diff --git a/docs/my-website/docs/providers/azure_ai/azure_ai_vector_stores_passthrough.md b/docs/my-website/docs/providers/azure_ai/azure_ai_vector_stores_passthrough.md
new file mode 100644
index 00000000000..a528b1ccfcf
--- /dev/null
+++ b/docs/my-website/docs/providers/azure_ai/azure_ai_vector_stores_passthrough.md
@@ -0,0 +1,391 @@
+# Azure AI Search - Vector Store (Passthrough API)
+
+Use this to allow developers to **create** and **search** vector stores using the Azure AI Search API in the **native** Azure AI Search API format, without giving them the Azure AI credentials.
+
+This is for the proxy only. 
+
+## Admin Flow
+
+### 1. Add the vector store to LiteLLM 
+
+```yaml
+model_list:  
+  - model_name: embedding-model
+    litellm_params:
+      model: openai/text-embedding-3-large
+
+
+vector_store_registry:
+  - vector_store_name: "azure-ai-search"
+    litellm_params:
+      vector_store_id: "can-be-anything" # vector store id can be anything for the purpose of passthrough api
+      custom_llm_provider: "azure_ai"
+      api_key: os.environ/AZURE_SEARCH_API_KEY
+      api_base: https://azure-kb-search.search.windows.net
+      litellm_embedding_model: "azure/text-embedding-3-large"
+      litellm_embedding_config:
+          api_base: https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/
+          api_key: os.environ/AZURE_API_KEY
+          api_version: "2025-09-01"
+
+general_settings:
+    database_url: "postgresql://user:password@host:port/database"
+    master_key: "sk-1234"
+```
+
+Add your vector store credentials to LiteLLM. 
+
+### 2. Start the proxy. 
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+### 3. Create a virtual index. 
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/v1/indexes' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{ 
+    "index_name": "dall-e-4",
+    "litellm_params": {
+        "vector_store_index": "real-index-name-2",
+        "vector_store_name": "azure-ai-search"
+    }
+
+}'
+```
+
+This is a virtual index, which the developer can use to create and search vector stores.
+
+### 4. Create a key with the vector store permissions. 
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+    "allowed_vector_store_indexes": [{"index_name": "dall-e-4", "index_permissions": ["write", "read"]}],
+    "models": ["embedding-model"]
+}'
+```
+
+Give the key access to the virtual index and the embedding model.
+
+**Expected response**
+
+```json
+{
+    "key": "sk-my-virtual-key"
+}
+```
+
+## Developer Flow
+
+### 1. Create a vector store with some documents. 
+
+Note: Use the '/azure_ai' endpoint for the passthrough api that uses the `azure_ai` provider in your `_new_secret_config.yaml` file. 
+
+```python
+import requests
+import json
+
+# ----------------------------
+# 🔐 CONFIGURATION
+# ----------------------------
+# Azure OpenAI (for embeddings)
+AZURE_OPENAI_ENDPOINT = "http://0.0.0.0:4000"
+AZURE_OPENAI_KEY = "sk-my-virtual-key"
+EMBEDDING_DEPLOYMENT_NAME = "embedding-model"
+
+# Azure AI Search
+AZURE_AI_SEARCH_ENDPOINT = "http://0.0.0.0:4000/azure_ai" # IMPORTANT: Use the '/azure_ai' endpoint for the passthrough api to Azure 
+SEARCH_API_KEY = "sk-my-virtual-key"
+INDEX_NAME = "dall-e-4"
+
+
+
+# Vector dimensions (text-embedding-3-large uses 3072 dimensions)
+VECTOR_DIMENSIONS = 3072
+
+# Example docs (replace with your own)
+documents = [
+    {"id": "1", "content": "Refunds must be requested within 30 days."},
+    {"id": "2", "content": "We offer 24/7 support for all enterprise customers."},
+]
+
+
+# ----------------------------
+# 📋 STEP 0 — Create Index Schema
+# ----------------------------
+def delete_index_if_exists():
+    """Delete the index if it exists"""
+    index_url = f"{AZURE_AI_SEARCH_ENDPOINT}/indexes/{INDEX_NAME}?api-version=2024-07-01"
+    headers = {"api-key": SEARCH_API_KEY}
+
+    response = requests.delete(index_url, headers=headers)
+
+    if response.status_code == 204:
+        print(f"🗑️  Deleted existing index '{INDEX_NAME}'")
+        return True
+    elif response.status_code == 404:
+        print(f"ℹ️  Index '{INDEX_NAME}' does not exist yet")
+        return False
+    else:
+        print(f"⚠️  Delete response: {response.status_code}")
+        print(f"    Message: {response.text}")
+        return False
+
+
+def create_index():
+    """Create the Azure AI Search index with proper schema"""
+    index_url = f"{AZURE_AI_SEARCH_ENDPOINT}/indexes/{INDEX_NAME}?api-version=2024-07-01"
+    headers = {"Content-Type": "application/json", "api-key": SEARCH_API_KEY}
+
+    index_schema = {
+        "name": INDEX_NAME,
+        "fields": [
+            {"name": "id", "type": "Edm.String", "key": True, "filterable": True},
+            {
+                "name": "content",
+                "type": "Edm.String",
+                "searchable": True,
+                "filterable": False,
+            },
+            {
+                "name": "contentVector",
+                "type": "Collection(Edm.Single)",
+                "searchable": True,
+                "dimensions": VECTOR_DIMENSIONS,
+                "vectorSearchProfile": "my-vector-profile",
+            },
+        ],
+        "vectorSearch": {
+            "algorithms": [
+                {
+                    "name": "my-hnsw-algorithm",
+                    "kind": "hnsw",
+                    "hnswParameters": {
+                        "metric": "cosine",
+                        "m": 4,
+                        "efConstruction": 400,
+                        "efSearch": 500,
+                    },
+                }
+            ],
+            "profiles": [
+                {"name": "my-vector-profile", "algorithm": "my-hnsw-algorithm"}
+            ],
+        },
+    }
+
+    # Create the index
+    response = requests.put(index_url, headers=headers, json=index_schema)
+
+    if response.status_code == 201:
+        print(f"✅ Index '{INDEX_NAME}' created successfully.")
+        return True
+    elif response.status_code == 204:
+        print(f"✅ Index '{INDEX_NAME}' updated successfully.")
+        return True
+    else:
+        print(f"❌ Failed to create index: {response.status_code}")
+        print(f"    Message: {response.text}")
+        return False
+
+
+# Delete and recreate the index with correct schema
+print("🔄 Setting up Azure AI Search index...")
+delete_index_if_exists()
+if not create_index():
+    print("❌ Could not create index. Exiting.")
+    exit(1)
+
+
+# ----------------------------
+# 🧠 STEP 1 — Generate Embeddings
+# ----------------------------
+def get_embedding(text: str):
+    url = f"{AZURE_OPENAI_ENDPOINT}/openai/deployments/{EMBEDDING_DEPLOYMENT_NAME}/embeddings?api-version=2024-10-21"
+    headers = {"Content-Type": "application/json", "api-key": AZURE_OPENAI_KEY}
+    payload = {"input": text}
+    response = requests.post(url, headers=headers, json=payload)
+
+    if response.status_code != 200:
+        raise Exception(f"Embedding failed: {response.status_code}\n{response.text}")
+    return response.json()["data"][0]["embedding"]
+
+
+# Generate embeddings for each document
+for doc in documents:
+    doc["contentVector"] = get_embedding(doc["content"])
+    print(f"✅ Embedded doc {doc['id']} (vector length: {len(doc['contentVector'])})")
+
+# ----------------------------
+# 📤 STEP 2 — Upload to Azure AI Search
+# ----------------------------
+upload_url = f"{AZURE_AI_SEARCH_ENDPOINT}/indexes/{INDEX_NAME}/docs/index?api-version=2024-07-01"
+headers = {"Content-Type": "application/json", "api-key": SEARCH_API_KEY}
+
+payload = {
+    "value": [
+        {
+            "@search.action": "upload",
+            "id": doc["id"],
+            "content": doc["content"],
+            "contentVector": doc["contentVector"],
+        }
+        for doc in documents
+    ]
+}
+
+response = requests.post(upload_url, headers=headers, data=json.dumps(payload))
+
+# ----------------------------
+# 🧾 RESULT
+# ----------------------------
+if response.status_code == 200:
+    print("✅ Documents uploaded successfully.")
+else:
+    print(f"❌ Upload failed: {response.status_code}")
+    print(response.text)
+
+```
+
+
+### 2. Search the vector store. 
+
+
+```python
+import requests
+import json
+
+# ----------------------------
+# 🔐 CONFIGURATION
+# ----------------------------
+# Azure OpenAI (for embeddings)
+AZURE_OPENAI_ENDPOINT = "http://0.0.0.0:4000"
+AZURE_OPENAI_KEY = "sk-my-virtual-key"
+EMBEDDING_DEPLOYMENT_NAME = "embedding-model"
+
+# Azure AI Search
+AZURE_AI_SEARCH_ENDPOINT = "http://0.0.0.0:4000/azure_ai"
+SEARCH_API_KEY = "sk-my-virtual-key"
+INDEX_NAME = "dall-e-4"
+
+
+# ----------------------------
+# 🧠 Generate Query Embedding
+# ----------------------------
+def get_embedding(text: str):
+    """Generate embedding for the query text"""
+    url = f"{AZURE_OPENAI_ENDPOINT}/openai/deployments/{EMBEDDING_DEPLOYMENT_NAME}/embeddings?api-version=2024-10-21"
+    headers = {"Content-Type": "application/json", "api-key": AZURE_OPENAI_KEY}
+    payload = {"input": text}
+    response = requests.post(url, headers=headers, json=payload)
+
+    if response.status_code != 200:
+        raise Exception(f"Embedding failed: {response.status_code}\n{response.text}")
+    return response.json()["data"][0]["embedding"]
+
+
+# ----------------------------
+# 🔍 Vector Search Function
+# ----------------------------
+def search_knowledge_base(query: str, top_k: int = 3):
+    """
+    Search the knowledge base using vector similarity
+
+    Args:
+        query: The search query string
+        top_k: Number of top results to return (default: 3)
+
+    Returns:
+        List of search results with content and scores
+    """
+    print(f"🔍 Searching for: '{query}'")
+
+    # Step 1: Generate embedding for the query
+    print("   Generating query embedding...")
+    query_vector = get_embedding(query)
+
+    # Step 2: Perform vector search
+    search_url = f"{AZURE_AI_SEARCH_ENDPOINT}/indexes/{INDEX_NAME}/docs/search?api-version=2024-07-01"
+    headers = {"Content-Type": "application/json", "api-key": SEARCH_API_KEY}
+
+    # Build the search request with vector search
+    search_payload = {
+        "search": "*",  # Get all documents
+        "vectorQueries": [
+            {
+                "vector": query_vector,
+                "fields": "contentVector",
+                "kind": "vector",
+                "k": top_k,  # Number of nearest neighbors to return
+            }
+        ],
+        "select": "id,content",  # Fields to return
+        "top": top_k,
+    }
+
+    # Execute the search
+    response = requests.post(search_url, headers=headers, json=search_payload)
+
+    if response.status_code != 200:
+        raise Exception(f"Search failed: {response.status_code}\n{response.text}")
+
+    # Parse and return results
+    results = response.json()
+    return results.get("value", [])
+
+
+# ----------------------------
+# 📊 Display Results
+# ----------------------------
+def display_results(results):
+    """Pretty print the search results"""
+    if not results:
+        print("\n❌ No results found.")
+        return
+
+    print(f"\n✅ Found {len(results)} results:\n")
+    print("=" * 80)
+
+    for i, result in enumerate(results, 1):
+        print(f"\n📄 Result #{i}")
+        print(f"   ID: {result.get('id', 'N/A')}")
+        print(f"   Score: {result.get('@search.score', 'N/A')}")
+        print(f"   Content: {result.get('content', 'N/A')}")
+        print("-" * 80)
+
+
+# ----------------------------
+# 🎯 MAIN - Example Queries
+# ----------------------------
+if __name__ == "__main__":
+    # Example 1: Search for refund policy
+    print("\n" + "=" * 80)
+    print("EXAMPLE 1: Refund Policy Query")
+    print("=" * 80)
+    results = search_knowledge_base("How do I get a refund?", top_k=2)
+    display_results(results)
+
+    # Example 2: Search for customer support
+    print("\n\n" + "=" * 80)
+    print("EXAMPLE 2: Customer Support Query")
+    print("=" * 80)
+    results = search_knowledge_base("When can I contact support?", top_k=2)
+    display_results(results)
+
+    # Example 3: Custom query - uncomment to use
+    # print("\n\n" + "=" * 80)
+    # print("CUSTOM QUERY")
+    # print("=" * 80)
+    # custom_query = input("Enter your query: ")
+    # results = search_knowledge_base(custom_query, top_k=3)
+    # display_results(results)
+
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/azure_ai_vector_stores.md b/docs/my-website/docs/providers/azure_ai_vector_stores.md
index d3abb78bbe4..b9dfa3bdc9c 100644
--- a/docs/my-website/docs/providers/azure_ai_vector_stores.md
+++ b/docs/my-website/docs/providers/azure_ai_vector_stores.md
@@ -1,9 +1,9 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# Azure AI Search - Vector Store
+# Azure AI Search - Vector Store (Unified API)
 
-Use Azure AI Search as a vector store for RAG.
+Use this to **search** Azure AI Search Vector Stores, with LiteLLM's unified `/chat/completions` API.
 
