Problema observado: Daniela chamou generate_pix com arguments vazios apos
cliente informar "27/4". Tool retornou missing_fields=[check_in, amount] e
LLM caiu no fallback silenciosamente.
Correcoes:
- DDMMYYYY_REGEX agora aceita "DD/MM" sem ano (assume ano corrente, empurra
pro proximo ano se a data ja passou)
- parse_date_without_year com fallback explicito
- Instruction da scenario Daniela_Reservas (DB, scenario_id=2) atualizada
para listar todos os 4 parametros obrigatorios de generate_pix e
distinguir requires_input (erro do LLM) de success=false (erro tecnico)
Backup da instruction anterior: /tmp/daniela_instruction_backup_20260418.txt
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- 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)
- Controller grava cpf/ultima_suite/ultima_permanencia/ultima_reserva_em/total_reservas
em contact.custom_attributes (aparece no painel lateral do Chatwoot)
- GenerateReservationLinkTool exige marca/unidade/categoria/permanencia/checkin_at;
retorna erro se Jasmine chamar sem esses dados
Mirrors CheckPixPaymentTool resolve_conversation helpers. No fallback,
Jasmine so precisa passar categoria/permanencia/checkin_at - a tool
preenche nome/telefone/cpf/email a partir do contato.
Cria Captain::Tools::GenerateReservationLinkTool que constrói URL
pré-preenchida do reserva-1001 com dados coletados em conversa.
Registra entrada generate_reservation_link em tools.yml e documenta
RESERVA_1001_BASE_URL no .env.example.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
# Pull Request Template
## Description
Reply suggestions uses `search_documentation`. While this is useful,
there is a subtle bug, a user's message may be in a different language
(say spanish) than the FAQs present (english).
This results in embedding search in spanish and compared against english
vectors, which results in poor retrieval and poor suggestions.
Fixes # (issue)
This PR fixes the above behaviour by making a small llm call translate
the query before searching in the search documentation tool
## Type of change
- [x] Bug fix (non-breaking change which fixes an issue)
## How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide
instructions so we can reproduce. Please also list any relevant details
for your test configuration.
before:
<img width="894" height="157" alt="image"
src="https://github.com/user-attachments/assets/83871ee5-511e-4432-8b99-39e803759f63"
/>
after:
<img width="1149" height="294" alt="image"
src="https://github.com/user-attachments/assets/f9617d7a-6d48-4ca1-ad1c-2181e16c1f3d"
/>
test on rails console:
<img width="2094" height="380" alt="image"
src="https://github.com/user-attachments/assets/159fdaa5-8808-49d2-be5d-304d69fa97f7"
/>
## Checklist:
- [x] My code follows the style guidelines of this project
- [x] I have performed a self-review of my code
- [x] I have commented on my code, particularly in hard-to-understand
areas
- [ ] I have made corresponding changes to the documentation
- [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
- [x] Any dependent changes have been merged and published in downstream
modules
This PR is the first of many to simplify the process of building an
assistant. The new flow will only require the user’s website. We’ll
automatically crawl it, identify the business name and what the business
does, and then generate a suggested assistant persona, complete with a
proposed name and description.
This service returns the following.
Example: tooljet.com
<img width="795" height="217" alt="Screenshot 2025-10-25 at 2 55 04 PM"
src="https://github.com/user-attachments/assets/9cb3594a-9c9c-4970-a0a1-4c9c8869c193"
/>
Example: replit.com
<img width="797" height="176" alt="Screenshot 2025-10-25 at 2 56 42 PM"
src="https://github.com/user-attachments/assets/6a1b4266-aab6-455f-a5e3-696d3a8243c9"
/>
# Pull Request Template
## Linear task:
https://linear.app/chatwoot/issue/CW-4482/captain-should-be-able-to-access-private-notes-only-on-copilot
## Description
Captain should be able to access private notes (only on copilot)
## Type of change
- [x] New feature (non-breaking change which adds functionality)
## How Has This Been Tested?
## 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
---------
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
- Enable jobs by default when a copilot thread or a message is created.
- Rename thread_id to copilot_thread_id to keep it consistent with the
model name
- Add a spec for search_linear_issues service
Earlier, we were manually checking if a user was an agent and filtering
their conversations based on inboxes. This logic should have been part
of the conversation permissions service.
This PR moves the check to the right place and updates the logic
accordingly.
Other updates:
- Add support for search_conversations service for copilot.
- Use PermissionFilterService in contacts/conversations, conversations,
copilot search_conversations.
---------
Co-authored-by: Sojan <sojan@pepalo.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
This PR adds a tool to search Linear issues. If the integration is
enabled for the account, the tool will return results as expected. Also
introduces support for an `active?` method, which allows third-party
Copilot tools to be conditionally enabled based on the status of the
integration on the account.
This PR introduces the concept of a tool registry. The implementation is
straightforward: you can define a tool by creating a class with a
function name. The function name gets registered in the registry and can
be referenced during LLM calls. When the LLM invokes a tool using the
registered name, the registry locates and executes the appropriate tool.
If the LLM calls an unregistered tool, the registry returns an error
indicating that the tool is not defined.
- Fixed Firecrawl webhook payloads to ensure proper data handling and
delivery.
- Removed unused Robin AI code to improve codebase cleanliness and
maintainability.
- Implement authentication for the Firecrawl endpoint to improve
security. A key is generated to secure the webhook URLs from FireCrawl.
---------
Co-authored-by: Pranav <pranavrajs@gmail.com>
Migration Guide: https://chwt.app/v4/migration
This PR imports all the work related to Captain into the EE codebase. Captain represents the AI-based features in Chatwoot and includes the following key components:
- Assistant: An assistant has a persona, the product it would be trained on. At the moment, the data at which it is trained is from websites. Future integrations on Notion documents, PDF etc. This PR enables connecting an assistant to an inbox. The assistant would run the conversation every time before transferring it to an agent.
- Copilot for Agents: When an agent is supporting a customer, we will be able to offer additional help to lookup some data or fetch information from integrations etc via copilot.
- Conversation FAQ generator: When a conversation is resolved, the Captain integration would identify questions which were not in the knowledge base.
- CRM memory: Learns from the conversations and identifies important information about the contact.
---------
Co-authored-by: Vishnu Narayanan <vishnu@chatwoot.com>
Co-authored-by: Sojan <sojan@pepalo.com>
Co-authored-by: iamsivin <iamsivin@gmail.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
