diff --git a/document.txt b/document.txt
new file mode 100644
index 0000000000..4a91207970
--- /dev/null
+++ b/document.txt
@@ -0,0 +1,19 @@
+LiteLLM provides a unified interface for calling 100+ different LLM providers.
+
+Key capabilities:
+- Translate requests to provider-specific formats
+- Consistent OpenAI-compatible responses
+- Retry and fallback logic across deployments
+- Proxy server with authentication and rate limiting
+- Support for streaming, function calling, and embeddings
+
+Popular providers supported:
+- OpenAI (GPT-4, GPT-3.5)
+- Anthropic (Claude)
+- AWS Bedrock
+- Azure OpenAI
+- Google Vertex AI
+- Cohere
+- And 95+ more
+
+This allows developers to easily switch between providers without code changes.
diff --git a/litellm/llms/vertex_ai/rag_engine/ingestion.py b/litellm/llms/vertex_ai/rag_engine/ingestion.py
index 386a38c729..6b435a46bc 100644
--- a/litellm/llms/vertex_ai/rag_engine/ingestion.py
+++ b/litellm/llms/vertex_ai/rag_engine/ingestion.py
@@ -14,21 +14,16 @@ Key differences from OpenAI:
 from __future__ import annotations
 
 import os
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, List, Optional, Tuple
 
 from litellm import get_secret_str
 from litellm._logging import verbose_logger
-from litellm.llms.custom_httpx.http_handler import (
-    AsyncHTTPHandler,
-    get_async_httpx_client,
-)
 from litellm.llms.vertex_ai.rag_engine.transformation import VertexAIRAGTransformation
 from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion
-from litellm.types.llms.custom_http import httpxSpecialProvider
 
 if TYPE_CHECKING:
     from litellm import Router
-    from litellm.types.rag import RAGChunkingStrategy, RAGIngestOptions
+    from litellm.types.rag import RAGIngestOptions
 
 
 def _get_str_or_none(value: Any) -> Optional[str]:
diff --git a/litellm/llms/vertex_ai/rag_engine/transformation.py b/litellm/llms/vertex_ai/rag_engine/transformation.py
index 469bfe990e..b601da1951 100644
--- a/litellm/llms/vertex_ai/rag_engine/transformation.py
+++ b/litellm/llms/vertex_ai/rag_engine/transformation.py
@@ -4,7 +4,7 @@ Transformation utilities for Vertex AI RAG Engine.
 Handles transforming LiteLLM's unified formats to Vertex AI RAG Engine API format.
 """
 
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Dict, Optional
 
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_CHUNK_OVERLAP, DEFAULT_CHUNK_SIZE
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 243b5318a3..0287736af1 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -20020,6 +20020,25 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 159
     },
+    "openrouter/anthropic/claude-opus-4.5": {
+        "cache_creation_input_token_cost": 6.25e-06,
+        "cache_read_input_token_cost": 5e-07,
+        "input_cost_per_token": 5e-06,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 200000,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "mode": "chat",
+        "output_cost_per_token": 2.5e-05,
+        "supports_assistant_prefill": true,
+        "supports_computer_use": true,
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "tool_use_system_prompt_tokens": 159
+    },
     "openrouter/anthropic/claude-sonnet-4.5": {
         "input_cost_per_image": 0.0048,
         "cache_creation_input_token_cost": 3.75e-06,
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index d05454c578..7e30079e78 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -379,6 +379,11 @@ class LiteLLMRoutes(enum.Enum):
     #########################################################
     passthrough_routes_wildcard = [f"{route}/*" for route in mapped_pass_through_routes]
 
