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Release version 0.17.11
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‎docs-build/cookbook/instructor/advanced/custom_llm_with_dsn.mdx‎

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---
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title: 'Customize parameters via DSN'
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docname: 'custom_llm_with_dsn'
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---
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## Overview
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You can provide your own LLM configuration data to `Instructor` object with DSN string.
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This is useful for inline configuration or for building configuration from admin UI,
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CLI arguments or environment variables.
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## Example
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```php
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<?php
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require 'examples/boot.php';
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use Cognesy\Instructor\Instructor;
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use Cognesy\Polyglot\LLM\Data\LLMConfig;
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use Cognesy\Polyglot\LLM\Enums\OutputMode;
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use Cognesy\Utils\Env;
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class User {
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public int $age;
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public string $name;
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}
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$user = (new Instructor)
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::fromDSN('connection=xai,model=grok-2')
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->respond(
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messages: "Our user Jason is 25 years old.",
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responseModel: User::class,
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mode: OutputMode::Tools,
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);
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dump($user);
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assert(isset($user->name));
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assert(isset($user->age));
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?>
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```

‎docs-build/cookbook/instructor/advanced/demonstrations.mdx‎

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require 'examples/boot.php';
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use Cognesy\Http\Events\HttpRequestSent;
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use Cognesy\Instructor\Features\Core\Data\Example;
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use Cognesy\Instructor\Data\Example;
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use Cognesy\Instructor\Instructor;
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use Cognesy\Polyglot\LLM\Enums\OutputMode;
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‎docs-build/cookbook/polyglot/llm_advanced/custom_llm_via_dsn.mdx‎

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---
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title: 'Customize LLM Configuration with DSN string'
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docname: 'custom_llm_via_dsn'
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---
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## Overview
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You can provide your own LLM configuration data to `Inference` object with DSN string.
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This is useful for inline configuration or for building configuration from admin UI,
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CLI arguments or environment variables.
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## Example
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```php
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<?php
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require 'examples/boot.php';
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use Cognesy\Polyglot\LLM\Inference;
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use Cognesy\Utils\Str;
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$answer = Inference
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::fromDSN('connection=xai,model=grok-2')
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->create(
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messages: [['role' => 'user', 'content' => 'What is the capital of France']],
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options: ['max_tokens' => 64]
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)
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->toText();
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echo "USER: What is capital of France\n";
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echo "ASSISTANT: $answer\n";
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assert(Str::contains($answer, 'Paris'));
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?>
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```

‎docs-build/cookbook/prompting/few_shot/in_context_examples.mdx‎

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<?php
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require 'examples/boot.php';
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use Cognesy\Instructor\Data\Example;
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use Cognesy\Instructor\Extras\Scalar\Scalar;
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use Cognesy\Instructor\Extras\Sequence\Sequence;
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use Cognesy\Instructor\Features\Core\Data\Example;
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use Cognesy\Instructor\Instructor;
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enum ReviewSentiment : string {

‎docs-build/instructor/concepts/overview.mdx‎

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Instructor uses Large Language Models (LLMs) to process data and return structured information you can easily use in your code.
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![image](../../images/concept.png)
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![image](../images/concept.png)
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## Instructor in action
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Here's a simple CLI demo app using Instructor to extract structured data from text:
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![image](../../images/extraction.gif)
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![image](../images/extraction.gif)
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‎docs-build/instructor/essentials/demonstrations.mdx‎

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```php
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<?php
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use Cognesy\Instructor\Features\Core\Data\Example;
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use Cognesy\Instructor\Data\Example;
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class User {
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public int $age;

