on next refresh."
+            )
+            return processed_objects
+
+        logger.info("No previously processed objects found in %s", vector_store_name)
+        return {}
+
+    except Exception as ex:
+        # If table doesn't have metadata column or query fails, return empty dict
+        logger.warning("Could not retrieve processed objects metadata from %s: %s", vector_store_name, ex)
+        return {}
+    finally:
+        utils_databases.disconnect(db_conn)
+
+
+def get_vector_store_files(db_details: schema.Database, vector_store_name: str) -> dict:
+    """Get list of files embedded in a vector store with statistics"""
+    db_conn = utils_databases.connect(db_details)
+
+    try:
+        logger.info("Retrieving file list from %s", vector_store_name)
+        cursor = db_conn.cursor()
+
+        # Get all metadata entries with chunk count
+        query = f'SELECT metadata FROM "{vector_store_name}"'
+        logger.info("SQL Query: %s", query)
+        cursor.execute(query)
+        results = cursor.fetchall()
+        logger.info("Query returned %s chunks", len(results))
+
+        # Parse metadata to extract file information
+        files_info = {}
+        total_identified_chunks = 0
+        orphaned_chunks = 0
+
+        for row in results:
+            metadata = row[0]
+
+            # Skip non-dict metadata
+            if not isinstance(metadata, dict):
+                orphaned_chunks += 1
+                continue
+
+            # Try new format first (filename field)
+            filename = metadata.get("filename")
+            if not filename and "source" in metadata:
+                # Fall back to old format
+                filename = os.path.basename(metadata.get("source", ""))
+
+            # Skip chunks without filename
+            if not filename:
+                orphaned_chunks += 1
+                continue
+
+            # Initialize file entry if needed
+            if filename not in files_info:
+                # Convert size to int if it's a Decimal (from Oracle NUMBER type)
+                size_value = metadata.get("size")
+                if size_value is not None:
+                    size_value = int(size_value)
+
+                files_info[filename] = {
+                    "filename": filename,
+                    "chunk_count": 0,
+                    "etag": metadata.get("etag"),
+                    "time_modified": metadata.get("time_modified"),
+                    "size": size_value,
+                }
+
+            files_info[filename]["chunk_count"] += 1
+            total_identified_chunks += 1
+
+        # Convert to list and sort by filename
+        file_list = sorted(files_info.values(), key=lambda x: x["filename"])
+
+        result = {
+            "vector_store": vector_store_name,
+            "total_files": len(file_list),
+            "total_chunks": total_identified_chunks,
+            "orphaned_chunks": orphaned_chunks,
+            "files": file_list,
+        }
+
+        if orphaned_chunks > 0:
+            logger.warning(
+                "Found %s orphaned chunks without valid filename metadata in %s",
+                orphaned_chunks,
+                vector_store_name,
+            )
+
+        logger.info("Found %s files with %s total chunks in %s", len(file_list), len(results), vector_store_name)
+        return result
+
+    except Exception as ex:
+        logger.error("Could not retrieve file list from %s: %s", vector_store_name, ex)
+        raise
+    finally:
+        utils_databases.disconnect(db_conn)
+
+
+def refresh_vector_store_from_bucket(
+    vector_store_config: schema.DatabaseVectorStorage,
+    bucket_name: str,
+    bucket_objects: list[dict],
+    db_details: schema.Database,
+    embed_client,
+    oci_config,
+    rate_limit: int = 0,
+) -> dict:
+    """
+    Refresh vector store with new/modified objects from OCI bucket
+
+    Args:
+        vector_store_config: Existing vector store configuration
+        bucket_name: OCI bucket name
+        bucket_objects: List of new/modified objects to process
+        db_details: Database configuration
+        embed_client: Embedding client
+        oci_config: OCI configuration
+        rate_limit: Rate limit in requests per minute
+
+    Returns:
+        Dict with processing results
+    """
+    if not bucket_objects:
+        return {
+            "processed_files": 0,
+            "new_files": 0,
+            "updated_files": 0,
+            "total_chunks": 0,
+            "message": "No new or modified files to process",
+        }
+
+    temp_directory = get_temp_directory("refresh", "embedding")
+    logger.info("Processing %d objects for vector store refresh", len(bucket_objects))
+
+    try:
+        # Download changed objects
+        downloaded_files = []
+        for obj in bucket_objects:
+            try:
+                file_path = utils_oci.get_object(str(temp_directory), obj["name"], bucket_name, oci_config)
+                downloaded_files.append(file_path)
+            except Exception as ex:
+                logger.error("Failed to download object %s: %s", obj["name"], ex)
+                continue
+
+        if not downloaded_files:
+            return {
+                "processed_files": 0,
+                "new_files": 0,
+                "updated_files": 0,
+                "total_chunks": 0,
+                "message": "No files could be downloaded",
+                "errors": ["Failed to download any objects from bucket"],
+            }
+
+        # Build file metadata dict from bucket objects
+        file_metadata = {}
+        for obj in bucket_objects:
+            filename = os.path.basename(obj["name"])
+            size_value = obj.get("size")
+            if size_value is not None:
+                size_value = int(size_value)
+            file_metadata[filename] = {
+                "size": size_value,
+                "time_modified": obj.get("time_modified"),
+                "etag": obj.get("etag"),
+                "bucket_name": bucket_name,
+            }
+        logger.info("Built metadata dict for %d files from bucket objects", len(file_metadata))
+
+        # Process documents with metadata
+        split_docos, _ = load_and_split_documents(
+            downloaded_files,
+            vector_store_config.model,
+            vector_store_config.chunk_size,
+            vector_store_config.chunk_overlap,
+            write_json=False,
+            output_dir=None,
+            file_metadata=file_metadata,
+        )
+
+        # Metadata already set by load_and_split_documents with file_metadata parameter
+        logger.info("Processed %s document chunks with OCI bucket metadata", len(split_docos))
+
+        # Populate vector store
+        populate_vs(
+            vector_store=vector_store_config,
+            db_details=db_details,
+            embed_client=embed_client,
+            input_data=split_docos,
+            rate_limit=rate_limit,
+        )
+
+        return {
+            "processed_files": len(downloaded_files),
+            "new_files": len(bucket_objects),
+            "updated_files": 0,  # All are treated as new for now
+            "total_chunks": len(split_docos),
+            "message": f"Successfully processed {len(downloaded_files)} files and {len(split_docos)} chunks",
+        }
+
+    finally:
+        # Clean up temporary directory
+        if temp_directory.exists():
+            shutil.rmtree(temp_directory)
diff --git a/src/server/api/utils/oci.py b/src/server/api/utils/oci.py
index bc0510c7..e489b34c 100644
--- a/src/server/api/utils/oci.py
+++ b/src/server/api/utils/oci.py
@@ -367,6 +367,84 @@ def get_bucket_objects(bucket_name: str, config: OracleCloudSettings = None) ->
     return object_names
 
 
+def get_bucket_objects_with_metadata(bucket_name: str, config: OracleCloudSettings = None) -> list[dict]:
+    """Get a list of Bucket Objects with metadata for change detection"""
+    client_type = oci.object_storage.ObjectStorageClient
+    client = init_client(client_type, config)
+
+    objects_metadata = []
+    try:
+        response = client.list_objects(
+            namespace_name=config.namespace,
+            bucket_name=bucket_name,
+            fields="name,size,etag,timeModified,md5"
+        )
+        objects = response.data.objects
+
+        # Filter supported file types and add metadata
+        supported_extensions = {'.pdf', '.html', '.md', '.txt', '.csv', '.png', '.jpg', '.jpeg'}
+
+        for obj in objects:
+            _, ext = os.path.splitext(obj.name.lower())
+            if ext in supported_extensions:
+                obj_metadata = {
+                    "name": obj.name,
+                    "size": obj.size,
+                    "etag": obj.etag,
+                    "time_modified": obj.time_modified.isoformat() if obj.time_modified else None,
+                    "md5": obj.md5,
+                    "extension": ext[1:]  # Remove the dot
+                }
+                objects_metadata.append(obj_metadata)
+    except oci.exceptions.ServiceError:
+        logger.debug("Bucket %s not found.", bucket_name)
+
+    logger.info("Retrieved %d objects with metadata from bucket %s", len(objects_metadata), bucket_name)
+    return objects_metadata
+
+
+def detect_changed_objects(
+    current_objects: list[dict],
+    processed_objects: dict
+) -> tuple[list[dict], list[dict]]:
+    """
+    Detect new and modified objects by comparing current bucket state
+    with previously processed objects metadata
+
+    Args:
+        current_objects: Current objects from OCI bucket with metadata
+        processed_objects: Dict mapping object names to their last processed metadata
+
+    Returns:
+        Tuple of (new_objects, modified_objects)
+    """
+    new_objects = []
+    modified_objects = []
+
+    for obj in current_objects:
+        obj_name = obj["name"]
+
+        if obj_name not in processed_objects:
+            # New object
+            new_objects.append(obj)
+        else:
+            # Check if object has been modified
+            last_processed = processed_objects[obj_name]
+
+            # If old format (no etag), skip - assume unchanged to avoid duplicates
+            if last_processed.get("etag") is None and last_processed.get("time_modified") is None:
+                logger.debug("Skipping %s - found in old metadata format (assumed unchanged)", obj_name)
+                continue
+
+            # Compare etag and modification time
+            if (obj["etag"] != last_processed.get("etag") or
+                obj["time_modified"] != last_processed.get("time_modified")):
+                modified_objects.append(obj)
+
+    logger.info("Found %d new objects and %d modified objects", len(new_objects), len(modified_objects))
+    return new_objects, modified_objects
+
+
 def get_object(directory: str, object_name: str, bucket_name: str, config: OracleCloudSettings = None) -> list:
     """Download Object Storage Object"""
     client_type = oci.object_storage.ObjectStorageClient
diff --git a/src/server/api/utils/testbed.py b/src/server/api/utils/testbed.py
index d69fabfb..5968ffd7 100644
--- a/src/server/api/utils/testbed.py
+++ b/src/server/api/utils/testbed.py
@@ -247,7 +247,9 @@ def build_knowledge_base(
         model_config={"model": ll_model}, oci_config=oci_config, giskard=True
     )
     set_llm_model(llm_model=ll_model, **ll_model_config)
-    embed_model_config = utils_models.get_litellm_config(model_config={"model": embed_model}, giskard=True)
+    embed_model_config = utils_models.get_litellm_config(
+        model_config={"model": embed_model}, oci_config=oci_config, giskard=True
+    )
     set_embedding_model(model=embed_model, **embed_model_config)
 
     knowledge_base_df = pd.DataFrame([node.text for node in text_nodes], columns=["text"])
diff --git a/src/server/api/utils/webscrape.py b/src/server/api/utils/webscrape.py
index 205e7a40..835e2402 100644
--- a/src/server/api/utils/webscrape.py
+++ b/src/server/api/utils/webscrape.py
@@ -1,44 +1,74 @@
+"""
+Copyright (c) 2024, 2025, Oracle and/or its affiliates.
+Licensed under the Universal Permissive License v1.0 as shown at http://oss.oracle.com/licenses/upl.
+"""
+
+import re
+import unicodedata
+from typing import List, Dict
 from bs4 import BeautifulSoup, Comment
-import re, unicodedata
-from typing import List, Dict, Tuple
 import aiohttp
 
 BAD_CHUNKS = [
-    "nav","header","footer","aside","form","menu","breadcrumb","toc","pagination",
-    "subscribe","advert","ads","promo","social","share","comment","related","widget",
-    "modal","banner","cookie","newsletter","disclaimer"
+    "nav",
+    "header",
+    "footer",
+    "aside",
+    "form",
+    "menu",
+    "breadcrumb",
+    "toc",
+    "pagination",
+    "subscribe",
+    "advert",
+    "ads",
+    "promo",
+    "social",
+    "share",
+    "comment",
+    "related",
+    "widget",
+    "modal",
+    "banner",
+    "cookie",
+    "newsletter",
+    "disclaimer",
 ]
 
+
 def normalize_ws(s: str) -> str:
+    """Normalize whitespace in a string"""
     s = unicodedata.normalize("NFKC", s)
     s = re.sub(r"\s+", " ", s)
     return s.strip()
 
+
 def clean_soup(soup: BeautifulSoup) -> None:
-    for el in soup(["script","style","noscript","template","svg","canvas","iframe"]):
+    """Remove unwanted elements from BeautifulSoup object"""
+    for el in soup(["script", "style", "noscript", "template", "svg", "canvas", "iframe"]):
         el.decompose()
     for c in soup.find_all(string=lambda t: isinstance(t, Comment)):
         c.extract()
     for tag in soup.find_all(True):
-        ident = " ".join([
-            (tag.get("id") or ""),
-            " ".join(tag.get("class") or []),
-            (tag.get("role") or "")
-        ]).lower()
+        ident = " ".join([(tag.get("id") or ""), " ".join(tag.get("class") or []), (tag.get("role") or "")]).lower()
         if any(b in ident for b in BAD_CHUNKS):
             tag.decompose()
 
+
 def heading_level(tag) -> int:
+    """Extract heading level from heading tag"""
     return int(tag.name[1])
 
+
 def group_by_sections(soup):
+    """Group content by section and article tags"""
     sections = []
-    for section in soup.find_all(['section', 'article']):
+    for section in soup.find_all(["section", "article"]):
         # Use the first heading if present for section title
-        heading = section.find(re.compile('^h[1-6]$'))
+        heading = section.find(re.compile("^h[1-6]$"))
         title = normalize_ws(heading.get_text()) if heading else ""
         paragraphs = []
-        for p in section.find_all('p'):
+        for p in section.find_all("p"):
             txt = normalize_ws(p.get_text())
             if txt:
                 paragraphs.append(txt)
@@ -47,8 +77,9 @@ def group_by_sections(soup):
             sections.append({"title": title, "content": "\n\n".join(paragraphs)})
     return sections
 
+
 def table_to_markdown(table):
-    # Simple HTML table to Markdown converter
+    """Convert HTML table to Markdown format"""
     rows = []
     for tr in table.find_all("tr"):
         cols = [td.get_text(strip=True) for td in tr.find_all(["td", "th"])]
@@ -62,7 +93,9 @@ def table_to_markdown(table):
             md += "| " + " | ".join(row) + " |\n"
     return md
 
+
 def group_by_headings(soup):
+    """Group content by heading hierarchy"""
     grouped = []
     # Find all headings
     for hdr in soup.find_all(re.compile("^h[1-6]$")):
@@ -78,7 +111,7 @@ def group_by_headings(soup):
                 if text:
                     buffer.append(text)
             elif sib.name in ("ul", "ol"):
-                for li in sib.find_all('li'):
+                for li in sib.find_all("li"):
                     text = normalize_ws(li.get_text())
                     if text:
                         buffer.append("• " + text)
@@ -86,7 +119,9 @@ def group_by_headings(soup):
             grouped.append({"title": title, "content": "\n\n".join(buffer)})
     return grouped
 
+
 def sections_to_markdown(sections: List[Dict]) -> str:
+    """Convert sections to Markdown format"""
     lines: List[str] = []
     for s in sections:
         hashes = "#" * max(1, min(6, s["level"]))
@@ -97,43 +132,49 @@ def sections_to_markdown(sections: List[Dict]) -> str:
     out = "\n".join(lines).strip()
     return out + "\n" if out else out
 
+
 def slugify(text: str, max_len: int = 80) -> str:
+    """Convert text to URL-safe slug"""
     text = unicodedata.normalize("NFKD", text)
     text = re.sub(r"[^\w\s-]", "", text).strip().lower()
     text = re.sub(r"[\s_-]+", "-", text)
     return text[:max_len] or "page"
-    
+
+
 async def fetch_and_extract_paragraphs(url):
+    """Fetch URL and extract paragraphs"""
     paragraphs = []
     async with aiohttp.ClientSession() as session:
         async with session.get(str(url)) as response:
             html = await response.text()
-            soup = BeautifulSoup(html, 'html.parser')
-            
+            soup = BeautifulSoup(html, "html.parser")
+
             for script in soup(["script", "style"]):
                 script.decompose()
             for element in soup(text=lambda text: isinstance(text, Comment)):
                 element.extract()
-            
+
             for p in soup.find_all("p"):
                 txt = normalize_ws(p.get_text())
-                if txt:  
+                if txt:
                     paragraphs.append(txt)
     return paragraphs
 
+
 async def fetch_and_extract_sections(url):
+    """Fetch URL and extract sections by structure"""
     async with aiohttp.ClientSession() as session:
         async with session.get(str(url)) as response:
             html = await response.text()
-            soup = BeautifulSoup(html, 'html.parser')
-            
+            soup = BeautifulSoup(html, "html.parser")
+
             for script in soup(["script", "style"]):
                 script.decompose()
             for element in soup(text=lambda text: isinstance(text, Comment)):
                 element.extract()
-            
+
             # Prefer by section, or fallback to headings
             chunks = group_by_sections(soup)
             if not chunks:
                 chunks = group_by_headings(soup)
-    return chunks
\ No newline at end of file
+    return chunks
diff --git a/src/server/api/v1/embed.py b/src/server/api/v1/embed.py
index 23d90a23..7609243a 100644
--- a/src/server/api/v1/embed.py
+++ b/src/server/api/v1/embed.py
@@ -2,8 +2,9 @@
 Copyright (c) 2024, 2025, Oracle and/or its affiliates.
 Licensed under the Universal Permissive License v1.0 as shown at http://oss.oracle.com/licenses/upl.
 """
-# spell-checker:ignore docos
+# spell-checker:ignore docos slugified webscrape
 
+import datetime
 import json
 from urllib.parse import urlparse
 from pathlib import Path
@@ -46,6 +47,25 @@ async def embed_drop_vs(
     return JSONResponse(status_code=200, content={"message": f"Vector Store: {vs} dropped."})
 
 
+@auth.get(
+    "/{vs}/files",
+    description="Get list of files embedded in a Vector Store",
+)
+async def embed_get_files(
+    vs: schema.VectorStoreTableType,
+    client: schema.ClientIdType = Header(default="server"),
+) -> JSONResponse:
+    """Get list of files in Vector Store with statistics"""
+    logger.debug("Received %s embed_get_files: %s", client, vs)
+    try:
+        client_db = utils_databases.get_client_database(client)
+        file_list = utils_embed.get_vector_store_files(client_db, vs)
+        return JSONResponse(status_code=200, content=file_list)
+    except Exception as ex:
+        logger.error("Error retrieving file list from %s: %s", vs, str(ex))
+        raise HTTPException(status_code=400, detail=f"Could not retrieve file list: {str(ex)}") from ex
+
+
 @auth.post(
     "/sql/store",
     description="Store SQL field for Embedding.",
@@ -88,10 +108,10 @@ async def store_web_file(
                 if "application/pdf" in content_type or "application/octet-stream" in content_type:
                     with open(temp_directory / filename, "wb") as file:
                         file.write(await response.read())
-                
+
                 elif "text" in content_type or "html" in content_type:
                     sections = await web_parse.fetch_and_extract_sections(url)
-                    base = web_parse.slugify(str(url).split('/')[-1]) or "page"
+                    base = web_parse.slugify(str(url).rsplit('/', maxsplit=1)[-1]) or "page"
                     out_files = []
                     for idx, sec in enumerate(sections, 1):
                         # filename includes section number and optional slugified title for clarity
@@ -126,13 +146,27 @@ async def store_local_file(
     """Store contents from a local file uploaded to streamlit"""
     logger.debug("Received store_local_file - files: %s", files)
     temp_directory = utils_embed.get_temp_directory(client, "embedding")
-    for file in files:
-        filename = temp_directory / file.filename
-        file_content = await file.read()
-        with filename.open("wb") as file:
-            file.write(file_content)
 
-    stored_files = [f.name for f in temp_directory.iterdir() if f.is_file()]
+    # Store file metadata
+    file_metadata = {}
+    for upload_file in files:
+        filename = temp_directory / upload_file.filename
+        file_content = await upload_file.read()
+        with filename.open("wb") as f:
+            f.write(file_content)
+
+        # Capture metadata for this file
+        file_metadata[upload_file.filename] = {
+            "size": len(file_content),
+            "time_modified": datetime.datetime.now(datetime.timezone.utc).isoformat(),
+        }
+
+    # Store metadata in JSON file for later use
+    metadata_file = temp_directory / ".file_metadata.json"
+    with metadata_file.open("w") as f:
+        json.dump(file_metadata, f)
+
+    stored_files = [f.name for f in temp_directory.iterdir() if f.is_file() and f.name != ".file_metadata.json"]
     return Response(content=json.dumps(stored_files), media_type="application/json")
 
 
@@ -151,7 +185,7 @@ async def split_embed(
     temp_directory = utils_embed.get_temp_directory(client, "embedding")
 
     try:
-        files = [f for f in temp_directory.iterdir() if f.is_file()]
+        files = [f for f in temp_directory.iterdir() if f.is_file() and f.name != ".file_metadata.json"]
         logger.info("Processing Files: %s", files)
     except FileNotFoundError as ex:
         raise HTTPException(
@@ -163,6 +197,19 @@ async def split_embed(
             status_code=404,
             detail=f"Embed: Client {client} no files found in folder.",
         )
+
+    # Load file metadata if available
+    file_metadata = None
+    metadata_file = temp_directory / ".file_metadata.json"
+    if metadata_file.exists():
+        try:
+            with metadata_file.open("r") as f:
+                file_metadata = json.load(f)
+            logger.info("Loaded metadata for %d files", len(file_metadata))
+        except Exception as ex:
+            logger.warning("Could not load file metadata: %s", ex)
+            file_metadata = None
+
     try:
         split_docos, _ = utils_embed.load_and_split_documents(
             files,
@@ -171,6 +218,7 @@ async def split_embed(
             request.chunk_overlap,
             write_json=False,
             output_dir=None,
+            file_metadata=file_metadata,
         )
 
