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https://github.com/InvoiceShelf/InvoiceShelf.git
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* refactor: stabilize model identities for domain migration * refactor: extract module platform context * refactor: assign models to domain contexts * refactor: extract ai platform context * refactor: extract storage platform context * refactor: extract mail platform context * refactor: extract pdf platform context * refactor: extract operations platform context * refactor: move installation into operations platform * refactor: extract money domain context * refactor: extract taxation domain context * refactor: extract catalog domain context * refactor: extract metadata domain context * refactor: extract reporting domain context * refactor: extract purchases domain context * refactor: extract receivables domain context * refactor: extract accounts domain context * refactor: complete reporting statement boundary * refactor: extract contacts domain context * refactor: extract sales domain context * refactor: remove legacy application layers * fix: migrate legacy bouncer role identities
89 lines
3.0 KiB
PHP
89 lines
3.0 KiB
PHP
<?php
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namespace App\Platform\Ai\Contracts;
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use App\Platform\Ai\Data\AiChatResponse;
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use App\Platform\Ai\Exceptions\AiException;
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/**
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* Abstract base for AI drivers.
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*
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* Concrete drivers adapt a specific AI provider (OpenRouter, Anthropic direct,
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* local Ollama, etc.) into the two operations the host app needs:
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*
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* - `chatCompletion()` — for the chat assistant. OpenAI chat format, optional
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* tool-calling support. Drivers MUST translate their provider's tool-call
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* shape into the OpenAI-style `tool_calls` array on AiChatResponse so the
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* AiAssistantService orchestration loop stays provider-agnostic.
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*
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* - `textCompletion()` — for the text generation popup on WYSIWYG editors.
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* Stateless single-shot prompt → text.
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*
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* Drivers are registered with the host via the module Registry (see
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* App\Platform\Ai\AiServiceProvider for built-ins and
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* InvoiceShelf\Modules\Registry::registerAiDriver() for module-contributed ones).
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*/
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abstract class AiDriver
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{
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public function __construct(
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protected string $apiKey,
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protected array $config = [],
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) {}
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/**
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* Perform a chat completion.
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*
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* @param array<int, array<string, mixed>> $messages OpenAI chat format: [['role' => 'user', 'content' => '...'], ...]
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* @param string $model Provider-specific model identifier, e.g. 'openai/gpt-4o'
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* @param array<int, array<string, mixed>> $tools OpenAI tools schema array (empty = no tool calling)
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* @param array<string, mixed> $options Provider-specific options (temperature, max_tokens, etc.)
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*
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* @throws AiException
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*/
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abstract public function chatCompletion(
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array $messages,
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string $model,
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array $tools = [],
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array $options = [],
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): AiChatResponse;
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/**
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* Perform a single-shot text completion.
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*
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* Implementations may route this through chatCompletion() with a single
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* user message — it's a convenience for callers that don't need history.
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*
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* @throws AiException
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*/
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abstract public function textCompletion(
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string $prompt,
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string $model,
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array $options = [],
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): string;
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/**
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* Validate that the configured API key and base URL can reach the provider.
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*
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* Called from admin "Test connection" buttons. Should make a cheap round-trip
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* (list models, short completion, etc.) and throw AiException on failure.
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*
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* @return array<string, mixed> Provider info the UI can display (e.g. echoed model list)
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*
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* @throws AiException
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*/
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abstract public function validateConnection(): array;
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/**
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* Optional: return the list of available model identifiers from the provider.
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*
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* Drivers that don't expose a models endpoint can leave this as an empty array.
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* The UI falls back to the `suggested_models` declared in driver metadata.
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*
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* @return array<int, array{value: string, label: string}>
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*/
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public function listModels(): array
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{
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return [];
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}
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}
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