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.
88 lines
2.4 KiB
TypeScript
88 lines
2.4 KiB
TypeScript
import { client } from '../client'
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import { API } from '../endpoints'
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import type {
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AiChatSendResponse,
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AiConfig,
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AiConversationDetail,
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AiConversationSummary,
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AiDriversResponse,
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AiTestPayload,
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AiTestResponse,
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CompanyAiConfig,
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} from '@/scripts/types/ai-config'
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export const aiService = {
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// Driver catalog — same shape across admin, company, installer contexts.
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async getDrivers(): Promise<AiDriversResponse> {
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const { data } = await client.get(API.AI_DRIVERS)
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return data
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},
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// --- Global (admin) ---
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async getGlobalConfig(): Promise<AiConfig> {
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const { data } = await client.get(API.AI_CONFIG)
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return data
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},
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async saveGlobalConfig(payload: AiConfig): Promise<{ success?: string; error?: string }> {
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const { data } = await client.post(API.AI_CONFIG, payload)
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return data
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},
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async testGlobalConnection(payload: AiTestPayload): Promise<AiTestResponse> {
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const { data } = await client.post(API.AI_TEST, payload)
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return data
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},
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// --- Per-company ---
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async getCompanyConfig(): Promise<CompanyAiConfig> {
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const { data } = await client.get(API.COMPANY_AI_CONFIG)
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return data
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},
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async saveCompanyConfig(payload: CompanyAiConfig): Promise<{ success?: boolean; error?: string }> {
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const { data } = await client.post(API.COMPANY_AI_CONFIG, payload)
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return data
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},
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async testCompanyConnection(payload: AiTestPayload): Promise<AiTestResponse> {
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const { data } = await client.post(API.COMPANY_AI_TEST, payload)
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return data
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},
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// --- Phase 2: chat ---
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async sendChatMessage(
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conversationId: number | null,
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message: string,
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): Promise<AiChatSendResponse> {
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const { data } = await client.post(API.AI_CHAT, {
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conversation_id: conversationId,
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message,
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})
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return data
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},
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async listConversations(): Promise<{ conversations: AiConversationSummary[] }> {
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const { data } = await client.get(API.AI_CONVERSATIONS)
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return data
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},
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async getConversation(id: number): Promise<AiConversationDetail> {
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const { data } = await client.get(`${API.AI_CONVERSATIONS}/${id}`)
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return data
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},
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async renameConversation(id: number, title: string): Promise<{ success: boolean }> {
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const { data } = await client.patch(`${API.AI_CONVERSATIONS}/${id}`, { title })
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return data
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},
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async deleteConversation(id: number): Promise<{ success: boolean }> {
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const { data } = await client.delete(`${API.AI_CONVERSATIONS}/${id}`)
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return data
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},
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}
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