revert: do not use MySecretStr

We don't need this if we can set it to empty string.

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/core/secret_types.py

@@ -1,21 +0,0 @@
-# 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 pydantic.types import SecretStr
-
-
-class MySecretStr(SecretStr):
-    """A SecretStr that can accept None values to avoid mypy type errors.
-
-    This is useful for optional secret fields where you want to avoid
-    explicit None checks in consuming code.
-
-    We chose to not use the SecretStr from pydantic because it does not allow None values and will
-    let the provider's library fail if the secret is not provided.
-    """
-
-    def __init__(self, secret_value: str | None = None) -> None:
-        SecretStr.__init__(self, secret_value)  # type: ignore[arg-type]

llama_stack/core/stack.py

@@ -288,6 +288,12 @@ def _convert_string_to_proper_type_with_config(value: str, path: str, provider_c
         field_name = path.split(".")[-1] if "." in path else path
 
         config_class = provider_context["config_class"]
+        # Only instantiate if the class hasn't been instantiated already
+        # This handles the case we entered replace_env_vars() with a dict, which
+        # could happen if we use a sample_run_config() method that returns a dict. Our unit tests do
+        # this on the adhoc config spec creation.
+        if isinstance(config_class, str):
+            config_class = instantiate_class_type(config_class)
 
         if hasattr(config_class, "model_fields") and field_name in config_class.model_fields:
             field_info = config_class.model_fields[field_name]
@@ -563,7 +569,9 @@ def run_config_from_adhoc_config_spec(
         # call method "sample_run_config" on the provider spec config class
         provider_config_type = instantiate_class_type(provider_spec.config_class)
         provider_config = replace_env_vars(
-            provider_config_type.sample_run_config(__distro_dir__=distro_dir), provider_registry=provider_registry
+            provider_config_type.sample_run_config(__distro_dir__=distro_dir),
+            provider_registry=provider_registry,
+            current_provider_context=provider_spec.model_dump(),
         )
 
         provider_configs_by_api[api_str] = [

llama_stack/providers/inline/scoring/braintrust/__init__.py

@@ -5,16 +5,15 @@
 # the root directory of this source tree.
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, SecretStr
 
 from llama_stack.core.datatypes import Api
-from llama_stack.core.secret_types import MySecretStr
 
 from .config import BraintrustScoringConfig
 
 
 class BraintrustProviderDataValidator(BaseModel):
-    openai_api_key: MySecretStr
+    openai_api_key: SecretStr
 
 
 async def get_provider_impl(

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -17,7 +17,7 @@ from autoevals.ragas import (
     ContextRelevancy,
     Faithfulness,
 )
-from pydantic import BaseModel
+from pydantic import BaseModel, SecretStr
 
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
@@ -31,7 +31,6 @@ from llama_stack.apis.scoring import (
 from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
 from llama_stack.core.datatypes import Api
 from llama_stack.core.request_headers import NeedsRequestProviderData
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 from llama_stack.providers.utils.common.data_schema_validator import (
     get_valid_schemas,
@@ -153,7 +152,7 @@ class BraintrustScoringImpl(
                 raise ValueError(
                     'Pass OpenAI API Key in the header X-LlamaStack-Provider-Data as { "openai_api_key": <your api key>}'
                 )
-            self.config.openai_api_key = MySecretStr(provider_data.openai_api_key)
+            self.config.openai_api_key = SecretStr(provider_data.openai_api_key)
 
         os.environ["OPENAI_API_KEY"] = self.config.openai_api_key.get_secret_value()
 

llama_stack/providers/inline/scoring/braintrust/config.py

@@ -5,13 +5,11 @@
 # the root directory of this source tree.
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BraintrustScoringConfig(BaseModel):
-    openai_api_key: MySecretStr = Field(
+    openai_api_key: SecretStr = Field(
         description="The OpenAI API Key",
     )
 

llama_stack/providers/remote/datasetio/nvidia/config.py

@@ -8,16 +8,14 @@ import os
 import warnings
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class NvidiaDatasetIOConfig(BaseModel):
     """Configuration for NVIDIA DatasetIO implementation."""
 
