Support for Custom Vertex AI Models via PSC Endpoint with api_base

docs/my-website/docs/providers/vertex.md

@@ -1604,6 +1604,53 @@ litellm.vertex_location = "us-central1 # Your Location
 | gemini-2.5-flash-preview-09-2025   | `completion('gemini-2.5-flash-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-preview-09-2025', messages)` |
 | gemini-2.5-flash-lite-preview-09-2025   | `completion('gemini-2.5-flash-lite-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-lite-preview-09-2025', messages)` |
 
+## Private Service Connect (PSC) Endpoints
+
+LiteLLM supports Vertex AI models deployed to Private Service Connect (PSC) endpoints, allowing you to use custom `api_base` URLs for private deployments.
+
+### Usage
+
+```python
+from litellm import completion
+
+# Use PSC endpoint with custom api_base
+response = completion(
+    model="vertex_ai/1234567890",  # Numeric endpoint ID
+    messages=[{"role": "user", "content": "Hello!"}],
+    api_base="http://10.96.32.8",  # Your PSC endpoint
+    vertex_project="my-project-id",
+    vertex_location="us-central1"
+)
+```
+
+**Key Features:**
+- Supports both numeric endpoint IDs and custom model names
+- Works with both completion and embedding endpoints
+- Automatically constructs full PSC URL: `{api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint}`
+- Compatible with streaming requests
+
+### Configuration
+
+Add PSC endpoints to your `config.yaml`:
+
+```yaml
+model_list:
+  - model_name: psc-gemini
+    litellm_params:
+      model: vertex_ai/1234567890  # Numeric endpoint ID
+      api_base: "http://10.96.32.8"  # Your PSC endpoint
+      vertex_project: "my-project-id"
+      vertex_location: "us-central1"
+      vertex_credentials: "/path/to/service_account.json"
+  - model_name: psc-embedding
+    litellm_params:
+      model: vertex_ai/text-embedding-004
+      api_base: "http://10.96.32.8"  # Your PSC endpoint
+      vertex_project: "my-project-id"
+      vertex_location: "us-central1"
+      vertex_credentials: "/path/to/service_account.json"
+```
+
 ## Fine-tuned Models
 
 You can call fine-tuned Vertex AI Gemini models through LiteLLM

litellm/llms/vertex_ai/batches/handler.py

@@ -61,6 +61,10 @@ def create_batch(
             stream=None,
             auth_header=None,
             url=default_api_base,
+            model=None,
+            vertex_project=vertex_project or project_id,
+            vertex_location=vertex_location or "us-central1",
+            vertex_api_version="v1",
         )
 
         headers = {
@@ -166,6 +170,10 @@ def retrieve_batch(
             stream=None,
             auth_header=None,
             url=default_api_base,
+            model=None,
+            vertex_project=vertex_project or project_id,
+            vertex_location=vertex_location or "us-central1",
+            vertex_api_version="v1",
         )
 
         headers = {

litellm/llms/vertex_ai/common_utils.py

@@ -57,6 +57,9 @@ def get_vertex_ai_model_route(model: str, litellm_params: Optional[dict] = None)
 
         >>> get_vertex_ai_model_route("openai/gpt-oss-120b")
         VertexAIModelRoute.MODEL_GARDEN
+
+        >>> get_vertex_ai_model_route("1234567890", {"api_base": "http://10.96.32.8"})
+        VertexAIModelRoute.GEMINI  # Numeric endpoints with api_base use HTTP path
     """
     from litellm.llms.vertex_ai.vertex_ai_partner_models.main import (
         VertexAIPartnerModels,
@@ -67,6 +70,11 @@ def get_vertex_ai_model_route(model: str, litellm_params: Optional[dict] = None)
         if "gemini" in litellm_params["base_model"]:
             return VertexAIModelRoute.GEMINI
 