+    litellm_native_routes = [
+        "/rag/ingest",
+        "/v1/rag/ingest",
+    ]
+
     anthropic_routes = [
         "/v1/messages",
         "/v1/messages/count_tokens",
@@ -416,6 +421,7 @@ class LiteLLMRoutes(enum.Enum):
         + passthrough_routes_wildcard
         + apply_guardrail_routes
         + mcp_routes
+        + litellm_native_routes
     )
     info_routes = [
         "/key/info",
diff --git a/litellm/rag/ingestion/base_ingestion.py b/litellm/rag/ingestion/base_ingestion.py
index 538a72b5ca..20059487b4 100644
--- a/litellm/rag/ingestion/base_ingestion.py
+++ b/litellm/rag/ingestion/base_ingestion.py
@@ -61,6 +61,26 @@ class BaseRAGIngestion(ABC):
         )
         self.ingest_name = ingest_options.get("name")
 
+        # Load credentials from litellm_credential_name if provided in vector_store config
+        self._load_credentials_from_config()
+
+    def _load_credentials_from_config(self) -> None:
+        """
+        Load credentials from litellm_credential_name if provided in vector_store config.
+
+        This allows users to specify a credential name in the vector_store config
+        which will be resolved from litellm.credential_list.
+        """
+        from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
+
+        credential_name = self.vector_store_config.get("litellm_credential_name")
+        if credential_name and litellm.credential_list:
+            credential_values = CredentialAccessor.get_credential_values(credential_name)
+            # Merge credentials into vector_store_config (don't overwrite existing values)
+            for key, value in credential_values.items():
+                if key not in self.vector_store_config:
+                    self.vector_store_config[key] = value
+
     @property
     def custom_llm_provider(self) -> str:
         """Get the vector store provider."""
@@ -317,5 +337,6 @@ class BaseRAGIngestion(ABC):
                 status="failed",
                 vector_store_id="",
                 file_id=None,
+                error=str(e),
             )
 
diff --git a/litellm/rag/ingestion/bedrock_ingestion.py b/litellm/rag/ingestion/bedrock_ingestion.py
index 5fa6145e24..3c880b8849 100644
--- a/litellm/rag/ingestion/bedrock_ingestion.py
+++ b/litellm/rag/ingestion/bedrock_ingestion.py
@@ -11,8 +11,8 @@ Supports two modes:
 
 from __future__ import annotations
 
+import asyncio
 import json
-import time
 import uuid
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
@@ -37,6 +37,29 @@ def _get_int(value: Any, default: int) -> int:
     return int(value)
 
 
+def _normalize_principal_arn(caller_arn: str, account_id: str) -> str:
+    """
+    Normalize a caller ARN to the format required by OpenSearch data access policies.
+    
+    OpenSearch Serverless data access policies require:
+    - IAM users: arn:aws:iam::account-id:user/user-name
+    - IAM roles: arn:aws:iam::account-id:role/role-name
+    
+    But get_caller_identity() returns for assumed roles:
+    - arn:aws:sts::account-id:assumed-role/role-name/session-name
+    
+    This function converts assumed-role ARNs to the proper IAM role ARN format.
+    """
+    if ":assumed-role/" in caller_arn:
+        # Extract role name from assumed-role ARN
+        # Format: arn:aws:sts::ACCOUNT:assumed-role/ROLE-NAME/SESSION-NAME
+        parts = caller_arn.split("/")
+        if len(parts) >= 2:
+            role_name = parts[1]
+            return f"arn:aws:iam::{account_id}:role/{role_name}"
+    return caller_arn
+
+
 class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
     """
     Bedrock Knowledge Base RAG ingestion.
@@ -99,7 +122,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         # Track resources we create (for cleanup if needed)
         self._created_resources: Dict[str, Any] = {}
 
-    def _ensure_config_initialized(self):
+    async def _ensure_config_initialized(self):
         """Lazily initialize KB config - either detect from existing or create new."""
         if self._config_initialized:
             return
@@ -109,7 +132,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
             self._auto_detect_config()
         else:
             # No KB provided - create everything from scratch
-            self._create_knowledge_base_infrastructure()
+            await self._create_knowledge_base_infrastructure()
 
         self._config_initialized = True
 
@@ -170,7 +193,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
                 )
             self.s3_bucket = self._s3_bucket
 
-    def _create_knowledge_base_infrastructure(self):
+    async def _create_knowledge_base_infrastructure(self):
         """Create all AWS resources needed for a new Knowledge Base."""
         verbose_logger.info("Creating new Bedrock Knowledge Base infrastructure...")
 