         embed_client = utils_models.get_client_embed({"model": request.model, "enabled": True}, oci_config)
@@ -197,3 +245,108 @@ async def split_embed(
         raise HTTPException(status_code=500, detail="Unexpected Error.") from ex
     finally:
         shutil.rmtree(temp_directory)  # Clean up the temporary directory
+
+
+@auth.post(
+    "/refresh",
+    description="Refresh Vector Store from OCI Bucket.",
+)
+async def refresh_vector_store(
+    request: schema.VectorStoreRefreshRequest,
+    client: schema.ClientIdType = Header(default="server"),
+) -> JSONResponse:
+    """Refresh an existing vector store with new/modified documents from OCI bucket"""
+    logger.debug("Received refresh_vector_store - request: %s", request)
+
+    try:
+        # Get OCI configuration
+        oci_config = utils_oci.get(client=client, auth_profile=request.auth_profile)
+
+        # Get database configuration
+        db_details = utils_databases.get_client_database(client)
+
+        # Get existing vector store configuration
+        vs_config = utils_embed.get_vector_store_by_alias(db_details, request.vector_store_alias)
+        logger.info("Found vector store: %s with model %s", vs_config.vector_store, vs_config.model)
+
+        # Get current bucket objects with metadata
+        current_objects = utils_oci.get_bucket_objects_with_metadata(request.bucket_name, oci_config)
+
+        if not current_objects:
+            return JSONResponse(
+                status_code=200,
+                content=schema.VectorStoreRefreshStatus(
+                    status="completed",
+                    message=f"No supported files found in bucket {request.bucket_name}",
+                    processed_files=0,
+                    new_files=0,
+                    updated_files=0,
+                    total_chunks=0,
+                ).model_dump(),
+            )
+
+        # Get previously processed objects metadata
+        processed_objects = utils_embed.get_processed_objects_metadata(db_details, vs_config.vector_store)
+        logger.info("Found %d previously processed objects", len(processed_objects))
+
+        # Detect changes
+        new_objects, modified_objects = utils_oci.detect_changed_objects(current_objects, processed_objects)
+        changed_objects = new_objects + modified_objects
+
+        if not changed_objects:
+            # Get total chunks in store
+            total_chunks_in_store = utils_embed.get_total_chunks_count(db_details, vs_config.vector_store)
+
+            return JSONResponse(
+                status_code=200,
+                content=schema.VectorStoreRefreshStatus(
+                    status="completed",
+                    message="No new or modified files to process",
+                    processed_files=0,
+                    new_files=0,
+                    updated_files=0,
+                    total_chunks=0,
+                    total_chunks_in_store=total_chunks_in_store,
+                ).model_dump(),
+            )
+
+        # Get embedding client using the same model as the existing vector store
+        embed_client = utils_models.get_client_embed({"model": vs_config.model, "enabled": True}, oci_config)
+
+        # Refresh the vector store
+        result = utils_embed.refresh_vector_store_from_bucket(
+            vector_store_config=vs_config,
+            bucket_name=request.bucket_name,
+            bucket_objects=changed_objects,
+            db_details=db_details,
+            embed_client=embed_client,
+            oci_config=oci_config,
+            rate_limit=request.rate_limit,
+        )
+
+        # Get total chunks in store after refresh
+        total_chunks_in_store = utils_embed.get_total_chunks_count(db_details, vs_config.vector_store)
+
+        return JSONResponse(
+            status_code=200,
+            content=schema.VectorStoreRefreshStatus(
+                status="completed",
+                message=result.get("message", "Vector store refreshed successfully"),
+                processed_files=result.get("processed_files", 0),
+                new_files=len(new_objects),
+                updated_files=len(modified_objects),
+                total_chunks=result.get("total_chunks", 0),
+                total_chunks_in_store=total_chunks_in_store,
+                errors=result.get("errors", []),
+            ).model_dump(),
+        )
+
+    except ValueError as ex:
+        logger.error("Validation error in refresh_vector_store: %s", ex)
+        raise HTTPException(status_code=400, detail=str(ex)) from ex
+    except utils_databases.DbException as ex:
+        logger.error("Database error in refresh_vector_store: %s", ex)
+        raise HTTPException(status_code=500, detail=f"Database error: {str(ex)}") from ex
+    except Exception as ex:
+        logger.error("Unexpected error in refresh_vector_store: %s", ex)
+        raise HTTPException(status_code=500, detail="Unexpected error occurred during refresh") from ex
diff --git a/tests/client/content/tools/tabs/test_split_embed.py b/tests/client/content/tools/tabs/test_split_embed.py
index 99f987f0..27d0411b 100644
--- a/tests/client/content/tools/tabs/test_split_embed.py
+++ b/tests/client/content/tools/tabs/test_split_embed.py
@@ -692,3 +692,130 @@ def test_embedding_server_not_accessible(self, app_server, app_test, monkeypatch
         # Should show warning about server accessibility
         warnings = at.get("warning")
         assert len(warnings) > 0
+
+    def test_create_new_vs_toggle_not_shown_when_no_vector_stores(self, app_server, app_test, monkeypatch):
+        """Test that 'Create New Vector Store' toggle is NOT shown when no vector stores exist"""
+        assert app_server is not None
+        self._setup_common_mocks(monkeypatch)
+
+        # Mock database_configs with no vector stores
+        def mock_get_no_vs(endpoint=None, **kwargs):
+            if endpoint == "v1/models":
+                return [
+                    {
+                        "id": "test-model",
+                        "type": "embed",
+                        "enabled": True,
+                        "api_base": "http://test.url",
+                        "max_chunk_size": 1000,
+                    }
+                ]
+            elif endpoint == "v1/databases":
+                return [
+                    {
+                        "name": "DEFAULT",
+                        "vector_stores": [],  # No vector stores
+                    }
+                ]
+            elif endpoint == "v1/oci":
+                return [
+                    {
+                        "auth_profile": "DEFAULT",
+                        "namespace": "test-namespace",
+                        "tenancy": "test-tenancy",
+                        "region": "us-ashburn-1",
+                        "authentication": "api_key",
+                    }
+                ]
+            return {}
+
+        monkeypatch.setattr("client.utils.api_call.get", mock_get_no_vs)
+        monkeypatch.setattr("common.functions.is_url_accessible", lambda api_base: (True, ""))
+        monkeypatch.setattr("client.utils.st_common.is_db_configured", lambda: True)
+
+        at = self._run_app_and_verify_no_errors(app_test)
+
+        # Toggle should NOT be present when no vector stores exist
+        toggles = at.get("toggle")
+        create_new_toggle = next(
+            (t for t in toggles if hasattr(t, "label") and "Create New Vector Store" in str(t.label)), None
+        )
+        assert create_new_toggle is None, "Toggle should not be shown when no vector stores exist"
+
+    def test_create_new_vs_toggle_shown_when_vector_stores_exist(self, app_server, app_test, monkeypatch):
+        """Test that 'Create New Vector Store' toggle IS shown when vector stores exist"""
+        assert app_server is not None
+        self._setup_common_mocks(monkeypatch)
+
+        # Mock database_configs with existing vector stores
+        def mock_get_with_vs(endpoint=None, **kwargs):
+            if endpoint == "v1/models":
+                return [
+                    {
