Scanned PDFs without a successful OCR pass (e.g. when no VLM is configured
on the KB, or when the VLM returned a "no text content" reply that the
sanitizer discarded) reach the summary and wiki-ingest pipelines with
nothing but markdown image references for content. The downstream LLM
prompts used to receive the filename, file type, and title alongside that
empty content, and the generate_summary template explicitly forbade the
model from refusing — so the model would invent a topic from the filename
alone, producing e.g. a "Canon scanner manual" summary for an MX5280.pdf
that is actually a scanned legal letter.
Two changes shut this down:
1. Drop filename / title / file-type from every LLM prompt input. The
wiki summary, knowledge extract, candidate slug, and chunk citation
prompts now see only the document content. The knowledge summary
path no longer prepends the "Document Type: X / File Name: Y" intro.
2. Add a content-sufficiency guard in front of the LLM call. A new
helper strips markdown image references and HTML image tags and
refuses to invoke the model when fewer than 50 real-text runes
remain. getSummary returns a sentinel error in that case and the
caller marks the knowledge as SummaryStatusFailed instead of
surfacing the first chunk's bare image reference. Wiki ingest skips
LLM extraction with a clear log message.
The generate_summary.yaml prompt is also updated: the "NEVER output
refusal phrases" clause is removed and replaced with an explicit
empty-content rule that returns a deterministic placeholder line.
Real OCR (Tesseract / PaddleOCR) and filename-pattern sanitisation are
intentionally left for separate PRs.
When users ask broad queries like "请整理知识库中的数据" in RAG mode,
vector/keyword search returns nothing because the query has no specific
content to match. The user needs to see what documents exist, not search
results.
- Add buildKBDocumentListing() to fetch document titles/filenames from
the knowledge base and inject them into the fallback prompt via
{{kb_documents}} placeholder
- Update fallback prompt templates (model_fallback, default_fallback_prompt)
to include document listing context so the LLM can guide users
- Improve rewrite prompt: tighten summarize vs kb_search classification,
expand kb_search to cover browse/organize/list operations, add examples
Closes#959
Revised the agent system prompt to clarify the synthesis process and the handling of factual errors. The instructions now emphasize the mandatory use of the `final_answer` tool for submitting responses and the need to flag issues before calling `final_answer`. This enhances the clarity and consistency of the agent's operational guidelines.
- Introduced a unified capability requirements system for agent tools, ensuring consistent filtering of knowledge bases based on their capabilities.
- Implemented derived `kb_filter` logic to streamline compatibility checks between agent tools and knowledge bases, reducing redundancy in configuration.
- Updated frontend components to reflect new filtering logic, including improved handling of empty states and user feedback when no compatible knowledge bases are available.
- Enhanced internationalization support by adding relevant messages for tool compatibility issues across multiple languages.
- Changed `kb_selection_mode` in agent type presets from "selected" to "all" to allow agents to access all knowledge bases by default.
- Revised prompt templates to clarify the handling of bound knowledge bases, replacing the `{{knowledge_bases}}` placeholder with a reference to the `<bound_knowledge_bases>` block in the user message's `<runtime_context>`.
- Enhanced internal logic to ensure that only searchable knowledge bases are considered during retrieval, improving the efficiency of knowledge searches.
- Added new functions for better formatting and handling of knowledge base metadata in the runtime context.
- Updated documentation and comments for clarity on the changes made to knowledge base interactions.
- Introduced extraction granularity settings in the wiki configuration, allowing users to control the level of detail in entity and concept extraction (focused, standard, exhaustive).
- Updated the UI to reflect these new options, including tooltips for better user guidance.
- Refactored the knowledge base editor to support the new extraction granularity feature, ensuring a seamless user experience.
- Improved the backend logic for wiki ingest tasks to utilize the new granularity settings, enhancing the accuracy of extracted content.
- Added tests to validate the new extraction granularity functionality and ensure robust performance across different configurations.
- Eliminated the "thinking" and "todo_write" tools from the allowed tools list in multiple sections of the `builtin_agents.yaml` file, streamlining the agent capabilities.
- This change enhances the clarity and relevance of the tools available to built-in agents, ensuring a more focused and efficient configuration.