@@ -178,26 +201,28 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         unique_id = uuid.uuid4().hex[:8]
         kb_name = self.ingest_name or f"litellm-kb-{unique_id}"
 
-        # Get AWS account ID
+        # Get AWS account ID and caller ARN (for data access policy)
         sts = self._get_boto3_client("sts")
-        account_id = sts.get_caller_identity()["Account"]
+        caller_identity = sts.get_caller_identity()
+        account_id = caller_identity["Account"]
+        caller_arn = caller_identity["Arn"]
 
         # Step 1: Create S3 bucket (if not provided)
         self.s3_bucket = self._s3_bucket or self._create_s3_bucket(unique_id)
 
         # Step 2: Create OpenSearch Serverless collection
-        collection_name, collection_arn = self._create_opensearch_collection(
-            unique_id, account_id
+        collection_name, collection_arn = await self._create_opensearch_collection(
+            unique_id, account_id, caller_arn
         )
 
         # Step 3: Create OpenSearch index
-        self._create_opensearch_index(collection_name)
+        await self._create_opensearch_index(collection_name)
 
         # Step 4: Create IAM role for Bedrock
-        role_arn = self._create_bedrock_role(unique_id, account_id, collection_arn)
+        role_arn = await self._create_bedrock_role(unique_id, account_id, collection_arn)
 
         # Step 5: Create Knowledge Base
-        self.knowledge_base_id = self._create_knowledge_base(
+        self.knowledge_base_id = await self._create_knowledge_base(
             kb_name, role_arn, collection_arn
         )
 
@@ -228,8 +253,8 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         verbose_logger.info(f"Created S3 bucket: {bucket_name}")
         return bucket_name
 
-    def _create_opensearch_collection(
-        self, unique_id: str, account_id: str
+    async def _create_opensearch_collection(
+        self, unique_id: str, account_id: str, caller_arn: str
     ) -> Tuple[str, str]:
         """Create OpenSearch Serverless collection for vector storage."""
         oss = self._get_boto3_client("opensearchserverless")
@@ -258,7 +283,16 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
             }]),
         )
 
-        # Create data access policy
+        # Create data access policy - include both root and actual caller ARN
+        # This ensures the credentials being used have access to the collection
+        # Normalize the caller ARN (convert assumed-role ARN to IAM role ARN if needed)
+        normalized_caller_arn = _normalize_principal_arn(caller_arn, account_id)
+        verbose_logger.debug(f"Caller ARN: {caller_arn}, Normalized: {normalized_caller_arn}")
+        
+        principals = [f"arn:aws:iam::{account_id}:root", normalized_caller_arn]
+        # Deduplicate in case caller is root
+        principals = list(set(principals))
+        
         oss.create_access_policy(
             name=f"{collection_name}-access",
             type="data",
@@ -267,7 +301,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
                     {"ResourceType": "index", "Resource": [f"index/{collection_name}/*"], "Permission": ["aoss:*"]},
                     {"ResourceType": "collection", "Resource": [f"collection/{collection_name}"], "Permission": ["aoss:*"]},
                 ],
-                "Principal": [f"arn:aws:iam::{account_id}:root"],
+                "Principal": principals,
             }]),
         )
 
@@ -279,24 +313,29 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         collection_id = response["createCollectionDetail"]["id"]
         self._created_resources["opensearch_collection"] = collection_name
 
-        # Wait for collection to be active
+        # Wait for collection to be active (use asyncio.sleep to avoid blocking)
         verbose_logger.debug("Waiting for OpenSearch collection to be active...")
         for _ in range(60):  # 5 min timeout
             status_response = oss.batch_get_collection(ids=[collection_id])
             status = status_response["collectionDetails"][0]["status"]
             if status == "ACTIVE":
                 break
-            time.sleep(5)
+            await asyncio.sleep(5)
         else:
             raise TimeoutError("OpenSearch collection did not become active in time")
 
         collection_arn = status_response["collectionDetails"][0]["arn"]
         verbose_logger.info(f"Created OpenSearch collection: {collection_name}")
 
