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revert: do not use MySecretStr

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

Signed-off-by: Sébastien Han <seb@redhat.com>
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Sébastien Han 2025-09-26 10:33:33 +02:00
parent bc64635835
commit 2a34226727
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86 changed files with 208 additions and 263 deletions
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2
docs/docs/providers/datasetio/remote_nvidia.mdx
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@ -14,7 +14,7 @@ NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The NVIDIA API key. |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The NVIDIA API key. |
| `dataset_namespace` | `str \| None` | No | default | The NVIDIA dataset namespace. |
| `project_id` | `str \| None` | No | test-project | The NVIDIA project ID. |
| `datasets_url` | `<class 'str'>` | No | http://nemo.test | Base URL for the NeMo Dataset API |

2
docs/docs/providers/files/remote_s3.mdx
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@ -17,7 +17,7 @@ AWS S3-based file storage provider for scalable cloud file management with metad
| `bucket_name` | `<class 'str'>` | No | | S3 bucket name to store files |
| `region` | `<class 'str'>` | No | us-east-1 | AWS region where the bucket is located |
| `aws_access_key_id` | `str \| None` | No | | AWS access key ID (optional if using IAM roles) |
| `aws_secret_access_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | AWS secret access key (optional if using IAM roles) |
| `aws_secret_access_key` | `<class 'pydantic.types.SecretStr'>` | No | | AWS secret access key (optional if using IAM roles) |
| `endpoint_url` | `str \| None` | No | | Custom S3 endpoint URL (for MinIO, LocalStack, etc.) |
| `auto_create_bucket` | `<class 'bool'>` | No | False | Automatically create the S3 bucket if it doesn't exist |
| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |

2
docs/docs/providers/inference/remote_anthropic.mdx
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@ -14,7 +14,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | API key for Anthropic models |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | API key for Anthropic models |
## Sample Configuration

2
docs/docs/providers/inference/remote_azure.mdx
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@ -21,7 +21,7 @@ https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | Azure API key for Azure |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | Azure API key for Azure |
| `api_base` | `<class 'pydantic.networks.HttpUrl'>` | No | | Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com) |
| `api_version` | `str \| None` | No | | Azure API version for Azure (e.g., 2024-12-01-preview) |
| `api_type` | `str \| None` | No | azure | Azure API type for Azure (e.g., azure) |

4
docs/docs/providers/inference/remote_bedrock.mdx
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@ -15,8 +15,8 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `aws_access_key_id` | `str \| None` | No | | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
| `aws_secret_access_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
| `aws_session_token` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
| `aws_secret_access_key` | `<class 'pydantic.types.SecretStr'>` | No | | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
| `aws_session_token` | `<class 'pydantic.types.SecretStr'>` | No | | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
| `region_name` | `str \| None` | No | | The default AWS Region to use, for example, us-west-1 or us-west-2.Default use environment variable: AWS_DEFAULT_REGION |
| `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE |
| `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS |

2
docs/docs/providers/inference/remote_cerebras.mdx
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@ -15,7 +15,7 @@ Cerebras inference provider for running models on Cerebras Cloud platform.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `base_url` | `<class 'str'>` | No | https://api.cerebras.ai | Base URL for the Cerebras API |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | Cerebras API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | Cerebras API Key |
## Sample Configuration

2
docs/docs/providers/inference/remote_databricks.mdx
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@ -15,7 +15,7 @@ Databricks inference provider for running models on Databricks' unified analytic
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | | The URL for the Databricks model serving endpoint |
| `api_token` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Databricks API token |
| `api_token` | `<class 'pydantic.types.SecretStr'>` | No | | The Databricks API token |
## Sample Configuration

2
docs/docs/providers/inference/remote_fireworks.mdx
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@ -16,7 +16,7 @@ Fireworks AI inference provider for Llama models and other AI models on the Fire
|-------|------|----------|---------|-------------|
| `allowed_models` | `list[str \| None` | No | | List of models that should be registered with the model registry. If None, all models are allowed. |
| `url` | `<class 'str'>` | No | https://api.fireworks.ai/inference/v1 | The URL for the Fireworks server |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Fireworks.ai API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Fireworks.ai API Key |
## Sample Configuration