‎docs-build/mint.json‎

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"instructor/misc/llm_providers"
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]
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},
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{
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"group": "Release Notes",
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"pages": [
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"release-notes/v0.17.4",
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"release-notes/v0.17.3",
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"release-notes/v0.17.1",
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"release-notes/v0.17.0",
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"release-notes/v0.16.0",
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"release-notes/v0.15.2",
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"release-notes/v0.15.1",
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"release-notes/v0.15.0",
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"release-notes/v0.14.7",
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"release-notes/v0.14.6",
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"release-notes/v0.14.5",
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"release-notes/v0.14.4",
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"release-notes/v0.14.3",
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"release-notes/v0.14.2",
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"release-notes/v0.14.1",
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"release-notes/v0.14.0",
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"release-notes/v0.13.0",
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"release-notes/v0.12.13",
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"release-notes/v0.12.12",
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"release-notes/v0.12.11",
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"release-notes/v0.12.10",
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"release-notes/v0.12.9",
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"release-notes/v0.12.8",
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"release-notes/v0.12.7",
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"release-notes/v0.12.6",
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"release-notes/v0.12.5",
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"release-notes/v0.12.4",
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"release-notes/v0.12.3",
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"release-notes/v0.12.2",
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"release-notes/v0.12.0"
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]
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},
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{
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"group": "Polyglot",
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"pages": [
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"cookbook/contributing"
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]
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},
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{
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"group": "Release Notes",
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"pages": [
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"release-notes/v0.17.11",
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"release-notes/v0.17.10",
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"release-notes/v0.17.9",
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"release-notes/v0.17.8",
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"release-notes/v0.17.7",
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"release-notes/v0.17.6",
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"release-notes/v0.17.5",
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"release-notes/v0.17.4",
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"release-notes/v0.17.3",
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"release-notes/v0.17.1",
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"release-notes/v0.17.0",
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"release-notes/v0.16.0",
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"release-notes/v0.15.2",
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"release-notes/v0.15.1",
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"release-notes/v0.15.0",
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"release-notes/v0.14.7",
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"release-notes/v0.14.6",
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"release-notes/v0.14.5",
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"release-notes/v0.14.4",
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"release-notes/v0.14.3",
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"release-notes/v0.14.2",
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"release-notes/v0.14.1",
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"release-notes/v0.14.0",
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"release-notes/v0.13.0",
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"release-notes/v0.12.13",
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"release-notes/v0.12.12",
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"release-notes/v0.12.11",
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"release-notes/v0.12.10",
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"release-notes/v0.12.9",
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"release-notes/v0.12.8",
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"release-notes/v0.12.7",
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"release-notes/v0.12.6",
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"release-notes/v0.12.5",
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"release-notes/v0.12.4",
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"release-notes/v0.12.3",
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"release-notes/v0.12.2",
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"release-notes/v0.12.0",
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"release-notes/v0.8.0"
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]
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},
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{
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"group": "Cookbook \\ Instructor \\ Basics",
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"pages": [
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"cookbook/instructor/advanced/context_cache_structured",
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"cookbook/instructor/advanced/custom_llm",
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"cookbook/instructor/advanced/custom_prompts",
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"cookbook/instructor/advanced/custom_llm_with_dsn",
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"cookbook/instructor/advanced/structured_input",
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"cookbook/instructor/advanced/function_calls",
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"cookbook/instructor/advanced/partials",
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"cookbook/polyglot/llm_advanced/custom_llm",
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"cookbook/polyglot/llm_advanced/custom_embeddings_driver",
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"cookbook/polyglot/llm_advanced/custom_llm_driver",
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"cookbook/polyglot/llm_advanced/custom_llm_via_dsn",
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"cookbook/polyglot/llm_advanced/embeddings",
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"cookbook/polyglot/llm_advanced/parallel_calls",
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"cookbook/polyglot/llm_advanced/reasoning_content"

‎docs-build/polyglot/advanced/json-schema.mdx‎

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---
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title: Structured outputs with `JsonSchema` class
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title: Structured outputs with JsonSchema class
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description: Learn how to use JSON Schemas to generate structured outputs using LLMs.
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---
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// Additional configuration...
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);
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```
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## Conclusion
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JsonSchema in Polyglot provides a powerful way to structure your LLM interactions. By defining clear schemas, you ensure consistent, typed responses from language models and improve the reliability of your AI-powered applications.
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Remember that while schemas help define the structure of your data, they don't automatically validate the data against these schemas - they primarily serve as guidance for the LLM to structure its responses correctly. Always implement server-side validation for production applications.

‎docs-build/release-notes/v0.17.11.mdx‎

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- (polyglot) Support for DSN string containing parameters of LLM provider connection - Inference, Embeddings classes and their configs
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- (instructor) Support for DSN string containing parameters of LLM provider connection - Instructor class
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- (utils) DSN class with DSN-like string parsing capabilities
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- (docs) DSN examples for Inference and Instructor
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- (instructor) Internal refactoring - moved some Instructor/Features/Core code to Instructor namespace
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- (hub) Automated build of changelog section in docs

‎docs-build/release-notes/v0.8.0.mdx‎

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- 'Structured-to-structured' processing - provide objects or arrays to be processed with LLM, get object as a result
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- Composite language programs with Module classes (inspired by DSPy)
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- `FunctionCall` helper class for extracting arguments for functions, methods or closures
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- (experimental) Anthropic tool calls mode support
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- (experimental) Cohere API support - MdJson only, other modes unstable
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- (experimental) Gemini API client - MdJson & sync only, streaming is unstable
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- (internals) Simplified, cleaner API client code
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- (internals) Consolidated message building logic to support formats required by different APIs
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- (internals) Better control over complex chat message sequences with `Scripts` and `Sections`
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- (internals) Code cleanup and bug fixes
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- Additions to docs and examples

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