Merge branch 'main' into chroma

llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py

@@ -15,6 +15,7 @@ from llama_stack.apis.safety import (
     RunShieldResponse,
     Safety,
     SafetyViolation,
+    ShieldStore,
     ViolationLevel,
 )
 from llama_stack.apis.shields import Shield
@@ -32,6 +33,8 @@ PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
 
 
 class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
+    shield_store: ShieldStore
+
     def __init__(self, config: PromptGuardConfig, _deps) -> None:
         self.config = config
 
@@ -53,7 +56,7 @@ class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
         self,
         shield_id: str,
         messages: list[Message],
-        params: dict[str, Any] = None,
+        params: dict[str, Any],
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)
         if not shield:
@@ -117,8 +120,10 @@ class PromptGuardShield:
         elif self.config.guard_type == PromptGuardType.jailbreak.value and score_malicious > self.threshold:
             violation = SafetyViolation(
                 violation_level=ViolationLevel.ERROR,
-                violation_type=f"prompt_injection:malicious={score_malicious}",
-                violation_return_message="Sorry, I cannot do this.",
+                user_message="Sorry, I cannot do this.",
+                metadata={
+                    "violation_type": f"prompt_injection:malicious={score_malicious}",
+                },
             )
 
         return RunShieldResponse(violation=violation)

llama_stack/providers/registry/inference.py

@@ -213,6 +213,36 @@ def available_providers() -> list[ProviderSpec]:
                 description="Google Gemini inference provider for accessing Gemini models and Google's AI services.",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="vertexai",
+                pip_packages=["litellm", "google-cloud-aiplatform"],
+                module="llama_stack.providers.remote.inference.vertexai",
+                config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig",
+                provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator",
+                description="""Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages:
+
+• Enterprise-grade security: Uses Google Cloud's security controls and IAM
+• Better integration: Seamless integration with other Google Cloud services
+• Advanced features: Access to additional Vertex AI features like model tuning and monitoring
+• Authentication: Uses Google Cloud Application Default Credentials (ADC) instead of API keys
+
+Configuration:
+- Set VERTEX_AI_PROJECT environment variable (required)
+- Set VERTEX_AI_LOCATION environment variable (optional, defaults to us-central1)
+- Use Google Cloud Application Default Credentials or service account key
+
+Authentication Setup:
+Option 1 (Recommended): gcloud auth application-default login
+Option 2: Set GOOGLE_APPLICATION_CREDENTIALS to service account key path
+
+Available Models:
+- vertex_ai/gemini-2.0-flash
+- vertex_ai/gemini-2.5-flash
+- vertex_ai/gemini-2.5-pro""",
+            ),
+        ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(

llama_stack/providers/remote/inference/vertexai/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .config import VertexAIConfig
+
+
+async def get_adapter_impl(config: VertexAIConfig, _deps):
+    from .vertexai import VertexAIInferenceAdapter
+
+    impl = VertexAIInferenceAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/inference/vertexai/config.py

@@ -0,0 +1,45 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from llama_stack.schema_utils import json_schema_type
+
+
+class VertexAIProviderDataValidator(BaseModel):
+    vertex_project: str | None = Field(
+        default=None,
+        description="Google Cloud project ID for Vertex AI",
+    )
+    vertex_location: str | None = Field(
+        default=None,
+        description="Google Cloud location for Vertex AI (e.g., us-central1)",
+    )
+
+
+@json_schema_type
+class VertexAIConfig(BaseModel):
+    project: str = Field(
+        description="Google Cloud project ID for Vertex AI",
+    )
+    location: str = Field(
+        default="us-central1",
+        description="Google Cloud location for Vertex AI",
+    )
+
+    @classmethod
+    def sample_run_config(
+        cls,
+        project: str = "${env.VERTEX_AI_PROJECT:=}",
+        location: str = "${env.VERTEX_AI_LOCATION:=us-central1}",
+        **kwargs,
+    ) -> dict[str, Any]:
+        return {
+            "project": project,
+            "location": location,
+        }

llama_stack/providers/remote/inference/vertexai/models.py

@@ -0,0 +1,20 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.providers.utils.inference.model_registry import (
+    ProviderModelEntry,
+)
+
+# Vertex AI model IDs with vertex_ai/ prefix as required by litellm
+LLM_MODEL_IDS = [
+    "vertex_ai/gemini-2.0-flash",
+    "vertex_ai/gemini-2.5-flash",
+    "vertex_ai/gemini-2.5-pro",
+]
+
+SAFETY_MODELS_ENTRIES = list[ProviderModelEntry]()
+
+MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + SAFETY_MODELS_ENTRIES

llama_stack/providers/remote/inference/vertexai/vertexai.py

@@ -0,0 +1,52 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from llama_stack.apis.inference import ChatCompletionRequest
+from llama_stack.providers.utils.inference.litellm_openai_mixin import (
+    LiteLLMOpenAIMixin,
+)
+
+from .config import VertexAIConfig
+from .models import MODEL_ENTRIES
+
+
+class VertexAIInferenceAdapter(LiteLLMOpenAIMixin):
+    def __init__(self, config: VertexAIConfig) -> None:
+        LiteLLMOpenAIMixin.__init__(
+            self,
+            MODEL_ENTRIES,
+            litellm_provider_name="vertex_ai",
+            api_key_from_config=None,  # Vertex AI uses ADC, not API keys
+            provider_data_api_key_field="vertex_project",  # Use project for validation
+        )
+        self.config = config
+
+    def get_api_key(self) -> str:
+        # Vertex AI doesn't use API keys, it uses Application Default Credentials
+        # Return empty string to let litellm handle authentication via ADC
+        return ""
+
+    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
+        # Get base parameters from parent
+        params = await super()._get_params(request)
+
+        # Add Vertex AI specific parameters
+        provider_data = self.get_request_provider_data()
+        if provider_data:
+            if getattr(provider_data, "vertex_project", None):
+                params["vertex_project"] = provider_data.vertex_project
+            if getattr(provider_data, "vertex_location", None):
+                params["vertex_location"] = provider_data.vertex_location
+        else:
+            params["vertex_project"] = self.config.project
+            params["vertex_location"] = self.config.location
+
+        # Remove api_key since Vertex AI uses ADC
+        params.pop("api_key", None)
+
+        return params