-    api_key: MySecretStr = Field(
-        default_factory=lambda: MySecretStr(os.getenv("NVIDIA_API_KEY", "")),
+    api_key: SecretStr = Field(
+        default_factory=lambda: SecretStr(os.getenv("NVIDIA_API_KEY", "")),
         description="The NVIDIA API key.",
     )
 

llama_stack/providers/remote/files/s3/config.py

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
 
 
@@ -18,7 +17,7 @@ class S3FilesImplConfig(BaseModel):
     bucket_name: str = Field(description="S3 bucket name to store files")
     region: str = Field(default="us-east-1", description="AWS region where the bucket is located")
     aws_access_key_id: str | None = Field(default=None, description="AWS access key ID (optional if using IAM roles)")
-    aws_secret_access_key: MySecretStr = Field(description="AWS secret access key (optional if using IAM roles)")
+    aws_secret_access_key: SecretStr = Field(description="AWS secret access key (optional if using IAM roles)")
     endpoint_url: str | None = Field(default=None, description="Custom S3 endpoint URL (for MinIO, LocalStack, etc.)")
     auto_create_bucket: bool = Field(
         default=False, description="Automatically create the S3 bucket if it doesn't exist"

llama_stack/providers/remote/inference/anthropic/anthropic.py

@@ -28,7 +28,7 @@ class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="anthropic",
-            api_key_from_config=config.api_key.get_secret_value() if config.api_key else None,
+            api_key_from_config=config.api_key,
             provider_data_api_key_field="anthropic_api_key",
         )
         self.config = config

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

@@ -6,21 +6,20 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class AnthropicProviderDataValidator(BaseModel):
-    anthropic_api_key: MySecretStr = Field(
+    anthropic_api_key: SecretStr = Field(
         description="API key for Anthropic models",
     )
 
 
 @json_schema_type
 class AnthropicConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="API key for Anthropic models",
     )
 

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

@@ -7,14 +7,13 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, Field, HttpUrl
+from pydantic import BaseModel, Field, HttpUrl, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class AzureProviderDataValidator(BaseModel):
-    azure_api_key: MySecretStr = Field(
+    azure_api_key: SecretStr = Field(
         description="Azure API key for Azure",
     )
     azure_api_base: HttpUrl = Field(
@@ -32,7 +31,7 @@ class AzureProviderDataValidator(BaseModel):
 
 @json_schema_type
 class AzureConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="Azure API key for Azure",
     )
     api_base: HttpUrl = Field(

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

@@ -7,9 +7,8 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 DEFAULT_BASE_URL = "https://api.cerebras.ai"
@@ -21,8 +20,8 @@ class CerebrasImplConfig(BaseModel):
         default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL),
         description="Base URL for the Cerebras API",
     )
-    api_key: MySecretStr = Field(
-        default=MySecretStr(os.environ.get("CEREBRAS_API_KEY")),
+    api_key: SecretStr = Field(
+        default=SecretStr(os.environ.get("CEREBRAS_API_KEY")),
         description="Cerebras API Key",
     )
 

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

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -18,7 +17,7 @@ class DatabricksImplConfig(BaseModel):
         default=None,
         description="The URL for the Databricks model serving endpoint",
     )
-    api_token: MySecretStr = Field(
+    api_token: SecretStr = Field(
         description="The Databricks API token",
     )
 

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

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import Field
+from pydantic import Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
@@ -19,7 +18,7 @@ class FireworksImplConfig(RemoteInferenceProviderConfig):
         default="https://api.fireworks.ai/inference/v1",
         description="The URL for the Fireworks server",
     )
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The Fireworks.ai API Key",
     )
 

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

@@ -6,21 +6,20 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class GeminiProviderDataValidator(BaseModel):
-    gemini_api_key: MySecretStr = Field(
+    gemini_api_key: SecretStr = Field(
         description="API key for Gemini models",
     )
 
 
 @json_schema_type
 class GeminiConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="API key for Gemini models",
     )
 

llama_stack/providers/remote/inference/gemini/gemini.py

@@ -20,7 +20,7 @@ class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="gemini",
-            api_key_from_config=config.api_key.get_secret_value() if config.api_key else None,
+            api_key_from_config=config.api_key,
             provider_data_api_key_field="gemini_api_key",
         )
         self.config = config

