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.
from adaline_api.models.prompt_snapshot_config import PromptSnapshotConfig
Fields
The AI provider name (e.g., "openai", "anthropic", "google", "azure", "bedrock").
Adaline’s internal provider identifier.
The model identifier (e.g., "gpt-4o", "claude-sonnet-4-20250514").
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
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
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"),
)
