- Implemented a new API method to fetch suggested questions based on an agent's knowledge bases, enhancing user interaction in the chat interface.
- Introduced data structures for request and response handling, including options for limiting results and specifying knowledge base IDs.
- Enhanced query construction to support flexible filtering of suggested questions, improving the overall chat experience.
- Updated the CreateKnowledgeFromFile function to include a new optional `channel` parameter, allowing for better tracking of knowledge entry sources.
- Enhanced documentation to reflect the addition of the channel parameter, improving clarity for future developers.
- Introduced a new `channel` field in the Knowledge struct and associated request types to track the source channel (e.g., "web", "api", "browser_extension").
- Updated various frontend components to display channel information and enhance user experience with channel labels.
- Enhanced localization files to support channel labels in English and Chinese.
- Modified backend services and database migrations to accommodate the new channel feature, ensuring consistent tracking across knowledge entries.
- Refactored related functions to integrate channel handling, improving overall knowledge management and context.
- Introduced a new `channel` field in multiple request and message structures to track the source channel (e.g., "web", "api", "im").
- Updated frontend API calls and chat components to include the `channel` parameter, ensuring consistent channel tracking in user messages.
- Enhanced localization files to support channel labels in English, Korean, Russian, and Chinese.
- Added database migration scripts to incorporate the `channel` column in the messages table, facilitating the storage of channel information.
- Refactored related functions and components to accommodate the new channel feature, improving overall message context and tracking.
- Implemented ClearKnowledgeBaseContents function in the Client to asynchronously delete all knowledge entries while preserving the knowledge base.
- Added corresponding ClearKnowledgeBaseContents handler in the KnowledgeHandler to validate access and enqueue deletion tasks.
- Updated router to include a new DELETE endpoint for clearing knowledge base contents.
- Added support for SO_REUSEPORT in the listenWithRetry function to improve port binding during hot-reloads.
- Implemented graceful shutdown by closing the listener immediately upon receiving a shutdown signal, allowing for quicker port release.
- Updated logging to provide clearer feedback during server shutdown and error handling.
When an MCP tool returns image content items, the agent engine now
automatically generates text descriptions using the configured VLM
model and appends them to the tool result message. This makes image
content accessible to LLMs that receive tool results as text.
Images are not passed directly to the LLM because Chat Completions API
does not reliably support images in tool role messages across providers
(tested: gpt-5.4 silently ignores them, Qwen3.5 processes them).
Changes:
- Add Images field to ToolResult for preserving base64 data URIs
- Add extractContentAndImages() with MIME whitelist, size/count limits
- Add ImageDescriberFunc injection to AgentEngine (no vlm package dep)
- Add describeImages() with graceful error handling and ctx cancellation
- Add VLM model injection in CreateAgentEngine (same pattern as
SetAppConfig/SetSkillsManager)
- Redact image base64 from Data map to prevent log/SSE exposure
- Added support for image attachments in agent Q&A requests, enabling multimodal chat capabilities.
- Introduced a new UserInfo struct to represent user information in API responses, improving user data management.
- Updated existing request structures to include image handling, enhancing the overall functionality of the chat system.
These changes significantly improve the application's ability to handle multimodal interactions and manage user data effectively.
- Introduced a new package for managing custom agents, including CRUD operations for agent creation, retrieval, updating, and deletion.
- Implemented API endpoints for listing agents and retrieving agent placeholders.
- Added data structures for agent configuration and requests, enhancing the overall agent management capabilities.
- Enhanced the client with methods to interact with the new agent management features, improving user experience in managing agents.
These changes significantly expand the application's functionality for handling custom agents, providing users with a comprehensive toolset for agent management.
- Added a new `StorageProviderConfig` struct to the `KnowledgeBase` for improved storage provider management, separating provider selection from storage credentials.
- Updated the `UnmarshalJSON` method to auto-populate `StorageProviderConfig` from the legacy `StorageConfig` for backward compatibility.
- Refactored various service methods to utilize the new storage provider configuration, ensuring a smooth transition from the legacy system.
- Implemented database migrations to add the `storage_provider_config` column and migrate existing data, maintaining compatibility with historical knowledge bases.
This update enhances the clarity and flexibility of storage configurations while ensuring compatibility with existing data structures.
- Updated the `KnowledgeBase` struct to rename `cos_config` to `storage_config` for consistency across the application.
- Implemented a custom `UnmarshalJSON` method to maintain backward compatibility with legacy responses that still use `cos_config`.
- Modified various frontend components to reflect the new `storage_config` naming, ensuring seamless integration with the updated backend structure.
- Enhanced Dockerfiles and service configurations to support the new storage engine setup.
This update improves the clarity of storage configurations and ensures compatibility with existing data structures.
cn-kali-team