Merge 7774c308ca into 67c7b97fd2

litellm/proxy/auth/auth_utils.py

@@ -1568,6 +1568,7 @@ _MODEL_ROUTING_ROUTE_MARKERS: Final = (
 _MODEL_ROUTING_HEADER_OR_QUERY_ROUTE_MARKERS: Final = (
     "/files",
     "/batches",
+    "/vector_stores",
     "/skills",
     "/evals",
 )

litellm/proxy/vector_store_endpoints/endpoints.py

@@ -94,6 +94,31 @@ async def _update_request_data_with_litellm_managed_vector_store_registry(
     return data
 
 
+def _update_request_data_with_model_from_request(
+    data: Dict,
+    request: Request,
+) -> Dict:
+    """
+    Add model routing from request query/header when no body or registry model exists.
+
+    The vector store retrieve/delete endpoints do not have request bodies, but
+    clients can still pass the proxy model through query params or
+    x-litellm-model. Preserve registry-derived routing first.
+    """
+    if (
+        data.get("model")
+        or data.get("custom_llm_provider")
+        or data.get("litellm_credential_name")
+    ):
+        return data
+
+    model = request.query_params.get("model") or request.headers.get("x-litellm-model")
+    if model:
+        data["model"] = model
+
+    return data
+
+
 @router.post(
     "/v1/vector_stores/{vector_store_id:path}/search",
     dependencies=[Depends(user_api_key_auth)],
@@ -312,6 +337,7 @@ async def vector_store_retrieve(
     data = await _update_request_data_with_litellm_managed_vector_store_registry(
         data=data, vector_store_id=vector_store_id, user_api_key_dict=user_api_key_dict
     )
+    data = _update_request_data_with_model_from_request(data=data, request=request)
 
     processor: Final = ProxyBaseLLMRequestProcessing(data=data)
     try:
@@ -511,6 +537,7 @@ async def vector_store_delete(
     data = await _update_request_data_with_litellm_managed_vector_store_registry(
         data=data, vector_store_id=vector_store_id, user_api_key_dict=user_api_key_dict
     )
+    data = _update_request_data_with_model_from_request(data=data, request=request)
 
     processor: Final = ProxyBaseLLMRequestProcessing(data=data)
     try:

tests/test_litellm/proxy/auth/test_auth_utils.py

@@ -439,6 +439,28 @@ def test_get_model_from_request_includes_file_endpoint_header_model():
     )
 
 
+def test_get_model_from_request_includes_vector_store_query_model():
+    assert (
+        get_model_from_request(
+            request_data={"vector_store_id": "vs_plain_openai"},
+            route="/v1/vector_stores/{vector_store_id}",
+            request_query_params={"model": "restricted-model"},
+        )
+        == "restricted-model"
+    )
+
+
+def test_get_model_from_request_includes_vector_store_header_model():
+    assert (
+        get_model_from_request(
+            request_data={"vector_store_id": "vs_plain_openai"},
+            route="/v1/vector_stores/{vector_store_id}",
+            request_headers={"x-litellm-model": "restricted-model"},
+        )
+        == "restricted-model"
+    )
+
+
 def test_get_model_from_request_ignores_routing_header_on_standard_llm_routes():
     assert (
         get_model_from_request(

tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_tenant_guard.py

@@ -55,6 +55,157 @@ async def test_vector_store_search_forces_path_id_over_body_id():
     assert captured_data["vector_store_id"] == "vs_path_allowed"
 