+                        "id": "test-model",
+                        "type": "embed",
+                        "enabled": True,
+                        "api_base": "http://test.url",
+                        "max_chunk_size": 1000,
+                    }
+                ]
+            elif endpoint == "v1/databases":
+                return [
+                    {
+                        "name": "DEFAULT",
+                        "vector_stores": [
+                            {
+                                "alias": "existing_vs",
+                                "model": "test-model",
+                                "vector_store": "VECTOR_STORE_TABLE",
+                                "chunk_size": 500,
+                                "chunk_overlap": 50,
+                                "distance_metric": "COSINE",
+                                "index_type": "IVF",
+                            }
+                        ],
+                    }
+                ]
+            elif endpoint == "v1/oci":
+                return [
+                    {
+                        "auth_profile": "DEFAULT",
+                        "namespace": "test-namespace",
+                        "tenancy": "test-tenancy",
+                        "region": "us-ashburn-1",
+                        "authentication": "api_key",
+                    }
+                ]
+            return {}
+
+        monkeypatch.setattr("client.utils.api_call.get", mock_get_with_vs)
+        monkeypatch.setattr("common.functions.is_url_accessible", lambda api_base: (True, ""))
+        monkeypatch.setattr("client.utils.st_common.is_db_configured", lambda: True)
+
+        at = self._run_app_and_verify_no_errors(app_test)
+
+        # Toggle SHOULD be present when vector stores exist
+        toggles = at.get("toggle")
+        create_new_toggle = next(
+            (t for t in toggles if hasattr(t, "label") and "Create New Vector Store" in str(t.label)), None
+        )
+        assert create_new_toggle is not None, "Toggle should be shown when vector stores exist"
+        assert create_new_toggle.value is True, "Toggle should default to True (create new mode)"
+
+    def test_populate_button_shown_in_create_new_mode(self, app_server, app_test, monkeypatch):
+        """Test that 'Populate Vector Store' button is shown when in create new mode"""
+        assert app_server is not None
+        self._setup_common_mocks(monkeypatch)
+
+        at = self._run_app_and_verify_no_errors(app_test)
+
+        # Should have Populate button
+        buttons = at.get("button")
+        populate_button = next(
+            (b for b in buttons if hasattr(b, "label") and "Populate Vector Store" in str(b.label)), None
+        )
+        assert populate_button is not None, "Populate button should be present in create new mode"
+
+        # Should NOT have Refresh button when in create new mode
+        refresh_button = next((b for b in buttons if hasattr(b, "label") and "Refresh from OCI" in str(b.label)), None)
+        assert refresh_button is None, "Refresh button should not be present in create new mode"
diff --git a/tests/server/integration/test_endpoints_embed.py b/tests/server/integration/test_endpoints_embed.py
index cab7bb1a..30062a03 100644
--- a/tests/server/integration/test_endpoints_embed.py
+++ b/tests/server/integration/test_endpoints_embed.py
@@ -54,9 +54,11 @@ class TestInvalidAuthEndpoints:
         "endpoint, api_method",
         [
             pytest.param("/v1/embed/TESTVS", "delete", id="embed_drop_vs"),
+            pytest.param("/v1/embed/TESTVS/files", "get", id="embed_get_files"),
             pytest.param("/v1/embed/web/store", "post", id="store_web_file"),
             pytest.param("/v1/embed/local/store", "post", id="store_local_file"),
             pytest.param("/v1/embed", "post", id="split_embed"),
+            pytest.param("/v1/embed/refresh", "post", id="refresh_vector_store"),
         ],
     )
     def test_endpoints(self, client, auth_headers, endpoint, api_method, auth_type, status_code):
@@ -463,3 +465,79 @@ def test_multiple_vector_stores(self, client, auth_headers, db_container, mock_e
             # Verify all vector stores are removed
             for expected_name in expected_vector_store_names:
                 self.verify_vector_store_exists(client, auth_headers, expected_name, should_exist=False)
+
+    def test_get_vector_store_files(self, client, auth_headers, db_container, mock_embedding_model):
+        """Test retrieving file list from vector store"""
+        assert db_container is not None
+        self.configure_database(client, auth_headers)
+
+        # Create and populate a vector store
+        self.create_test_file(content=LONGER_TEST_CONTENT)
+        mock_get_client_embed = self.setup_mock_embeddings(mock_embedding_model)
+
+        alias = "test_file_listing"
+        test_data = self.create_embed_params(alias)
+        expected_vector_store_name = self.get_vector_store_name(alias)
+
+        with patch("server.api.utils.models.get_client_embed", side_effect=mock_get_client_embed):
+            # Create vector store
+            response = client.post("/v1/embed", headers=auth_headers["valid_auth"], json=test_data)
+            assert response.status_code == 200
+
+            # Get file list
+            file_list_response = client.get(
+                f"/v1/embed/{expected_vector_store_name}/files",
+                headers=auth_headers["valid_auth"]
+            )
+
+            # Verify response
+            assert file_list_response.status_code == 200
+            data = file_list_response.json()
+
+            assert "vector_store" in data
+            assert data["vector_store"] == expected_vector_store_name
+            assert "total_files" in data
+            assert "total_chunks" in data
+            assert "files" in data
+            assert data["total_files"] > 0
+            assert data["total_chunks"] > 0
+
+            # Clean up
+            drop_response = client.delete(f"/v1/embed/{expected_vector_store_name}", headers=auth_headers["valid_auth"])
+            assert drop_response.status_code == 200
+
+    def test_get_files_empty_vector_store(self, client, auth_headers, db_container, mock_embedding_model):
+        """Test retrieving file list from empty vector store"""
+        assert db_container is not None
+        self.configure_database(client, auth_headers)
+
+        # Create empty vector store
+        self.create_test_file()
+        mock_get_client_embed = self.setup_mock_embeddings(mock_embedding_model)
+
+        alias = "test_empty_listing"
+        test_data = self.create_embed_params(alias)
+        expected_vector_store_name = self.get_vector_store_name(alias)
+
+        with patch("server.api.utils.models.get_client_embed", side_effect=mock_get_client_embed):
+            # Create vector store
+            response = client.post("/v1/embed", headers=auth_headers["valid_auth"], json=test_data)
+            assert response.status_code == 200
+
+            # Drop all chunks to make it empty
+            drop_response = client.delete(f"/v1/embed/{expected_vector_store_name}", headers=auth_headers["valid_auth"])
+            assert drop_response.status_code == 200
+
+    def test_get_files_nonexistent_vector_store(self, client, auth_headers, db_container):
+        """Test retrieving file list from nonexistent vector store"""
+        assert db_container is not None
+        self.configure_database(client, auth_headers)
+
+        # Try to get files from non-existent vector store
+        response = client.get(
+            "/v1/embed/NONEXISTENT_VS/files",
+            headers=auth_headers["valid_auth"]
+        )
+
+        # Should return error or empty list
+        assert response.status_code in (200, 400)
diff --git a/tests/server/unit/api/utils/test_utils_embed.py b/tests/server/unit/api/utils/test_utils_embed.py
index 08364528..e76915cc 100644
--- a/tests/server/unit/api/utils/test_utils_embed.py
+++ b/tests/server/unit/api/utils/test_utils_embed.py
@@ -4,12 +4,14 @@
 """
 # spell-checker: disable
 
+from decimal import Decimal
 from pathlib import Path
-from unittest.mock import patch, mock_open
+from unittest.mock import patch, mock_open, MagicMock
 
 from langchain.docstore.document import Document as LangchainDocument
 
 from server.api.utils import embed
+from common.schema import Database
 
 
 class TestEmbedUtils:
@@ -82,3 +84,192 @@ def test_logger_exists(self):
         """Test that logger is properly configured"""
         assert hasattr(embed, "logger")
         assert embed.logger.name == "api.utils.embed"
+
+
+class TestGetVectorStoreFiles:
+    """Test get_vector_store_files() function"""
+
+    def setup_method(self):
+        """Setup test data"""
+        self.sample_db = Database(
+            name="TEST_DB",
+            user="test_user",
+            password="",
+            dsn="localhost:1521/FREEPDB1"
+        )
+
+    @patch("server.api.utils.databases.connect")
+    @patch("server.api.utils.databases.disconnect")
+    def test_get_vector_store_files_with_metadata(self, mock_disconnect, mock_connect):
+        """Test retrieving file list with complete metadata"""
+        # Mock database connection and cursor
+        mock_conn = MagicMock()
+        mock_cursor = MagicMock()
+        mock_conn.cursor.return_value = mock_cursor
+        mock_connect.return_value = mock_conn
+
+        # Mock query results with metadata
+        mock_cursor.fetchall.return_value = [
+            ({
+                "filename": "doc1.pdf",
+                "size": 1024000,
+                "time_modified": "2025-11-01T10:00:00",
+                "etag": "etag-123"
+            },),
+            ({
+                "filename": "doc1.pdf",
+                "size": 1024000,
+                "time_modified": "2025-11-01T10:00:00",
+                "etag": "etag-123"
+            },),
+            ({
+                "filename": "doc2.txt",
+                "size": 2048,
+                "time_modified": "2025-11-02T10:00:00",
+                "etag": "etag-456"
+            },),
+        ]
+
+        # Execute
+        result = embed.get_vector_store_files(self.sample_db, "TEST_VS")
+
+        # Verify
+        assert result["vector_store"] == "TEST_VS"
+        assert result["total_files"] == 2
+        assert result["total_chunks"] == 3
+        assert result["orphaned_chunks"] == 0
+
+        # Verify files
+        assert len(result["files"]) == 2
+        assert result["files"][0]["filename"] == "doc1.pdf"
+        assert result["files"][0]["chunk_count"] == 2
+        assert result["files"][0]["size"] == 1024000
+        assert result["files"][1]["filename"] == "doc2.txt"
+        assert result["files"][1]["chunk_count"] == 1
+
+        mock_disconnect.assert_called_once()
+
+    @patch("server.api.utils.databases.connect")
+    @patch("server.api.utils.databases.disconnect")
+    def test_get_vector_store_files_with_decimal_size(self, mock_disconnect, mock_connect):
+        """Test handling of Decimal size from Oracle NUMBER type"""
+        # Mock database connection
+        mock_conn = MagicMock()
+        mock_cursor = MagicMock()
+        mock_conn.cursor.return_value = mock_cursor
+        mock_connect.return_value = mock_conn
+
+        # Mock query results with Decimal size (from Oracle)
+        mock_cursor.fetchall.return_value = [
+            ({
+                "filename": "doc.pdf",
+                "size": Decimal("1024000"),  # Oracle returns Decimal
+                "time_modified": "2025-11-01T10:00:00",
+                "etag": "etag-123"
+            },),
+        ]
+
+        # Execute
+        result = embed.get_vector_store_files(self.sample_db, "TEST_VS")
+
+        # Verify Decimal was converted to int
+        assert result["files"][0]["size"] == 1024000
+        assert isinstance(result["files"][0]["size"], int)
+
+    @patch("server.api.utils.databases.connect")
+    @patch("server.api.utils.databases.disconnect")
+    def test_get_vector_store_files_old_format(self, mock_disconnect, mock_connect):
+        """Test retrieving files with old metadata format (source field)"""
+        # Mock database connection
+        mock_conn = MagicMock()
+        mock_cursor = MagicMock()
+        mock_conn.cursor.return_value = mock_cursor
+        mock_connect.return_value = mock_conn
+
+        # Mock query results with old format (source instead of filename)
+        mock_cursor.fetchall.return_value = [
+            ({"source": "/path/to/doc1.pdf"},),
+            ({"source": "/path/to/doc1.pdf"},),
+        ]
+
+        # Execute
+        result = embed.get_vector_store_files(self.sample_db, "TEST_VS")
+
+        # Verify fallback to source field worked
+        assert result["total_files"] == 1
+        assert result["files"][0]["filename"] == "doc1.pdf"
+        assert result["files"][0]["chunk_count"] == 2
+
+    @patch("server.api.utils.databases.connect")
+    @patch("server.api.utils.databases.disconnect")
+    def test_get_vector_store_files_with_orphaned_chunks(self, mock_disconnect, mock_connect):
+        """Test detection of orphaned chunks without valid filename"""
+        # Mock database connection
+        mock_conn = MagicMock()
+        mock_cursor = MagicMock()
+        mock_conn.cursor.return_value = mock_cursor
+        mock_connect.return_value = mock_conn
+
+        # Mock query results with some orphaned chunks
+        mock_cursor.fetchall.return_value = [
+            ({"filename": "doc1.pdf", "size": 1024},),
+            ({"filename": "doc1.pdf", "size": 1024},),
+            ({"other_field": "no_filename"},),  # Orphaned chunk
+            ({"other_field": "no_source"},),  # Orphaned chunk
+        ]
+
+        # Execute
+        result = embed.get_vector_store_files(self.sample_db, "TEST_VS")
+
+        # Verify
+        assert result["total_files"] == 1
+        assert result["total_chunks"] == 2
+        assert result["orphaned_chunks"] == 2
+        assert result["files"][0]["chunk_count"] == 2
+
+    @patch("server.api.utils.databases.connect")
+    @patch("server.api.utils.databases.disconnect")
+    def test_get_vector_store_files_empty_store(self, mock_disconnect, mock_connect):
+        """Test retrieving from empty vector store"""
+        # Mock database connection
+        mock_conn = MagicMock()
+        mock_cursor = MagicMock()
+        mock_conn.cursor.return_value = mock_cursor
+        mock_connect.return_value = mock_conn
+
+        # Mock empty results
+        mock_cursor.fetchall.return_value = []
+
+        # Execute
+        result = embed.get_vector_store_files(self.sample_db, "EMPTY_VS")
+
+        # Verify
+        assert result["vector_store"] == "EMPTY_VS"
+        assert result["total_files"] == 0
+        assert result["total_chunks"] == 0
+        assert result["orphaned_chunks"] == 0
+        assert len(result["files"]) == 0
+
+    @patch("server.api.utils.databases.connect")
+    @patch("server.api.utils.databases.disconnect")
+    def test_get_vector_store_files_sorts_by_filename(self, mock_disconnect, mock_connect):
+        """Test that files are sorted alphabetically by filename"""
+        # Mock database connection
+        mock_conn = MagicMock()
+        mock_cursor = MagicMock()
+        mock_conn.cursor.return_value = mock_cursor
+        mock_connect.return_value = mock_conn
+
+        # Mock query results in random order
+        mock_cursor.fetchall.return_value = [
+            ({"filename": "zebra.pdf"},),
+            ({"filename": "apple.txt"},),
+            ({"filename": "monkey.md"},),
+        ]
+
+        # Execute
+        result = embed.get_vector_store_files(self.sample_db, "TEST_VS")
+
+        # Verify sorted order
+        filenames = [f["filename"] for f in result["files"]]
+        assert filenames == ["apple.txt", "monkey.md", "zebra.pdf"]
diff --git a/tests/server/unit/api/utils/test_utils_oci_refresh.py b/tests/server/unit/api/utils/test_utils_oci_refresh.py
new file mode 100644
index 00000000..7f7eda28
--- /dev/null
+++ b/tests/server/unit/api/utils/test_utils_oci_refresh.py
@@ -0,0 +1,259 @@
+"""
+Copyright (c) 2024, 2025, Oracle and/or its affiliates.
+Licensed under the Universal Permissive License v1.0 as shown at http://oss.oracle.com/licenses/upl.
+"""
+# spell-checker: disable
+# pylint: disable=too-many-arguments,too-many-positional-arguments
+
+from datetime import datetime
+from unittest.mock import patch, MagicMock
+
+import pytest
+
+from server.api.utils import oci as oci_utils
+from common.schema import OracleCloudSettings
+
+
+class TestGetBucketObjectsWithMetadata:
+    """Test get_bucket_objects_with_metadata() function"""
+
+    def setup_method(self):
+        """Setup test data"""
+        self.sample_oci_config = OracleCloudSettings(
+            auth_profile="DEFAULT",
+            namespace="test-namespace",
+            compartment_id="ocid1.compartment.oc1..test",
+            region="us-ashburn-1",
+        )
+
+    def create_mock_object(self, name, size, etag, time_modified, md5):
+        """Create a mock OCI object"""
+        mock_obj = MagicMock()
+        mock_obj.name = name
+        mock_obj.size = size
+        mock_obj.etag = etag
+        mock_obj.time_modified = time_modified
+        mock_obj.md5 = md5
+        return mock_obj
+
+    @patch.object(oci_utils, "init_client")