- Changed the agent type from "custom" to "rag-qa" in the `builtin_agents.yaml` file, aligning with the new agent type presets introduced previously.
- This update enhances the configuration of built-in agents, ensuring they utilize the appropriate agent type for improved functionality and performance.
These changes contribute to a more coherent and effective agent system by standardizing agent types across configurations.
- Enhanced the agent system prompt to clarify citation formats for wiki and chunk sources, ensuring accurate inline citations.
- Introduced a new citation format section in the prompt, detailing the required syntax for both wiki pages and knowledge chunks.
- Updated the frontend to visually align wiki citations with knowledge base citations, improving user experience and consistency in citation presentation.
- Implemented truncation for long wiki labels in citations to maintain visual consistency in chat bubbles.
These changes improve the clarity and usability of citations in the agent system, enhancing the overall information retrieval process.
- Updated the agent system prompt to emphasize the use of regex for chunk searches, improving retrieval accuracy and efficiency.
- Introduced a new `Capabilities` field in the `KnowledgeBaseInfo` struct to define the retrieval surfaces available for each knowledge base, guiding the agent's strategy selection.
- Enhanced the `grep_chunks` tool to support regex queries, allowing for more flexible and powerful text pattern matching in knowledge base chunks.
- Improved JSON handling in the `RepairJSON` function to address invalid escape sequences, particularly for regex patterns, ensuring robust parsing and error handling.
- Updated documentation and comments across various files to clarify usage and expectations for regex and knowledge base capabilities.
These changes significantly enhance the agent's ability to retrieve relevant information while ensuring the integrity of input data handling.
- Added a new configuration file `agent_type_presets.yaml` to define various agent type presets, including RAG Q&A, Wiki Q&A, Hybrid (Wiki + RAG), and Data Analysis.
- Updated the `builtin_agents.yaml` to associate built-in agents with the new agent type presets, enhancing their configuration options.
- Enhanced the agent system prompt templates to reflect the new agent types and their functionalities.
- Implemented API endpoints to retrieve agent type presets, allowing for dynamic loading in the frontend.
- Updated the frontend to support agent type selection in the agent editor modal, improving user experience by auto-filling relevant configurations based on selected presets.
These changes significantly enhance the flexibility and usability of the agent system, allowing users to easily configure agents based on predefined types.
- Updated the agent system prompt to include new auxiliary tools and improved synthesis and issue flagging steps, enhancing the clarity of the workflow.
- Revised constraints to emphasize the importance of retrieving information from the wiki first and using skills when applicable.
- Expanded localization files for English, Korean, Russian, and Chinese to include new wiki tool descriptions and statuses, ensuring consistency across languages.
- Improved the agent editor modal to provide a clearer overview of tool statuses and retrieval preferences, enhancing user experience.
These changes significantly improve the functionality and usability of the agent system, particularly in relation to wiki interactions and tool management.
- Renamed the "Wiki Researcher" to "Wiki Questioner" and adjusted its description for better alignment with its functionality.
- Updated the "Wiki Fixer" to "Wiki Revising" with a revised description to reflect its focus on revising Wiki pages based on inspection issues.
- Made corresponding changes to localized names and descriptions in Chinese, Japanese, and Korean to ensure consistency across languages.
These updates enhance the clarity and usability of the built-in agents in the system.
- Updated the agent system prompt to require wiki-links in citations, improving user navigation to source pages.
- Implemented logic in AgentStreamDisplay to handle wiki link clicks and keyboard interactions, facilitating seamless navigation to knowledge base wiki pages.
- Enhanced WikiBrowser to auto-select pages based on query parameters, improving user experience when accessing specific wiki content.
- Added localization support for new error messages related to knowledge base identification, ensuring clarity in user interactions.
These changes significantly improve the usability and functionality of the wiki navigation system, providing users with a more intuitive experience when accessing and citing wiki content.
- Updated the agent system prompt to refine the workflow for fixing issues, emphasizing the need to verify if issues still exist before making edits.
- Introduced a new `embeddedMode` prop in the frontend components to manage UI behavior based on the context of use.
- Simplified issue fix prompts in multiple languages for clarity, ensuring users receive concise instructions for resolving issues.
- Enhanced the WikiBrowser component to improve the display of issues and actions, including updated icons and streamlined interaction elements.
These changes significantly improve the user experience and functionality of the Wiki Fixer agent, fostering more efficient issue resolution and content management.
- Added a new built-in agent, "Wiki Fixer," designed to repair and optimize Wiki pages based on linter issues, with multilingual support for enhanced accessibility.
- Implemented a corresponding system prompt detailing the agent's role, mission, and workflow for effective issue resolution.
- Introduced new API functions for listing and updating wiki issues, allowing for better management of content conflicts and errors.
- Enhanced the frontend with new UI elements to display pending issues and facilitate auto-fixing, improving user interaction with the Wiki content.
These changes significantly enhance the agent's capabilities for maintaining the accuracy and quality of Wiki pages, fostering a more reliable knowledge base.
- Added a new tool, `wiki_flag_issue`, enabling users to report factual errors, mixed entities, or outdated information on wiki pages.
- Updated the agent system prompt and tool definitions to include the new tool, enhancing the agent's capabilities for maintaining wiki accuracy.
- Implemented backend functionality for creating, listing, and updating the status of flagged issues, ensuring effective tracking and resolution.
These changes significantly improve the agent's ability to manage and report issues within the wiki, fostering a more reliable knowledge base.
- Added a new tool, `wiki_read_source_doc`, allowing agents to access specific source documents for detailed information retrieval when wiki page content is insufficient.
- Updated the agent system prompt and tool definitions to incorporate the new tool, enhancing the agent's capabilities for in-depth knowledge extraction.
- Modified existing tools to support the new functionality, ensuring seamless integration within the agent's workflow.
These changes significantly improve the agent's ability to provide accurate and detailed responses by leveraging source documents alongside wiki content.
- Updated the agent system prompt to clarify the workflow for knowledge retrieval, emphasizing the use of `wiki_search` and `wiki_read_page` tools for specific queries and general overviews.
- Modified the `wikiReadPageTool` to support reading multiple wiki pages simultaneously, improving efficiency in fetching content.
- Enhanced the `wikiSearchTool` to utilize PostgreSQL POSIX regular expressions for more effective search queries, allowing for complex pattern matching.
- Adjusted the `wikiPageRepository` to replace `ILIKE` with regex matching for search queries, increasing the precision of search results.
These changes significantly improve the agent's ability to retrieve and synthesize information from the wiki, enhancing user interactions and response accuracy.
- Added a new constraint to the agent system prompt emphasizing the need for fresh searches and reads for each question, ensuring up-to-date information retrieval.
- Updated the agent tool configuration to include `ToolWikiSearch` and `ToolWikiReadPage`, enhancing the agent's capabilities for accessing and utilizing wiki content effectively.
These changes improve the agent's performance in providing accurate and current responses based on wiki knowledge.
- Introduced a new built-in agent, "Wiki Researcher," designed for navigating and answering questions based on Wiki knowledge bases, complete with multilingual support.
- Added a corresponding system prompt that outlines the agent's role, mission, and workflow for effective knowledge graph traversal.
- Updated the agent configuration to include specific tools and parameters tailored for the Wiki Researcher, enhancing its functionality and user interaction.
- Removed deprecated wiki tools from the agent service to streamline the toolset and improve performance.
These changes significantly enhance the capabilities of the agent framework, providing users with a specialized tool for in-depth Wiki-based research.
- Introduced a new built-in agent, "Wiki Researcher," designed for navigating and answering questions based on Wiki knowledge bases, complete with multilingual support.
- Added a corresponding system prompt that outlines the agent's role, mission, and workflow for effective knowledge graph traversal.
- Updated the agent configuration to include specific tools and parameters tailored for the Wiki Researcher, enhancing its functionality and user interaction.
- Removed deprecated wiki tools from the agent service to streamline the toolset and improve performance.
These changes significantly enhance the capabilities of the agent system, providing users with a specialized tool for in-depth research and information retrieval from Wiki sources.
Enhanced the system prompts for the knowledge assistant to better guide users when web search is disabled. The updates clarify the assistant's role in answering questions based on the user's personal knowledge base and emphasize the importance of indicating when information may be outdated or uncertain. This aims to improve user experience and ensure accurate communication of capabilities.
- Updated the prompt template to clarify the task of understanding and rewriting user questions, improving the instructions for coreference resolution and ellipsis completion.
- Renamed sections for better clarity, changing "Rewriting Goals" to "Query Understanding."
- Added critical notes for intent classification regarding image and document attachments, ensuring accurate processing based on the presence of attachments.
- Improved examples to reflect the updated intent classification logic, enhancing the overall user experience in query handling.
- Introduced a new prompt template for document analysis, allowing the assistant to provide detailed responses based on attached documents.
- Updated the rewrite template descriptions to clarify the handling of image and document attachments, ensuring better user understanding of intent classification.
- Added a new intent type for document-only queries to improve response accuracy.
This update enhances the assistant's capabilities in processing and analyzing documents while refining the user experience in intent classification.
- Added max_input_chars configuration to limit input size for summary generation.
- Updated generate_summary.yaml to clarify summary generation steps and requirements.
- Introduced a new summary section in the document content view to display generated summaries or loading states.
- Refactored doc-content.vue to utilize computed properties for improved performance and readability.
- Enhanced knowledge base hooks to include description and summary status fields for better data handling.
- Updated internationalization files to include new summary-related labels in multiple languages.
- Updated the question generation template to clarify the role of surrounding context and main content.
- Enhanced quality rules for generated questions to better align with user search intent.
- Revised output format and added explicit instructions on what not to generate.
- Improved logging and output in the web parser for better visibility of parsed content and metadata.
- guard against empty content completion in analyzeResponse/executeLoop
with retry (max 2), nudge message, and fallback response
- propagate actual finish_reason through streaming pipeline instead of
hardcoding "stop" in streamThinkingToEventBus
- add FinishReason field to StreamResponse and streamLLMResult
- add finish marker chunk in processStreamDelta for empty content scenarios
- propagate lastFinishReason through EOF handler in processStream
- strengthen PureAgent system prompt with MCP tool guidelines and
graceful fallback instructions for unanswerable questions
- add unit tests for empty response bug and finish_reason propagation
- Updated context template to include runtime metadata such as current time and week, improving contextual awareness in user queries.
- Enhanced rewrite template with critical instructions for intent classification, ensuring that rewritten questions preserve essential entities and keywords.
- Refined intent classification logic to prioritize user intents more effectively, improving the accuracy of responses based on user queries.
- Added examples to clarify expected input and output formats for intent classification, enhancing usability for developers.
- Removed redundant instructions from the context template to simplify user guidance.
- Added context retrieval information to the system prompt, ensuring consistency in how context is presented across templates.
- Enhanced parameter casting to allow single strings to be treated as string arrays, improving flexibility in parameter handling.
- Adjusted minimum thought requirements in sequential thinking to allow for more varied input scenarios.
- Updated the assistant descriptions in various prompt templates to specify that they are developed by Tencent.
- Added new intent-specific system prompt templates for greeting, chitchat, follow-up, image analysis, summarization, and web search unavailability scenarios.
- Enhanced citation rules in the system prompts to ensure proper inline citation formatting.
- Refactored the rewrite prompt to classify user intent more accurately and output structured JSON responses.
- 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.
- 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.
- 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
Convert all LLM-facing prompts, templates, and tool output labels to
language-neutral English so the model responds in the user's input
language. Update frontend relevance/match-type mappings to match.
Output language strategy:
- User-facing: reply in user's input language
- Vector-indexed: match source document language
- LLM-internal: fixed English
- Updated `config.yaml` to adjust conversation parameters for improved search performance.
- Added a new `knowledgeSemanticSearch` API function for semantic search capabilities.
- Introduced new SVG icons for search functionality.
- Created `KnowledgeSearch.vue` component for the search interface, including query input and result display.
- Enhanced menu and routing to support navigation to the new knowledge search page.
- Updated internationalization files to include translations for the knowledge search feature.
This update provides users with a powerful tool to perform semantic searches across knowledge bases, improving content discoverability.
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