+    # Check if numeric endpoint ID with custom api_base (PSC endpoint)
+    # Route to GEMINI (HTTP path) to support PSC endpoints properly
+    if model.isdigit() and litellm_params and litellm_params.get("api_base"):
+        return VertexAIModelRoute.GEMINI
+
     # Check for partner models (llama, mistral, claude, etc.)
     if VertexAIPartnerModels.is_vertex_partner_model(model=model):
         return VertexAIModelRoute.PARTNER_MODELS

litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py

@@ -79,6 +79,10 @@ def _get_token_and_url_context_caching(
             stream=None,
             auth_header=auth_header,
             url=url,
+            model=None,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_api_version="v1beta1" if custom_llm_provider == "vertex_ai_beta" else "v1",
         )
 
     def check_cache(

litellm/llms/vertex_ai/vertex_embeddings/transformation.py

@@ -167,6 +167,9 @@ def _transform_openai_request_to_fine_tuned_embedding_request(
         vertex_request["parameters"] = TextEmbeddingFineTunedParameters(
             **optional_params
         )
+        # Remove 'shared_session' from parameters if present
+        if vertex_request["parameters"] is not None and "shared_session" in vertex_request["parameters"]:
+            del vertex_request["parameters"]["shared_session"]  # type: ignore[typeddict-item]
 
         return vertex_request
 

litellm/llms/vertex_ai/vertex_llm_base.py

@@ -241,6 +241,9 @@ def get_complete_vertex_url(
             auth_header=None,
             url=default_api_base,
             model=model,
+            vertex_project=vertex_project or project_id,
+            vertex_location=vertex_location or "us-central1",
+            vertex_api_version="v1",  # Partner models typically use v1
         )
         return api_base
 
@@ -289,9 +292,18 @@ def _check_custom_proxy(
         auth_header: Optional[str],
         url: str,
         model: Optional[str] = None,
+        vertex_project: Optional[str] = None,
+        vertex_location: Optional[str] = None,
+        vertex_api_version: Optional[Literal["v1", "v1beta1"]] = None,
     ) -> Tuple[Optional[str], str]:
         """
         for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317
+
+        Handles custom api_base for:
+        1. Gemini (Google AI Studio) - constructs /models/{model}:{endpoint}
+        2. Vertex AI with standard proxies - constructs {api_base}:{endpoint}
+        3. Vertex AI with PSC endpoints - constructs full path structure
+           {api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint}
 
         ## Returns
         - (auth_header, url) - Tuple[Optional[str], str]
@@ -312,8 +324,34 @@ def _check_custom_proxy(
                     gemini_api_key  # cloudflare expects api key as bearer token
                 )
             else:
-                url = "{}:{}".format(api_base, endpoint)
-
+                # For Vertex AI
+                # Check if this is a PSC endpoint or custom deployment
+                # PSC/custom endpoints need the full path structure
+                if vertex_project and vertex_location and model:
+                    # Check if model is numeric (endpoint ID) or if api_base doesn't contain googleapis.com
+                    # These are indicators of PSC/custom endpoints
+                    is_psc_or_custom = (
+                        "googleapis.com" not in api_base.lower() or model.isdigit()
+                    )
+
+                    if is_psc_or_custom:
+                        # Construct full PSC/custom endpoint URL
+                        # Format: {api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint}
+                        version = vertex_api_version or "v1"
+                        url = "{}/{}/projects/{}/locations/{}/endpoints/{}:{}".format(
+                            api_base.rstrip("/"),
+                            version,
+                            vertex_project,
+                            vertex_location,
+                            model,
+                            endpoint,
+                        )
+                    else:
+                        # Standard proxy - just append endpoint
+                        url = "{}:{}".format(api_base, endpoint)
+                else:
+                    # Fallback to simple format if we don't have all parameters
+                    url = "{}:{}".format(api_base, endpoint)
             if stream is True:
                 url = url + "?alt=sse"
         return auth_header, url
@@ -340,6 +378,7 @@ def _get_token_and_url(
         Returns
             token, url
         """
+        version: Optional[Literal["v1beta1", "v1"]] = None
         if custom_llm_provider == "gemini":
             url, endpoint = _get_gemini_url(
                 mode=mode,
@@ -355,7 +394,7 @@ def _get_token_and_url(
             )
 
             ### SET RUNTIME ENDPOINT ###
-            version: Literal["v1beta1", "v1"] = (
+            version = (
                 "v1beta1" if should_use_v1beta1_features is True else "v1"
             )
             url, endpoint = _get_vertex_url(
@@ -376,6 +415,9 @@ def _get_token_and_url(
             stream=stream,
             url=url,
             model=model,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_api_version=version,
         )
 
     def _handle_reauthentication(

litellm/llms/vertex_ai/vertex_model_garden/main.py

@@ -123,6 +123,10 @@ def completion(
                 stream=stream,
                 auth_header=None,
                 url=default_api_base,
+                model=model,
+                vertex_project=vertex_project or project_id,
+                vertex_location=vertex_location or "us-central1",
+                vertex_api_version="v1beta1",
             )
             model = ""
             return openai_like_chat_completions.completion(