 
+@pytest.mark.asyncio
+async def test_vector_store_retrieve_uses_query_model_for_routing():
+    from litellm.proxy.vector_store_endpoints.endpoints import vector_store_retrieve
+
+    captured_data = {}
+
+    async def fake_base_process(self, **kwargs):
+        captured_data.update(self.data)
+        return {"ok": True}
+
+    request = _mock_request()
+    request.method = "GET"
+    request.query_params = {"model": "gpt-5.2"}
+    request.headers = {"x-litellm-model": "header-model"}
+
+    with (
+        patch(
+            "litellm.proxy.vector_store_endpoints.endpoints.get_litellm_managed_vector_store",
+            new=AsyncMock(return_value=None),
+        ),
+        patch(
+            "litellm.proxy.vector_store_endpoints.endpoints.ProxyBaseLLMRequestProcessing.base_process_llm_request",
+            new=fake_base_process,
+        ),
+    ):
+        response = await vector_store_retrieve(
+            request=request,
+            vector_store_id="vs_plain_openai",
+            fastapi_response=Response(),
+            user_api_key_dict=UserAPIKeyAuth(team_id="team-a"),
+        )
+
+    assert response == {"ok": True}
+    assert captured_data["vector_store_id"] == "vs_plain_openai"
+    assert captured_data["model"] == "gpt-5.2"
+
+
+@pytest.mark.asyncio
+async def test_vector_store_delete_uses_header_model_for_routing():
+    from litellm.proxy.vector_store_endpoints.endpoints import vector_store_delete
+
+    captured_data = {}
+
+    async def fake_base_process(self, **kwargs):
+        captured_data.update(self.data)
+        return {"ok": True}
+
+    request = _mock_request()
+    request.method = "DELETE"
+    request.headers = {"x-litellm-model": "gpt-5.2"}
+
+    with (
+        patch(
+            "litellm.proxy.vector_store_endpoints.endpoints.get_litellm_managed_vector_store",
+            new=AsyncMock(return_value=None),
+        ),
+        patch(
+            "litellm.proxy.vector_store_endpoints.endpoints.ProxyBaseLLMRequestProcessing.base_process_llm_request",
+            new=fake_base_process,
+        ),
+    ):
+        response = await vector_store_delete(
+            request=request,
+            vector_store_id="vs_plain_openai",
+            fastapi_response=Response(),
+            user_api_key_dict=UserAPIKeyAuth(team_id="team-a"),
+        )
+
+    assert response == {"ok": True}
+    assert captured_data["vector_store_id"] == "vs_plain_openai"
+    assert captured_data["model"] == "gpt-5.2"
+
+
+@pytest.mark.asyncio
+async def test_vector_store_delete_uses_query_model_for_routing():
+    from litellm.proxy.vector_store_endpoints.endpoints import vector_store_delete
+
+    captured_data = {}
+
+    async def fake_base_process(self, **kwargs):
+        captured_data.update(self.data)
+        return {"ok": True}
+
+    request = _mock_request()
+    request.method = "DELETE"
+    request.query_params = {"model": "gpt-5.2"}
+    request.headers = {"x-litellm-model": "header-model"}
+
+    with (
+        patch(
+            "litellm.proxy.vector_store_endpoints.endpoints.get_litellm_managed_vector_store",
+            new=AsyncMock(return_value=None),
+        ),
+        patch(
+            "litellm.proxy.vector_store_endpoints.endpoints.ProxyBaseLLMRequestProcessing.base_process_llm_request",
+            new=fake_base_process,
+        ),
+    ):
+        response = await vector_store_delete(
+            request=request,
+            vector_store_id="vs_plain_openai",
+            fastapi_response=Response(),
+            user_api_key_dict=UserAPIKeyAuth(team_id="team-a"),
+        )
+
+    assert response == {"ok": True}
+    assert captured_data["vector_store_id"] == "vs_plain_openai"
+    assert captured_data["model"] == "gpt-5.2"
+
+
+def test_vector_store_model_from_request_preserves_existing_model():
+    from litellm.proxy.vector_store_endpoints.endpoints import (
+        _update_request_data_with_model_from_request,
+    )
+
+    request = _mock_request()
+    request.query_params = {"model": "query-model"}
+    request.headers = {"x-litellm-model": "header-model"}
+    data = {"model": "registry-model"}
+
+    updated_data = _update_request_data_with_model_from_request(
+        data=data, request=request
+    )
+
+    assert updated_data is data
+    assert updated_data["model"] == "registry-model"
+
+
+def test_vector_store_model_from_request_preserves_registry_routing():
+    from litellm.proxy.vector_store_endpoints.endpoints import (
+        _update_request_data_with_model_from_request,
+    )
+
+    request = _mock_request()
+    request.query_params = {"model": "query-model"}
+    request.headers = {"x-litellm-model": "header-model"}
+    data = {
+        "custom_llm_provider": "openai",
+        "litellm_credential_name": "vector-store-credential",
+    }
+
+    updated_data = _update_request_data_with_model_from_request(
+        data=data, request=request
+    )
+
+    assert updated_data is data
+    assert "model" not in updated_data
+    assert updated_data["custom_llm_provider"] == "openai"
+    assert updated_data["litellm_credential_name"] == "vector-store-credential"
+
+
 @pytest.mark.asyncio
 async def test_vector_store_file_create_forces_path_id_over_body_id():
     from litellm.proxy.vector_store_files_endpoints.endpoints import (