atransform_search_vector_store_request

litellm/llms/custom_httpx/llm_http_handler.py

@@ -7033,17 +7033,31 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        (
-            url,
-            request_body,
-        ) = vector_store_provider_config.transform_search_vector_store_request(
-            vector_store_id=vector_store_id,
-            query=query,
-            vector_store_search_optional_params=vector_store_search_optional_params,
-            api_base=api_base,
-            litellm_logging_obj=logging_obj,
-            litellm_params=dict(litellm_params),
-        )
+        # Check if provider has async transform method
+        if hasattr(vector_store_provider_config, "atransform_search_vector_store_request"):
+            (
+                url,
+                request_body,
+            ) = await vector_store_provider_config.atransform_search_vector_store_request(
+                vector_store_id=vector_store_id,
+                query=query,
+                vector_store_search_optional_params=vector_store_search_optional_params,
+                api_base=api_base,
+                litellm_logging_obj=logging_obj,
+                litellm_params=dict(litellm_params),
+            )
+        else:
+            (
+                url,
+                request_body,
+            ) = vector_store_provider_config.transform_search_vector_store_request(
+                vector_store_id=vector_store_id,
+                query=query,
+                vector_store_search_optional_params=vector_store_search_optional_params,
+                api_base=api_base,
+                litellm_logging_obj=logging_obj,
+                litellm_params=dict(litellm_params),
+            )
         all_optional_params: Dict[str, Any] = dict(litellm_params)
         all_optional_params.update(vector_store_search_optional_params or {})
         headers, signed_json_body = vector_store_provider_config.sign_request(

litellm/llms/s3_vectors/__init__.py

@@ -0,0 +1 @@
+# S3 Vectors LLM integration

litellm/proxy/rag_endpoints/endpoints.py

@@ -129,6 +129,14 @@ async def _save_vector_store_to_db_from_rag_ingest(
     litellm_vector_store_params = ingest_options.get("litellm_vector_store_params", {})
     custom_vector_store_name = litellm_vector_store_params.get("vector_store_name")
     custom_vector_store_description = litellm_vector_store_params.get("vector_store_description")
+    
+    # Extract provider-specific params from vector_store_config to save as litellm_params
+    # This ensures params like aws_region_name, embedding_model, etc. are available for search
+    provider_specific_params = {}
+    excluded_keys = {"custom_llm_provider", "vector_store_id"}
+    for key, value in vector_store_config.items():
+        if key not in excluded_keys and value is not None:
+            provider_specific_params[key] = value
 
     # Build file metadata entry using helper
     file_entry = _build_file_metadata_entry(
@@ -167,6 +175,7 @@ async def _save_vector_store_to_db_from_rag_ingest(
                 vector_store_name=vector_store_name,
                 vector_store_description=vector_store_description,
                 vector_store_metadata=initial_metadata,
+                litellm_params=provider_specific_params if provider_specific_params else None,
             )
 
             verbose_proxy_logger.info(

litellm/rag/ingestion/s3_vectors_ingestion.py

@@ -478,7 +478,9 @@ class S3VectorsRAGIngestion(BaseRAGIngestion, BaseAWSLLM):
         # Call PutVectors API
         await self._put_vectors(vectors)
 
-        return self.index_name, filename
+        # Return vector_store_id in format bucket_name:index_name for S3 Vectors search compatibility
+        vector_store_id = f"{self.vector_bucket_name}:{self.index_name}"
+        return vector_store_id, filename
 
     async def query_vector_store(
         self, vector_store_id: str, query: str, top_k: int = 5

litellm/types/router.py

@@ -404,6 +404,10 @@ class LiteLLMParamsTypedDict(TypedDict, total=False):
     aws_access_key_id: Optional[str]
     aws_secret_access_key: Optional[str]
     aws_region_name: Optional[str]
+    ## AWS S3 VECTORS ##
+    vector_bucket_name: Optional[str]
+    index_name: Optional[str]
+    embedding_model: Optional[str]
     ## IBM WATSONX ##
     watsonx_region_name: Optional[str]
     ## CUSTOM PRICING ##

litellm/types/utils.py

@@ -4,20 +4,22 @@ from enum import Enum
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional, Union
 
 from openai._models import BaseModel as OpenAIObject
-from openai.types.audio.transcription_create_params import FileTypes as FileTypes  # type: ignore
+from openai.types.audio.transcription_create_params import (
+    FileTypes as FileTypes,  # type: ignore
+)
 from openai.types.chat.chat_completion import ChatCompletion as ChatCompletion
 from openai.types.completion_usage import (
     CompletionTokensDetails,
     CompletionUsage,
     PromptTokensDetails,
 )
+from openai.types.moderation import Categories as Categories
 from openai.types.moderation import (
-    Categories as Categories,
     CategoryAppliedInputTypes as CategoryAppliedInputTypes,
-    CategoryScores as CategoryScores,
 )
+from openai.types.moderation import CategoryScores as CategoryScores
+from openai.types.moderation_create_response import Moderation as Moderation
 from openai.types.moderation_create_response import (
-    Moderation as Moderation,
     ModerationCreateResponse as ModerationCreateResponse,
 )
 from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, model_validator
@@ -3075,6 +3077,7 @@ class LlmProviders(str, Enum):
     LLAMA = "meta_llama"
     NSCALE = "nscale"
     PG_VECTOR = "pg_vector"
+    S3_VECTORS = "s3_vectors"
     HELICONE = "helicone"
     HYPERBOLIC = "hyperbolic"
     RECRAFT = "recraft"

tests/test_litellm/llms/s3_vectors/__init__.py

@@ -0,0 +1 @@
+# S3 Vectors tests

tests/test_litellm/llms/s3_vectors/vector_stores/__init__.py

@@ -0,0 +1 @@
+# S3 Vectors vector store tests