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"
/>
## Linear Link:
https://linear.app/chatwoot/issue/CW-5636/pdf-faqs-captain-generates-faqs-in-the-english-only
## Description
PDF Faqs should be generated in the same language as set in account
## Type of change
- [ ] Bug fix (non-breaking change which fixes an issue)
## How Has This Been Tested?
This has been tested via UI, by setting account language to arabic and
upload the pdf for faq generation (pdf content in Hindi)
<img width="1045" height="1085" alt="image"
src="https://github.com/user-attachments/assets/10385181-578e-4933-afc4-4609a6abcec8"
/>
## 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>
This PR adds the ability to modify the embedding model used by Captain
AI.Previously, the embedding model was hardcoded which led to errors when
you used a different API provider which did not support that specific
embedding model.
Co-authored-by: Shivam Mishra <scm.mymail@gmail.com>
There were customer reported issues with FAQs which were generated in a
different langauge than what they were expecting. The reason behind this
was that the language of the account was not considered in the prompt
provided. If the language of the content was say Spanish, and the
account locale was english. The output was not predicable. The output
depends on the model and the execution time.
This PR would update the prompt to behave consistently with the account
locale. Even though the content provided is in a different language, it
would generate FAQs in the account locale.
Changes:
- Updated the prompt to include a detailed expectation of the FAQs
quality along with the language
- Added specs for the services where the prompt generator is called.
Tested the prompt using Phoenix playground across GPT 5, GPT 4.1, GPT
4.0. The reasoning setting for GPT 5 needs to be low so that it doesn't
generate random questions like "What was this updated?"
## Description
This PR introduces WhatsApp Embedded Signup functionality, enabling
users to connect their WhatsApp Business accounts through Meta's
streamlined OAuth flow without manual webhook configuration. This
significantly improves the user experience by automating the entire
setup process.
**Key Features:**
- Embedded signup flow using Facebook SDK and Meta's OAuth 2.0
- Automatic webhook registration and phone number configuration
- Enhanced provider selection UI with card-based design
- Real-time progress tracking during signup process
- Comprehensive error handling and user feedback
## Required Configuration
The following environment variables must be configured by administrators
before this feature can be used:
Super Admin Configuration (via
super_admin/app_config?config=whatsapp_embedded)
- `WHATSAPP_APP_ID`: The Facebook App ID for WhatsApp Business API
integration
- `WHATSAPP_CONFIGURATION_ID`: The Configuration ID for WhatsApp
Embedded Signup flow (obtained from Meta Developer Portal)
- `WHATSAPP_APP_SECRET`: The App Secret for WhatsApp Embedded Signup
flow (required for token exchange)
## How Has This Been Tested?
#### Backend Tests (RSpec):
- Authentication validation for embedded signup endpoints
- Authorization code validation and error handling
- Missing business parameter validation
- Proper response format for configuration endpoint
- Unauthorized access prevention
#### Manual Test Cases:
- Complete embedded signup flow (happy path)
- Provider selection UI navigation
- Facebook authentication popup handling
- Error scenarios (cancelled auth, invalid business data, API failures)
- Configuration presence/absence behavior
## Related Screenshots:
Fixes
https://linear.app/chatwoot/issue/CW-2131/spec-for-whatsapp-cloud-channels-sign-in-with-facebook
---------
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: iamsivin <iamsivin@gmail.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
Co-authored-by: Sojan Jose <sojan@pepalo.com>
# Pull Request Template
## Linear links:
-
https://linear.app/chatwoot/issue/CW-4479/if-image-is-sent-by-the-customer-send-it-to-openai
## Description
This pull request adds “Captain image support” to Chatwoot. It
introduces multimodal message handling so that when a customer sends an
image, Captain can forward the file to OpenAI’s vision endpoint,
generate a caption/analysis
## Type of change
Please delete options that are not relevant.
- [x] New feature (non-breaking change which adds functionality)
## How Has This Been Tested?
<img width="891" alt="image"
src="https://github.com/user-attachments/assets/c7cc98ed-cc44-4865-a53a-83d129e2fe2c"
/>
## 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: Pranav <pranav@chatwoot.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.
Show captain messages under the name of the assistant which generated
the message.
- Add support for `Captain::Assistant` sender type
- Add push_event_data for captain_assistants
- Add activity message handler for captain_assistants
- Update UI to show captain messages under the name of the assistant
- Fix the issue where openAI errors when image is sent
- Add support for custom name of the assistant
---------
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
This PR implements the following features
- FAQs from conversations will be generated in account language
- Contact notes will be generated in account language
- Copilot chat will respond in user language, unless the agent asks the
question in a different language
## Changes
### Copilot Chat
- Update the prompt to include an instruction for the language, the bot
will reply in asked language, but will default to account language
- Update the `ChatService` class to include pass the language to
`SystemPromptsService`
### FAQ and Contact note generation
- Update contact note generator and conversation generator to include
account locale
- Pass the account locale to `SystemPromptsService`
<details><summary>Screenshots</summary>
#### FAQs being generated in system langauge
#### Copilot responding in system language
</details>
---------
Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.com>
Co-authored-by: Pranav <pranav@chatwoot.com>
This PR adds service to automate account abuse detection. Currently
based on the signup name and URL, could potentially add more context
such as usage analysis, message metadata etc.
This PR ensures that only conversations from quick conversation channels
are resolved, avoiding resolutions on the email channel (we still need
to improve the UX here). It also updates the FAQ generation logic,
limiting it to conversations that had at least one human interaction.
- 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>
This pull request introduces several changes to implement and manage
usage limits for the Captain AI service. The key changes include adding
configuration for plan limits, updating error messages, modifying
controllers and models to handle usage limits, and updating tests to
ensure the new functionality works correctly.
## Implementation Checklist
- [x] Ability to configure captain limits per check
- [x] Update response for `usage_limits` to include captain limits
- [x] Methods to increment or reset captain responses limits in the
`limits` column for the `Account` model
- [x] Check documents limit using a count query
- [x] Ensure Captain hand-off if a limit is reached
- [x] Ensure limits are enforced for Copilot Chat
- [x] Ensure limits are reset when stripe webhook comes in
- [x] Increment usage for FAQ generation and Contact notes
- [x] Ensure documents limit is enforced
These changes ensure that the Captain AI service operates within the defined usage limits for different subscription plans, providing appropriate error messages and handling when limits are exceeded.
Currently, it’s unclear whether an FAQ item is generated from a
document, derived from a conversation, or added manually.
This PR resolves the issue by providing visibility into the source of
each FAQ. Users can now see whether an FAQ was generated or manually
added and, if applicable, by whom.
- Move the document_id to a polymorphic relation (documentable).
- Updated the APIs to accommodate the change.
- Update the service to add corresponding references.
- Updated the specs.
<img width="1007" alt="Screenshot 2025-01-15 at 11 27 56 PM"
src="https://github.com/user-attachments/assets/7d58f798-19c0-4407-b3e2-748a919d14af"
/>
---------
Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
This PR introduces a review step for generated FAQs, allowing a human to
validate and approve them before use in customer interactions. While
hallucinations are minimal, this step ensures accurate and reliable FAQs
for Captain to use during LLM calls when responding to customers.
- Added a status field for the FAQ
- Allow the filter on the UI.
<img width="1072" alt="Screenshot 2025-01-15 at 6 39 26 PM"
src="https://github.com/user-attachments/assets/81dfc038-31e9-40e6-8a09-586ebc4e8384"
/>
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>