 ## Quick Start
 
diff --git a/docs/my-website/docs/vector_stores/create.md b/docs/my-website/docs/vector_stores/create.md
index c97f88a2543..19b4f39cd9e 100644
--- a/docs/my-website/docs/vector_stores/create.md
+++ b/docs/my-website/docs/vector_stores/create.md
@@ -12,7 +12,8 @@ Create a vector store which can be used to store and search document chunks for
 | Cost Tracking | ✅ | Tracked per vector store operation |
 | Logging | ✅ | Works across all integrations |
 | End-user Tracking | ✅ | |
-| Support LLM Providers | **OpenAI** | Full vector stores API support across providers |
+| Support LLM Providers (OpenAI `/vector_stores` API) | **OpenAI** | Full vector stores API support across providers |
+| Support LLM Providers (Passthrough API) | [**Azure AI**](/docs/providers/azure_ai/azure_ai_vector_stores_passthrough) | Full vector stores API support across providers |
 
 ## Usage
 
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 1ed2840d29e..d8ba2b1c0a5 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -457,6 +457,7 @@ const sidebars = {
             "providers/azure_ai_speech",
             "providers/azure_ai_img",
             "providers/azure_ai_vector_stores",
+            "providers/azure_ai/azure_ai_vector_stores_passthrough",
           ]
         },
         {
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 04463266947..d141a3fb28f 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -90,7 +90,11 @@ from litellm.types.proxy.management_endpoints.ui_sso import (
     DefaultTeamSSOParams,
     LiteLLM_UpperboundKeyGenerateParams,
 )
-from litellm.types.utils import StandardKeyGenerationConfig, LlmProviders, SearchProviders
+from litellm.types.utils import (
+    StandardKeyGenerationConfig,
+    LlmProviders,
+    SearchProviders,
+)
 from litellm.types.utils import PriorityReservationSettings
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.logging_callback_manager import LoggingCallbackManager
@@ -157,9 +161,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
     "cloudzero",
     "posthog",
 ]
-cold_storage_custom_logger: Optional[
-    _custom_logger_compatible_callbacks_literal
-] = None
+cold_storage_custom_logger: Optional[_custom_logger_compatible_callbacks_literal] = None
 logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
 _known_custom_logger_compatible_callbacks: List = list(
     get_args(_custom_logger_compatible_callbacks_literal)
@@ -174,22 +176,22 @@ prometheus_initialize_budget_metrics: Optional[bool] = False
 require_auth_for_metrics_endpoint: Optional[bool] = False
 argilla_batch_size: Optional[int] = None
 datadog_use_v1: Optional[bool] = False  # if you want to use v1 datadog logged payload.
-gcs_pub_sub_use_v1: Optional[
-    bool
-] = False  # if you want to use v1 gcs pubsub logged payload
-generic_api_use_v1: Optional[
-    bool
-] = False  # if you want to use v1 generic api logged payload
+gcs_pub_sub_use_v1: Optional[bool] = (
+    False  # if you want to use v1 gcs pubsub logged payload
+)
+generic_api_use_v1: Optional[bool] = (
+    False  # if you want to use v1 generic api logged payload
+)
 argilla_transformation_object: Optional[Dict[str, Any]] = None
-_async_input_callback: List[
-    Union[str, Callable, CustomLogger]
-] = []  # internal variable - async custom callbacks are routed here.
-_async_success_callback: List[
-    Union[str, Callable, CustomLogger]
-] = []  # internal variable - async custom callbacks are routed here.
-_async_failure_callback: List[
-    Union[str, Callable, CustomLogger]
-] = []  # internal variable - async custom callbacks are routed here.
+_async_input_callback: List[Union[str, Callable, CustomLogger]] = (
+    []
+)  # internal variable - async custom callbacks are routed here.
+_async_success_callback: List[Union[str, Callable, CustomLogger]] = (
+    []
+)  # internal variable - async custom callbacks are routed here.
+_async_failure_callback: List[Union[str, Callable, CustomLogger]] = (
+    []
+)  # internal variable - async custom callbacks are routed here.
 pre_call_rules: List[Callable] = []
 post_call_rules: List[Callable] = []
 turn_off_message_logging: Optional[bool] = False
@@ -197,18 +199,18 @@ log_raw_request_response: bool = False
 redact_messages_in_exceptions: Optional[bool] = False
 redact_user_api_key_info: Optional[bool] = False
 filter_invalid_headers: Optional[bool] = False
-add_user_information_to_llm_headers: Optional[
-    bool
-] = None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
+add_user_information_to_llm_headers: Optional[bool] = (
+    None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
+)
 store_audit_logs = False  # Enterprise feature, allow users to see audit logs
 ### end of callbacks #############
 
-email: Optional[
-    str
-] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-token: Optional[
-    str
-] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+email: Optional[str] = (
+    None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+)
+token: Optional[str] = (
+    None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+)
 telemetry = True
 max_tokens: int = DEFAULT_MAX_TOKENS  # OpenAI Defaults
 drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
@@ -264,7 +266,9 @@ use_client: bool = False
 ssl_verify: Union[str, bool] = True
 ssl_security_level: Optional[str] = None
 ssl_certificate: Optional[str] = None
-ssl_ecdh_curve: Optional[str] = None  # Set to 'X25519' to disable PQC and improve performance
+ssl_ecdh_curve: Optional[str] = (
+    None  # Set to 'X25519' to disable PQC and improve performance
+)
 disable_streaming_logging: bool = False
 disable_token_counter: bool = False
 disable_add_transform_inline_image_block: bool = False
@@ -310,20 +314,24 @@ enable_loadbalancing_on_batch_endpoints: Optional[bool] = None
 enable_caching_on_provider_specific_optional_params: bool = (
     False  # feature-flag for caching on optional params - e.g. 'top_k'
 )
-caching: bool = False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-caching_with_models: bool = False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-cache: Optional[
-    Cache
-] = None  # cache object <- use this - https://docs.litellm.ai/docs/caching
+caching: bool = (
+    False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+)
+caching_with_models: bool = (
+    False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+)
+cache: Optional[Cache] = (
+    None  # cache object <- use this - https://docs.litellm.ai/docs/caching
+)
 default_in_memory_ttl: Optional[float] = None
 default_redis_ttl: Optional[float] = None
 default_redis_batch_cache_expiry: Optional[float] = None
 model_alias_map: Dict[str, str] = {}
 model_group_settings: Optional["ModelGroupSettings"] = None
 max_budget: float = 0.0  # set the max budget across all providers
-budget_duration: Optional[
-    str
-] = None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
+budget_duration: Optional[str] = (
+    None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
+)
 default_soft_budget: float = (
     DEFAULT_SOFT_BUDGET  # by default all litellm proxy keys have a soft budget of 50.0
 )
@@ -332,11 +340,15 @@ forward_traceparent_to_llm_provider: bool = False
 
 _current_cost = 0.0  # private variable, used if max budget is set
 error_logs: Dict = {}
-add_function_to_prompt: bool = False  # if function calling not supported by api, append function call details to system prompt
+add_function_to_prompt: bool = (
+    False  # if function calling not supported by api, append function call details to system prompt
+)
 client_session: Optional[httpx.Client] = None
 aclient_session: Optional[httpx.AsyncClient] = None
 model_fallbacks: Optional[List] = None  # Deprecated for 'litellm.fallbacks'
-model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+model_cost_map_url: str = (
+    "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+)
 suppress_debug_info = False
 dynamodb_table_name: Optional[str] = None
 s3_callback_params: Optional[Dict] = None
@@ -366,7 +378,9 @@ prometheus_metrics_config: Optional[List] = None
 disable_add_prefix_to_prompt: bool = (
     False  # used by anthropic, to disable adding prefix to prompt
 )
-disable_copilot_system_to_assistant: bool = False  # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
+disable_copilot_system_to_assistant: bool = (
+    False  # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
+)
 public_model_groups: Optional[List[str]] = None
 public_model_groups_links: Dict[str, str] = {}
 #### REQUEST PRIORITIZATION #######
@@ -377,13 +391,17 @@ priority_reservation_settings: "PriorityReservationSettings" = (
 
 
 ######## Networking Settings ########
-use_aiohttp_transport: bool = True  # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
+use_aiohttp_transport: bool = (
+    True  # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
+)
 aiohttp_trust_env: bool = False  # set to true to use HTTP_ Proxy settings
 disable_aiohttp_transport: bool = False  # Set this to true to use httpx instead
 disable_aiohttp_trust_env: bool = (
     False  # When False, aiohttp will respect HTTP(S)_PROXY env vars
 )
-force_ipv4: bool = False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
+force_ipv4: bool = (
+    False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
+)
 module_level_aclient = AsyncHTTPHandler(
     timeout=request_timeout, client_alias="module level aclient"
 )
@@ -397,13 +415,13 @@ fallbacks: Optional[List] = None
 context_window_fallbacks: Optional[List] = None
 content_policy_fallbacks: Optional[List] = None
 allowed_fails: int = 3
-num_retries_per_request: Optional[
-    int
-] = None  # for the request overall (incl. fallbacks + model retries)
+num_retries_per_request: Optional[int] = (
+    None  # for the request overall (incl. fallbacks + model retries)
+)
 ####### SECRET MANAGERS #####################
-secret_manager_client: Optional[
-    Any
-] = None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
+secret_manager_client: Optional[Any] = (
+    None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
+)
 _google_kms_resource_name: Optional[str] = None
 _key_management_system: Optional[KeyManagementSystem] = None
 _key_management_settings: KeyManagementSettings = KeyManagementSettings()
@@ -413,7 +431,9 @@ output_parse_pii: bool = False
 from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
 
 model_cost = get_model_cost_map(url=model_cost_map_url)
-cost_discount_config: Dict[str, float] = {}  # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
+cost_discount_config: Dict[str, float] = (
+    {}
+)  # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
 custom_prompt_dict: Dict[str, dict] = {}
 check_provider_endpoint = False
 
@@ -771,9 +791,9 @@ azure_llms = {
     "gpt-35-turbo": "azure/gpt-35-turbo",
     "gpt-35-turbo-16k": "azure/gpt-35-turbo-16k",
     "gpt-35-turbo-instruct": "azure/gpt-35-turbo-instruct",
-    "azure/gpt-41":"gpt-4.1", 
-    "azure/gpt-41-mini":"gpt-4.1-mini",
-    "azure/gpt-41-nano":"gpt-4.1-nano"
+    "azure/gpt-41": "gpt-4.1",
+    "azure/gpt-41-mini": "gpt-4.1-mini",
+    "azure/gpt-41-nano": "gpt-4.1-nano",
 }
 
 azure_embedding_models = {
@@ -1188,7 +1208,9 @@ from .llms.bedrock.embed.amazon_titan_v2_transformation import (
 from .llms.cohere.chat.transformation import CohereChatConfig
 from .llms.cohere.chat.v2_transformation import CohereV2ChatConfig
 from .llms.bedrock.embed.cohere_transformation import BedrockCohereEmbeddingConfig
-from .llms.bedrock.embed.twelvelabs_marengo_transformation import TwelveLabsMarengoEmbeddingConfig
+from .llms.bedrock.embed.twelvelabs_marengo_transformation import (
+    TwelveLabsMarengoEmbeddingConfig,
+)
 from .llms.openai.openai import OpenAIConfig, MistralEmbeddingConfig
 from .llms.openai.image_variations.transformation import OpenAIImageVariationConfig
 from .llms.deepinfra.chat.transformation import DeepInfraConfig
@@ -1360,21 +1382,25 @@ import litellm.anthropic_interface as anthropic
 adapters: List[AdapterItem] = []
 
 ### Vector Store Registry ###
-from .vector_stores.vector_store_registry import VectorStoreRegistry
+from .vector_stores.vector_store_registry import (
+    VectorStoreRegistry,
+    VectorStoreIndexRegistry,
+)
 
 vector_store_registry: Optional[VectorStoreRegistry] = None
+vector_store_index_registry: Optional[VectorStoreIndexRegistry] = None
 
 ### CUSTOM LLMs ###
 from .types.llms.custom_llm import CustomLLMItem
 from .types.utils import GenericStreamingChunk
 
 custom_provider_map: List[CustomLLMItem] = []
-_custom_providers: List[
-    str
-] = []  # internal helper util, used to track names of custom providers
-disable_hf_tokenizer_download: Optional[
-    bool
-] = None  # disable huggingface tokenizer download. Defaults to openai clk100
+_custom_providers: List[str] = (
+    []
+)  # internal helper util, used to track names of custom providers
+disable_hf_tokenizer_download: Optional[bool] = (
+    None  # disable huggingface tokenizer download. Defaults to openai clk100
+)
 global_disable_no_log_param: bool = False
 
 ### CLI UTILITIES ###
@@ -1393,9 +1419,11 @@ def set_global_bitbucket_config(config: Dict[str, Any]) -> None:
     global global_bitbucket_config
     global_bitbucket_config = config
 
+
 ### GLOBAL CONFIG ###
 global_gitlab_config: Optional[Dict[str, Any]] = None
 
+
 def set_global_gitlab_config(config: Dict[str, Any]) -> None:
     """Set global BitBucket configuration for prompt management."""
     global global_gitlab_config
diff --git a/litellm/llms/azure_ai/vector_stores/transformation.py b/litellm/llms/azure_ai/vector_stores/transformation.py
index f99d2c4c4b2..96cea064ce1 100644
--- a/litellm/llms/azure_ai/vector_stores/transformation.py
+++ b/litellm/llms/azure_ai/vector_stores/transformation.py
@@ -7,8 +7,10 @@ from litellm.llms.azure.common_utils import BaseAzureLLM
 from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
     VectorStoreCreateOptionalRequestParams,
     VectorStoreCreateResponse,
+    VectorStoreIndexEndpoints,
     VectorStoreResultContent,
     VectorStoreSearchOptionalRequestParams,
     VectorStoreSearchResponse,
@@ -34,6 +36,25 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM):
     def __init__(self):
         super().__init__()
 
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        return {
+            "read": [("GET", "/docs/search"), ("POST", "/docs/search")],
+            "write": [("PUT", "/docs")],
+        }
+
+    def get_auth_credentials(
+        self, litellm_params: dict
+    ) -> BaseVectorStoreAuthCredentials:
+        api_key = litellm_params.get("api_key")
+        if api_key is None:
+            raise ValueError("api_key is required")
+
+        return {
+            "headers": {
+                "api-key": api_key,
+            }
+        }
+
     def validate_environment(
         self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
     ) -> dict:
diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py
index daca48fc323..89f2094d5df 100644
--- a/litellm/llms/base_llm/vector_store/transformation.py
+++ b/litellm/llms/base_llm/vector_store/transformation.py
@@ -5,9 +5,11 @@ import httpx
 
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
     VECTOR_STORE_OPENAI_PARAMS,
     VectorStoreCreateOptionalRequestParams,
     VectorStoreCreateResponse,
+    VectorStoreIndexEndpoints,
     VectorStoreSearchOptionalRequestParams,
     VectorStoreSearchResponse,
 )
@@ -39,6 +41,16 @@ class BaseVectorStoreConfig:
     ) -> dict:
         return optional_params
 
+    @abstractmethod
+    def get_auth_credentials(
+        self, litellm_params: dict
+    ) -> BaseVectorStoreAuthCredentials:
+        pass
+
+    @abstractmethod
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        pass
+
     @abstractmethod
     def transform_search_vector_store_request(
         self,
diff --git a/litellm/llms/bedrock/vector_stores/transformation.py b/litellm/llms/bedrock/vector_stores/transformation.py
index c485defe8a8..72e1e1470d3 100644
--- a/litellm/llms/bedrock/vector_stores/transformation.py
+++ b/litellm/llms/bedrock/vector_stores/transformation.py
@@ -13,6 +13,8 @@ from litellm.types.integrations.rag.bedrock_knowledgebase import (
 )
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
+    VectorStoreIndexEndpoints,
     VECTOR_STORE_OPENAI_PARAMS,
     VectorStoreResultContent,
     VectorStoreSearchOptionalRequestParams,
@@ -33,6 +35,17 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
         BaseVectorStoreConfig.__init__(self)
         BaseAWSLLM.__init__(self)
 
+    def get_auth_credentials(
+        self, litellm_params: dict
+    ) -> BaseVectorStoreAuthCredentials:
+        return {}
+
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        return {
+            "read": [("POST", "/knowledgebases/{knowledge_base_id}/retrieve")],
+            "write": [],
+        }
+
     def get_supported_openai_params(
         self, model: str
     ) -> List[VECTOR_STORE_OPENAI_PARAMS]:
diff --git a/litellm/llms/openai/vector_stores/transformation.py b/litellm/llms/openai/vector_stores/transformation.py
index 76cd12be8ee..5689e249bc0 100644
--- a/litellm/llms/openai/vector_stores/transformation.py
+++ b/litellm/llms/openai/vector_stores/transformation.py
@@ -7,9 +7,11 @@ from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreCon
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
     VectorStoreCreateOptionalRequestParams,
     VectorStoreCreateRequest,
     VectorStoreCreateResponse,
+    VectorStoreIndexEndpoints,
     VectorStoreSearchOptionalRequestParams,
     VectorStoreSearchRequest,
     VectorStoreSearchResponse,
@@ -23,10 +25,29 @@ if TYPE_CHECKING:
 else:
     LiteLLMLoggingObj = Any
 
+
 class OpenAIVectorStoreConfig(BaseVectorStoreConfig):
     ASSISTANTS_HEADER_KEY = "OpenAI-Beta"
     ASSISTANTS_HEADER_VALUE = "assistants=v2"
 
+    def get_auth_credentials(
+        self, litellm_params: dict
+    ) -> BaseVectorStoreAuthCredentials:
+        api_key = litellm_params.get("api_key")
+        if api_key is None:
+            raise ValueError("api_key is required")
+        return {
+            "headers": {
+                "Authorization": f"Bearer {api_key}",
+            },
+        }
+
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        return {
+            "read": [("GET", "/vector_stores/{index_name}/search")],
+            "write": [("POST", "/vector_stores")],
+        }
+
     def validate_environment(
         self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
     ) -> dict:
@@ -51,8 +72,8 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig):
             headers.update(
                 {
                     self.ASSISTANTS_HEADER_KEY: self.ASSISTANTS_HEADER_VALUE,
-            }
-        )
+                }
+            )
 
         return headers
 
@@ -76,7 +97,6 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig):
         api_base = api_base.rstrip("/")
 
         return f"{api_base}/vector_stores"
-    
 
     def transform_search_vector_store_request(
         self,
@@ -91,27 +111,31 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig):
         typed_request_body = VectorStoreSearchRequest(
             query=query,
             filters=vector_store_search_optional_params.get("filters", None),
-            max_num_results=vector_store_search_optional_params.get("max_num_results", None),
-            ranking_options=vector_store_search_optional_params.get("ranking_options", None),
-            rewrite_query=vector_store_search_optional_params.get("rewrite_query", None),
+            max_num_results=vector_store_search_optional_params.get(
+                "max_num_results", None
+            ),
+            ranking_options=vector_store_search_optional_params.get(
+                "ranking_options", None
+            ),
+            rewrite_query=vector_store_search_optional_params.get(
+                "rewrite_query", None
+            ),
         )
 
         dict_request_body = cast(dict, typed_request_body)
         return url, dict_request_body
-    
 
-
-    def transform_search_vector_store_response(self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj) -> VectorStoreSearchResponse:
+    def transform_search_vector_store_response(
+        self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
+    ) -> VectorStoreSearchResponse:
         try:
             response_json = response.json()
-            return VectorStoreSearchResponse(
-                **response_json
-            )
+            return VectorStoreSearchResponse(**response_json)
         except Exception as e:
             raise self.get_error_class(
-                error_message=str(e), 
-                status_code=response.status_code, 
-                headers=response.headers
+                error_message=str(e),
+                status_code=response.status_code,
+                headers=response.headers,
             )
 
     def transform_create_vector_store_request(
@@ -124,28 +148,27 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig):
         typed_request_body = VectorStoreCreateRequest(
             name=vector_store_create_optional_params.get("name", None),
             file_ids=vector_store_create_optional_params.get("file_ids", None),
-            expires_after=vector_store_create_optional_params.get("expires_after", None),
-            chunking_strategy=vector_store_create_optional_params.get("chunking_strategy", None),
+            expires_after=vector_store_create_optional_params.get(
+                "expires_after", None
+            ),
+            chunking_strategy=vector_store_create_optional_params.get(
+                "chunking_strategy", None
+            ),
             metadata=add_openai_metadata(metadata) if metadata is not None else None,
         )
 
         dict_request_body = cast(dict, typed_request_body)
         return url, dict_request_body
 
-    def transform_create_vector_store_response(self, response: httpx.Response) -> VectorStoreCreateResponse:
+    def transform_create_vector_store_response(
+        self, response: httpx.Response
+    ) -> VectorStoreCreateResponse:
         try:
             response_json = response.json()
-            return VectorStoreCreateResponse(
-                **response_json
-            )
+            return VectorStoreCreateResponse(**response_json)
         except Exception as e:
             raise self.get_error_class(
-                error_message=str(e), 
-                status_code=response.status_code, 
-                headers=response.headers
+                error_message=str(e),
+                status_code=response.status_code,
+                headers=response.headers,
             )
-
-    
-
-    
-    
\ No newline at end of file
diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py
index 5296b11e883..6f258bc04a6 100644
--- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py
+++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py
@@ -6,8 +6,10 @@ from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreCon
 from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
     VectorStoreCreateOptionalRequestParams,
     VectorStoreCreateResponse,
+    VectorStoreIndexEndpoints,
     VectorStoreResultContent,
     VectorStoreSearchOptionalRequestParams,
     VectorStoreSearchResponse,
@@ -25,13 +27,40 @@ else:
 class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
     """
     Configuration for Vertex AI Vector Store RAG API
-    
+
     This implementation uses the Vertex AI RAG Engine API for vector store operations.
     """
 
     def __init__(self):
         super().__init__()
 
+    def get_auth_credentials(
+        self, litellm_params: dict
+    ) -> BaseVectorStoreAuthCredentials:
+        # Get credentials and project info
+        vertex_credentials = self.get_vertex_ai_credentials(dict(litellm_params))
+        vertex_project = self.get_vertex_ai_project(dict(litellm_params))
+
+        # Get access token using the base class method
+        access_token, project_id = self._ensure_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider="vertex_ai",
+        )
+
+        return {
+            "headers": {
+                "Authorization": f"Bearer {access_token}",
+                "Content-Type": "application/json",
+            },
+        }
+
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        return {
+            "read": [("POST", ":retrieveContexts")],
+            "write": [("POST", "/ragCorpora")],
+        }
+
     def validate_environment(
         self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
     ) -> dict:
@@ -39,23 +68,9 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
         Validate and set up authentication for Vertex AI RAG API
         """
         litellm_params = litellm_params or GenericLiteLLMParams()
-        
-        # Get credentials and project info
-        vertex_credentials = self.get_vertex_ai_credentials(dict(litellm_params))
-        vertex_project = self.get_vertex_ai_project(dict(litellm_params))
-        
-        # Get access token using the base class method
-        access_token, project_id = self._ensure_access_token(
-            credentials=vertex_credentials,
-            project_id=vertex_project,
-            custom_llm_provider="vertex_ai",
-        )
-        
-        headers.update({
-            "Authorization": f"Bearer {access_token}",
-            "Content-Type": "application/json",
-        })
-        
+
+        auth_headers = self.get_auth_credentials(litellm_params.model_dump())
+        headers.update(auth_headers.get("headers", {}))
         return headers
 
     def get_complete_url(
@@ -68,10 +83,10 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
         """
         vertex_location = self.get_vertex_ai_location(litellm_params)
         vertex_project = self.get_vertex_ai_project(litellm_params)
-        
+
         if api_base:
             return api_base.rstrip("/")
-        
+
         # Vertex AI RAG API endpoint for retrieveContexts
         return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}"
 
@@ -90,60 +105,52 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
         # Convert query to string if it's a list
         if isinstance(query, list):
             query = " ".join(query)
-        
+
         # Vertex AI RAG API endpoint for retrieving contexts
         url = f"{api_base}:retrieveContexts"
-        
+
         # Use helper methods to get project and location, then construct full rag corpus path
         vertex_project = self.get_vertex_ai_project(litellm_params)
         vertex_location = self.get_vertex_ai_location(litellm_params)
-        
+
         # Construct full rag corpus path
         full_rag_corpus = f"projects/{vertex_project}/locations/{vertex_location}/ragCorpora/{vector_store_id}"
-        
+
         # Build the request body for Vertex AI RAG API
         request_body: Dict[str, Any] = {
-            "vertex_rag_store": {
-                "rag_resources": [
-                    {
-                        "rag_corpus": full_rag_corpus
-                    }
-                ]
-            },
-            "query": {
-                "text": query
-            }
+            "vertex_rag_store": {"rag_resources": [{"rag_corpus": full_rag_corpus}]},
+            "query": {"text": query},
         }
 
         #########################################################
         # Update logging object with details of the request
         #########################################################
         litellm_logging_obj.model_call_details["query"] = query
-        
+
         # Add optional parameters
         max_num_results = vector_store_search_optional_params.get("max_num_results")
         if max_num_results is not None:
-            request_body["query"]["rag_retrieval_config"] = {
-                "top_k": max_num_results
-            }
-        
+            request_body["query"]["rag_retrieval_config"] = {"top_k": max_num_results}
+
         # Add filters if provided
         filters = vector_store_search_optional_params.get("filters")
         if filters is not None:
             if "rag_retrieval_config" not in request_body["query"]:
                 request_body["query"]["rag_retrieval_config"] = {}
             request_body["query"]["rag_retrieval_config"]["filter"] = filters
-        
+
         # Add ranking options if provided
         ranking_options = vector_store_search_optional_params.get("ranking_options")
         if ranking_options is not None:
             if "rag_retrieval_config" not in request_body["query"]:
                 request_body["query"]["rag_retrieval_config"] = {}
             request_body["query"]["rag_retrieval_config"]["ranking"] = ranking_options
-        
+
         return url, request_body
 
-    def transform_search_vector_store_response(self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj) -> VectorStoreSearchResponse:
+    def transform_search_vector_store_response(
+        self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
+    ) -> VectorStoreSearchResponse:
         """
         Transform Vertex AI RAG API response to standard vector store search response
         """
@@ -152,7 +159,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
             response_json = response.json()
             # Extract contexts from Vertex AI response - handle nested structure
             contexts = response_json.get("contexts", {}).get("contexts", [])
-            
+
             # Transform contexts to standard format
             search_results = []
             for context in contexts:
@@ -162,27 +169,29 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
                         type="text",
                     )
                 ]
-                
+
                 # Extract file information
                 source_uri = context.get("sourceUri", "")
                 source_display_name = context.get("sourceDisplayName", "")
-                
+
                 # Generate file_id from source URI or use display name as fallback
                 file_id = source_uri if source_uri else source_display_name
-                filename = source_display_name if source_display_name else "Unknown Document"
-                
+                filename = (
+                    source_display_name if source_display_name else "Unknown Document"
+                )
+
                 # Build attributes with available metadata
                 attributes = {}
                 if source_uri:
                     attributes["sourceUri"] = source_uri
                 if source_display_name:
                     attributes["sourceDisplayName"] = source_display_name
-                
+
                 # Add page span information if available
                 page_span = context.get("pageSpan", {})
                 if page_span:
                     attributes["pageSpan"] = page_span
-                
+
                 result = VectorStoreSearchResult(
                     score=context.get("score", 0.0),
                     content=content,
@@ -191,18 +200,18 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
                     attributes=attributes,
                 )
                 search_results.append(result)
-            
+
             return VectorStoreSearchResponse(
                 object="vector_store.search_results.page",
                 search_query=litellm_logging_obj.model_call_details.get("query", ""),
-                data=search_results
+                data=search_results,
             )
-            
+
         except Exception as e:
             raise self.get_error_class(
                 error_message=str(e),
                 status_code=response.status_code,
-                headers=response.headers
+                headers=response.headers,
             )
 
     def transform_create_vector_store_request(
@@ -214,48 +223,55 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
         Transform create request for Vertex AI RAG Corpus
         """
         url = f"{api_base}/ragCorpora"  # Base URL for creating RAG corpus
-        
+
         # Build the request body for Vertex AI RAG Corpus creation
         request_body: Dict[str, Any] = {
-            "display_name": vector_store_create_optional_params.get("name", "litellm-vector-store"),
-            "description": "Vector store created via LiteLLM"
+            "display_name": vector_store_create_optional_params.get(
+                "name", "litellm-vector-store"
+            ),
+            "description": "Vector store created via LiteLLM",
         }
-        
+
         # Add metadata if provided
         metadata = vector_store_create_optional_params.get("metadata")
         if metadata is not None:
             request_body["labels"] = metadata
-        
+
         return url, request_body
 
-    def transform_create_vector_store_response(self, response: httpx.Response) -> VectorStoreCreateResponse:
+    def transform_create_vector_store_response(
+        self, response: httpx.Response
+    ) -> VectorStoreCreateResponse:
         """
         Transform Vertex AI RAG Corpus creation response to standard vector store response
         """
         try:
             response_json = response.json()
-            
+
             # Extract the corpus ID from the response name
             corpus_name = response_json.get("name", "")
-            corpus_id = corpus_name.split("/")[-1] if "/" in corpus_name else corpus_name
-            
+            corpus_id = (
+                corpus_name.split("/")[-1] if "/" in corpus_name else corpus_name
+            )
+
             # Handle createTime conversion
             create_time = response_json.get("createTime", 0)
             if isinstance(create_time, str):
                 # Convert ISO timestamp to Unix timestamp
                 from datetime import datetime
+
                 try:
-                    dt = datetime.fromisoformat(create_time.replace('Z', '+00:00'))
+                    dt = datetime.fromisoformat(create_time.replace("Z", "+00:00"))
                     create_time = int(dt.timestamp())
                 except ValueError:
                     create_time = 0
             elif not isinstance(create_time, int):
                 create_time = 0
-            
+
             # Handle labels safely
             labels = response_json.get("labels", {})
             metadata = labels if isinstance(labels, dict) else {}
-            
+
             return VectorStoreCreateResponse(
                 id=corpus_id,
                 object="vector_store",
@@ -267,18 +283,18 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
                     "completed": 0,
                     "failed": 0,
                     "cancelled": 0,
-                    "total": 0
+                    "total": 0,
                 },
                 status="completed",  # Vertex AI corpus creation is typically synchronous
                 expires_after=None,
                 expires_at=None,
                 last_active_at=None,
-                metadata=metadata
+                metadata=metadata,
             )
-            
+
         except Exception as e:
             raise self.get_error_class(
                 error_message=str(e),
                 status_code=response.status_code,
-                headers=response.headers
-            ) 
\ No newline at end of file
+                headers=response.headers,
+            )
diff --git a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py
index 31837146485..8fa65002d53 100644
--- a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py
+++ b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py
@@ -7,8 +7,10 @@ from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreCon
 from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
     VectorStoreCreateOptionalRequestParams,
     VectorStoreCreateResponse,
+    VectorStoreIndexEndpoints,
     VectorStoreResultContent,
     VectorStoreSearchOptionalRequestParams,
     VectorStoreSearchResponse,
@@ -33,14 +35,9 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
     def __init__(self):
         super().__init__()
 
-    def validate_environment(
-        self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
-    ) -> dict:
-        """
-        Validate and set up authentication for Vertex AI RAG API
-        """
-        litellm_params = litellm_params or GenericLiteLLMParams()
-
+    def get_auth_credentials(
+        self, litellm_params: dict
+    ) -> BaseVectorStoreAuthCredentials:
         # Get credentials and project info
         vertex_credentials = self.get_vertex_ai_credentials(dict(litellm_params))
         vertex_project = self.get_vertex_ai_project(dict(litellm_params))
@@ -52,13 +49,28 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
             custom_llm_provider="vertex_ai",
         )
 
-        headers.update(
-            {
+        return {
+            "headers": {
                 "Authorization": f"Bearer {access_token}",
                 "Content-Type": "application/json",
-            }
-        )
+            },
+        }
 
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        return {
+            "read": [("POST", ":search")],
+            "write": [],
+        }
+
+    def validate_environment(
+        self, headers: dict, litellm_params: Optional[GenericLiteLLMParams]
+    ) -> dict:
+        """
+        Validate and set up authentication for Vertex AI RAG API
+        """
+        litellm_params = litellm_params or GenericLiteLLMParams()
+        auth_headers = self.get_auth_credentials(litellm_params.model_dump())
+        headers.update(auth_headers.get("headers", {}))
         return headers
 
     def get_complete_url(
diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml
index 8544956db92..6d0936d1980 100644
--- a/litellm/proxy/_new_secret_config.yaml
+++ b/litellm/proxy/_new_secret_config.yaml
@@ -4,10 +4,7 @@ model_list:
       model: bedrock/global.anthropic.claude-sonnet-4-5-20250929-v1:0
   - model_name: embedding-model
     litellm_params:
-      model: azure/text-embedding-3-large
-      api_base: https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/
-      api_key: os.environ/AZURE_API_KEY
-      api_version: "2025-09-01"
+      model: openai/text-embedding-3-large
 
 general_settings:
   master_key: sk-1234
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index b1082c4cc49..20a78d839a3 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -340,6 +340,7 @@ class LiteLLMRoutes(enum.Enum):
         "/anthropic",
         "/langfuse",
         "/azure",
+        "/azure_ai",
         "/openai",
         "/assemblyai",
         "/eu.assemblyai",
@@ -777,6 +778,11 @@ class GenerateRequestBase(LiteLLMPydanticObjectBase):
     object_permission: Optional[LiteLLM_ObjectPermissionBase] = None
 
 
+class AllowedVectorStoreIndexItem(LiteLLMPydanticObjectBase):
+    index_name: str
+    index_permissions: List[Literal["read", "write"]]
+
+
 class KeyRequestBase(GenerateRequestBase):
     key: Optional[str] = None
     budget_id: Optional[str] = None
@@ -784,6 +790,7 @@ class KeyRequestBase(GenerateRequestBase):
     enforced_params: Optional[List[str]] = None
     allowed_routes: Optional[list] = []
     allowed_passthrough_routes: Optional[list] = None
+    allowed_vector_store_indexes: Optional[List[AllowedVectorStoreIndexItem]] = None
     rpm_limit_type: Optional[
         Literal["guaranteed_throughput", "best_effort_throughput", "dynamic"]
     ] = None  # raise an error if 'guaranteed_throughput' is set and we're overallocating rpm
@@ -1311,6 +1318,7 @@ class NewTeamRequest(TeamBase):
         None  # allow user to set TPM limit for all team members
     )
     team_member_key_duration: Optional[str] = None  # e.g. "1d", "1w", "1m"
+    allowed_vector_store_indexes: Optional[List[AllowedVectorStoreIndexItem]] = None
 
     model_config = ConfigDict(protected_namespaces=())
 
@@ -1359,6 +1367,7 @@ class UpdateTeamRequest(LiteLLMPydanticObjectBase):
     allowed_passthrough_routes: Optional[list] = None
     model_rpm_limit: Optional[Dict[str, int]] = None
     model_tpm_limit: Optional[Dict[str, int]] = None
+    allowed_vector_store_indexes: Optional[List[AllowedVectorStoreIndexItem]] = None
 
 
 class ResetTeamBudgetRequest(LiteLLMPydanticObjectBase):
@@ -3177,11 +3186,10 @@ LiteLLM_ManagementEndpoint_MetadataFields = [
     "model_tpm_limit",
     "rpm_limit_type",
     "tpm_limit_type",
-    "guardrails",
-    "tags",
     "enforced_params",
     "temp_budget_increase",
     "temp_budget_expiry",
+    "allowed_vector_store_indexes",
 ]
 
 LiteLLM_ManagementEndpoint_MetadataFields_Premium = [
diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py
index eeae54055f8..472cb81f93d 100644
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@@ -551,6 +551,7 @@ async def _common_key_generation_helper(  # noqa: PLR0915
                 field_name=field,
                 value=getattr(data, field),
             )
+            delattr(data, field)
 
     for field in LiteLLM_ManagementEndpoint_MetadataFields:
         if getattr(data, field, None) is not None:
@@ -1019,6 +1020,7 @@ async def generate_key_fn(
     - prompts: Optional[List[str]] - List of allowed prompts for the key. If specified, the key will only be able to use these specific prompts.
     - auto_rotate: Optional[bool] - Whether this key should be automatically rotated (regenerated)
     - rotation_interval: Optional[str] - How often to auto-rotate this key (e.g., '30s', '30m', '30h', '30d'). Required if auto_rotate=True.
+    - allowed_vector_store_indexes: Optional[List[dict]] - List of allowed vector store indexes for the key. Example - [{"index_name": "my-index", "index_permissions": ["write", "read"]}]. If specified, the key will only be able to use these specific vector store indexes. Create index, using `/v1/indexes` endpoint.
 
 
     Examples:
@@ -1167,6 +1169,8 @@ async def generate_service_account_key_fn(
     - allowed_routes: Optional[list] - List of allowed routes for the key. Store the actual route or store a wildcard pattern for a set of routes. Example - ["/chat/completions", "/embeddings", "/keys/*"]
     - object_permission: Optional[LiteLLM_ObjectPermissionBase] - key-specific object permission. Example - {"vector_stores": ["vector_store_1", "vector_store_2"], "mcp_tool_permissions": {"server_id_1": ["tool1", "tool2"]}}. IF null or {} then no object permission.
     Examples:
+    - allowed_vector_store_indexes: Optional[List[dict]] - List of allowed vector store indexes for the key. Example - [{"index_name": "my-index", "index_permissions": ["write", "read"]}]. If specified, the key will only be able to use these specific vector store indexes. Create index, using `/v1/indexes` endpoint.
+
 
     1. Allow users to turn on/off pii masking
 
@@ -1457,6 +1461,8 @@ async def update_key_fn(
     - object_permission: Optional[LiteLLM_ObjectPermissionBase] - key-specific object permission. Example - {"vector_stores": ["vector_store_1", "vector_store_2"], "mcp_tool_permissions": {"server_id_1": ["tool1", "tool2"]}}. IF null or {} then no object permission.
     - auto_rotate: Optional[bool] - Whether this key should be automatically rotated
     - rotation_interval: Optional[str] - How often to rotate this key (e.g., '30d', '90d'). Required if auto_rotate=True
+    - allowed_vector_store_indexes: Optional[List[dict]] - List of allowed vector store indexes for the key. Example - [{"index_name": "my-index", "index_permissions": ["write", "read"]}]. If specified, the key will only be able to use these specific vector store indexes. Create index, using `/v1/indexes` endpoint.
+
     Example:
     ```bash
     curl --location 'http://0.0.0.0:4000/key/update' \
diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py
index 582f5f8654b..d4bdde02bbd 100644
--- a/litellm/proxy/management_endpoints/team_endpoints.py
+++ b/litellm/proxy/management_endpoints/team_endpoints.py
@@ -556,6 +556,8 @@ async def new_team(  # noqa: PLR0915
     - team_member_key_duration: Optional[str] - The duration for a team member's key. e.g. "1d", "1w", "1mo"
     - prompts: Optional[List[str]] - List of allowed prompts for the team. If specified, the team will only be able to use these specific prompts.
     - allowed_passthrough_routes: Optional[List[str]] - List of allowed pass through routes for the team.
+    - allowed_vector_store_indexes: Optional[List[dict]] - List of allowed vector store indexes for the key. Example - [{"index_name": "my-index", "index_permissions": ["write", "read"]}]. If specified, the key will only be able to use these specific vector store indexes. Create index, using `/v1/indexes` endpoint.
+
     
 
     Returns:
@@ -1114,6 +1116,8 @@ async def update_team(
     - model_rpm_limit: Optional[Dict[str, int]] - The RPM (Requests Per Minute) limit per model for this team. Example: {"gpt-4": 100, "gpt-3.5-turbo": 200}
     - model_tpm_limit: Optional[Dict[str, int]] - The TPM (Tokens Per Minute) limit per model for this team. Example: {"gpt-4": 10000, "gpt-3.5-turbo": 20000}
     Example - update team TPM Limit
+    - allowed_vector_store_indexes: Optional[List[dict]] - List of allowed vector store indexes for the key. Example - [{"index_name": "my-index", "index_permissions": ["write", "read"]}]. If specified, the key will only be able to use these specific vector store indexes. Create index, using `/v1/indexes` endpoint.
+
 
     ```
     curl --location 'http://0.0.0.0:4000/team/update' \
diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
index 749b421c427..5d1c2febad8 100644
--- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
@@ -35,7 +35,11 @@ from litellm.proxy.pass_through_endpoints.pass_through_endpoints import (
     websocket_passthrough_request,
 )
 from litellm.proxy.utils import is_known_model
+from litellm.proxy.vector_store_endpoints.utils import (
+    is_allowed_to_call_vector_store_endpoint,
+)
 from litellm.secret_managers.main import get_secret_str
+from litellm.utils import ProviderConfigManager
 
 from .passthrough_endpoint_router import PassthroughEndpointRouter
 
@@ -988,6 +992,11 @@ async def assemblyai_proxy_route(
     return received_value
 
 
+@router.api_route(
+    "/azure_ai/{endpoint:path}",
+    methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
+    tags=["Azure AI Pass-through", "pass-through"],
+)
 @router.api_route(
     "/azure/{endpoint:path}",
     methods=["GET", "POST", "PUT", "DELETE", "PATCH"],
@@ -1014,9 +1023,21 @@ async def azure_proxy_route(
 
     if len(parts) > 1 and llm_router:
         for part in parts:
+            # check if LLM MODEL
             is_router_model = is_passthrough_request_using_router_model(
                 request_body={"model": part}, llm_router=llm_router
             )
+            # check if vector store index
+            is_vector_store_index = (
+                (
+                    litellm.vector_store_index_registry.is_vector_store_index(
+                        vector_store_index_name=part
+                    )
+                )
+                if litellm.vector_store_index_registry is not None
+                else False
+            )
+
             if is_router_model:
                 request_body = await get_request_body(request)
                 is_streaming_request = is_passthrough_request_streaming(request_body)
@@ -1074,6 +1095,66 @@ async def azure_proxy_route(
                         custom_headers=None,
                     ),
                 )
+            elif is_vector_store_index:
+                # get the api key from the provider config
+                provider_config = (
+                    ProviderConfigManager.get_provider_vector_stores_config(
+                        provider=litellm.LlmProviders.AZURE_AI
+                    )
+                )
+                if provider_config is None:
+                    raise Exception("Provider config not found for Azure AI")
+                # get the index from registry
+                if litellm.vector_store_registry is None:
+                    raise Exception("Vector store registry not found")
+
+                is_allowed_to_call_vector_store_endpoint(
+                    index_name=part,
+                    provider=litellm.LlmProviders.AZURE_AI,
+                    request=request,
+                    user_api_key_dict=user_api_key_dict,
+                )
+                # get the vector store name from index registry
+                index_object = (
+                    (
+                        litellm.vector_store_index_registry.get_vector_store_index_by_name(
+                            vector_store_index_name=part
+                        )
+                    )
+                    if litellm.vector_store_index_registry is not None
+                    else None
+                )
+                if index_object is None:
+                    raise Exception(f"Vector store index not found for {part}")
+
+                vector_store_name = index_object.litellm_params.vector_store_name
+
+                vector_store = litellm.vector_store_registry.get_litellm_managed_vector_store_from_registry_by_name(
+                    vector_store_name=vector_store_name
+                )
+                if vector_store is None:
+                    raise Exception(f"Vector store not found for {vector_store_name}")
+                litellm_params = vector_store.get("litellm_params") or {}
+                auth_credentials = provider_config.get_auth_credentials(
+                    litellm_params=litellm_params
+                )
+
+                extra_headers = auth_credentials.get("headers") or {}
+
+                base_target_url = litellm_params.get("api_base")
+                if base_target_url is None:
+                    raise Exception(f"API base not found for {part}")
+                return await BaseOpenAIPassThroughHandler._base_openai_pass_through_handler(
+                    endpoint=endpoint,
+                    request=request,
+                    fastapi_response=fastapi_response,
+                    user_api_key_dict=user_api_key_dict,
+                    base_target_url=base_target_url,
+                    api_key=None,
+                    custom_llm_provider=litellm.LlmProviders.AZURE_AI,
+                    extra_headers=cast(dict, extra_headers),
+                )
+
     base_target_url = get_secret_str(secret_name="AZURE_API_BASE")
     if base_target_url is None:
         raise Exception(
@@ -1577,8 +1658,9 @@ class BaseOpenAIPassThroughHandler:
         fastapi_response: Response,
         user_api_key_dict: UserAPIKeyAuth,
         base_target_url: str,
-        api_key: str,
+        api_key: Optional[str],
         custom_llm_provider: litellm.LlmProviders,
+        extra_headers: Optional[dict] = None,
     ):
         encoded_endpoint = httpx.URL(endpoint).path
         # Ensure endpoint starts with '/' for proper URL construction
@@ -1603,7 +1685,7 @@ class BaseOpenAIPassThroughHandler:
             endpoint=endpoint,
             target=str(updated_url),
             custom_headers=BaseOpenAIPassThroughHandler._assemble_headers(
-                api_key=api_key, request=request
+                api_key=api_key, request=request, extra_headers=extra_headers
             ),
         )  # dynamically construct pass-through endpoint based on incoming path
         received_value = await endpoint_func(
@@ -1629,11 +1711,17 @@ class BaseOpenAIPassThroughHandler:
         return headers
 
     @staticmethod
-    def _assemble_headers(api_key: str, request: Request) -> dict:
-        base_headers = {
-            "authorization": "Bearer {}".format(api_key),
-            "api-key": "{}".format(api_key),
-        }
+    def _assemble_headers(
+        api_key: Optional[str], request: Request, extra_headers: Optional[dict] = None
+    ) -> dict:
+        base_headers = {}
+        if api_key is not None:
+            base_headers = {
+                "authorization": "Bearer {}".format(api_key),
+                "api-key": "{}".format(api_key),
+            }
+        if extra_headers is not None:
+            base_headers.update(extra_headers)
         return BaseOpenAIPassThroughHandler._append_openai_beta_header(
             headers=base_headers,
             request=request,
diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
index 72e139dea52..39187edb523 100644
--- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
@@ -771,14 +771,6 @@ async def pass_through_request(  # noqa: PLR0915
                 status_code=response.status_code,
             )
 
-        verbose_proxy_logger.debug("request method: {}".format(request.method))
-        verbose_proxy_logger.debug("request url: {}".format(url))
-        verbose_proxy_logger.debug("request headers: {}".format(headers))
-        verbose_proxy_logger.debug(
-            "requested_query_params={}".format(requested_query_params)
-        )
-        verbose_proxy_logger.debug("request body: {}".format(_parsed_body))
-
         response = (
             await HttpPassThroughEndpointHelpers.non_streaming_http_request_handler(
                 request=request,
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 14cf848f70e..12e6164f424 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -269,9 +269,7 @@ from litellm.proxy.management_endpoints.customer_endpoints import (
 from litellm.proxy.management_endpoints.internal_user_endpoints import (
     router as internal_user_router,
 )
-from litellm.proxy.management_endpoints.internal_user_endpoints import (
-    user_update,
-)
+from litellm.proxy.management_endpoints.internal_user_endpoints import user_update
 from litellm.proxy.management_endpoints.key_management_endpoints import (
     delete_verification_tokens,
     duration_in_seconds,
@@ -322,9 +320,7 @@ from litellm.proxy.ocr_endpoints.endpoints import router as ocr_router
 from litellm.proxy.openai_files_endpoints.files_endpoints import (
     router as openai_files_router,
 )
-from litellm.proxy.openai_files_endpoints.files_endpoints import (
-    set_files_config,
-)
+from litellm.proxy.openai_files_endpoints.files_endpoints import set_files_config
 from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
     passthrough_endpoint_router,
 )
@@ -411,9 +407,7 @@ from litellm.types.proxy.management_endpoints.ui_sso import (
     LiteLLM_UpperboundKeyGenerateParams,
 )
 from litellm.types.realtime import RealtimeQueryParams
-from litellm.types.router import (
-    DeploymentTypedDict,
-)
+from litellm.types.router import DeploymentTypedDict
 from litellm.types.router import ModelInfo as RouterModelInfo
 from litellm.types.router import (
     RouterGeneralSettings,
@@ -1928,29 +1922,31 @@ class ProxyConfig:
         """
         Parse and validate search tools from config.
         Loads environment variables and casts to SearchToolTypedDict.
-        
+
         Args:
             config: Config dictionary containing search_tools
-            
+
         Returns:
             List of validated SearchToolTypedDict or None if not configured
         """
         search_tools_raw = config.get("search_tools", None)
         if not search_tools_raw:
             return None
-        
+
         search_tools_parsed: List[SearchToolTypedDict] = []
-        
+
         print(  # noqa
             "\033[32mLiteLLM: Proxy initialized with Search Tools:\033[0m"
         )  # noqa
-        
+
         for search_tool in search_tools_raw:
             # Display loaded search tool
             search_tool_name = search_tool.get("search_tool_name", "")
-            search_provider = search_tool.get("litellm_params", {}).get("search_provider", "")
+            search_provider = search_tool.get("litellm_params", {}).get(
+                "search_provider", ""
+            )
             print(f"\033[32m    {search_tool_name} ({search_provider})\033[0m")  # noqa
-            
+
             # Cast to SearchToolTypedDict for type safety
             try:
                 search_tool_typed: SearchToolTypedDict = SearchToolTypedDict(**search_tool)  # type: ignore
@@ -1960,7 +1956,7 @@ class ProxyConfig:
                     f"Error parsing search tool {search_tool_name}: {str(e)}"
                 )
                 continue
-        
+
         return search_tools_parsed if search_tools_parsed else None
 
     def _load_environment_variables(self, config: dict):
@@ -2466,7 +2462,9 @@ class ProxyConfig:
             assistants_config = AssistantsTypedDict(**assistant_settings)  # type: ignore
 
         ## SEARCH TOOLS SETTINGS
-        search_tools: Optional[List[SearchToolTypedDict]] = self.parse_search_tools(config)
+        search_tools: Optional[List[SearchToolTypedDict]] = self.parse_search_tools(
+            config
+        )
 
         ## /fine_tuning/jobs endpoints config
         finetuning_config = config.get("finetune_settings", None)
@@ -3366,6 +3364,10 @@ class ProxyConfig:
         if self._should_load_db_object(object_type="vector_stores"):
             await self._init_vector_stores_in_db(prisma_client=prisma_client)
 
+        if self._should_load_db_object(object_type="vector_store_indexes"):
+
+            await self._init_vector_store_indexes_in_db(prisma_client=prisma_client)
+
         if self._should_load_db_object(object_type="mcp"):
             await self._init_mcp_servers_in_db()
 
@@ -3394,21 +3396,26 @@ class ProxyConfig:
         """
         Initialize SSO settings from database into the router on startup.
         """
-        
+
         try:
             sso_settings = await prisma_client.db.litellm_ssoconfig.find_unique(
                 where={"id": "sso_config"}
             )
             if sso_settings is not None:
                 # Capitalize all keys in sso_settings dictionary
-                uppercase_sso_settings = {key.upper(): value for key, value in sso_settings.sso_settings.items()}
-                self._decrypt_and_set_db_env_variables(environment_variables=uppercase_sso_settings)
+                uppercase_sso_settings = {
+                    key.upper(): value
+                    for key, value in sso_settings.sso_settings.items()
+                }
+                self._decrypt_and_set_db_env_variables(
+                    environment_variables=uppercase_sso_settings
+                )
         except Exception as e:
             verbose_proxy_logger.exception(
                 "litellm.proxy.proxy_server.py::ProxyConfig:_init_sso_settings_in_db - {}".format(
                     str(e)
                 )
-            )   
+            )
 
     async def _check_and_reload_model_cost_map(self, prisma_client: PrismaClient):
         """
@@ -3579,6 +3586,36 @@ class ProxyConfig:
                 )
             )
 
+    async def _init_vector_store_indexes_in_db(self, prisma_client: PrismaClient):
+        from litellm.vector_stores.vector_store_registry import VectorStoreIndexRegistry
+
+        try:
+            # read vector stores from db table
+            vector_store_indexes = (
+                await VectorStoreIndexRegistry._get_vector_store_indexes_from_db(
+                    prisma_client=prisma_client
+                )
+            )
+
+            if len(vector_store_indexes) <= 0:
+                return
+
+            if litellm.vector_store_index_registry is None:
+                litellm.vector_store_index_registry = VectorStoreIndexRegistry(
+                    vector_store_indexes=vector_store_indexes
+                )
+            else:
+                for vector_store_index in vector_store_indexes:
+                    litellm.vector_store_index_registry.upsert_vector_store_index(
+                        vector_store_index=vector_store_index
+                    )
+        except Exception as e:
+            verbose_proxy_logger.exception(
+                "litellm.proxy.proxy_server.py::ProxyConfig:_init_vector_stores_in_db - {}".format(
+                    str(e)
+                )
+            )
+
     async def _init_mcp_servers_in_db(self):
         from litellm.proxy._experimental.mcp_server.utils import is_mcp_available
 
@@ -3600,7 +3637,7 @@ class ProxyConfig:
                     str(e)
                 )
             )
-    
+
     async def _init_search_tools_in_db(self, prisma_client: PrismaClient):
         """
         Initialize search tools from database into the router on startup.
@@ -3611,19 +3648,20 @@ class ProxyConfig:
             SearchToolRegistry,
         )
         from litellm.router_utils.search_api_router import SearchAPIRouter
-        
+
         try:
-            search_tools = await SearchToolRegistry.get_all_search_tools_from_db(prisma_client=prisma_client)
-            
+            search_tools = await SearchToolRegistry.get_all_search_tools_from_db(
+                prisma_client=prisma_client
+            )
+
             verbose_proxy_logger.info(
                 f"Loading {len(search_tools)} search tool(s) from database into router"
             )
-            
+
             if llm_router is not None:
                 # Add search tools to the router
                 await SearchAPIRouter.update_router_search_tools(
-                    router_instance=llm_router,
-                    search_tools=search_tools
+                    router_instance=llm_router, search_tools=search_tools
                 )
                 verbose_proxy_logger.info(
                     f"Successfully loaded {len(search_tools)} search tool(s) into router"
@@ -3632,14 +3670,14 @@ class ProxyConfig:
                 verbose_proxy_logger.debug(
                     "Router not initialized yet, search tools will be added when router is created"
                 )
-                
+
         except Exception as e:
             verbose_proxy_logger.exception(
                 "litellm.proxy.proxy_server.py::ProxyConfig:_init_search_tools_in_db - {}".format(
                     str(e)
                 )
             )
-    
+
     async def _init_pass_through_endpoints_in_db(self):
         from litellm.proxy.pass_through_endpoints.pass_through_endpoints import (
             initialize_pass_through_endpoints_in_db,
@@ -4138,21 +4176,24 @@ class ProxyStartupEvent:
                 # It must be passed individually when calling add_job()
             },
             # Limit job store size to prevent memory growth
-            jobstores={
-                'default': MemoryJobStore()    # explicitly use memory job store
-            },
+            jobstores={"default": MemoryJobStore()},  # explicitly use memory job store
             # Use simple executor to minimize overhead
             executors={
-                'default': AsyncIOExecutor(),
+                "default": AsyncIOExecutor(),
             },
             # Disable timezone awareness to reduce computation
-            timezone=None
+            timezone=None,
         )
 
         # Use fixed intervals with small random offset instead of jitter
         # This avoids the expensive jitter calculations in APScheduler
-        budget_interval = proxy_budget_rescheduler_min_time + random.randint(0,
-            min(30, proxy_budget_rescheduler_max_time - proxy_budget_rescheduler_min_time))
+        budget_interval = proxy_budget_rescheduler_min_time + random.randint(
+            0,
+            min(
+                30,
+                proxy_budget_rescheduler_max_time - proxy_budget_rescheduler_min_time,
+            ),
+        )
 
         # Ensure minimum interval of 30 seconds for batch writing to prevent memory issues
         batch_writing_interval = proxy_batch_write_at + random.randint(0, 5)
@@ -4248,7 +4289,9 @@ class ProxyStartupEvent:
                 # REMOVED jitter parameter - major cause of memory leak
                 # Use random start time instead for distribution
                 next_run_time=datetime.now()
-                + timedelta(seconds=10 + random.randint(0, 300)),  # Random 0-5 min offset
+                + timedelta(
+                    seconds=10 + random.randint(0, 300)
+                ),  # Random 0-5 min offset
                 args=[spend_report_frequency],
                 id="weekly_spend_report_job",
                 replace_existing=True,
@@ -4292,7 +4335,8 @@ class ProxyStartupEvent:
                 scheduler.add_job(
                     spend_log_cleanup.cleanup_old_spend_logs,
                     "interval",
-                    seconds=interval_seconds + random.randint(0, 60),  # Add small random offset
+                    seconds=interval_seconds
+                    + random.randint(0, 60),  # Add small random offset
                     # REMOVED jitter parameter - major cause of memory leak
                     args=[prisma_client],
                     id="spend_log_cleanup_job",
@@ -4318,7 +4362,8 @@ class ProxyStartupEvent:
                 scheduler.add_job(
                     check_batch_cost_job.check_batch_cost,
                     "interval",
-                    seconds=proxy_batch_polling_interval + random.randint(0, 30),  # Add small random offset
+                    seconds=proxy_batch_polling_interval
+                    + random.randint(0, 30),  # Add small random offset
                     # REMOVED jitter parameter - major cause of memory leak
                     id="check_batch_cost_job",
                     replace_existing=True,
@@ -4327,9 +4372,7 @@ class ProxyStartupEvent:
                 verbose_proxy_logger.info("Batch cost check job scheduled successfully")
 
             except Exception as e:
-                verbose_proxy_logger.error(
-                    f"Failed to setup batch cost checking: {e}"
-                )
+                verbose_proxy_logger.error(f"Failed to setup batch cost checking: {e}")
                 verbose_proxy_logger.debug(
                     "Checking batch cost for LiteLLM Managed Files is an Enterprise Feature. Skipping..."
                 )
diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma
index ac1acee908a..605a312db4d 100644
--- a/litellm/proxy/schema.prisma
+++ b/litellm/proxy/schema.prisma
@@ -590,6 +590,17 @@ model LiteLLM_SSOConfig {
   updated_at DateTime @updatedAt
 }
 
+model LiteLLM_ManagedVectorStoreIndexTable {
+  id String @id @default(uuid())
+  index_name String @unique
+  litellm_params Json
+  index_info Json?
+  created_at DateTime @default(now())
+  created_by String?
+  updated_at DateTime @updatedAt
+  updated_by String?
+}
+
 // Cache configuration table
 model LiteLLM_CacheConfig {
   id String @id @default("cache_config")
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index dbd795e1b31..7ad39bff6b1 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -985,7 +985,11 @@ class ProxyLogging:
                     )
                 else:
                     _callback = callback  # type: ignore
-                if _callback is not None and isinstance(_callback, CustomGuardrail) and data is not None:
+                if (
+                    _callback is not None
+                    and isinstance(_callback, CustomGuardrail)
+                    and data is not None
+                ):
                     result = await self._process_guardrail_callback(
                         callback=_callback,
                         data=data,
@@ -3691,6 +3695,16 @@ def is_known_model(model: Optional[str], llm_router: Optional[Router]) -> bool:
     return is_in_list
 
 
+def is_known_vector_store_index(index_name: str) -> bool:
+    """
+    Returns True if the vector store index is in the llm_router vector store indexes
+    """
+
+    if litellm.vector_store_index_registry is None:
+        return False
+    return index_name in litellm.vector_store_index_registry.get_vector_store_indexes()
+
+
 def join_paths(base_path: str, route: str) -> str:
     # Remove trailing slashes from base_path and leading slashes from route
     base_path = base_path.rstrip("/")
diff --git a/litellm/proxy/vector_store_endpoints/endpoints.py b/litellm/proxy/vector_store_endpoints/endpoints.py
index 7859c1c4b83..509869d9bb0 100644
--- a/litellm/proxy/vector_store_endpoints/endpoints.py
+++ b/litellm/proxy/vector_store_endpoints/endpoints.py
@@ -1,20 +1,23 @@
 from typing import Dict, Optional
 
-from fastapi import APIRouter, Depends, Request, Response
+from fastapi import APIRouter, Depends, HTTPException, Request, Response
 
 import litellm
 from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import (
     LiteLLM_ManagedVectorStore,
 )
-from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy._types import CommonProxyErrors, UserAPIKeyAuth
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
+from litellm.proxy.utils import jsonify_object
+from litellm.types.vector_stores import IndexCreateRequest
 
 router = APIRouter()
 ########################################################
 # OpenAI Compatible Endpoints
 ########################################################
 
+
 def _update_request_data_with_litellm_managed_vector_store_registry(
     data: Dict,
     vector_store_id: str,
@@ -24,23 +27,35 @@ def _update_request_data_with_litellm_managed_vector_store_registry(
 
     """
     if litellm.vector_store_registry is not None:
-        vector_store_to_run: Optional[LiteLLM_ManagedVectorStore] = litellm.vector_store_registry.get_litellm_managed_vector_store_from_registry(
-            vector_store_id=vector_store_id
+        vector_store_to_run: Optional[LiteLLM_ManagedVectorStore] = (
+            litellm.vector_store_registry.get_litellm_managed_vector_store_from_registry(
+                vector_store_id=vector_store_id
+            )
         )
         if vector_store_to_run is not None:
             if "custom_llm_provider" in vector_store_to_run:
-                data["custom_llm_provider"] = vector_store_to_run.get("custom_llm_provider")
-            
+                data["custom_llm_provider"] = vector_store_to_run.get(
+                    "custom_llm_provider"
+                )
+
             if "litellm_credential_name" in vector_store_to_run:
-                data["litellm_credential_name"] = vector_store_to_run.get("litellm_credential_name")
+                data["litellm_credential_name"] = vector_store_to_run.get(
+                    "litellm_credential_name"
+                )
 
             if "litellm_params" in vector_store_to_run:
                 litellm_params = vector_store_to_run.get("litellm_params", {}) or {}
                 data.update(litellm_params)
     return data
 
-@router.post("/v1/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)])
-@router.post("/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)])
+
+@router.post(
+    "/v1/vector_stores/{vector_store_id}/search",
+    dependencies=[Depends(user_api_key_auth)],
+)
+@router.post(
+    "/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)]
+)
 async def vector_store_search(
     request: Request,
     vector_store_id: str,
@@ -71,12 +86,11 @@ async def vector_store_search(
     data = await _read_request_body(request=request)
     if "vector_store_id" not in data:
         data["vector_store_id"] = vector_store_id
-    
+
     data = _update_request_data_with_litellm_managed_vector_store_registry(
-        data=data,
-        vector_store_id=vector_store_id
+        data=data, vector_store_id=vector_store_id
     )
-    
+
     processor = ProxyBaseLLMRequestProcessing(data=data)
     try:
         return await processor.base_process_llm_request(
@@ -106,7 +120,6 @@ async def vector_store_search(
         )
 
 
-
 @router.post("/v1/vector_stores", dependencies=[Depends(user_api_key_auth)])
 @router.post("/vector_stores", dependencies=[Depends(user_api_key_auth)])
 async def vector_store_create(
@@ -163,3 +176,61 @@ async def vector_store_create(
             proxy_logging_obj=proxy_logging_obj,
             version=version,
         )
+
+
+@router.post(
+    "/v1/indexes",
+    dependencies=[Depends(user_api_key_auth)],
+)
+async def index_create(
+    request: Request,
+    index_create_request: IndexCreateRequest,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Create an index. Just writes the index to the database.
+
+    ```bash
+    curl -L -X POST 'http://0.0.0.0:4000/indexes/create' \
+        -H 'Content-Type: application/json' \
+        -H 'Authorization: Bearer sk-1234' \
+        -H 'LiteLLM-Beta: indexes_beta=v1' \
+        -d '{ 
+            "index_name": "dall-e-3",
+            "vector_store_index": "real-index-name",
+            "vector_store_name": "azure-ai-search"
+        }'
+    ```
+    """
+    from litellm.proxy.proxy_server import prisma_client
+
+    if prisma_client is None:
+        raise HTTPException(
+            status_code=500,
+            detail=CommonProxyErrors.db_not_connected_error.value,
+        )
+    ## 1. check if index already exists
+    existing_index = (
+        await prisma_client.db.litellm_managedvectorstoreindextable.find_unique(
+            where={"index_name": index_create_request.index_name}
+        )
+    )
+
+    ## 2. set created_by and updated_by
+
+    if existing_index is not None:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Index {index_create_request.index_name} already exists",
+        )
+
+    ## 2. create index
+    index_data = index_create_request.model_dump(exclude_none=True)
+    index_data["created_by"] = user_api_key_dict.user_id
+    index_data["updated_by"] = user_api_key_dict.user_id
+    new_index = await prisma_client.db.litellm_managedvectorstoreindextable.create(
+        data=jsonify_object(index_data)
+    )
+
+    return new_index.model_dump()
diff --git a/litellm/proxy/vector_store_endpoints/utils.py b/litellm/proxy/vector_store_endpoints/utils.py
new file mode 100644
index 00000000000..1d88ad0edf4
--- /dev/null
+++ b/litellm/proxy/vector_store_endpoints/utils.py
@@ -0,0 +1,120 @@
+from typing import Any, Dict, Literal, Optional
+
+from fastapi import HTTPException, Request
+
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.types.utils import LlmProviders
+from litellm.utils import ProviderConfigManager
+
+
+def check_vector_store_permission(
+    index_name: str,
+    permission: str,
+    key_metadata: Optional[Dict[str, Any]],
+    team_metadata: Optional[Dict[str, Any]],
+) -> bool:
+    """
+    Check if a specific permission is allowed for a given vector store index.
+
+    Args:
+        index_name: The name of the vector store index
+        permission: The permission to check (e.g., "read", "write")
+        key_metadata: Metadata from the API key
+        team_metadata: Metadata from the team
+
+    Returns:
+        True if the permission is allowed, False otherwise
+
+    Example metadata format:
+        "metadata": {
+            "allowed_vector_store_indexes": [
+                {
+                    "index_name": "dall-e-3",
+                    "index_permissions": ["write"]
+                }
+            ]
+        }
+    """
+    # Check both key_metadata and team_metadata
+    for metadata in [key_metadata, team_metadata]:
+        if metadata is None:
+            continue
+
+        allowed_indexes = metadata.get("allowed_vector_store_indexes")
+        if not allowed_indexes or not isinstance(allowed_indexes, list):
+            continue
+
+        # Look for matching index
+        for index_config in allowed_indexes:
+            if not isinstance(index_config, dict):
+                continue
+
+            if index_config.get("index_name") == index_name:
+                index_permissions = index_config.get("index_permissions", [])
+                if (
+                    isinstance(index_permissions, list)
+                    and permission in index_permissions
+                ):
+                    return True
+
+    return False
+
+
+def is_allowed_to_call_vector_store_endpoint(
+    provider: LlmProviders,
+    index_name: str,
+    request: Request,
+    user_api_key_dict: UserAPIKeyAuth,
+) -> Optional[Literal[True]]:
+    """
+    Check if the user is allowed to call the vector store endpoint.
+
+    Cover:
+    1. Creating a vector store index
+    2. Reading a vector store index (Search / List / Get)
+    """
+    # check what allowed permissions are for the key
+    key_metadata = user_api_key_dict.metadata
+    team_metadata = user_api_key_dict.team_metadata
+
+    provider_config = ProviderConfigManager.get_provider_vector_stores_config(
+        provider=provider
+    )
+    if provider_config is None:
+        return None
+
+    provider_vector_store_endpoints = (
+        provider_config.get_vector_store_endpoints_by_type()
+    )
+
+    # Determine the permission type based on the request
+    permission_type = None
+    for endpoint in provider_vector_store_endpoints["read"]:
+        if request.method == endpoint[0] and endpoint[1] in request.url.path:
+            permission_type = "read"
+            break
+
+    if permission_type is None:
+        for endpoint in provider_vector_store_endpoints["write"]:
+            if request.method == endpoint[0] and endpoint[1] in request.url.path:
+                permission_type = "write"
+                break
+
+    if permission_type is None:
+        return None
+
+    # Check if key has specific permission for allowed_vector_store_indexes
+    has_permission = check_vector_store_permission(
+        index_name=index_name,
+        permission=permission_type,
+        key_metadata=key_metadata,
+        team_metadata=team_metadata,
+    )
+
+    if not has_permission:
+        raise HTTPException(
+            status_code=403,
+            detail=f"User does not have permission to call vector store endpoint {index_name}. Ask your administrator to add the necessary permissions to your API key/Team.",
+        )
+
+    return has_permission
diff --git a/litellm/types/vector_stores.py b/litellm/types/vector_stores.py
index 144b1853382..5456eb90e30 100644
--- a/litellm/types/vector_stores.py
+++ b/litellm/types/vector_stores.py
@@ -1,6 +1,6 @@
 from datetime import datetime
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 from annotated_types import Ge
 from pydantic import BaseModel
@@ -195,6 +195,51 @@ class VectorStoreCreateResponse(TypedDict, total=False):
     metadata: Optional[Dict[str, str]]  # Metadata associated with the vector store
 
 
+class IndexCreateLiteLLMParams(BaseModel):
+    vector_store_index: str
+    vector_store_name: str
+
+
+class IndexCreateRequest(BaseModel):
+    index_name: str
+    litellm_params: IndexCreateLiteLLMParams
+    index_info: Optional[Dict[str, Any]] = None
+
+
+class BaseVectorStoreAuthCredentials(TypedDict, total=False):
+    headers: dict
+    query_params: dict
+
+
+class LiteLLM_ManagedVectorStoreIndex(BaseModel):
+    """LiteLLM managed vector store index object - this is is the object stored in the database"""
+
+    id: str
+    index_name: str
+    litellm_params: IndexCreateLiteLLMParams
+    index_info: Optional[Dict[str, Any]] = None
+    created_at: Optional[datetime] = None
+    created_by: Optional[str] = None
+    updated_at: Optional[datetime] = None
+    updated_by: Optional[str] = None
+
+
+class VectorStoreIndexType(str, Enum):
+    """Type of vector store index"""
+
+    READ = "read"
+    WRITE = "write"
+
+
+class VectorStoreIndexEndpoints(TypedDict):
+    """Endpoints for vector store index"""
+
+    read: List[
+        Tuple[Literal["GET", "POST", "PUT", "DELETE", "PATCH"], str]
+    ]  # endpoints for reading a vector store index
+    write: List[
+        Tuple[Literal["GET", "POST", "PUT", "DELETE", "PATCH"], str]
+    ]  # endpoints for writing a vector store index
 VECTOR_STORE_OPENAI_PARAMS = Literal[
     "filters",
     "max_num_results",
diff --git a/litellm/vector_stores/vector_store_registry.py b/litellm/vector_stores/vector_store_registry.py
index bdd14cb2d99..f7a2ddaec8d 100644
--- a/litellm/vector_stores/vector_store_registry.py
+++ b/litellm/vector_stores/vector_store_registry.py
@@ -7,6 +7,7 @@ from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.core_helpers import remove_items_at_indices
 from litellm.types.vector_stores import (
     LiteLLM_ManagedVectorStore,
+    LiteLLM_ManagedVectorStoreIndex,
     LiteLLM_ManagedVectorStoreListResponse,
     LiteLLM_VectorStoreConfig,
 )
@@ -17,6 +18,91 @@ else:
     PrismaClient = Any
 
 
+class VectorStoreIndexRegistry:
+    def __init__(
+        self, vector_store_indexes: List[LiteLLM_ManagedVectorStoreIndex] = []
+    ):
+        self.vector_store_indexes: List[LiteLLM_ManagedVectorStoreIndex] = (
+            vector_store_indexes
+        )
+
+    def get_vector_store_indexes(self) -> List[LiteLLM_ManagedVectorStoreIndex]:
+        """
+        Returns the vector store indexes
+        """
+        return self.vector_store_indexes
+
+    def get_vector_store_index_by_name(
+        self, vector_store_index_name: str
+    ) -> Optional[LiteLLM_ManagedVectorStoreIndex]:
+        """
+        Returns the vector store index by name
+        """
+        for vector_store_index in self.vector_store_indexes:
+            if vector_store_index.index_name == vector_store_index_name:
+                return vector_store_index
+        return None
+
+    def upsert_vector_store_index(
+        self, vector_store_index: LiteLLM_ManagedVectorStoreIndex
+    ):
+        """
+        Adds a vector store index to the registry.
+
+        If it already exists, it will be updated.
+        """
+        for i, _vector_store_index in enumerate[LiteLLM_ManagedVectorStoreIndex](
+            self.vector_store_indexes
+        ):
+            if _vector_store_index.index_name == vector_store_index.index_name:
+                self.vector_store_indexes[i] = vector_store_index
+                return
+        self.vector_store_indexes.append(vector_store_index)
+
+    def delete_vector_store_index(self, vector_store_index: str):
+        """
+        Deletes a vector store index from the registry
+        """
+        self.vector_store_indexes = [
+            index for index in self.vector_store_indexes if index != vector_store_index
+        ]
+
+    def is_vector_store_index(self, vector_store_index_name: str) -> bool:
+        """
+        Returns True if the vector store index is in the registry
+        """
+        for vector_store_index in self.vector_store_indexes:
+            if vector_store_index.index_name == vector_store_index_name:
+                return True
+        return False
+
+    #########################################################
+    ########### DB management helpers for vector stores ###########
+    #########################################################
+
+    @staticmethod
+    async def _get_vector_store_indexes_from_db(
+        prisma_client: Optional[PrismaClient],
+    ) -> List[LiteLLM_ManagedVectorStoreIndex]:
+        """
+        Get vector stores from the database
+        """
+        vector_stores_from_db: List[LiteLLM_ManagedVectorStoreIndex] = []
+        if prisma_client is not None:
+            _vector_stores_from_db = (
+                await prisma_client.db.litellm_managedvectorstoreindextable.find_many(
+                    order={"created_at": "desc"},
+                )
+            )
+            for vector_store in _vector_stores_from_db:
+                _dict_vector_store = dict(vector_store)
+                _litellm_managed_vector_store = LiteLLM_ManagedVectorStoreIndex(
+                    **_dict_vector_store
+                )
+                vector_stores_from_db.append(_litellm_managed_vector_store)
+        return vector_stores_from_db
+
+
 class VectorStoreRegistry:
     def __init__(self, vector_stores: List[LiteLLM_ManagedVectorStore] = []):
         self.vector_stores: List[LiteLLM_ManagedVectorStore] = vector_stores
@@ -139,6 +225,17 @@ class VectorStoreRegistry:
                 return vector_store
         return None
 
+    def get_litellm_managed_vector_store_from_registry_by_name(
+        self, vector_store_name: str
+    ) -> Optional[LiteLLM_ManagedVectorStore]:
+        """
+        Returns the vector store from the registry by name
+        """
+        for vector_store in self.vector_stores:
+            if vector_store.get("vector_store_name") == vector_store_name:
+                return vector_store
+        return None
+
     def pop_vector_stores_to_run(
         self, non_default_params: Dict, tools: Optional[List[Dict]] = None
     ) -> List[LiteLLM_ManagedVectorStore]:
diff --git a/schema.prisma b/schema.prisma
index ac1acee908a..a142b388b24 100644
--- a/schema.prisma
+++ b/schema.prisma
@@ -590,6 +590,17 @@ model LiteLLM_SSOConfig {
   updated_at DateTime @updatedAt
 }
 
+model LiteLLM_IndexTable {
+  id String @id @default(uuid())
+  index_name String @unique
+  litellm_params Json
+  index_info Json?
+  created_at DateTime @default(now())
+  created_by String?
+  updated_at DateTime @updatedAt
+  updated_by String?
+}
+
 // Cache configuration table
 model LiteLLM_CacheConfig {
   id String @id @default("cache_config")
diff --git a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
index 384c37ec1e6..1a287e95746 100644
--- a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
+++ b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
@@ -3,48 +3,57 @@ import sys
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
+from fastapi import Request
 
 sys.path.insert(
     0, os.path.abspath("../../../../..")
 )  # Adds the parent directory to the system path
 
+from fastapi import HTTPException
+
 import litellm
 from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import (
     LiteLLM_ManagedVectorStore,
 )
+from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.vector_store_endpoints.endpoints import (
     _update_request_data_with_litellm_managed_vector_store_registry,
 )
+from litellm.proxy.vector_store_endpoints.utils import (
+    check_vector_store_permission,
+    is_allowed_to_call_vector_store_endpoint,
+)
+from litellm.types.utils import LlmProviders
 
 
 @pytest.mark.asyncio
 async def test_router_avector_store_search_passes_correct_args():
     """
-    Test that router.avector_store_search() passes the correct arguments 
+    Test that router.avector_store_search() passes the correct arguments
     to downstream litellm.vector_stores.asearch() with custom_llm_provider and query.
     """
     # Create a router
     router = litellm.Router(model_list=[])
-    
+
     # Mock the router's _init_vector_store_api_endpoints method to avoid real API calls
-    with patch.object(router, '_init_vector_store_api_endpoints') as mock_init:
+    with patch.object(router, "_init_vector_store_api_endpoints") as mock_init:
         mock_init.return_value = {
             "object": "vector_store.search_results.page",
             "search_query": "test query",
-            "data": []
+            "data": [],
         }
-        
+
         # Call router's avector_store_search
         result = await router.avector_store_search(
             vector_store_id="test_store_id",
             query="test query",
-            custom_llm_provider="bedrock"
+            custom_llm_provider="bedrock",
         )
-        
+
         # Verify the internal method was called with correct args
         mock_init.assert_called_once()
         call_args = mock_init.call_args
-        
+
         # Check that the original function is passed correctly
         assert call_args[1]["vector_store_id"] == "test_store_id"
         assert call_args[1]["query"] == "test query"
@@ -59,44 +68,553 @@ def test_update_request_data_with_litellm_managed_vector_store_registry():
     # Setup test data
     data = {"existing_key": "existing_value"}
     vector_store_id = "test_store_id"
-    
+
     # Mock vector store registry
     mock_vector_store: LiteLLM_ManagedVectorStore = {
-        "vector_store_id": "test_store_id", 
+        "vector_store_id": "test_store_id",
         "custom_llm_provider": "bedrock",
         "litellm_credential_name": "test_credential",
-        "litellm_params": {"api_key": "test_key", "aws_region_name": "us-east-1"}
+        "litellm_params": {"api_key": "test_key", "aws_region_name": "us-east-1"},
     }
-    
+
     mock_registry = MagicMock()
-    mock_registry.get_litellm_managed_vector_store_from_registry.return_value = mock_vector_store
-    
+    mock_registry.get_litellm_managed_vector_store_from_registry.return_value = (
+        mock_vector_store
+    )
+
     # Test with vector store registry
-    with patch.object(litellm, 'vector_store_registry', mock_registry):
+    with patch.object(litellm, "vector_store_registry", mock_registry):
         result = _update_request_data_with_litellm_managed_vector_store_registry(
-            data=data,
-            vector_store_id=vector_store_id
+            data=data, vector_store_id=vector_store_id
         )
-        
+
         # Verify the data was updated correctly
         assert result["existing_key"] == "existing_value"  # Original data preserved
         assert result["custom_llm_provider"] == "bedrock"
         assert result["litellm_credential_name"] == "test_credential"
         assert result["api_key"] == "test_key"
         assert result["aws_region_name"] == "us-east-1"
-        
+
         # Verify registry was called correctly
         mock_registry.get_litellm_managed_vector_store_from_registry.assert_called_once_with(
             vector_store_id="test_store_id"
         )
-    
+
     # Test with no vector store registry
-    with patch.object(litellm, 'vector_store_registry', None):
+    with patch.object(litellm, "vector_store_registry", None):
         original_data = {"existing_key": "existing_value"}
         result = _update_request_data_with_litellm_managed_vector_store_registry(
-            data=original_data,
-            vector_store_id=vector_store_id
+            data=original_data, vector_store_id=vector_store_id
         )
-        
+
         # Verify data remains unchanged when no registry
-        assert result == original_data 
\ No newline at end of file
+        assert result == original_data
+
+
+class TestCheckVectorStorePermission:
+    """Test suite for check_vector_store_permission function."""
+
+    def test_permission_allowed_in_key_metadata(self):
+        """Test that permission is allowed when found in key metadata."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["read", "write"]}
+            ]
+        }
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        assert result is True
+
+    def test_permission_allowed_in_team_metadata(self):
+        """Test that permission is allowed when found in team metadata."""
+        team_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "team-index", "index_permissions": ["write"]}
+            ]
+        }
+
+        result = check_vector_store_permission(
+            index_name="team-index",
+            permission="write",
+            key_metadata=None,
+            team_metadata=team_metadata,
+        )
+
+        assert result is True
+
+    def test_permission_denied_wrong_permission(self):
+        """Test that permission is denied when index exists but wrong permission."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["read"]}
+            ]
+        }
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="write",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        assert result is False
+
+    def test_permission_denied_index_not_found(self):
+        """Test that permission is denied when index doesn't exist."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "other-index", "index_permissions": ["read", "write"]}
+            ]
+        }
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        assert result is False
+
+    def test_permission_denied_no_metadata(self):
+        """Test that permission is denied when no metadata provided."""
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=None,
+            team_metadata=None,
+        )
+
+        assert result is False
+
+    def test_permission_denied_no_allowed_indexes_field(self):
+        """Test that permission is denied when metadata has no allowed_vector_store_indexes."""
+        key_metadata = {"some_other_field": "value"}
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        assert result is False
+
+    def test_key_metadata_takes_precedence(self):
+        """Test that key metadata is checked and returns permission successfully."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["read"]}
+            ]
+        }
+        team_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["write"]}
+            ]
+        }
+
+        # Should find permission in key_metadata (checked first)
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=team_metadata,
+        )
+
+        assert result is True
+
+    def test_team_metadata_as_fallback(self):
+        """Test that team metadata is checked when key metadata doesn't have permission."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "other-index", "index_permissions": ["read"]}
+            ]
+        }
+        team_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["write"]}
+            ]
+        }
+
+        # Should find permission in team_metadata
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="write",
+            key_metadata=key_metadata,
+            team_metadata=team_metadata,
+        )
+
+        assert result is True
+
+    def test_multiple_indexes_in_metadata(self):
+        """Test handling multiple indexes in metadata."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "index-1", "index_permissions": ["read"]},
+                {"index_name": "index-2", "index_permissions": ["write"]},
+                {"index_name": "index-3", "index_permissions": ["read", "write"]},
+            ]
+        }
+
+        # Test each index
+        assert (
+            check_vector_store_permission("index-1", "read", key_metadata, None) is True
+        )
+        assert (
+            check_vector_store_permission("index-1", "write", key_metadata, None)
+            is False
+        )
+        assert (
+            check_vector_store_permission("index-2", "write", key_metadata, None)
+            is True
+        )
+        assert (
+            check_vector_store_permission("index-2", "read", key_metadata, None)
+            is False
+        )
+        assert (
+            check_vector_store_permission("index-3", "read", key_metadata, None) is True
+        )
+        assert (
+            check_vector_store_permission("index-3", "write", key_metadata, None)
+            is True
+        )
+
+    def test_invalid_metadata_structure(self):
+        """Test handling of invalid metadata structures."""
+        # Test when allowed_vector_store_indexes is not a list
+        key_metadata = {"allowed_vector_store_indexes": "not-a-list"}
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        assert result is False
+
+        # Test when index config is not a dict
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                "not-a-dict",
+                {"index_name": "my-index", "index_permissions": ["read"]},
+            ]
+        }
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        # Should still work because it skips invalid entries
+        assert result is True
+
+    def test_missing_index_permissions_field(self):
+        """Test when index_permissions field is missing."""
+        key_metadata = {
+            "allowed_vector_store_indexes": [
+                {
+                    "index_name": "my-index"
+                    # Missing index_permissions field
+                }
+            ]
+        }
+
+        result = check_vector_store_permission(
+            index_name="my-index",
+            permission="read",
+            key_metadata=key_metadata,
+            team_metadata=None,
+        )
+
+        assert result is False
+
+
+class TestIsAllowedToCallVectorStoreEndpoint:
+    """Test suite for is_allowed_to_call_vector_store_endpoint function."""
+
+    def test_read_permission_allowed(self):
+        """Test read permission is checked correctly."""
+        # Mock request
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "GET"
+        mock_request.url.path = "/v1/vector_stores/my-index/search"
+
+        # Mock user API key with permissions
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["read"]}
+            ]
+        }
+        mock_user_api_key.team_metadata = None
+
+        # Mock provider config
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            result = is_allowed_to_call_vector_store_endpoint(
+                provider=LlmProviders.OPENAI,
+                index_name="my-index",
+                request=mock_request,
+                user_api_key_dict=mock_user_api_key,
+            )
+
+        assert result is True
+
+    def test_write_permission_allowed(self):
+        """Test write permission is checked correctly."""
+        # Mock request
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "POST"
+        mock_request.url.path = "/v1/vector_stores/my-index/create"
+
+        # Mock user API key with permissions
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["write"]}
+            ]
+        }
+        mock_user_api_key.team_metadata = None
+
+        # Mock provider config
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            result = is_allowed_to_call_vector_store_endpoint(
+                provider=LlmProviders.OPENAI,
+                index_name="my-index",
+                request=mock_request,
+                user_api_key_dict=mock_user_api_key,
+            )
+
+        assert result is True
+
+    def test_permission_denied_wrong_permission(self):
+        """Test permission denied when user has read but tries write."""
+        # Mock request for write operation
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "POST"
+        mock_request.url.path = "/v1/vector_stores/my-index/create"
+
+        # Mock user API key with only read permissions
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["read"]}
+            ]
+        }
+        mock_user_api_key.team_metadata = None
+
+        # Mock provider config
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                result = is_allowed_to_call_vector_store_endpoint(
+                    provider=LlmProviders.OPENAI,
+                    index_name="my-index",
+                    request=mock_request,
+                    user_api_key_dict=mock_user_api_key,
+                )
+
+        assert exc_info.value.status_code == 403
+
+    def test_provider_config_not_found(self):
+        """Test when provider config is not found."""
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "GET"
+        mock_request.url.path = "/v1/vector_stores/my-index/search"
+
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = {}
+        mock_user_api_key.team_metadata = None
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=None,
+        ):
+            result = is_allowed_to_call_vector_store_endpoint(
+                provider=LlmProviders.OPENAI,
+                index_name="my-index",
+                request=mock_request,
+                user_api_key_dict=mock_user_api_key,
+            )
+
+        assert result is None
+
+    def test_endpoint_not_recognized(self):
+        """Test when endpoint doesn't match any read or write patterns."""
+        # Mock request with unrecognized path
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "DELETE"
+        mock_request.url.path = "/v1/vector_stores/my-index/unknown"
+
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "my-index", "index_permissions": ["read", "write"]}
+            ]
+        }
+        mock_user_api_key.team_metadata = None
+
+        # Mock provider config
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            result = is_allowed_to_call_vector_store_endpoint(
+                provider=LlmProviders.OPENAI,
+                index_name="my-index",
+                request=mock_request,
+                user_api_key_dict=mock_user_api_key,
+            )
+
+        assert result is None
+
+    def test_team_metadata_permissions(self):
+        """Test that team metadata permissions work."""
+        # Mock request
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "GET"
+        mock_request.url.path = "/v1/vector_stores/team-index/search"
+
+        # Mock user API key with no key metadata but team metadata
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = None
+        mock_user_api_key.team_metadata = {
+            "allowed_vector_store_indexes": [
+                {"index_name": "team-index", "index_permissions": ["read"]}
+            ]
+        }
+
+        # Mock provider config
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            result = is_allowed_to_call_vector_store_endpoint(
+                provider=LlmProviders.OPENAI,
+                index_name="team-index",
+                request=mock_request,
+                user_api_key_dict=mock_user_api_key,
+            )
+
+        assert result is True
+
+    def test_no_permissions_configured(self):
+        """Test when user has no vector store permissions configured."""
+
+        mock_request = MagicMock(spec=Request)
+        mock_request.method = "GET"
+        mock_request.url.path = "/v1/vector_stores/my-index/search"
+
+        mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
+        mock_user_api_key.metadata = {}
+        mock_user_api_key.team_metadata = {}
+
+        # Mock provider config
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                result = is_allowed_to_call_vector_store_endpoint(
+                    provider=LlmProviders.OPENAI,
+                    index_name="my-index",
+                    request=mock_request,
+                    user_api_key_dict=mock_user_api_key,
+                )
+
+        assert exc_info.value.status_code == 403
+
+    def test_azure_ai_permission_allowed(self):
+        from datetime import datetime, timezone
+        from unittest.mock import MagicMock
+
+        from fastapi import HTTPException
+
+        from litellm.proxy._types import UserAPIKeyAuth
+        from litellm.types.utils import LlmProviders
+
+        mock_request = MagicMock()
+        mock_request.method = "GET"
+        mock_request.url.path = "/azure_ai/indexes/dall-e-4/docs/search"
+        mock_user_api_key = UserAPIKeyAuth(
+            token="b637312ebffb9745321224644430ba9e4916a291c8281f293d21182c5e80bc5a",
+            key_name="sk-...plNQ",
+            metadata={
+                "allowed_vector_store_indexes": [
+                    {"index_name": "dall-e-4", "index_permissions": ["write"]}
+                ]
+            },
+            spend=0.015,
+        )
+        mock_provider_config = MagicMock()
+        mock_provider_config.get_vector_store_endpoints_by_type.return_value = {
+            "read": [("GET", "/docs/search")],
+            "write": [("POST", "/create")],
+        }
+
+        with patch(
+            "litellm.proxy.vector_store_endpoints.utils.ProviderConfigManager.get_provider_vector_stores_config",
+            return_value=mock_provider_config,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                is_allowed_to_call_vector_store_endpoint(
+                    provider=LlmProviders.AZURE_AI,
+                    index_name="dall-e-4",
+                    request=mock_request,
+                    user_api_key_dict=mock_user_api_key,
+                )
+
+        assert exc_info.value.status_code == 403