Captain::Assistant agora aponta direto pra Captain::Unit. Antes a relação
ia via CaptainInbox, mas isso quebrou quando re-apontamos as inboxes pros
Hermes — assistants captain_interno (Juliana, Bianca, Lara, Nina,
Valentina) ficaram SEM CaptainInbox associada e o lookup
unit_for(assistant) retornava nil.
Resultado: get_assistant_pricing(3) (Lara) caía no fallback de scenario
text. Construtor reportava "veio cenário/prompt, não tabela estruturada".
Migration adiciona captain_unit_id (FK opcional). Backfill explícito:
- 1 Juliana → unit 3 (Qnn01)
- 2 Bianca → unit 2 (PrimeAL)
- 3 Lara → unit 2 (PrimeAL — mesmo brand)
- 4 Nina → unit 5 (Express)
- 6 Valentina → unit 4 (Dolce Amore)
- 9 Lara.H → unit 2 (via parent_assistant_id=3)
Tools get_assistant_pricing_tool e save_agent_spec_tool atualizados pra
usar assistant.captain_unit primeiro (nova relação direta), com fallback
pro CaptainInbox se nulo (pra retrocompatibilidade).
Validado live: tool retorna grid markdown com Stilo/Alexa/Hidromassagem
em Seg-Qua + Qui-Dom corretamente.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ROOT FIX (não paliativo) das 3 lacunas que travavam o Construtor:
1. get_assistant_pricing_tool: lia de Captain::Mcp::PricingTables::TABLES
(hash Ruby) que NÃO EXISTE MAIS desde a migração pra DB. Caía no
fallback de scenario raw. Refactor: lê de Captain::PricingCategory +
Captain::PricingAmount, formata grid markdown agrupado por day_bucket.
2. save_agent_spec_tool: Construtor salvava REFERÊNCIAS
(pricing_source.copied_from_assistant_id) mas hermes-provision script
espera DADOS EXPANDIDOS (categories[] com amounts, soul_md+skill_md).
Refactor: tool agora EXPANDE server-side — busca PricingCategory do
parent, monta categories array, gera SOUL.md (template + identity +
disclosure_policy) e SKILL.md (template + pricing + rules + identity).
Output já é spec consumível pelo script.
3. Captain::PricingAmount::PERIODS: adicionado '1h' (Prime tem 1h).
4. Seed pras 3 units faltando: Hotel Recanto (1) + PrimeAL (2) + Qnn01
(3). Agora os 6 units existentes têm pricing em DB.
Hot-patched ambos tools + USR1 no Puma. Construtor pronto pra criar
Bianca/Juliana etc end-to-end sem intervenção manual.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Migra a tabela de preços do PricingTables.rb hardcoded pras tabelas
captain_pricing_categories + captain_pricing_amounts no DB. Mantém a
mesma API pública Captain::Mcp::PricingTables.calculate(...) — código
chama o banco via novos modelos Captain::PricingCategory e
Captain::PricingAmount.
Seed db/seed_pricing_tables.rb faz backfill idempotente pra Dolce Amore
(unit 4) e Express (unit 5) com a mesma estrutura que tava no Ruby.
Adiciona em captain_assistants:
- hermes_subscription_secret (gerado pelo script de provisionamento)
- hermes_port (alocado no range 8650-8699)
- parent_assistant_id (link informativo Hermes → captain_interno parent
pra sombrear FAQs/scenarios via header X-Captain-Assistant-Id)
Adiciona em captain_units: extra_person_fee + currency.
Primeiro milestone do roadmap arquitetural pro Construtor autônomo
(decisões em memory/project_construtor_autonomo_decisions.md).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Marca cada Captain::Assistant com engine ('captain_interno' | 'hermes')
e move o roteamento Hermes do env var pro banco — admin troca engine
re-apontando a inbox no painel, sem deploy. Mantém fallback pras env
vars antigas (CAPTAIN_HERMES_INBOX_IDS etc) durante a migração gradual,
pra não quebrar Valentina antes da re-associação.
Frontend: badge "Hermes" (âmbar) ou "Interno" (cinza) ao lado de cada
assistant no dropdown switcher e no card da listagem, com chaves i18n
em en + pt_BR.
Tabela de preço (pricing_tables.rb): adiciona unit Express (id=5) e
estende a estrutura pra aceitar preço por dia da semana
(mon_wed/thu_sun) — necessário pro Express, retrocompatível com Dolce
Amore (preço único).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Pequenos ajustes em Captain::Unit (app + enterprise), migration de seed
inicial dos prompts Jasmine/Daniela, schema regenerado, e atualização do
README de seed_prompts pra refletir o estado atual dos modelos.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Hook after_commit on:create no Captain::Unit dispara
ProvisionUnitInSupabaseJob, que upserta a unit em reserva_hotel.unidades
via Supabase REST (UNIQUE on tenant_id+chatwoot_unit_id) e grava IDs no
Captain::Unit (supabase_unit_id, supabase_tenant_id, supabase_marca_id).
Sem isso, criar nova unidade no painel Pix não habilitava roleta — a row
no Supabase ficava ausente e OfferService caía em "tenant não resolvido".
Inclui rake captain:reprovision_unit_in_supabase[id] + provision_all
pra reconciliação manual e migration retroativa.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Resolve duas camadas de problema identificadas em teste end-to-end:
1. Embeddings falhavam com HTTP 404 (/codex/v1/embeddings não existe).
Solução: Captain::Llm::EmbeddingService sempre usa OpenAI tradicional
via Llm::Config.with_api_key(legacy_settings). ProviderConfig expõe
legacy_openai_settings pra isso.
2. Servidor Codex ocasionalmente responde com response.failed +
code=server_error (instabilidade transitória). Client agora retenta
até 2x com backoff exponencial (0.5s, 1.5s) em erros retryable:
HTTP 5xx, server_error no response.failed, ou stream inacabado.
Outras correções nesta etapa:
- Scenario#agent_model: em modo Codex, ignora CAPTAIN_OPEN_AI_MODEL_SCENARIO
(que pode ter gpt-4o legado) e usa ProviderConfig.model.
- ExtractionService/ContradictionCheckerService/TranslateQueryService:
trocam constantes hardcoded gpt-4o-mini/gpt-4.1-nano por
ProviderConfig.light_model (respeitando o provider ativo).
- ProviderConfig.DEFAULT_CODEX_MODEL agora é gpt-5.2 (reconhecido pelo
RubyLLM; gpt-5.4 não está no catalog do gem).
Validado ponta-a-ponta: WhatsApp → Chatwoot → Jasmine → handoff Daniela
→ faq_lookup com embedding OK → resposta com preços corretos.
Docs em docs/captain-codex-oauth.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Lays the data + job foundation for tracking customer interactions,
recurrence, and Pix conversion on Contact. Design decisions negotiated
with Rodrigo (see docs to come):
Rules:
- Gap of 30h from last message defines separate interactions
- Qualified interaction = >=2 customer msgs + >=2 attendant msgs,
both with textual content (>= 2 letters)
- One-shot consultation = >=1+1 but below the qualified threshold
(tracked as secondary KPI)
- Excludes contacts labeled `equipe_interna`
- is_recurring = interactions_count >= 2
- pix_generated_count counts all PixCharges; reservations_paid_count
only counts those with status = paid
Surface area:
- Migration adds denormalized stats to contacts + indexes for fast filtering
- Captain::ContactStats::InteractionCalculatorService computes the stats
for a single contact (pure, no persistence)
- Captain::Retention::RecalculateContactStatsJob persists them for one
contact (idempotent)
- Captain::Retention::RecalculateAllContactStatsJob runs daily at 3am BRT,
enqueues per-contact jobs for everyone active in the last 120 days
- Event-driven refresh: CaptainListener#conversation_resolved enqueues
recalc; Captain::PixCharge after_create/after_update enqueues recalc
on status change
No UI yet — that's the next layer.
Consolida o trabalho desta branch de abril/2026 em um bloco pronto pra
testar em staging antes do merge pra main.
## Correções de memória semântica
- ExtractionService: Princípio Zero + Regra de Ouro (ação consumada vs intenção).
- Cenário Daniela_Reservas: Passo 0 de classificação (consulta/intenção/fora).
## Roleta da Sorte (end-to-end)
- Schema Supabase + 7 RPCs atômicas (server-side, idempotentes).
- Services: Offer, Redeem, WeeklyReport.
- Jobs: OfferRouletteJob (hook em ConfirmationService após Pix pago),
NotifyRevealed + Scheduler de fallback.
- Tool manual GenerateRoletaLinkTool + endpoint público /roleta/notify.
- Dashboard /captain/roleta com Resgate + Relatório + anomaly detection.
## Cenário Reclamacoes_Ouvidoria
- Triagem P1-P4, framework LAST, Three-level listening, Self-check.
- Sem compensação material, detecção de cliente frustrado eleva prioridade.
## Analytics
- Funil de conversão /captain/funnel: 5 etapas via regex, zero LLM.
- Detector de churn via ChurnOutreach* (cron dias úteis 10h-17h BRT).
## Trabalho pré-existente incluído
- Captain Executive Reports (ceo_digest, mattermost_delivery).
- get_reserva_preco_tool, Lifecycle ajustes, Reservations UI polimentos.
## Outros
- .gitignore: patterns pra credenciais.
- Migrations de scenarios idempotentes.
- i18n completa pt_BR+en pra roleta/funnel.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two orthogonal cost optimizations to the Captain agent pipeline:
1. Hierarchical model routing (optimization A)
Captain::Scenario now overrides agent_model to read a dedicated
InstallationConfig CAPTAIN_OPEN_AI_MODEL_SCENARIO, falling back to the
global CAPTAIN_OPEN_AI_MODEL used by the orchestrator (Assistant).
Rationale: the orchestrator (Jasmine) does cheap triage (is this a
reservation intent? a greeting? escalate to human?) — a smaller model
handles this well. Scenarios (Daniela — reserva) run complex flows with
tool calling, strict taxonomies, and JSON schema output — they benefit
from a stronger model.
Config in this install: CAPTAIN_OPEN_AI_MODEL=gpt-4o-mini (orchestrator)
and CAPTAIN_OPEN_AI_MODEL_SCENARIO=gpt-4o (scenarios). Estimated ~60%
cost reduction vs everything on gpt-4o, preserving quality where it
matters for the business flow.
2. Conversation-level memory cache (optimization B)
MemoryPromptInjector now persists the computed memory block on
conversation.custom_attributes[captain_cached_memory_block]. First turn
computes once (embedding + pgvector query + XML formatting); subsequent
turns reuse. The customer's profile does not change during an open
conversation, so re-running the pipeline on every turn was pure waste.
Graceful fallbacks:
- Cache write failure → per-service-instance in-memory fallback still
applies.
- Cache read failure → fresh recall runs (no regression).
- Contact mismatch → invalidates cache, fresh recall runs.
When a new conversation starts, custom_attributes is empty → fresh
recall populates the cache for that conversation's lifetime.
Estimated ~80% reduction in embedding + pgvector calls during
multi-turn conversations.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds concierge.* and reservation.* Liquid variables to agent_instructions
so Sofia's orchestrator_prompt receives unit persona/knowledge/variables
and reservation data resolved from conversation.custom_attributes.current_unit_id.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implement guards following the same pass/reschedule/too_stale pattern as QuietHours.
Also fix belongs_to :conversation on Delivery to use class_name: '::Conversation' to avoid namespace resolution failure inside Captain::Lifecycle module.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add after_commit callbacks to call Captain::Lifecycle::Scheduler on
create, status change (cancelled/no_show), and check_in_at change.
Each handler wraps in rescue StandardError to preserve existing behavior.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
TDD: 16 examples passing. Adds EVENTS constant, active/for_event scopes,
and matches_reservation? with unit_ids/categorias/permanencias filters.
Also adds captain_reservation factory used by the spec.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Cobre ambos os caminhos (generate_pix_tool e PublicReservationsController):
toda reserva criada recebe um after_create_commit que posta uma mensagem
privada na conversa com os detalhes (suite, check-in, valores, ID).
Remove a criacao duplicada do PublicReservationsController.
- GeneratePixTool: envia payment_link como mensagem outgoing direta (bypassa
hallucination de [Link do Pix] placeholder pela LLM)
- GeneratePixTool: extrai email das mensagens recentes via regex e persiste
em contact.email
- GenerateReservationLinkTool: mesmo padrao de envio direto do link
- Captain::Reservation: after_create_commit callback atualiza
ultima_suite/permanencia/reserva_em/total_reservas em contact.custom_attributes
(aparece no painel lateral)
- Arquitetura corrigida: templates agora pertencem à inbox (WhatsApp),
não à unidade PIX (que é uma config financeira, não de mensagens)
- Migration: troca FK captain_unit_id -> inbox_id (up/down explícito)
- Model: belongs_to :inbox; scope for_inbox
- Controller: escopo via account.inboxes.find(inbox_id)
- Rotas: move de captain/units/:id → inboxes/:id/notification_templates
- Scanner job: joins(:conversation).where(conversations: {inbox_id:})
- UI: página /captain/notifications com seletor de inbox no topo
(chips clicáveis, templates carregam por watch no selectedInboxId)
- i18n PT/EN: novas keys INBOX_LABEL, SELECT_INBOX_HINT, EMPTY
- Adiciona check_in_at/duration_hours ao schema do tool CreateReservationIntent
para que a IA capture o horário EXATO de chegada informado pelo cliente
- Cria captain_notification_templates: label, content, timing_minutes,
timing_direction (before/after), active, position
- Implementa SendNotificationService com interpolação de variáveis
(guest_name, check_in_time, check_out_time, suite_name, unit_name)
- Implementa NotificationScannerJob (Sidekiq-cron a cada 5min) com
janela de tolerância de ±5min e idempotência via metadata JSONB
- API REST: /captain/units/:unit_id/notification_templates (CRUD)
- Store Vuex captainNotificationTemplates + API client
- UI: página de gestão de templates com editor inline e botão '+'
- Configura rota captain_settings_notifications
- i18n PT/EN para todas as strings novas
- Rubocop e ESLint: zero offenses
Implementa a página Relatórios IA com geração de análises semanais
por IA baseadas nas conversas de cada unidade/caixa de entrada.
Funcionalidades:
- Página /settings/captain/reports com dois tabs (Insights IA / Operacional)
- Botão "Gerar Análise" que enfileira job Sidekiq
- Filtro por unidade ou caixa de entrada
- Exibe insights com status (pendente/processando/concluído/falhou)
- Mostra top_topics, ai_failures e period_summary
- Estado vazio com CTA para gerar primeiro relatório
Backend:
- InsightsController com endpoints index/show/generate
- GenerateInsightsJob que processa conversas com LLM
- ConversationInsightService com chunking e merge inteligente
- Migração para adicionar inbox_id à tabela captain_conversation_insights
- Link sidebar "Relatórios IA" em /settings/captain/reports
Frontend:
- Vuex store captainReports com actions/mutations/getters
- API client CaptainReportsAPI (getInsights, generateInsight)
- i18n en e pt_BR para CAPTAIN_REPORTS.*
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Melhorias na ferramenta send_suite_images para resolver confusão entre
categoria e número de suíte:
1. **Descrições de parâmetros mais claras**
- suite_category: exemplos específicos (Hidromassagem, ALEXA, STILO)
- suite_number: apenas números (101, 102, 103) - remove exemplos confusos
2. **Instruções explícitas no system prompt**
- Seção [Galeria de Fotos] com regras claras
- Prioriza suite_category quando ambíguo
- Evita confirmações desnecessárias com cliente
3. **Mensagens de erro melhoradas**
- Sugere buscar por categoria quando busca por número falha
- Feedback mais útil para a IA
Resultado esperado:
- Cliente: "Me manda foto da suite Alexa"
- IA: busca por suite_category="Alexa" ✓ (sem pedir confirmação)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
## Linear Ticket:
https://linear.app/chatwoot/issue/CW-6081/review-feedback
## Description
Assignment V2 Service Enhancements
- Enable Assignment V2 on plan upgrade
- Fix UI issue with fair distribution policy display
- Add advanced assignment feature flag and enhance Assignment V2
capabilities
## Type of change
- [ ] Bug fix (non-breaking change which fixes an issue)
## How Has This Been Tested?
This has been tested using the UI.
## Checklist:
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented on my code, particularly in hard-to-understand
areas
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged and published in downstream
modules
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Changes auto-assignment execution paths, rate limiting defaults, and
feature-flag gating (including premium plan behavior), which could
affect which conversations get assigned and when. UI rewires inbox
settings and policy flows, so regressions are possible around
navigation/linking and feature visibility.
>
> **Overview**
> **Adds a new premium `advanced_assignment` feature flag** and uses it
to gate capacity/balanced assignment features in the UI (sidebar entry,
settings routes, assignment-policy landing cards) and backend
(Enterprise balanced selector + capacity filtering).
`advanced_assignment` is marked premium, included in Business plan
entitlements, and auto-synced in Enterprise accounts when
`assignment_v2` is toggled.
>
> **Improves Assignment V2 policy UX** by adding an inbox-level
“Conversation Assignment” section (behind `assignment_v2`) that can
link/unlink an assignment policy, navigate to create/edit policy flows
with `inboxId` query context, and show an inbox-link prompt after
creating a policy. The policy form now defaults to enabled, disables the
`balanced` option with a premium badge/message when unavailable, and
inbox lists support click-to-navigate.
>
> **Tightens/adjusts auto-assignment behavior**: bulk assignment now
requires `inbox.enable_auto_assignment?`, conversation ordering uses the
attached `assignment_policy` priority, and rate limiting uses
`assignment_policy` config with an infinite default limit while still
tracking assignments. Tests and i18n strings are updated accordingly.
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
23bc03bf75ee4376071e4d7fc7cd564c601d33d7. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Pranav <pranav@chatwoot.com>
Co-authored-by: iamsivin <iamsivin@gmail.com>
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
Co-authored-by: Shivam Mishra <scm.mymail@gmail.com>
We are expanding Chatwoot’s automation capabilities by
introducing **Conversation Workflows**, a dedicated section in settings
where teams can configure rules that govern how conversations are closed
and what information agents must fill before resolving. This feature
helps teams enforce data consistency, collect structured resolution
information, and ensure downstream reporting is accurate.
Instead of having auto‑resolution buried inside Account Settings, we
introduced a new sidebar item:
- Auto‑resolve conversations (existing behaviour)
- Required attributes on resolution (new)
This groups all conversation‑closing logic into a single place.
#### Required Attributes on Resolve
Admins can now pick which custom conversation attributes must be filled
before an agent can resolve a conversation.
**How it works**
- Admin selects one or more attributes from the list of existing
conversation level custom attributes.
- These selected attributes become mandatory during resolution.
- List all the attributes configured via Required Attributes (Text,
Number, Link, Date, List, Checkbox)
- When an agent clicks Resolve Conversation:
If attributes already have values → the conversation resolves normally.
If attributes are missing → a modal appears prompting the agent to fill
them.
<img width="1554" height="1282" alt="CleanShot 2025-12-10 at 11 42
23@2x"
src="https://github.com/user-attachments/assets/4cd5d6e1-abe8-4999-accd-d4a08913b373"
/>
#### Custom Attributes Integration
On the Custom Attributes page, we will surfaced indicators showing how
each attribute is being used.
Each attribute will show badges such as:
- Resolution → used in the required‑on‑resolve workflow
- Pre‑chat form → already existing
<img width="2390" height="1822" alt="CleanShot 2025-12-10 at 11 43
42@2x"
src="https://github.com/user-attachments/assets/b92a6eb7-7f6c-40e6-bf23-6a5310f2d9c5"
/>
#### Admin Flow
- Navigate to Settings → Conversation Workflows.
- Under Required attributes on resolve, click Add Required Attribute.
- Pick from the dropdown list of conversation attributes.
- Save changes.
Agents will now be prompted automatically whenever they resolve.
<img width="2434" height="872" alt="CleanShot 2025-12-10 at 11 44 42@2x"
src="https://github.com/user-attachments/assets/632fc0e5-767c-4a1c-8cf4-ffe3d058d319"
/>
#### NOTES
- The Required Attributes on Resolve modal should only appear when
values are missing.
- Required attributes must block the resolution action until satisfied.
- Bulk‑resolve actions should follow the same rules — any conversation
missing attributes cannot be bulk‑resolved, rest will be resolved, show
a notification that the resolution cannot be done.
- API resolution does not respect the attributes.
---------
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: iamsivin <iamsivin@gmail.com>
Co-authored-by: Pranav <pranav@chatwoot.com>
## Description
Adds the ability to sort companies by the number of contacts they have
(contacts_count) in ascending or descending order. This is part of the
Chatwoot 5.0 release requirements for the companies feature.
The implementation uses a scope-based approach consistent with other
sorting implementations in the codebase (e.g., contacts sorting by
last_activity_at).
## Type of change
- [x] New feature (non-breaking change which adds functionality)
## Available Sorting Options
After this change, the Companies API supports the following sorting
options:
| Sort Field | Type | Ascending | Descending |
|------------|------|-----------|------------|
| `name` | string | `?sort=name` | `?sort=-name` |
| `domain` | string | `?sort=domain` | `?sort=-domain` |
| `created_at` | datetime | `?sort=created_at` | `?sort=-created_at` |
| `contacts_count` | integer (scope) | `?sort=contacts_count` |
`?sort=-contacts_count` |
**Note:** Prefix with `-` for descending order. Companies with NULL
contacts_count will appear last (NULLS LAST).
## CURL Examples
**Sort by contacts count (ascending):**
```bash
curl -X GET 'https://app.chatwoot.com/api/v1/accounts/{account_id}/companies?sort=contacts_count' \
-H 'api_access_token: YOUR_API_TOKEN'
```
**Sort by contacts count (descending):**
```bash
curl -X GET 'https://app.chatwoot.com/api/v1/accounts/{account_id}/companies?sort=-contacts_count' \
-H 'api_access_token: YOUR_API_TOKEN'
```
**Sort by name (ascending):**
```bash
curl -X GET 'https://app.chatwoot.com/api/v1/accounts/{account_id}/companies?sort=name' \
-H 'api_access_token: YOUR_API_TOKEN'
```
**Sort by created_at (descending):**
```bash
curl -X GET 'https://app.chatwoot.com/api/v1/accounts/{account_id}/companies?sort=-created_at' \
-H 'api_access_token: YOUR_API_TOKEN'
```
**With pagination:**
```bash
curl -X GET 'https://app.chatwoot.com/api/v1/accounts/{account_id}/companies?sort=-contacts_count&page=2' \
-H 'api_access_token: YOUR_API_TOKEN'
```
## How Has This Been Tested?
- Added RSpec tests for both ascending and descending sort
- All 24 existing specs pass
- Manually tested the sorting functionality with test data
**Test configuration:**
- Ruby 3.4.4
- Rails 7.1.5.2
- PostgreSQL (test database)
**To reproduce:**
1. Run `bundle exec rspec
spec/enterprise/controllers/api/v1/accounts/companies_controller_spec.rb`
2. All tests should pass (24 examples, 0 failures)
## Checklist:
- [x] My code follows the style guidelines of this project
- [x] I have performed a self-review of my code
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] New and existing unit tests pass locally with my changes
## Technical Details
**Backend changes:**
- Controller: Added `sort_on :contacts_count` with scope-based sorting
- Model: Added `order_on_contacts_count` scope using
`Arel::Nodes::SqlLiteral` and `sanitize_sql_for_order` with `NULLS LAST`
for consistent NULL handling
- Specs: Added 2 new tests for ascending/descending sort validation
**Files changed:**
- `enterprise/app/controllers/api/v1/accounts/companies_controller.rb`
- `enterprise/app/models/company.rb`
-
`spec/enterprise/controllers/api/v1/accounts/companies_controller_spec.rb`
**Note:** This PR only includes the backend implementation. Frontend
changes (sort menu UI + i18n) will follow in a separate commit.
---------
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
Co-authored-by: iamsivin <iamsivin@gmail.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
Co-authored-by: Pranav <pranav@chatwoot.com>
This PR fixes the HTTP 500 timeout errors occurring when deleting SLA
policies that have large volumes of historical data.
The fix moves the deletion workflow to asynchronous background
processing using the existing `DeleteObjectJob`.
By offloading heavy cascaded deletions (applied SLAs, SLA events,
conversation nullifications) from the request cycle, the API can now
return immediately while the cleanup continues in the background
avoiding the `Rack::Timeout::RequestTimeoutException`. This ensures that
SLA policies can be deleted reliably, regardless of data size.
### Problem
Deleting an SLA policy via `DELETE
/api/v1/accounts/{account_id}/sla_policies/{id}` fails consistently with
`Rack::Timeout::RequestTimeoutException (15s)` for policies with large
amounts of related data.
Because the current implementation performs all dependent deletions
**synchronously**, Rails processes:
- `has_many :applied_slas, dependent: :destroy` (thousands)
- Each `AppliedSla#destroy` → triggers destruction of many `SlaEvent`
records
- `has_many :conversations, dependent: :nullify` (thousands)
This processing far exceeds the Rack timeout window and consistently
triggers HTTP 500 errors for users.
### Solution
This PR applies the same pattern used successfully in Inbox deletion.
**Move deletion to async background jobs**
- Uses `DeleteObjectJob` for centralized, reliable cleanup.
- Allows the DELETE API call to respond immediately.
**Chunk large datasets**
- Records are processed in **batches of 5,000** to reduce DB load and
avoid job timeouts.