2
docs/docs/providers/inference/remote_gemini.mdx
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@ -14,7 +14,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | API key for Gemini models |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | API key for Gemini models |
## Sample Configuration

2
docs/docs/providers/inference/remote_groq.mdx
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@ -14,7 +14,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Groq API key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Groq API key |
| `url` | `<class 'str'>` | No | https://api.groq.com | The URL for the Groq AI server |
## Sample Configuration

2
docs/docs/providers/inference/remote_hf_endpoint.mdx
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@ -15,7 +15,7 @@ HuggingFace Inference Endpoints provider for dedicated model serving.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `endpoint_name` | `<class 'str'>` | No | | The name of the Hugging Face Inference Endpoint in the format of '&#123;namespace&#125;/&#123;endpoint_name&#125;' (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` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) |
| `api_token` | `<class 'pydantic.types.SecretStr'>` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) |
## Sample Configuration

2
docs/docs/providers/inference/remote_hf_serverless.mdx
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@ -15,7 +15,7 @@ HuggingFace Inference API serverless provider for on-demand model inference.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `huggingface_repo` | `<class 'str'>` | No | | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
| `api_token` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) |
| `api_token` | `<class 'pydantic.types.SecretStr'>` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) |
## Sample Configuration

2
docs/docs/providers/inference/remote_llama-openai-compat.mdx
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@ -14,7 +14,7 @@ Llama OpenAI-compatible provider for using Llama models with OpenAI API format.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Llama API key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Llama API key |
| `openai_compat_api_base` | `<class 'str'>` | No | https://api.llama.com/compat/v1/ | The URL for the Llama API server |
## Sample Configuration

2
docs/docs/providers/inference/remote_nvidia.mdx
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@ -15,7 +15,7 @@ NVIDIA inference provider for accessing NVIDIA NIM models and AI services.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | https://integrate.api.nvidia.com | A base url for accessing the NVIDIA NIM |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The NVIDIA API key, only needed of using the hosted service |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The NVIDIA API key, only needed of using the hosted service |
| `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |
| `append_api_version` | `<class 'bool'>` | No | True | When set to false, the API version will not be appended to the base_url. By default, it is true. |

2
docs/docs/providers/inference/remote_openai.mdx
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@ -14,7 +14,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | API key for OpenAI models |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | API key for OpenAI models |
| `base_url` | `<class 'str'>` | No | https://api.openai.com/v1 | Base URL for OpenAI API |
## Sample Configuration

2
docs/docs/providers/inference/remote_passthrough.mdx
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@ -15,7 +15,7 @@ Passthrough inference provider for connecting to any external inference service
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | | The URL for the passthrough endpoint |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | API Key for the passthrouth endpoint |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | API Key for the passthrouth endpoint |
## Sample Configuration

2
docs/docs/providers/inference/remote_runpod.mdx
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@ -15,7 +15,7 @@ RunPod inference provider for running models on RunPod's cloud GPU platform.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `str \| None` | No | | The URL for the Runpod model serving endpoint |
| `api_token` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The API token |
| `api_token` | `<class 'pydantic.types.SecretStr'>` | No | | The API token |
## Sample Configuration

2
docs/docs/providers/inference/remote_sambanova.mdx
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@ -15,7 +15,7 @@ SambaNova inference provider for running models on SambaNova's dataflow architec
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | https://api.sambanova.ai/v1 | The URL for the SambaNova AI server |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The SambaNova cloud API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The SambaNova cloud API Key |
## Sample Configuration

2
docs/docs/providers/inference/remote_together.mdx
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@ -16,7 +16,7 @@ Together AI inference provider for open-source models and collaborative AI devel
|-------|------|----------|---------|-------------|
| `allowed_models` | `list[str \| None` | No | | List of models that should be registered with the model registry. If None, all models are allowed. |
| `url` | `<class 'str'>` | No | https://api.together.xyz/v1 | The URL for the Together AI server |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Together AI API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Together AI API Key |
## Sample Configuration

2
docs/docs/providers/inference/remote_vllm.mdx
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@ -16,7 +16,7 @@ Remote vLLM inference provider for connecting to vLLM servers.
|-------|------|----------|---------|-------------|
| `url` | `str \| None` | No | | The URL for the vLLM model serving endpoint |
| `max_tokens` | `<class 'int'>` | No | 4096 | Maximum number of tokens to generate. |
| `api_token` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The API token |
| `api_token` | `<class 'pydantic.types.SecretStr'>` | No | ********** | The API token |
| `tls_verify` | `bool \| str` | No | True | Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file. |
| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically |

2
docs/docs/providers/inference/remote_watsonx.mdx
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@ -15,7 +15,7 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The watsonx API key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The watsonx API key |
| `project_id` | `str \| None` | No | | The Project ID key |
| `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |

2
docs/docs/providers/post_training/remote_nvidia.mdx
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@ -14,7 +14,7 @@ NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The NVIDIA API key. |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The NVIDIA API key. |
| `dataset_namespace` | `str \| None` | No | default | The NVIDIA dataset namespace. |
| `project_id` | `str \| None` | No | test-example-model@v1 | The NVIDIA project ID. |
| `customizer_url` | `str \| None` | No | | Base URL for the NeMo Customizer API |

4
docs/docs/providers/safety/remote_bedrock.mdx
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@ -15,8 +15,8 @@ AWS Bedrock safety provider for content moderation using AWS's safety services.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `aws_access_key_id` | `str \| None` | No | | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
| `aws_secret_access_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
| `aws_session_token` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
| `aws_secret_access_key` | `<class 'pydantic.types.SecretStr'>` | No | | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
| `aws_session_token` | `<class 'pydantic.types.SecretStr'>` | No | | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
| `region_name` | `str \| None` | No | | The default AWS Region to use, for example, us-west-1 or us-west-2.Default use environment variable: AWS_DEFAULT_REGION |
| `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE |
| `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS |

2
docs/docs/providers/safety/remote_sambanova.mdx
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@ -15,7 +15,7 @@ SambaNova's safety provider for content moderation and safety filtering.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | https://api.sambanova.ai/v1 | The URL for the SambaNova AI server |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The SambaNova cloud API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The SambaNova cloud API Key |
## Sample Configuration

2
docs/docs/providers/scoring/inline_braintrust.mdx
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@ -14,7 +14,7 @@ Braintrust scoring provider for evaluation and scoring using the Braintrust plat
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `openai_api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The OpenAI API Key |
| `openai_api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The OpenAI API Key |
## Sample Configuration

2
docs/docs/providers/tool_runtime/remote_bing-search.mdx
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@ -14,7 +14,7 @@ Bing Search tool for web search capabilities using Microsoft's search engine.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Bing API key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Bing API key |
| `top_k` | `<class 'int'>` | No | 3 | |
## Sample Configuration

2
docs/docs/providers/tool_runtime/remote_brave-search.mdx
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@ -14,7 +14,7 @@ Brave Search tool for web search capabilities with privacy-focused results.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Brave Search API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Brave Search API Key |
| `max_results` | `<class 'int'>` | No | 3 | The maximum number of results to return |
## Sample Configuration

2
docs/docs/providers/tool_runtime/remote_tavily-search.mdx
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@ -14,7 +14,7 @@ Tavily Search tool for AI-optimized web search with structured results.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The Tavily Search API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The Tavily Search API Key |
| `max_results` | `<class 'int'>` | No | 3 | The maximum number of results to return |
## Sample Configuration

2
docs/docs/providers/tool_runtime/remote_wolfram-alpha.mdx
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@ -14,7 +14,7 @@ Wolfram Alpha tool for computational knowledge and mathematical calculations.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The WolframAlpha API Key |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The WolframAlpha API Key |
## Sample Configuration

2
docs/docs/providers/vector_io/remote_milvus.mdx
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@ -406,7 +406,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `uri` | `<class 'str'>` | No | | The URI of the Milvus server |
| `token` | `str \| None` | No | | The token of the Milvus server |
| `token` | `<class 'pydantic.types.SecretStr'>` | No | | The token of the Milvus server |
| `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
| `config` | `dict` | No | `{}` | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |

2
docs/docs/providers/vector_io/remote_pgvector.mdx
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@ -217,7 +217,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
| `port` | `int \| None` | No | 5432 | |
| `db` | `str \| None` | No | postgres | |
| `user` | `str \| None` | No | postgres | |
| `password` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | ********** | |
| `password` | `<class 'pydantic.types.SecretStr'>` | No | ********** | |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) |
## Sample Configuration

2
docs/docs/providers/vector_io/remote_qdrant.mdx
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@ -22,7 +22,7 @@ Please refer to the inline provider documentation.
| `grpc_port` | `<class 'int'>` | No | 6334 | |
| `prefer_grpc` | `<class 'bool'>` | No | False | |
| `https` | `bool \| None` | No | | |
| `api_key` | `<class 'llama_stack.core.secret_types.MySecretStr'>` | No | | The API key for the Qdrant instance |
| `api_key` | `<class 'pydantic.types.SecretStr'>` | No | | The API key for the Qdrant instance |
| `prefix` | `str \| None` | No | | |
| `timeout` | `int \| None` | No | | |
| `host` | `str \| None` | No | | |

21
llama_stack/core/secret_types.py
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@ -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]

10
llama_stack/core/stack.py
View file

@ -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] = [

5
llama_stack/providers/inline/scoring/braintrust/__init__.py
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(

5
llama_stack/providers/inline/scoring/braintrust/braintrust.py
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()

6
llama_stack/providers/inline/scoring/braintrust/config.py
View file

@ -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",
)

8
llama_stack/providers/remote/datasetio/nvidia/config.py
View file

@ -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.",
)

5
llama_stack/providers/remote/files/s3/config.py
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"

2
llama_stack/providers/remote/inference/anthropic/anthropic.py
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

7
llama_stack/providers/remote/inference/anthropic/config.py
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",
)

7
llama_stack/providers/remote/inference/azure/config.py
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(

7
llama_stack/providers/remote/inference/cerebras/config.py
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",
)

5
llama_stack/providers/remote/inference/databricks/config.py
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",
)

5
llama_stack/providers/remote/inference/fireworks/config.py
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",
)

7
llama_stack/providers/remote/inference/gemini/config.py
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",
)

2
llama_stack/providers/remote/inference/gemini/gemini.py
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

7
llama_stack/providers/remote/inference/groq/config.py
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",
)

7
llama_stack/providers/remote/inference/llama_openai_compat/config.py
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",
)

7
llama_stack/providers/remote/inference/nvidia/config.py
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(

7
llama_stack/providers/remote/inference/openai/config.py
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 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(

5
llama_stack/providers/remote/inference/passthrough/config.py
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
@ -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",
)

5
llama_stack/providers/remote/inference/runpod/config.py
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",
)

7
llama_stack/providers/remote/inference/sambanova/config.py
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 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",
)

2
llama_stack/providers/remote/inference/sambanova/sambanova.py
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

7
llama_stack/providers/remote/inference/tgi/config.py
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)",
)

6
llama_stack/providers/remote/inference/tgi/tgi.py
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):

5
llama_stack/providers/remote/inference/together/config.py
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 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",
)

10
llama_stack/providers/remote/inference/vertexai/vertexai.py
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:
"""

6
llama_stack/providers/remote/inference/vllm/__init__.py
View file

@ -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",
)

6
llama_stack/providers/remote/inference/vllm/config.py
View file

@ -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(

7
llama_stack/providers/remote/inference/watsonx/config.py
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(

8
llama_stack/providers/remote/post_training/nvidia/config.py
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.",
)

7
llama_stack/providers/remote/safety/sambanova/config.py
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",
)

6
llama_stack/providers/remote/tool_runtime/bing_search/config.py
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

6
llama_stack/providers/remote/tool_runtime/brave_search/config.py
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 BraveSearchToolConfig(BaseModel):
api_key: MySecretStr = Field(
api_key: SecretStr = Field(
description="The Brave Search API Key",
)
max_results: int = Field(

6
llama_stack/providers/remote/tool_runtime/tavily_search/config.py
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(

6
llama_stack/providers/remote/tool_runtime/wolfram_alpha/config.py
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",
)

4
llama_stack/providers/remote/vector_io/milvus/config.py
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")

5
llama_stack/providers/remote/vector_io/pgvector/config.py
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

5
llama_stack/providers/remote/vector_io/qdrant/config.py
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

2
llama_stack/providers/remote/vector_io/qdrant/qdrant.py
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)

12
llama_stack/providers/utils/bedrock/config.py
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(

6
llama_stack/providers/utils/inference/litellm_openai_mixin.py
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):

6
llama_stack/providers/utils/inference/openai_mixin.py
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

7
llama_stack/providers/utils/kvstore/config.py
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

5
llama_stack/providers/utils/sqlstore/sqlstore.py
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:

2
tests/unit/providers/files/conftest.py
View file

@ -7,6 +7,7 @@
import boto3
import pytest
from moto import mock_aws
from pydantic import SecretStr
from llama_stack.providers.remote.files.s3 import S3FilesImplConfig, get_adapter_impl
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
@ -43,6 +44,7 @@ def s3_config(tmp_path):
region="not-a-region",
auto_create_bucket=True,
metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()),
aws_secret_access_key=SecretStr("fake"),
)

4
tests/unit/providers/inference/bedrock/test_config.py
View file

@ -17,7 +17,7 @@ class TestBedrockBaseConfig:
# Basic creds should be None
assert config.aws_access_key_id is None
assert config.aws_secret_access_key is None
assert not config.aws_secret_access_key
assert config.region_name is None
# Timeouts get defaults
@ -39,7 +39,7 @@ class TestBedrockBaseConfig:
config = BedrockBaseConfig()
assert config.aws_access_key_id == "AKIATEST123"
assert config.aws_secret_access_key == "secret123"
assert config.aws_secret_access_key.get_secret_value() == "secret123"
assert config.region_name == "us-west-2"
assert config.total_max_attempts == 5
assert config.retry_mode == "adaptive"

16
tests/unit/providers/inference/test_inference_client_caching.py
View file

@ -7,6 +7,8 @@
import json
from unittest.mock import MagicMock
from pydantic import SecretStr
from llama_stack.core.request_headers import request_provider_data_context
from llama_stack.providers.remote.inference.groq.config import GroqConfig
from llama_stack.providers.remote.inference.groq.groq import GroqInferenceAdapter
@ -21,7 +23,7 @@ from llama_stack.providers.remote.inference.together.together import TogetherInf
def test_groq_provider_openai_client_caching():
"""Ensure the Groq provider does not cache api keys across client requests"""
config = GroqConfig()
config = GroqConfig(api_key=SecretStr(""))
inference_adapter = GroqInferenceAdapter(config)
inference_adapter.__provider_spec__ = MagicMock()
@ -33,13 +35,13 @@ def test_groq_provider_openai_client_caching():
with request_provider_data_context(
{"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})}
):
assert inference_adapter.client.api_key.get_secret_value() == api_key
assert inference_adapter.client.api_key == api_key
def test_openai_provider_openai_client_caching():
"""Ensure the OpenAI provider does not cache api keys across client requests"""
config = OpenAIConfig()
config = OpenAIConfig(api_key=SecretStr(""))
inference_adapter = OpenAIInferenceAdapter(config)
inference_adapter.__provider_spec__ = MagicMock()
@ -52,13 +54,13 @@ def test_openai_provider_openai_client_caching():
{"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})}
):
openai_client = inference_adapter.client
assert openai_client.api_key.get_secret_value() == api_key
assert openai_client.api_key == api_key
def test_together_provider_openai_client_caching():
"""Ensure the Together provider does not cache api keys across client requests"""
config = TogetherImplConfig()
config = TogetherImplConfig(api_key=SecretStr(""))
inference_adapter = TogetherInferenceAdapter(config)
inference_adapter.__provider_spec__ = MagicMock()
@ -76,7 +78,7 @@ def test_together_provider_openai_client_caching():
def test_llama_compat_provider_openai_client_caching():
"""Ensure the LlamaCompat provider does not cache api keys across client requests"""
config = LlamaCompatConfig()
config = LlamaCompatConfig(api_key=SecretStr(""))
inference_adapter = LlamaCompatInferenceAdapter(config)
inference_adapter.__provider_spec__ = MagicMock()
@ -86,4 +88,4 @@ def test_llama_compat_provider_openai_client_caching():
for api_key in ["test1", "test2"]:
with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"llama_api_key": api_key})}):
assert inference_adapter.client.api_key.get_secret_value() == api_key
assert inference_adapter.client.api_key == api_key

9
tests/unit/providers/inference/test_litellm_openai_mixin.py
View file

@ -8,20 +8,19 @@ import json
from unittest.mock import MagicMock
import pytest
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, SecretStr
from llama_stack.core.request_headers import request_provider_data_context
from llama_stack.core.secret_types import MySecretStr
from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
# Test fixtures and helper classes
class TestConfig(BaseModel):
api_key: MySecretStr | None = Field(default=None)
api_key: SecretStr | None = Field(default=None)
class TestProviderDataValidator(BaseModel):
test_api_key: MySecretStr | None = Field(default=None)
test_api_key: SecretStr | None = Field(default=None)
class TestLiteLLMAdapter(LiteLLMOpenAIMixin):
@ -37,7 +36,7 @@ class TestLiteLLMAdapter(LiteLLMOpenAIMixin):
@pytest.fixture
def adapter_with_config_key():
"""Fixture to create adapter with API key in config"""
config = TestConfig(api_key=MySecretStr("config-api-key"))
config = TestConfig(api_key=SecretStr("config-api-key"))
adapter = TestLiteLLMAdapter(config)
adapter.__provider_spec__ = MagicMock()
adapter.__provider_spec__.provider_data_validator = (

25
tests/unit/providers/inference/test_openai_base_url_config.py
View file

@ -7,14 +7,9 @@
import os
from unittest.mock import MagicMock, patch
from llama_stack.core.secret_types import MySecretStr
# Wrapper for backward compatibility in tests
def replace_env_vars_compat(config, path=""):
return replace_env_vars_compat(config, path, None, None)
from pydantic import SecretStr
from llama_stack.core.stack import replace_env_vars
from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
from llama_stack.providers.remote.inference.openai.openai import OpenAIInferenceAdapter
@ -42,7 +37,7 @@ class TestOpenAIBaseURLConfig:
"""Test that the adapter uses base URL from OPENAI_BASE_URL environment variable."""
# Use sample_run_config which has proper environment variable syntax
config_data = OpenAIConfig.sample_run_config(api_key="test-key")
processed_config = replace_env_vars_compat(config_data)
processed_config = replace_env_vars(config_data)
config = OpenAIConfig.model_validate(processed_config)
adapter = OpenAIInferenceAdapter(config)
@ -66,14 +61,14 @@ class TestOpenAIBaseURLConfig:
adapter = OpenAIInferenceAdapter(config)
# Mock the get_api_key method since it's delegated to LiteLLMOpenAIMixin
adapter.get_api_key = MagicMock(return_value=MySecretStr("test-key"))
adapter.get_api_key = MagicMock(return_value=SecretStr("test-key"))
# Access the client property to trigger AsyncOpenAI initialization
_ = adapter.client
# Verify AsyncOpenAI was called with the correct base_url
mock_openai_class.assert_called_once_with(
api_key=MySecretStr("test-key"),
api_key=SecretStr("test-key").get_secret_value(),
base_url=custom_url,
)
@ -85,7 +80,7 @@ class TestOpenAIBaseURLConfig:
adapter = OpenAIInferenceAdapter(config)
# Mock the get_api_key method
adapter.get_api_key = MagicMock(return_value=MySecretStr("test-key"))
adapter.get_api_key = MagicMock(return_value=SecretStr("test-key"))
# Mock a model object that will be returned by models.list()
mock_model = MagicMock()
@ -108,7 +103,7 @@ class TestOpenAIBaseURLConfig:
# Verify the client was created with the custom URL
mock_openai_class.assert_called_with(
api_key=MySecretStr("test-key"),
api_key=SecretStr("test-key").get_secret_value(),
base_url=custom_url,
)
@ -121,12 +116,12 @@ class TestOpenAIBaseURLConfig:
"""Test that setting OPENAI_BASE_URL environment variable affects where model availability is checked."""
# Use sample_run_config which has proper environment variable syntax
config_data = OpenAIConfig.sample_run_config(api_key="test-key")
processed_config = replace_env_vars_compat(config_data)
processed_config = replace_env_vars(config_data)
config = OpenAIConfig.model_validate(processed_config)
adapter = OpenAIInferenceAdapter(config)
# Mock the get_api_key method
adapter.get_api_key = MagicMock(return_value=MySecretStr("test-key"))
adapter.get_api_key = MagicMock(return_value=SecretStr("test-key"))
# Mock a model object that will be returned by models.list()
mock_model = MagicMock()
@ -149,6 +144,6 @@ class TestOpenAIBaseURLConfig:
# Verify the client was created with the environment variable URL
mock_openai_class.assert_called_with(
api_key=MySecretStr("test-key"),
api_key=SecretStr("test-key").get_secret_value(),
base_url="https://proxy.openai.com/v1",
)

17
tests/unit/providers/inference/test_remote_vllm.py
View file

@ -26,6 +26,7 @@ from openai.types.chat.chat_completion_chunk import (
ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction,
)
from openai.types.model import Model as OpenAIModel
from pydantic import SecretStr
from llama_stack.apis.inference import (
ChatCompletionRequest,
@ -688,31 +689,35 @@ async def test_should_refresh_models():
"""
# Test case 1: refresh_models is True, api_token is None
config1 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=None, refresh_models=True)
config1 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=SecretStr(""), refresh_models=True)
adapter1 = VLLMInferenceAdapter(config1)
result1 = await adapter1.should_refresh_models()
assert result1 is True, "should_refresh_models should return True when refresh_models is True"
# Test case 2: refresh_models is True, api_token is empty string
config2 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="", refresh_models=True)
config2 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=SecretStr(""), refresh_models=True)
adapter2 = VLLMInferenceAdapter(config2)
result2 = await adapter2.should_refresh_models()
assert result2 is True, "should_refresh_models should return True when refresh_models is True"
# Test case 3: refresh_models is True, api_token is "fake" (default)
config3 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="fake", refresh_models=True)
config3 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=SecretStr("fake"), refresh_models=True)
adapter3 = VLLMInferenceAdapter(config3)
result3 = await adapter3.should_refresh_models()
assert result3 is True, "should_refresh_models should return True when refresh_models is True"
# Test case 4: refresh_models is True, api_token is real token
config4 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-123", refresh_models=True)
config4 = VLLMInferenceAdapterConfig(
url="http://test.localhost", api_token=SecretStr("real-token-123"), refresh_models=True
)
adapter4 = VLLMInferenceAdapter(config4)
result4 = await adapter4.should_refresh_models()
assert result4 is True, "should_refresh_models should return True when refresh_models is True"
# Test case 5: refresh_models is False, api_token is real token
config5 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-456", refresh_models=False)
config5 = VLLMInferenceAdapterConfig(
url="http://test.localhost", api_token=SecretStr("real-token-456"), refresh_models=False
)
adapter5 = VLLMInferenceAdapter(config5)
result5 = await adapter5.should_refresh_models()
assert result5 is False, "should_refresh_models should return False when refresh_models is False"
@ -735,7 +740,7 @@ async def test_provider_data_var_context_propagation(vllm_inference_adapter):
# Mock provider data to return test data
mock_provider_data = MagicMock()
mock_provider_data.vllm_api_token = "test-token-123"
mock_provider_data.vllm_api_token = SecretStr("test-token-123")
mock_provider_data.vllm_url = "http://test-server:8000/v1"
mock_get_provider_data.return_value = mock_provider_data

3
tests/unit/providers/nvidia/test_parameters.py
View file

@ -9,6 +9,7 @@ import warnings
from unittest.mock import patch
import pytest
from pydantic import SecretStr
from llama_stack.apis.post_training.post_training import (
DataConfig,
@ -32,7 +33,7 @@ class TestNvidiaParameters:
"""Setup and teardown for each test method."""
os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None)
config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=SecretStr(""))
self.adapter = NvidiaPostTrainingAdapter(config)
self.make_request_patcher = patch(

3
tests/unit/providers/nvidia/test_supervised_fine_tuning.py
View file

@ -9,6 +9,7 @@ import warnings
from unittest.mock import patch
import pytest
from pydantic import SecretStr
from llama_stack.apis.post_training.post_training import (
DataConfig,
@ -34,7 +35,7 @@ def nvidia_post_training_adapter():
"""Fixture to create and configure the NVIDIA post training adapter."""
os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" # needed for nemo customizer
config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None)
config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=SecretStr(""))
adapter = NvidiaPostTrainingAdapter(config)
with patch.object(adapter, "_make_request") as mock_make_request:

37
tests/unit/server/test_replace_env_vars.py
View file

@ -8,10 +8,7 @@ import os
import pytest
# Wrapper for backward compatibility in tests
def replace_env_vars_compat(config, path=""):
return replace_env_vars_compat(config, path, None, None)
from llama_stack.core.stack import replace_env_vars
@pytest.fixture
@ -35,54 +32,52 @@ def setup_env_vars():
def test_simple_replacement(setup_env_vars):
assert replace_env_vars_compat("${env.TEST_VAR}") == "test_value"
assert replace_env_vars("${env.TEST_VAR}") == "test_value"
def test_default_value_when_not_set(setup_env_vars):
assert replace_env_vars_compat("${env.NOT_SET:=default}") == "default"
assert replace_env_vars("${env.NOT_SET:=default}") == "default"
def test_default_value_when_set(setup_env_vars):
assert replace_env_vars_compat("${env.TEST_VAR:=default}") == "test_value"
assert replace_env_vars("${env.TEST_VAR:=default}") == "test_value"
def test_default_value_when_empty(setup_env_vars):
assert replace_env_vars_compat("${env.EMPTY_VAR:=default}") == "default"
assert replace_env_vars("${env.EMPTY_VAR:=default}") == "default"
def test_none_value_when_empty(setup_env_vars):
assert replace_env_vars_compat("${env.EMPTY_VAR:=}") is None
assert replace_env_vars("${env.EMPTY_VAR:=}") is None
def test_value_when_set(setup_env_vars):
assert replace_env_vars_compat("${env.TEST_VAR:=}") == "test_value"
assert replace_env_vars("${env.TEST_VAR:=}") == "test_value"
def test_empty_var_no_default(setup_env_vars):
assert replace_env_vars_compat("${env.EMPTY_VAR_NO_DEFAULT:+}") is None
assert replace_env_vars("${env.EMPTY_VAR_NO_DEFAULT:+}") is None
def test_conditional_value_when_set(setup_env_vars):
assert replace_env_vars_compat("${env.TEST_VAR:+conditional}") == "conditional"
assert replace_env_vars("${env.TEST_VAR:+conditional}") == "conditional"
def test_conditional_value_when_not_set(setup_env_vars):
assert replace_env_vars_compat("${env.NOT_SET:+conditional}") is None
assert replace_env_vars("${env.NOT_SET:+conditional}") is None
def test_conditional_value_when_empty(setup_env_vars):
assert replace_env_vars_compat("${env.EMPTY_VAR:+conditional}") is None
assert replace_env_vars("${env.EMPTY_VAR:+conditional}") is None
def test_conditional_value_with_zero(setup_env_vars):
assert replace_env_vars_compat("${env.ZERO_VAR:+conditional}") == "conditional"
assert replace_env_vars("${env.ZERO_VAR:+conditional}") == "conditional"
def test_mixed_syntax(setup_env_vars):
assert replace_env_vars_compat("${env.TEST_VAR:=default} and ${env.NOT_SET:+conditional}") == "test_value and "
assert (
replace_env_vars_compat("${env.NOT_SET:=default} and ${env.TEST_VAR:+conditional}") == "default and conditional"
)
assert replace_env_vars("${env.TEST_VAR:=default} and ${env.NOT_SET:+conditional}") == "test_value and "
assert replace_env_vars("${env.NOT_SET:=default} and ${env.TEST_VAR:+conditional}") == "default and conditional"
def test_nested_structures(setup_env_vars):
@ -92,11 +87,11 @@ def test_nested_structures(setup_env_vars):
"key3": {"nested": "${env.NOT_SET:+conditional}"},
}
expected = {"key1": "test_value", "key2": ["default", "conditional"], "key3": {"nested": None}}
assert replace_env_vars_compat(data) == expected
assert replace_env_vars(data) == expected
def test_explicit_strings_preserved(setup_env_vars):
# Explicit strings that look like numbers/booleans should remain strings
data = {"port": "8080", "enabled": "true", "count": "123", "ratio": "3.14"}
expected = {"port": "8080", "enabled": "true", "count": "123", "ratio": "3.14"}
assert replace_env_vars_compat(data) == expected
assert replace_env_vars(data) == expected