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

@@ -6,21 +6,20 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class GroqProviderDataValidator(BaseModel):
-    groq_api_key: MySecretStr = Field(
+    groq_api_key: SecretStr = Field(
         description="API key for Groq models",
     )
 
 
 @json_schema_type
 class GroqConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         # The Groq client library loads the GROQ_API_KEY environment variable by default
         description="The Groq API key",
     )

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

@@ -6,21 +6,20 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class LlamaProviderDataValidator(BaseModel):
-    llama_api_key: MySecretStr = Field(
+    llama_api_key: SecretStr = Field(
         description="API key for api.llama models",
     )
 
 
 @json_schema_type
 class LlamaCompatConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The Llama API key",
     )
 

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

@@ -7,9 +7,8 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -40,8 +39,8 @@ class NVIDIAConfig(BaseModel):
         default_factory=lambda: os.getenv("NVIDIA_BASE_URL", "https://integrate.api.nvidia.com"),
         description="A base url for accessing the NVIDIA NIM",
     )
-    api_key: MySecretStr = Field(
-        default_factory=lambda: MySecretStr(os.getenv("NVIDIA_API_KEY", "")),
+    api_key: SecretStr = Field(
+        default_factory=lambda: SecretStr(os.getenv("NVIDIA_API_KEY", "")),
         description="The NVIDIA API key, only needed of using the hosted service",
     )
     timeout: int = Field(

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

@@ -6,21 +6,20 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class OpenAIProviderDataValidator(BaseModel):
-    openai_api_key: MySecretStr = Field(
+    openai_api_key: SecretStr = Field(
         description="API key for OpenAI models",
     )
 
 
 @json_schema_type
 class OpenAIConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="API key for OpenAI models",
     )
     base_url: str = Field(

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

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -19,7 +18,7 @@ class PassthroughImplConfig(BaseModel):
         description="The URL for the passthrough endpoint",
     )
 
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="API Key for the passthrouth endpoint",
     )
 

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

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -18,7 +17,7 @@ class RunpodImplConfig(BaseModel):
         default=None,
         description="The URL for the Runpod model serving endpoint",
     )
-    api_token: MySecretStr = Field(
+    api_token: SecretStr = Field(
         description="The API token",
     )
 

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

@@ -6,14 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class SambaNovaProviderDataValidator(BaseModel):
-    sambanova_api_key: MySecretStr = Field(
+    sambanova_api_key: SecretStr = Field(
         description="Sambanova Cloud API key",
     )
 
@@ -24,7 +23,7 @@ class SambaNovaImplConfig(BaseModel):
         default="https://api.sambanova.ai/v1",
         description="The URL for the SambaNova AI server",
     )
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The SambaNova cloud API Key",
     )
 

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -29,7 +29,7 @@ class SambaNovaInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="sambanova",
-            api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None,
+            api_key_from_config=self.config.api_key,
             provider_data_api_key_field="sambanova_api_key",
             openai_compat_api_base=self.config.url,
             download_images=True,  # SambaNova requires base64 image encoding

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

@@ -5,9 +5,8 @@
 # the root directory of this source tree.
 
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -33,7 +32,7 @@ class InferenceEndpointImplConfig(BaseModel):
     endpoint_name: str = Field(
         description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.",
     )
-    api_token: MySecretStr = Field(
+    api_token: SecretStr = Field(
         description="Your Hugging Face user access token (will default to locally saved token if not provided)",
     )
 
@@ -55,7 +54,7 @@ class InferenceAPIImplConfig(BaseModel):
     huggingface_repo: str = Field(
         description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
     )
-    api_token: MySecretStr = Field(
+    api_token: SecretStr = Field(
         description="Your Hugging Face user access token (will default to locally saved token if not provided)",
     )
 

