- Refactor HandleStripeEventService to better manage features by plan
- Add constants for features available in each plan tier (Startup,
Business, Enterprise)
- Add channel_instagram to Startup plan features
- Improve downgrade handling to properly disable higher-tier features
- Clean up and optimize tests for maintainability
- Add comprehensive test coverage for plan upgrades and downgrades
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Co-authored-by: Muhsin Keloth <muhsinkeramam@gmail.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.
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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"
/>
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Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.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.
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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>
- This PR adds a UI to validate the response source quality quickly. It also helps to test with sample questions and update responses in the database when missing.
Co-authored-by: Pranav Raj S <pranav@chatwoot.com>
- Reorganizing installation config settings to move more configurations into UI from environment variables
- Changes to installation config to support premium plans in the enterprise edition
- Fixes the broken premium indicator in account/show and accounts/edit page
This PR refactors the schema we introduced in #7518 based on the feedback from production tests. Here is the change log
- Decouple Inbox association to a new table inbox_response_sources -> this lets us share the same response source between multiple inboxes
- Add a status field to responses. This ensures that, by default, responses are created in pending status. You can do quality assurance before making them active. In future, this status can be leveraged by the bot to auto-generate response questions from conversations which require a handoff
- Add response_source association to responses and remove hard dependency from response_documents. This lets users write free-form question answers based on conversations, which doesn't necessarily need a response source.
This commit introduces the ability to associate response sources to an inbox, allowing external webpages to be parsed by Chatwoot. The parsed data is converted into embeddings for use with GPT models when managing customer queries.
The implementation relies on the `pgvector` extension for PostgreSQL. Database migrations related to this feature are handled separately by `Features::ResponseBotService`. A future update will integrate these migrations into the default rails migrations, once compatibility with Postgres extensions across all self-hosted installation options is confirmed.
Additionally, a new GitHub action has been added to the CI pipeline to ensure the execution of specs related to this feature.
