- Updated the AgentEngine to emit final answer events even when the LLM stops without calling the final_answer tool, ensuring the frontend can render responses correctly.
- Added fallback logic in the AgentStreamHandler to emit final answers as fallback events when no answer events are streamed, improving robustness in edge cases.
- Updated DeleteChunks method to split IDs into batches of 5000 to prevent MySQL Error 1390.
- Added unit tests for CalculateFAQContentHash to ensure consistent hashing behavior across various scenarios, including normalization and case sensitivity.
- Removed the validation for a maximum of 10 knowledge IDs in GetDocumentInfoTool to allow more flexibility.
- Adjusted pagination settings in chunk retrieval to limit results to 1 per request for more granular control.
- Updated the searchChunks method to use case-insensitive matching based on the database type, improving compatibility across different systems.
- Enhanced query structure for better readability and maintainability.
- Updated the message streaming logic to handle <think> blocks more effectively, ensuring proper formatting for Feishu.
- Introduced a new transformThinkBlocks function to convert <think> content into Feishu-compatible markdown blockquotes.
- Improved the handling of tool call events to prevent duplicate processing and ensure correct message formatting.
- Enhanced documentation for permission configuration in the IM channel setup.
- Updated formatToolCallStart and formatToolCallResult functions to return plain-text lines instead of Markdown snippets for better integration within <think> blocks.
- Introduced briefToolSummary function to extract concise summaries from tool output.
- Enhanced handleMessageStream to manage <think> blocks more effectively during agent interactions.
- Added handling for server-side events in WeCom long connection management to improve reconnection logic.
- Enhanced the SummaryGenerationPayload struct to include a Language field for better localization.
- Updated enqueueSummaryGenerationTask and ProcessSummaryGeneration methods to handle language context in summary generation tasks.
- Modified the session title generation template to focus on extracting only the intent of the user's question.
- Enhanced the session service to include language context when generating titles.
- Updated middleware to inject language into the request context.
- Added a new test suite for validating SSRF safety of URLs.
- Added critical language rules to fallback response templates to ensure consistent language usage in responses.
- Updated logger context to include language and session-related keys for improved context management.
- Added timezone and language settings to the environment configuration.
- Introduced built-in agent configurations with multilingual support for various agents.
- Updated Docker Compose to utilize new environment variables for timezone and language.
- Created new prompt templates for question generation, summary generation, and keywords extraction.
Add specific error handling for 'Endpoint url cannot have fully qualified paths'
to guide users to remove http:// or https:// prefix from endpoint URL."
Table-driven tests for sanitizeManualDownloadFilename covering normal titles,
each dangerous character replacement (/ \ " \n \r), blank/whitespace-only
titles falling back to "untitled.md", and safe passthrough of = ; characters.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Use mime.FormatMediaType for both attachment and inline Content-Disposition
headers in DownloadKnowledgeFile and the preview handler, preventing header
injection via a user-controlled knowledge title (e.g. containing ';' or '=')
- Extract inline sanitize logic into sanitizeManualDownloadFilename helper and
add a blank/whitespace-only title fallback to "untitled" so the download
never produces a degenerate ".md" filename
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add an early-return branch that streams manual knowledge content
(stored as Markdown in Metadata.content) directly as a .md file,
avoiding the runtime error that occurred when FilePath is empty.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Introduced MessageContentPart and ImageURL types to allow for multi-content messages that can include both text and images.
- Updated the Message struct to include a MultiContent field for handling multiple message parts.
- Modified the ConvertMessages function to prioritize processing of multi-content messages, improving the handling of user messages with images.
These changes enhance the flexibility and functionality of chat messages, enabling richer content delivery.
- Removed outdated instructions regarding the `skip_kb_search` field in the conversation configuration.
- Clarified requirements for `image_description` to ensure it is non-empty when images are present.
- Adjusted the handling of message parts in the chat pipeline to improve image processing.
- Deleted unused migration files related to image handling in the database schema.
These changes enhance the clarity and functionality of the conversation and image processing logic.
- Introduced a new IMChannelPanel component for managing WeCom and Feishu channels.
- Added CRUD operations for IM channels, including create, update, delete, and list functionalities.
- Enhanced the backend with new API endpoints for IM channel management.
- Updated documentation to reflect changes in IM integration and channel management.
- Improved localization support for new IM-related UI elements across multiple languages.
- Added blur handling for the textarea in the Input-field component to prevent errors when navigating away from the component.
- Improved the sanitization process in the AgentStreamDisplay component to preserve specific tags during markdown processing, ensuring accurate content display.
- Introduced new methods for handling FAQ-specific post-processing in the knowledge base service, optimizing retrieval and filtering of search results.
These changes enhance user experience by improving input handling and content rendering in the chat interface.
- Introduced a new `QARequest` type to streamline parameters for KnowledgeQA and AgentQA service calls, replacing multiple parameters with a single structured request.
- Updated the `KnowledgeQA` and `AgentQA` methods in the session service interface to accept the new `QARequest` type, enhancing clarity and maintainability.
- Refactored the request parsing logic to utilize the new `buildQARequest` method, improving code organization and reducing redundancy.
- Implemented a new `resolveAgent` method to simplify custom agent resolution logic, enhancing readability and maintainability.
These changes improve the overall structure and usability of the QA request handling, facilitating easier updates and better code organization.
- Introduced a new parameter `webSearchEnabled` to the `AgentQA` method, allowing for conditional web search capabilities.
- Updated the session service interface and handler to accommodate the new parameter, enhancing the flexibility of the AgentQA process.
- Adjusted the configuration to ensure that web search functionality can be toggled based on the provided input.
These changes improve the AgentQA feature by enabling users to control web search integration, enhancing the overall user experience.
- Introduced a new method to resolve embedding model IDs to their actual identity keys (name + endpoint), enabling optimal grouping of knowledge bases across different tenants.
- Updated the concurrent search functionality to utilize the resolved model keys, improving the efficiency of query embedding computations.
- Refactored search target grouping to reduce redundant API calls and enhance performance during hybrid searches.
These changes significantly improve the knowledge search process by ensuring that embeddings are computed efficiently and reused across multiple knowledge bases sharing the same underlying model.
- Enhanced the concurrent search functionality to group search targets by their embedding model, allowing for a single computation of query embeddings per model.
- Implemented batch fetching of knowledge base records to improve efficiency and reduce redundant API calls.
- Updated the HybridSearch method to support multiple knowledge base IDs, enabling combined retrieval for full-KB targets and improving overall search performance.
- Added a new GetQueryEmbedding method to compute embeddings for queries, facilitating reuse across multiple knowledge bases sharing the same model.
These changes significantly enhance the performance and efficiency of the knowledge search process, reducing API calls and improving response times.
- Replaced LLMConfig with model_id in TextRelationExtractionRequest and FabriTextRequest interfaces.
- Updated related API handlers to retrieve chat models based on model_id.
- Adjusted frontend components to pass model_id instead of LLMConfig, enhancing clarity and simplifying the configuration process.
These changes streamline the model configuration process across the application, improving maintainability and usability.
- Renamed the input parameter from "pattern" to "patterns" to reflect that multiple text patterns can be provided.
- Updated the schema definition and input structure accordingly to ensure consistency in handling multiple patterns.
- Enhanced error logging to specify "patterns" when no valid input is provided, improving clarity for debugging.
These changes improve the functionality of the grep_chunks tool by allowing users to search for multiple patterns simultaneously.
- Implemented a new API endpoint to clear all messages in a session, enhancing session management capabilities.
- Updated the frontend to include a clear messages option in the session menu, allowing users to easily remove chat history.
- Added localization support for the new feature in English, Korean, Russian, and Chinese, improving accessibility for a broader audience.
- Enhanced the chat view to handle session message clearing events, ensuring a responsive user experience.
These changes significantly improve the user experience by providing a straightforward method to manage session messages effectively.
- Refactored the chat model ID selection process to prioritize the request's summaryModelID, followed by the custom agent's ModelID, and finally a fallback to the default model selection.
- Improved logging to provide clearer insights into which model ID is being used during the selection process.
- Removed the deprecated selectChatModelIDWithOverride function to streamline the codebase and enhance maintainability.
These changes improve the flexibility and clarity of chat model selection, ensuring a more robust session management experience.
- Added support for updating the description field in knowledge records, improving the detail and context of knowledge entries.
- Refactored knowledge base update logic to ensure configuration updates are only applied when provided, enhancing robustness and preventing unnecessary overwrites.
- Modified the request structure to make the configuration field optional, allowing for more flexible updates.
These changes improve the knowledge management system by allowing for more comprehensive updates and better handling of configuration data.
- Updated configuration to support three tasks in the assistant's prompt: rewriting questions, classifying intent for knowledge base retrieval, and analyzing attached images.
- Implemented backend functionality to update image descriptions in user messages, ensuring that image context is preserved across conversation history.
- Modified session handling to return complete session data after updates, improving the accuracy of session information provided to users.
- Enhanced the chat pipeline to include detailed image descriptions and manage image captions effectively, enriching user interactions with visual content.
These improvements significantly enhance the application's multimodal capabilities, providing a more robust and user-friendly experience when handling image-based queries.
- Updated configuration to improve handling of multimodal queries, including clearer guidelines for when to use [NO_SEARCH] in responses.
- Enhanced the user interface to include dedicated sections for multimodal configuration, allowing users to manage image uploads and related settings more effectively.
- Added localization support for new multimodal features in both English and Chinese, improving accessibility for a broader audience.
- Implemented backend changes to support image analysis and extraction of OCR text, enriching the interaction experience for users.
These enhancements significantly improve the application's capability to process and respond to multimodal queries, fostering a more interactive and user-friendly environment.
- Added functionality for image uploads in chat, allowing users to attach images for multimodal Q&A.
- Enhanced the input field to handle image selection via drag-and-drop and paste, with validation for file types and sizes.
- Updated the backend to process images alongside text queries, including support for image analysis.
- Introduced new UI components for image previews and management, improving user interaction with uploaded content.
- Added localization strings for image upload features and error messages, enhancing accessibility for users in multiple languages.
These changes significantly improve the chat experience by enabling users to incorporate images into their queries, facilitating richer interactions and responses.
- Updated the summary length requirement for document summarization to 100-300 words.
- Added new localization strings for missing storage engine notifications in English, Korean, Russian, and Chinese.
- Implemented UI warnings for users regarding storage engine configuration when files exist, including confirmation dialogs for storage engine changes.
- Enhanced backend validation to ensure a storage engine is configured before allowing content uploads.
These changes improve user experience by providing clearer guidance on storage engine requirements and enhancing the overall functionality of the knowledge base management system.
- add StreamSender interface and streaming output for WeCom/Feishu
- refactor WeCom adapter into separate webhook and websocket files
- add output_mode config for stream/full toggle per platform
- add stream flush batching, dedup cleanup, and think-block filtering
- improve context template to handle irrelevant retrieved info gracefully
- add IM streaming integration tests
- add IM integration documentation with quick start guide
- support webhook and websocket modes for both platforms
- add im_channel_sessions migration for channel-session mapping
- register IM adapters and callback routes
- update config and docker-compose for IM env vars
Prevents 400 Bad Request errors when uploading documents with large
tables or code blocks by adding hard limits in buildUnitsWithProtection
and mergeUnits functions.
- Introduced a new method to resolve embedding model IDs to their actual identity keys (name + endpoint), enabling optimal grouping of knowledge bases across different tenants.
- Updated the concurrent search functionality to utilize the resolved model keys, improving the efficiency of query embedding computations.
- Refactored search target grouping to reduce redundant API calls and enhance performance during hybrid searches.
These changes significantly improve the knowledge search process by ensuring that embeddings are computed efficiently and reused across multiple knowledge bases sharing the same underlying model.
- Enhanced the concurrent search functionality to group search targets by their embedding model, allowing for a single computation of query embeddings per model.
- Implemented batch fetching of knowledge base records to improve efficiency and reduce redundant API calls.
- Updated the HybridSearch method to support multiple knowledge base IDs, enabling combined retrieval for full-KB targets and improving overall search performance.
- Added a new GetQueryEmbedding method to compute embeddings for queries, facilitating reuse across multiple knowledge bases sharing the same model.
These changes significantly enhance the performance and efficiency of the knowledge search process, reducing API calls and improving response times.
- Replaced LLMConfig with model_id in TextRelationExtractionRequest and FabriTextRequest interfaces.
- Updated related API handlers to retrieve chat models based on model_id.
- Adjusted frontend components to pass model_id instead of LLMConfig, enhancing clarity and simplifying the configuration process.
These changes streamline the model configuration process across the application, improving maintainability and usability.
wizardchen