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -8,6 +8,7 @@
 from collections.abc import AsyncGenerator
 
 from huggingface_hub import AsyncInferenceClient, HfApi
+from pydantic import SecretStr
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -34,7 +35,6 @@ from llama_stack.apis.inference import (
 )
 from llama_stack.apis.models import Model
 from llama_stack.apis.models.models import ModelType
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.log import get_logger
 from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
@@ -79,7 +79,7 @@ class _HfAdapter(
     ModelsProtocolPrivate,
 ):
     url: str
-    api_key: MySecretStr
+    api_key: SecretStr
 
     hf_client: AsyncInferenceClient
     max_tokens: int
@@ -337,7 +337,7 @@ class TGIAdapter(_HfAdapter):
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
         self.url = f"{config.url.rstrip('/')}/v1"
-        self.api_key = MySecretStr("NO_KEY")
+        self.api_key = SecretStr("NO_KEY")
 
 
 class InferenceAPIAdapter(_HfAdapter):

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

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import Field
+from pydantic import Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
@@ -19,7 +18,7 @@ class TogetherImplConfig(RemoteInferenceProviderConfig):
         default="https://api.together.xyz/v1",
         description="The URL for the Together AI server",
     )
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The Together AI API Key",
     )
 

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

@@ -8,9 +8,9 @@ from typing import Any
 
 import google.auth.transport.requests
 from google.auth import default
+from pydantic import SecretStr
 
 from llama_stack.apis.inference import ChatCompletionRequest
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.utils.inference.litellm_openai_mixin import (
     LiteLLMOpenAIMixin,
 )
@@ -24,12 +24,12 @@ class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="vertex_ai",
-            api_key_from_config=MySecretStr(None),  # Vertex AI uses ADC, not API keys
+            api_key_from_config=SecretStr(""),  # 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) -> MySecretStr:
+    def get_api_key(self) -> SecretStr:
         """
         Get an access token for Vertex AI using Application Default Credentials.
 
@@ -40,11 +40,11 @@ class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
             # Get default credentials - will read from GOOGLE_APPLICATION_CREDENTIALS
             credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
             credentials.refresh(google.auth.transport.requests.Request())
-            return MySecretStr(credentials.token)
+            return SecretStr(credentials.token)
         except Exception:
             # If we can't get credentials, return empty string to let LiteLLM handle it
             # This allows the LiteLLM mixin to work with ADC directly
-            return MySecretStr("")
+            return SecretStr("")
 
     def get_base_url(self) -> str:
         """

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

@@ -4,15 +4,13 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 from .config import VLLMInferenceAdapterConfig
 
 
 class VLLMProviderDataValidator(BaseModel):
-    vllm_api_token: MySecretStr = Field(
+    vllm_api_token: SecretStr = Field(
         description="API token for vLLM models",
     )
 

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

@@ -6,9 +6,8 @@
 
 from pathlib import Path
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, SecretStr, field_validator
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -22,7 +21,8 @@ class VLLMInferenceAdapterConfig(BaseModel):
         default=4096,
         description="Maximum number of tokens to generate.",
     )
-    api_token: MySecretStr = Field(
+    api_token: SecretStr = Field(
+        default=SecretStr("fake"),
         description="The API token",
     )
     tls_verify: bool | str = Field(

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

@@ -7,9 +7,8 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -25,8 +24,8 @@ class WatsonXConfig(BaseModel):
         default_factory=lambda: os.getenv("WATSONX_BASE_URL", "https://us-south.ml.cloud.ibm.com"),
         description="A base url for accessing the watsonx.ai",
     )
-    api_key: MySecretStr = Field(
-        default_factory=lambda: MySecretStr(os.getenv("WATSONX_API_KEY", "")),
+    api_key: SecretStr = Field(
+        default_factory=lambda: SecretStr(os.getenv("WATSONX_API_KEY", "")),
         description="The watsonx API key",
     )
     project_id: str | None = Field(

llama_stack/providers/remote/post_training/nvidia/config.py

@@ -7,9 +7,7 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 # TODO: add default values for all fields
 
@@ -17,8 +15,8 @@ from llama_stack.core.secret_types import MySecretStr
 class NvidiaPostTrainingConfig(BaseModel):
     """Configuration for NVIDIA Post Training implementation."""
 
