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>
This commit is contained in:
Sébastien Han 2025-09-26 10:33:33 +02:00
parent bc64635835
commit 2a34226727
No known key found for this signature in database
86 changed files with 208 additions and 263 deletions

View file

@ -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(

View file

@ -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()

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@ -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",
)

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@ -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.",
)

View file

@ -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"

View file

@ -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

View file

@ -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",
)

View file

@ -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(

View file

@ -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",
)

View file

@ -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",
)

View file

@ -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",
)

View file

@ -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",
)

View file

@ -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

View file

@ -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",
)

View file

@ -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",
)

View file

@ -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(

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@ -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(

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@ -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",
)

View file

@ -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",
)

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@ -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",
)

View file

@ -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

View file

@ -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)",
)

View file

@ -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):

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@ -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",
)

View file

@ -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:
"""

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@ -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",
)

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@ -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(

View file

@ -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(

View file

@ -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.",
)

View file

@ -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",
)

View file

@ -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

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@ -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(

View file

@ -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(

View file

@ -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",
)

View file

@ -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")

View file

@ -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

View file

@ -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

View file

@ -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)

View file

@ -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(

View file

@ -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):

View file

@ -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

View file

@ -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

View file

@ -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: