Settings Class

Overview¶

The structured-output package deliberately avoids a single global settings object. Configuration is split across purpose-specific classes, keeping request configuration local and shared behavior reusable.

Configuration Classes¶

`LLMConfig`¶

Holds provider connection settings: API URL, API key, model name, driver, token limits, and provider-specific options.

use Cognesy\Polyglot\Inference\Config\LLMConfig;

// From a named preset
$config = LLMConfig::fromPreset('openai');

// From explicit values
$config = new LLMConfig(
    apiUrl: 'https://api.openai.com/v1',
    apiKey: 'sk-...',
    model: 'gpt-4o',
    driver: 'openai-compatible',
    maxTokens: 4096,
);

// From an array (e.g., loaded from a config file)
$config = LLMConfig::fromArray($data);

// From a DSN string
$config = LLMConfig::fromDsn('driver=openai,model=gpt-4o,apiKey=sk-...');

`StructuredOutputConfig`¶

Controls the structured-output behavior: output mode, retry settings, prompt templates, schema naming, and response caching.

use Cognesy\Instructor\Config\StructuredOutputConfig;
use Cognesy\Instructor\Enums\OutputMode;

$config = new StructuredOutputConfig(
    outputMode: OutputMode::Tools,
    maxRetries: 2,
    toolName: 'extract_data',
    toolDescription: 'Extract structured data from the input.',
);

Key settings:

Setting	Default	Purpose
`outputMode`	`Tools`	How the schema is delivered to the LLM (`Tools`, `Json`, `JsonSchema`, `MdJson`, `Text`, `Unrestricted`)
`maxRetries`	`0`	Maximum retry attempts after the first call
`retryPrompt`	`"JSON generated incorrectly..."`	Template for retry feedback messages
`toolName`	`"extracted_data"`	Function name used in tool-call mode
`toolDescription`	`"Function call based on..."`	Description sent with the tool definition
`schemaName`	`"default_schema"`	Name used in JSON Schema mode
`useObjectReferences`	`false`	Enable `$ref` usage in generated schemas
`defaultToStdClass`	`false`	Return `stdClass` instead of arrays for untyped schemas
`responseCachePolicy`	`None`	Whether to cache streaming response snapshots for replay

`StructuredOutputRuntime`¶

Assembles the runtime by combining an inference provider, event dispatcher, structured-output config, and optional pipeline customizations:

use Cognesy\Instructor\StructuredOutputRuntime;

$runtime = StructuredOutputRuntime::fromConfig($llmConfig);

// Or with full customization
$runtime = new StructuredOutputRuntime(
    inference: $inferenceRuntime,
    events: $eventDispatcher,
    config: $structuredConfig,
    validators: [MyValidator::class],
    transformers: [MyTransformer::class],
);

Why No Global Settings?¶

Splitting configuration serves several goals:

Locality -- each request can use a different provider or different retry settings without affecting other requests.
Testability -- configuration objects are plain value objects that can be constructed in tests without touching global state.
Composability -- the same LLMConfig can be shared across the structured-output package, the Polyglot inference layer, and other companions without coupling them together.
Immutability -- both LLMConfig and StructuredOutputConfig are immutable. Mutation methods return new instances, making configuration safe to share across concurrent or reentrant code paths.