+        # Wait for data access policy to propagate before returning
+        # AWS recommends waiting 60+ seconds for policy propagation
+        verbose_logger.debug("Waiting for data access policy to propagate (60s)...")
+        await asyncio.sleep(60)
+
         return collection_name, collection_arn
 
-    def _create_opensearch_index(self, collection_name: str):
-        """Create vector index in OpenSearch collection."""
+    async def _create_opensearch_index(self, collection_name: str):
+        """Create vector index in OpenSearch collection with retry logic."""
         from opensearchpy import OpenSearch, RequestsHttpConnection
         from requests_aws4auth import AWS4Auth
 
@@ -348,10 +387,36 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
             },
         }
 
-        client.indices.create(index=index_name, body=index_body)
-        verbose_logger.info(f"Created OpenSearch index: {index_name}")
+        # Retry logic for index creation - data access policy may take time to propagate
+        max_retries = 8
+        retry_delay = 20  # seconds
+        last_error = None
+        
+        for attempt in range(max_retries):
+            try:
+                client.indices.create(index=index_name, body=index_body)
+                verbose_logger.info(f"Created OpenSearch index: {index_name}")
+                return
+            except Exception as e:
+                last_error = e
+                error_str = str(e)
+                if "authorization_exception" in error_str.lower() or "security_exception" in error_str.lower():
+                    verbose_logger.warning(
+                        f"OpenSearch index creation attempt {attempt + 1}/{max_retries} failed due to authorization. "
+                        f"Waiting {retry_delay}s for policy propagation..."
+                    )
+                    await asyncio.sleep(retry_delay)
+                else:
+                    # Non-auth error, raise immediately
+                    raise
+        
+        # All retries exhausted
+        raise RuntimeError(
+            f"Failed to create OpenSearch index after {max_retries} attempts. "
+            f"Data access policy may not have propagated. Last error: {last_error}"
+        )
 
-    def _create_bedrock_role(
+    async def _create_bedrock_role(
         self, unique_id: str, account_id: str, collection_arn: str
     ) -> str:
         """Create IAM role for Bedrock KB."""
@@ -408,13 +473,13 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
             PolicyDocument=json.dumps(permissions_policy),
         )
 
-        # Wait for role to propagate
-        time.sleep(10)
+        # Wait for role to propagate (use asyncio.sleep to avoid blocking)
+        await asyncio.sleep(10)
 
         verbose_logger.info(f"Created IAM role: {role_arn}")
         return role_arn
 
-    def _create_knowledge_base(
+    async def _create_knowledge_base(
         self, kb_name: str, role_arn: str, collection_arn: str
     ) -> str:
         """Create Bedrock Knowledge Base."""
@@ -447,14 +512,14 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         kb_id = response["knowledgeBase"]["knowledgeBaseId"]
         self._created_resources["knowledge_base"] = kb_id
 
-        # Wait for KB to be active
+        # Wait for KB to be active (use asyncio.sleep to avoid blocking)
         verbose_logger.debug("Waiting for Knowledge Base to be active...")
         for _ in range(30):
             kb_status = bedrock_agent.get_knowledge_base(knowledgeBaseId=kb_id)
             status = kb_status["knowledgeBase"]["status"]
             if status == "ACTIVE":
                 break
-            time.sleep(2)
+            await asyncio.sleep(2)
         else:
             raise TimeoutError("Knowledge Base did not become active in time")
 
