Adds three new read-only tools the chat LLM can call to answer
"who/what did the most X" questions that previously fell through
the cracks:
- rank_top_customers — ranks customers by invoiced_total, paid_total,
invoice_count, or outstanding_balance over a named time period
- rank_top_items — ranks catalog items by quantity_sold or revenue
- rank_expense_categories — ranks expense categories by total spend
All three share a new ResolvesPeriod trait that centralizes the
period-name → [start, end] logic. GetCompanyStatsTool is refactored
onto the same trait (identical public schema — the 'all_time' option
is only exposed on the new ranking tools, where an unbounded window
makes sense; stats over "all time" collapses every record into one
giant bucket and is rarely useful).
Each tool follows the existing pattern: snake_case name, one-sentence
description tuned for LLM tool selection, JSON-schema parameters
with injected company scoping (never trusting LLM-supplied company
IDs), and JSON-encodable output. outstanding_balance on the customer
tool explicitly ignores the period param since it's a current-state
snapshot.
Multi-company scoping tests lock down the session-authoritative
boundary on every new tool. Per-metric ordering tests verify the
aggregate queries actually rank correctly, and an ad-hoc-item
exclusion test verifies rank_top_items skips invoice lines where
item_id is null (free-typed entries that have no catalog row to
rank by id).
15 new tests added (tests/Feature/Ai/Tools/); test suite grows from
398 to 413 passing. LLM tool count goes from 9 to 12 — the model
will discover the new tools automatically via the function-calling
schema with no prompt changes required.
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.
