mirror of
https://github.com/InvoiceShelf/InvoiceShelf.git
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e861fc1fc1
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.
52 lines
1.4 KiB
PHP
52 lines
1.4 KiB
PHP
<?php
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namespace App\Models;
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use Illuminate\Database\Eloquent\Factories\HasFactory;
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use Illuminate\Database\Eloquent\Model;
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use Illuminate\Database\Eloquent\Relations\BelongsTo;
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/**
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* One message in an AI conversation.
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*
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* Column shapes match OpenAI's chat message format exactly so that
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* AiAssistantService can serialize a conversation into an API request
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* payload with no translation layer. Supported roles:
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*
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* - user — human-authored prompt
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* - assistant — model-generated reply (may carry tool_calls instead of content)
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* - tool — result of a tool invocation, tied to an assistant's tool_call_id
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* - system — persisted system prompts (not typically stored; reserved for future)
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*/
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class AiMessage extends Model
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{
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use HasFactory;
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public const ROLE_USER = 'user';
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public const ROLE_ASSISTANT = 'assistant';
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public const ROLE_TOOL = 'tool';
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public const ROLE_SYSTEM = 'system';
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public const UPDATED_AT = null; // created_at only — messages are immutable once written
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protected $guarded = ['id'];
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protected function casts(): array
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{
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return [
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'tool_calls' => 'array',
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'tokens_in' => 'integer',
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'tokens_out' => 'integer',
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'created_at' => 'datetime',
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];
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}
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public function conversation(): BelongsTo
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{
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return $this->belongsTo(AiConversation::class, 'conversation_id');
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}
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}
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