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https://github.com/InvoiceShelf/InvoiceShelf.git
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Second phase of the AI feature. Users can now open a slide-in chat drawer from the SiteHeader and ask natural-language questions about their company's invoices, customers, payments, and expenses. The LLM invokes pre-defined read-only tool functions (scoped to the current company at execute time) to fetch data and synthesize answers.
**Database** — new ai_conversations and ai_messages tables. Messages are stored in OpenAI's chat format so AiAssistantService serializes a conversation into an API request with zero translation. Columns: role, content, tool_call_id, tool_calls JSON, model, tokens_in, tokens_out. Conversations are scoped (company_id, user_id) — one user's chats are invisible to everyone else, even inside the same company. Foreign-key cascade deletes.
**Tool infrastructure** — AiTool abstract base + AiToolRegistry singleton (registered in a new AiServiceProvider). The base class enforces the security rule: every tool's execute() receives companyId and userId as injected parameters; tools' JSON schemas NEVER include a company_id field. An LLM physically cannot pass a company_id and escape tenancy. Modules can register their own tools by resolving the registry from their own ServiceProvider::boot().
**Nine built-in tools**: search_invoices, get_invoice, search_customers, get_customer, list_recent_payments, list_overdue_invoices, get_company_stats (aggregates for named periods), search_items, list_expense_categories. All read-only; no mutations. Each returns JSON-encodable data the LLM can parse.
**AiAssistantService orchestration loop** — the heart of Phase 2. Flow: persist user message → build payload from system prompt + recent history (40-message window) + new user message → call driver.chatCompletion with tools → if tool_calls, execute each one via the registry (with injected scope), persist tool result, loop → if plain text, persist and return. Hard cap at 5 iterations to prevent runaway LLMs. System prompt pins the assistant to this company's data and forbids mutation.
**Controllers + policy + rate limit** — POST /api/v1/ai/chat runs the orchestration loop. GET/PATCH/DELETE /api/v1/ai/conversations for CRUD. AiConversationPolicy enforces user_id+company_id match on every action. A new 'ai' RateLimiter in RouteServiceProvider throttles to 30 req/min per (user, company). New 'use ai' Gate defined in AppServiceProvider returns true for any authenticated user — the per-company kill-switch still goes through AiConfigurationService::resolveForCompany.
**Frontend** — new features/company/ai/ folder with a Pinia store (ai-chat.store.ts) holding drawer state, current conversation, messages, and loading flags. AiChatDrawer.vue is a slide-in panel teleported to <body>, mounted globally in CompanyLayout.vue when bootstrap reports ai.enabled && ai.chat_enabled. Sub-components: AiChatMessage (user bubbles vs assistant bubbles), AiChatMessageInput (Enter submits, Shift+Enter newline), AiChatConversationList (sidebar with 'new chat' button, rename, delete). A SparklesIcon button in SiteHeader toggles the drawer.
**Driver test double** — tests use a ScriptedAiDriver registered via AiDriverFactory::register('scripted', ...) that returns pre-queued AiChatResponse objects. Feature tests cover: happy path (new conversation + message persistence), tool-call loop (multi-round-trip with search_invoices), runaway-loop cap, driver-throws path, ai_enabled=NO rejection, chat role disabled rejection, per-user conversation visibility, cross-user policy enforcement, cascade delete.
388 tests pass (was 372, +16 new). Pint clean. npm run build clean. Phase 3 (WYSIWYG text generation popup) is the remaining follow-up.
93 lines
2.4 KiB
PHP
93 lines
2.4 KiB
PHP
<?php
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use App\Services\Ai\AiToolRegistry;
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use App\Services\Ai\Tools\AiTool;
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/**
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* A stub tool for exercising the registry without touching any real DB tables.
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*/
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class FakeAiTool extends AiTool
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{
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public array $lastArgs = [];
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public int $lastCompanyId = 0;
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public int $lastUserId = 0;
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public function __construct(
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private readonly string $toolName = 'fake_tool',
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) {}
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public function name(): string
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{
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return $this->toolName;
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}
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public function description(): string
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{
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return 'A fake tool for tests.';
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}
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public function parameterSchema(): array
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{
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return [
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'type' => 'object',
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'properties' => [
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'echo' => ['type' => 'string'],
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],
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'required' => [],
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];
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}
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public function execute(array $arguments, int $companyId, int $userId): mixed
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{
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$this->lastArgs = $arguments;
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$this->lastCompanyId = $companyId;
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$this->lastUserId = $userId;
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return ['echo' => $arguments['echo'] ?? null];
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}
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}
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test('register stores a tool by its name', function () {
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$registry = new AiToolRegistry;
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$tool = new FakeAiTool;
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$registry->register($tool);
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expect($registry->get('fake_tool'))->toBe($tool);
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expect($registry->all())->toHaveKey('fake_tool');
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});
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test('schemas returns OpenAI-format tool entries for every registered tool', function () {
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$registry = new AiToolRegistry;
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$registry->register(new FakeAiTool('alpha'));
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$registry->register(new FakeAiTool('beta'));
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$schemas = $registry->schemas();
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expect($schemas)->toHaveCount(2);
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expect($schemas[0]['type'])->toBe('function');
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expect($schemas[0]['function']['name'])->toBe('alpha');
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expect($schemas[1]['function']['name'])->toBe('beta');
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});
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test('execute injects companyId and userId into the tool and returns its result', function () {
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$registry = new AiToolRegistry;
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$tool = new FakeAiTool;
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$registry->register($tool);
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$result = $registry->execute('fake_tool', ['echo' => 'hello'], 42, 7);
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expect($result)->toEqual(['echo' => 'hello']);
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expect($tool->lastCompanyId)->toBe(42);
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expect($tool->lastUserId)->toBe(7);
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});
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test('execute throws for unknown tool names', function () {
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$registry = new AiToolRegistry;
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expect(fn () => $registry->execute('nonexistent', [], 1, 1))
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->toThrow(InvalidArgumentException::class);
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});
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