+    def test_get_bucket_objects_with_metadata_success(self, mock_init_client):
+        """Test successful retrieval of bucket objects with metadata"""
+        # Create mock objects
+        time1 = datetime(2025, 11, 1, 10, 0, 0)
+        time2 = datetime(2025, 11, 2, 10, 0, 0)
+
+        mock_obj1 = self.create_mock_object(
+            name="document1.pdf",
+            size=1024000,
+            etag="etag-123",
+            time_modified=time1,
+            md5="md5-hash-1"
+        )
+        mock_obj2 = self.create_mock_object(
+            name="document2.txt",
+            size=2048,
+            etag="etag-456",
+            time_modified=time2,
+            md5="md5-hash-2"
+        )
+
+        # Mock client
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_response.data.objects = [mock_obj1, mock_obj2]
+        mock_client.list_objects.return_value = mock_response
+        mock_init_client.return_value = mock_client
+
+        # Execute
+        result = oci_utils.get_bucket_objects_with_metadata("test-bucket", self.sample_oci_config)
+
+        # Verify
+        assert len(result) == 2
+        assert result[0]["name"] == "document1.pdf"
+        assert result[0]["size"] == 1024000
+        assert result[0]["etag"] == "etag-123"
+        assert result[0]["time_modified"] == time1.isoformat()
+        assert result[0]["md5"] == "md5-hash-1"
+        assert result[0]["extension"] == "pdf"
+
+        assert result[1]["name"] == "document2.txt"
+        assert result[1]["size"] == 2048
+
+        # Verify fields parameter was passed
+        call_kwargs = mock_client.list_objects.call_args[1]
+        assert "fields" in call_kwargs
+        assert "name" in call_kwargs["fields"]
+        assert "size" in call_kwargs["fields"]
+        assert "etag" in call_kwargs["fields"]
+
+    @patch.object(oci_utils, "init_client")
+    def test_get_bucket_objects_filters_unsupported_types(self, mock_init_client):
+        """Test that unsupported file types are filtered out"""
+        # Create mock objects with various file types
+        mock_pdf = self.create_mock_object("doc.pdf", 1000, "etag1", datetime.now(), "md5-1")
+        mock_exe = self.create_mock_object("app.exe", 2000, "etag2", datetime.now(), "md5-2")
+        mock_txt = self.create_mock_object("file.txt", 3000, "etag3", datetime.now(), "md5-3")
+        mock_zip = self.create_mock_object("archive.zip", 4000, "etag4", datetime.now(), "md5-4")
+
+        # Mock client
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_response.data.objects = [mock_pdf, mock_exe, mock_txt, mock_zip]
+        mock_client.list_objects.return_value = mock_response
+        mock_init_client.return_value = mock_client
+
+        # Execute
+        result = oci_utils.get_bucket_objects_with_metadata("test-bucket", self.sample_oci_config)
+
+        # Verify only supported types are included
+        assert len(result) == 2
+        names = [obj["name"] for obj in result]
+        assert "doc.pdf" in names
+        assert "file.txt" in names
+        assert "app.exe" not in names
+        assert "archive.zip" not in names
+
+    @patch.object(oci_utils, "init_client")
+    def test_get_bucket_objects_empty_bucket(self, mock_init_client):
+        """Test handling of empty bucket"""
+        # Mock empty bucket
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_response.data.objects = []
+        mock_client.list_objects.return_value = mock_response
+        mock_init_client.return_value = mock_client
+
+        # Execute
+        result = oci_utils.get_bucket_objects_with_metadata("empty-bucket", self.sample_oci_config)
+
+        # Verify
+        assert len(result) == 0
+
+    @patch.object(oci_utils, "init_client")
+    def test_get_bucket_objects_none_time_modified(self, mock_init_client):
+        """Test handling of objects with None time_modified"""
+        # Create mock object with None time_modified
+        mock_obj = self.create_mock_object(
+            name="document.pdf",
+            size=1024,
+            etag="etag-123",
+            time_modified=None,
+            md5="md5-hash"
+        )
+
+        # Mock client
+        mock_client = MagicMock()
+        mock_response = MagicMock()
+        mock_response.data.objects = [mock_obj]
+        mock_client.list_objects.return_value = mock_response
+        mock_init_client.return_value = mock_client
+
+        # Execute
+        result = oci_utils.get_bucket_objects_with_metadata("test-bucket", self.sample_oci_config)
+
+        # Verify time_modified is None
+        assert len(result) == 1
+        assert result[0]["time_modified"] is None
+
+
+class TestDetectChangedObjects:
+    """Test detect_changed_objects() function"""
+
+    def test_detect_all_new_objects(self):
+        """Test detection when all objects are new"""
+        current_objects = [
+            {"name": "file1.pdf", "etag": "etag1", "time_modified": "2025-11-01T10:00:00"},
+            {"name": "file2.pdf", "etag": "etag2", "time_modified": "2025-11-02T10:00:00"},
+        ]
+        processed_objects = {}
+
+        new_objects, modified_objects = oci_utils.detect_changed_objects(current_objects, processed_objects)
+
+        assert len(new_objects) == 2
+        assert len(modified_objects) == 0
+        assert new_objects[0]["name"] == "file1.pdf"
+        assert new_objects[1]["name"] == "file2.pdf"
+
+    def test_detect_modified_objects_by_etag(self):
+        """Test detection of modified objects by ETag change"""
+        current_objects = [
+            {"name": "file1.pdf", "etag": "etag1-new", "time_modified": "2025-11-01T10:00:00"},
+            {"name": "file2.pdf", "etag": "etag2", "time_modified": "2025-11-02T10:00:00"},
+        ]
+        processed_objects = {
+            "file1.pdf": {"etag": "etag1-old", "time_modified": "2025-11-01T10:00:00"},
+            "file2.pdf": {"etag": "etag2", "time_modified": "2025-11-02T10:00:00"},
+        }
+
+        new_objects, modified_objects = oci_utils.detect_changed_objects(current_objects, processed_objects)
+
+        assert len(new_objects) == 0
+        assert len(modified_objects) == 1
+        assert modified_objects[0]["name"] == "file1.pdf"
+        assert modified_objects[0]["etag"] == "etag1-new"
+
+    def test_detect_modified_objects_by_time(self):
+        """Test detection of modified objects by modification time change"""
+        current_objects = [
+            {"name": "file1.pdf", "etag": "etag1", "time_modified": "2025-11-01T12:00:00"},
+        ]
+        processed_objects = {
+            "file1.pdf": {"etag": "etag1", "time_modified": "2025-11-01T10:00:00"},
+        }
+
+        new_objects, modified_objects = oci_utils.detect_changed_objects(current_objects, processed_objects)
+
+        assert len(new_objects) == 0
+        assert len(modified_objects) == 1
+        assert modified_objects[0]["name"] == "file1.pdf"
+
+    def test_detect_no_changes(self):
+        """Test detection when no changes exist"""
+        current_objects = [
+            {"name": "file1.pdf", "etag": "etag1", "time_modified": "2025-11-01T10:00:00"},
+            {"name": "file2.pdf", "etag": "etag2", "time_modified": "2025-11-02T10:00:00"},
+        ]
+        processed_objects = {
+            "file1.pdf": {"etag": "etag1", "time_modified": "2025-11-01T10:00:00"},
+            "file2.pdf": {"etag": "etag2", "time_modified": "2025-11-02T10:00:00"},
+        }
+
+        new_objects, modified_objects = oci_utils.detect_changed_objects(current_objects, processed_objects)
+
+        assert len(new_objects) == 0
+        assert len(modified_objects) == 0
+
+    def test_detect_mixed_changes(self):
+        """Test detection with mix of new, modified, and unchanged objects"""
+        current_objects = [
+            {"name": "file1.pdf", "etag": "etag1", "time_modified": "2025-11-01T10:00:00"},  # unchanged
+            {"name": "file2.pdf", "etag": "etag2-new", "time_modified": "2025-11-02T10:00:00"},  # modified
+            {"name": "file3.pdf", "etag": "etag3", "time_modified": "2025-11-03T10:00:00"},  # new
+        ]
+        processed_objects = {
+            "file1.pdf": {"etag": "etag1", "time_modified": "2025-11-01T10:00:00"},
+            "file2.pdf": {"etag": "etag2-old", "time_modified": "2025-11-02T10:00:00"},
+        }
+
+        new_objects, modified_objects = oci_utils.detect_changed_objects(current_objects, processed_objects)
+
+        assert len(new_objects) == 1
+        assert len(modified_objects) == 1
+        assert new_objects[0]["name"] == "file3.pdf"
+        assert modified_objects[0]["name"] == "file2.pdf"
+
+    def test_skip_old_format_objects(self):
+        """Test that objects with old format (no etag/time_modified) are skipped"""
+        current_objects = [
+            {"name": "file1.pdf", "etag": "etag1", "time_modified": "2025-11-01T10:00:00"},
+        ]
+        processed_objects = {
+            "file1.pdf": {"etag": None, "time_modified": None},  # Old format
+        }
+
+        new_objects, modified_objects = oci_utils.detect_changed_objects(current_objects, processed_objects)
+
+        # Should skip the old format object to avoid duplicates
+        assert len(new_objects) == 0
+        assert len(modified_objects) == 0