-    api_key: MySecretStr = Field(
-        default_factory=lambda: MySecretStr(os.getenv("NVIDIA_API_KEY", "")),
+    api_key: SecretStr = Field(
+        default_factory=lambda: SecretStr(os.getenv("NVIDIA_API_KEY", "")),
         description="The NVIDIA API key.",
     )
 

llama_stack/providers/remote/safety/sambanova/config.py

@@ -6,14 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.schema_utils import json_schema_type
 
 
 class SambaNovaProviderDataValidator(BaseModel):
-    sambanova_api_key: MySecretStr = Field(
+    sambanova_api_key: SecretStr = Field(
         description="Sambanova Cloud API key",
     )
 
@@ -24,7 +23,7 @@ class SambaNovaSafetyConfig(BaseModel):
         default="https://api.sambanova.ai/v1",
         description="The URL for the SambaNova AI server",
     )
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The SambaNova cloud API Key",
     )
 

llama_stack/providers/remote/tool_runtime/bing_search/config.py

@@ -6,15 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BingSearchToolConfig(BaseModel):
     """Configuration for Bing Search Tool Runtime"""
 
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The Bing API key",
     )
     top_k: int = 3

llama_stack/providers/remote/tool_runtime/brave_search/config.py

@@ -6,13 +6,11 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BraveSearchToolConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The Brave Search API Key",
     )
     max_results: int = Field(

llama_stack/providers/remote/tool_runtime/tavily_search/config.py

@@ -6,13 +6,11 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class TavilySearchToolConfig(BaseModel):
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The Tavily Search API Key",
     )
     max_results: int = Field(

llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py

@@ -6,15 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class WolframAlphaToolConfig(BaseModel):
     """Configuration for WolframAlpha Tool Runtime"""
 
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The WolframAlpha API Key",
     )
 

llama_stack/providers/remote/vector_io/milvus/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, SecretStr
 
 from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 from llama_stack.schema_utils import json_schema_type
@@ -15,7 +15,7 @@ from llama_stack.schema_utils import json_schema_type
 @json_schema_type
 class MilvusVectorIOConfig(BaseModel):
     uri: str = Field(description="The URI of the Milvus server")
-    token: str | None = Field(description="The token of the Milvus server")
+    token: SecretStr = Field(description="The token of the Milvus server")
     consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong")
     kvstore: KVStoreConfig = Field(description="Config for KV store backend")
 

llama_stack/providers/remote/vector_io/pgvector/config.py

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
     SqliteKVStoreConfig,
@@ -22,7 +21,7 @@ class PGVectorVectorIOConfig(BaseModel):
     port: int | None = Field(default=5432)
     db: str | None = Field(default="postgres")
     user: str | None = Field(default="postgres")
-    password: MySecretStr = Field(default=MySecretStr("mysecretpassword"))
+    password: SecretStr = Field(default=SecretStr("mysecretpassword"))
     kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
 
     @classmethod

llama_stack/providers/remote/vector_io/qdrant/config.py

@@ -6,9 +6,8 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
     SqliteKVStoreConfig,
@@ -24,7 +23,7 @@ class QdrantVectorIOConfig(BaseModel):
     grpc_port: int = 6334
     prefer_grpc: bool = False
     https: bool | None = None
-    api_key: MySecretStr = Field(
+    api_key: SecretStr = Field(
         description="The API key for the Qdrant instance",
     )
     prefix: str | None = None

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -173,7 +173,7 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         self._qdrant_lock = asyncio.Lock()
 
     async def initialize(self) -> None:
-        client_config = self.config.model_dump(exclude_none=True, exclude={"kvstore"})
+        client_config = self.config.model_dump(exclude_none=True, exclude={"kvstore"}, mode="json")
         self.client = AsyncQdrantClient(**client_config)
         self.kvstore = await kvstore_impl(self.config.kvstore)
 

llama_stack/providers/utils/bedrock/config.py

@@ -6,9 +6,7 @@
 
 import os
 
-from pydantic import BaseModel, Field
-
-from llama_stack.core.secret_types import MySecretStr
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BedrockBaseConfig(BaseModel):
@@ -16,12 +14,12 @@ class BedrockBaseConfig(BaseModel):
         default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID"),
         description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID",
     )
-    aws_secret_access_key: MySecretStr = Field(
-        default_factory=lambda: MySecretStr(os.getenv("AWS_SECRET_ACCESS_KEY", "")),
+    aws_secret_access_key: SecretStr = Field(
+        default_factory=lambda: SecretStr(os.getenv("AWS_SECRET_ACCESS_KEY", "")),
         description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY",
     )
-    aws_session_token: MySecretStr = Field(
-        default_factory=lambda: MySecretStr(os.getenv("AWS_SESSION_TOKEN", "")),
+    aws_session_token: SecretStr = Field(
+        default_factory=lambda: SecretStr(os.getenv("AWS_SESSION_TOKEN", "")),
         description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN",
     )
     region_name: str | None = Field(

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator, AsyncIterator
 from typing import Any
 
 import litellm
+from pydantic import SecretStr
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -39,7 +40,6 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.core.request_headers import NeedsRequestProviderData
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, ProviderModelEntry
 from llama_stack.providers.utils.inference.openai_compat import (
@@ -69,7 +69,7 @@ class LiteLLMOpenAIMixin(
     def __init__(
         self,
         litellm_provider_name: str,
-        api_key_from_config: MySecretStr,
+        api_key_from_config: SecretStr,
         provider_data_api_key_field: str,
         model_entries: list[ProviderModelEntry] | None = None,
         openai_compat_api_base: str | None = None,
@@ -255,7 +255,7 @@ class LiteLLMOpenAIMixin(
             **get_sampling_options(request.sampling_params),
         }
 
-    def get_api_key(self) -> MySecretStr:
+    def get_api_key(self) -> SecretStr:
         provider_data = self.get_request_provider_data()
         key_field = self.provider_data_api_key_field
         if provider_data and getattr(provider_data, key_field, None):

llama_stack/providers/utils/inference/openai_mixin.py

@@ -11,6 +11,7 @@ from collections.abc import AsyncIterator
 from typing import Any
 
 from openai import NOT_GIVEN, AsyncOpenAI
+from pydantic import SecretStr
 
 from llama_stack.apis.inference import (
     Model,
@@ -24,7 +25,6 @@ from llama_stack.apis.inference import (
     OpenAIResponseFormatParam,
 )
 from llama_stack.apis.models import ModelType
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
@@ -71,14 +71,14 @@ class OpenAIMixin(ModelRegistryHelper, ABC):
     allowed_models: list[str] = []
 
     @abstractmethod
-    def get_api_key(self) -> MySecretStr:
+    def get_api_key(self) -> SecretStr:
         """
         Get the API key.
 
         This method must be implemented by child classes to provide the API key
         for authenticating with the OpenAI API or compatible endpoints.
 
-        :return: The API key as a MySecretStr
+        :return: The API key as a SecretStr
         """
         pass
 

llama_stack/providers/utils/kvstore/config.py

@@ -8,9 +8,8 @@ import re
 from enum import Enum
 from typing import Annotated, Literal
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, SecretStr, field_validator
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
 
 
@@ -75,7 +74,7 @@ class PostgresKVStoreConfig(CommonConfig):
     port: int = 5432
     db: str = "llamastack"
     user: str
-    password: MySecretStr = MySecretStr("")
+    password: SecretStr = SecretStr("")
     ssl_mode: str | None = None
     ca_cert_path: str | None = None
     table_name: str = "llamastack_kvstore"
@@ -119,7 +118,7 @@ class MongoDBKVStoreConfig(CommonConfig):
     port: int = 27017
     db: str = "llamastack"
     user: str | None = None
-    password: MySecretStr = MySecretStr("")
+    password: SecretStr = SecretStr("")
     collection_name: str = "llamastack_kvstore"
 
     @classmethod

llama_stack/providers/utils/sqlstore/sqlstore.py

@@ -9,9 +9,8 @@ from enum import StrEnum
 from pathlib import Path
 from typing import Annotated, Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
-from llama_stack.core.secret_types import MySecretStr
 from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
 
 from .api import SqlStore
@@ -64,7 +63,7 @@ class PostgresSqlStoreConfig(SqlAlchemySqlStoreConfig):
     port: int = 5432
     db: str = "llamastack"
     user: str
-    password: MySecretStr = MySecretStr("")
+    password: SecretStr = SecretStr("")
 
     @property
     def engine_str(self) -> str: