> ## Documentation Index
> Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# PromptSnapshotConfig

# PromptSnapshotConfig

Configuration types for prompt deployments in the Python SDK.

## PromptSnapshotConfig

The model and provider configuration returned as part of a deployment's prompt snapshot.

```python theme={null}
from adaline_api.models.prompt_snapshot_config import PromptSnapshotConfig
```

### Fields

**provider\_name** `str` *required*

The AI provider name (e.g., `"openai"`, `"anthropic"`, `"google"`, `"azure"`, `"bedrock"`).

**provider\_id** `str` *required*

Adaline's internal provider identifier.

**model** `str` *required*

The model identifier (e.g., `"gpt-4o"`, `"claude-sonnet-4-20250514"`).

**settings** `dict` *required*

Normalized model settings. Adaline uses provider-agnostic parameter names that map to each provider's native parameters.

### Settings

The `settings` dict uses Adaline's normalized parameter names. When you configure a prompt in Adaline, these are automatically mapped to the correct provider-specific parameter names.

| Adaline Key | OpenAI | Anthropic | Google | Description |
| - | - | - | - | - |
| `temperature` | `temperature` | `temperature` | `temperature` | Sampling temperature |
| `maxTokens` | `max_tokens` | `max_tokens` | `maxOutputTokens` | Maximum output tokens |
| `topP` | `top_p` | `top_p` | `topP` | Nucleus sampling threshold |
| `stopSequences` | `stop` | `stop_sequences` | `stopSequences` | Stop sequences |

***

## Examples

### Accessing Config from a Deployment

```python theme={null}
from adaline.main import Adaline

adaline = Adaline()

deployment = await adaline.get_latest_deployment(
    prompt_id="prompt_123",
    deployment_environment_id="environment_123"
)

config = deployment.prompt.config

print(f"Provider: {config.provider_name}")   # e.g. "openai"
print(f"Provider ID: {config.provider_id}")  # e.g. "provider_abc123"
print(f"Model: {config.model}")              # e.g. "gpt-4o"
print(f"Settings: {config.settings}")        # e.g. {"temperature": 0.7, "maxTokens": 1000}

temperature = config.settings.get("temperature")
max_tokens = config.settings.get("maxTokens")
```

### Using Config with a Provider SDK

```python theme={null}
from adaline.main import Adaline
from openai import OpenAI

adaline = Adaline()
openai = OpenAI()

deployment = await adaline.get_latest_deployment(
    prompt_id="prompt_123",
    deployment_environment_id="environment_123"
)

config = deployment.prompt.config
settings = config.settings

response = openai.chat.completions.create(
    model=config.model,
    messages=messages,
    temperature=settings.get("temperature"),
    max_tokens=settings.get("maxTokens"),
    top_p=settings.get("topP"),
)
```


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