@@ -555,7 +620,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
             Tuple of (knowledge_base_id, file_key)
         """
         # Auto-detect data source and S3 bucket if needed
-        self._ensure_config_initialized()
+        await self._ensure_config_initialized()
 
         if not file_content or not filename:
             verbose_logger.warning("No file content or filename provided for Bedrock ingestion")
@@ -587,10 +652,11 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         job_id = ingestion_response["ingestionJob"]["ingestionJobId"]
         verbose_logger.info(f"Started ingestion job: {job_id}")
 
-        # Step 3: Wait for ingestion (optional)
+        # Step 3: Wait for ingestion (optional) - use asyncio.sleep to avoid blocking
         if self.wait_for_ingestion:
-            start_time = time.time()
-            while time.time() - start_time < self.ingestion_timeout:
+            import time as time_module
+            start_time = time_module.time()
+            while time_module.time() - start_time < self.ingestion_timeout:
                 job_status = bedrock_agent.get_ingestion_job(
                     knowledgeBaseId=self.knowledge_base_id,
                     dataSourceId=self.data_source_id,
@@ -610,7 +676,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
                     verbose_logger.error(f"Ingestion failed: {failure_reasons}")
                     break
                 elif status in ("STARTING", "IN_PROGRESS"):
-                    time.sleep(2)
+                    await asyncio.sleep(2)
                 else:
                     verbose_logger.warning(f"Unknown ingestion status: {status}")
                     break
diff --git a/litellm/rag/ingestion/openai_ingestion.py b/litellm/rag/ingestion/openai_ingestion.py
index 034ad38d46..33fe8c06ec 100644
--- a/litellm/rag/ingestion/openai_ingestion.py
+++ b/litellm/rag/ingestion/openai_ingestion.py
@@ -78,6 +78,10 @@ class OpenAIRAGIngestion(BaseRAGIngestion):
         vector_store_id = self.vector_store_config.get("vector_store_id")
         ttl_days = self.vector_store_config.get("ttl_days")
 
+        # Get credentials from vector_store_config (loaded from litellm_credential_name if provided)
+        api_key = self.vector_store_config.get("api_key")
+        api_base = self.vector_store_config.get("api_base")
+
         # Create vector store if not provided
         if not vector_store_id:
             expires_after = {"anchor": "last_active_at", "days": ttl_days} if ttl_days else None
@@ -85,6 +89,8 @@ class OpenAIRAGIngestion(BaseRAGIngestion):
                 name=self.ingest_name or "litellm-rag-ingest",
                 custom_llm_provider="openai",
                 expires_after=expires_after,
+                api_key=api_key,
+                api_base=api_base,
             )
             vector_store_id = create_response.get("id")
 
@@ -96,6 +102,8 @@ class OpenAIRAGIngestion(BaseRAGIngestion):
                 file=(filename, file_content, content_type or "application/octet-stream"),
                 purpose="assistants",
                 custom_llm_provider="openai",
+                api_key=api_key,
+                api_base=api_base,
             )
             result_file_id = file_response.id
 
@@ -105,6 +113,8 @@ class OpenAIRAGIngestion(BaseRAGIngestion):
                 file_id=result_file_id,
                 custom_llm_provider="openai",
                 chunking_strategy=cast(Optional[Dict[str, Any]], self.chunking_strategy),
+                api_key=api_key,
+                api_base=api_base,
             )
 
         return vector_store_id, result_file_id
diff --git a/litellm/rag/main.py b/litellm/rag/main.py
index ef1a512998..e7a9d3a241 100644
--- a/litellm/rag/main.py
+++ b/litellm/rag/main.py
@@ -12,7 +12,7 @@ __all__ = ["ingest", "aingest"]
 import asyncio
 import contextvars
 from functools import partial
-from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Tuple, Type, Union
 
 import httpx
 
@@ -84,7 +84,7 @@ async def _execute_ingest_pipeline(
     provider = vector_store_config.get("custom_llm_provider", "openai")
 
     # Get provider-specific ingestion class
-    ingestion_class = get_rag_ingestion_class(provider)
+    ingestion_class = get_ingestion_class(provider)
 
     # Create ingestion instance
     ingestion = ingestion_class(
@@ -127,7 +127,10 @@ async def aingest(
         ```python
         response = await litellm.aingest(
             ingest_options={
-                "vector_store": {"custom_llm_provider": "openai"}
+                "vector_store": {
+                    "custom_llm_provider": "openai",
+                    "litellm_credential_name": "my-openai-creds",  # optional
+                }
             },
             file_url="https://example.com/doc.pdf",
         )
@@ -193,7 +196,10 @@ def ingest(
         ```python
         response = litellm.ingest(
             ingest_options={
-                "vector_store": {"custom_llm_provider": "openai"}
+                "vector_store": {
+                    "custom_llm_provider": "openai",
+                    "litellm_credential_name": "my-openai-creds",  # optional
+                }
             },
             file_data=("doc.txt", b"Hello world", "text/plain"),
         )
diff --git a/litellm/rag/utils.py b/litellm/rag/utils.py
index 2a0f3008dd..e8ab9c7517 100644
--- a/litellm/rag/utils.py
+++ b/litellm/rag/utils.py
@@ -4,7 +4,7 @@ RAG utility functions.
 Provides provider configuration utilities similar to ProviderConfigManager.
 """
 
-from typing import TYPE_CHECKING, Optional, Type
+from typing import TYPE_CHECKING, Type
 
 if TYPE_CHECKING:
     from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion
diff --git a/litellm/types/rag.py b/litellm/types/rag.py
index 51ac6eea04..7a964931af 100644
--- a/litellm/types/rag.py
+++ b/litellm/types/rag.py
@@ -41,12 +41,20 @@ class OpenAIVectorStoreOptions(TypedDict, total=False):
 
     Example (use existing):
         {"custom_llm_provider": "openai", "vector_store_id": "vs_xxx"}
+
+    Example (with credentials):
+        {"custom_llm_provider": "openai", "litellm_credential_name": "my-openai-creds"}
     """
 
     custom_llm_provider: Literal["openai"]
     vector_store_id: Optional[str]  # Existing VS ID (auto-creates if not provided)
     ttl_days: Optional[int]  # Time-to-live in days for indexed content
 
+    # Credentials (loaded from litellm.credential_list if litellm_credential_name is provided)
+    litellm_credential_name: Optional[str]  # Credential name to load from litellm.credential_list
+    api_key: Optional[str]  # Direct API key (alternative to litellm_credential_name)
+    api_base: Optional[str]  # Direct API base (alternative to litellm_credential_name)
+
 
 class BedrockVectorStoreOptions(TypedDict, total=False):
     """
@@ -58,6 +66,9 @@ class BedrockVectorStoreOptions(TypedDict, total=False):
     Example (use existing KB):
         {"custom_llm_provider": "bedrock", "vector_store_id": "KB_ID"}
 
+    Example (with credentials):
+        {"custom_llm_provider": "bedrock", "litellm_credential_name": "my-aws-creds"}
+
     Auto-creation creates: S3 bucket, OpenSearch Serverless collection,
     IAM role, Knowledge Base, and Data Source.
     """
@@ -73,6 +84,9 @@ class BedrockVectorStoreOptions(TypedDict, total=False):
     wait_for_ingestion: Optional[bool]  # Wait for completion (default: False - returns immediately)
     ingestion_timeout: Optional[int]  # Timeout in seconds if wait_for_ingestion=True (default: 300)
 
+    # Credentials (loaded from litellm.credential_list if litellm_credential_name is provided)
+    litellm_credential_name: Optional[str]  # Credential name to load from litellm.credential_list
+
     # AWS auth (uses BaseAWSLLM)
     aws_access_key_id: Optional[str]
     aws_secret_access_key: Optional[str]
@@ -160,6 +174,7 @@ class RAGIngestResponse(TypedDict, total=False):
     status: Literal["completed", "in_progress", "failed"]
     vector_store_id: str  # The vector store ID (created or existing)
     file_id: Optional[str]  # The file ID in the vector store
+    error: Optional[str]  # Error message if status is "failed"