Add a danger-themed reset button next to the existing copy/eye buttons
on the API Info settings page. Confirmation dialog warns that the old
key is revoked immediately; on success the new plaintext key replaces
the displayed value and is auto-revealed so the user can copy it.
Backend: new POST /api/v1/tenants/:id/api-key handler that wraps the
existing TenantService.UpdateAPIKey; access is gated by the same
authorizeTenantAccess check as other tenant endpoints. The handler
returns the freshly generated plaintext key, while the encrypted form
is persisted to the database.
Frontend: new resetTenantApiKey API client, reset button + confirm
dialog wiring in ApiInfo.vue, plus matching i18n entries for zh-CN,
en-US, ko-KR, and ru-RU.
Backend
- BatchDeleteKnowledge handler now enqueues an asynq
TypeKnowledgeListDelete task instead of calling DeleteKnowledgeList
synchronously, matching the pattern used by ClearKnowledgeBaseContents.
Avoids long HTTP timeouts on large batches and shares the existing
async cleanup pipeline.
- Extract a small enqueueKnowledgeListDelete helper on KnowledgeHandler
so BatchDeleteKnowledge and ClearKnowledgeBaseContents share the
payload/marshal/enqueue boilerplate.
Frontend (DocumentListView)
- softer outer border and lighter row separators
- header background switched to the page tone with placeholder-color
text, reducing institutional gray
- selected row uses an inset left accent bar and a quieter brand tint
instead of a saturated background; hover stays neutral
- consolidate the .row-more-btn { opacity: 1 } rule that was duplicated
across the hover and selected blocks
Add a list-view alongside the existing card grid (issue #957) and
multi-select batch delete (issue #1045) for documents inside a knowledge
base. The two views are toggled from the toolbar and the preference is
persisted in localStorage. Selection state is shared between views with
shift-range support and a sticky batch-action bar.
Backend
- POST /api/v1/knowledge/batch-delete: validates KB scope and editor
permission, then delegates to the existing DeleteKnowledgeList service
which already cascades vector/file/graph cleanup. Caps batch size at
200 and uses a single GetKnowledgeBatch call for membership validation.
Frontend
- DocumentListView: table layout with sticky header, checkbox column
(with indeterminate select-all), file-type icons, status badges,
per-row action menu.
- DocumentBatchBar: floating pill bar with selected count and delete
action; appears when items are selected.
- KnowledgeBase.vue: view-mode toggle, selection state, batch-delete
confirmation dialog, hover/active checkbox overlay on existing cards.
- Extracted formatFileSize and getFileIcon to utils/files.ts to share
with the new list view and avoid further drift from the existing
inline copies.
- i18n keys added for zh-CN, en-US, ko-KR, ru-RU.
Closes#1045Closes#957
Previously the Langfuse integration only traced in-process HTTP requests
(chat / search / eval), so file uploads and every downstream asynq task
(document parse, chunk embedding, OCR/VLM, summary / question gen, wiki
ingest, datasource sync, etc.) produced either disconnected shallow
traces or no observation at all.
This change threads one trace end-to-end:
- tracer: add SPAN observation type and StartSpan; add ResumeTrace so a
worker can attach to an upstream trace without emitting a duplicate
trace-create; StartGeneration now auto-picks parentObservationId from
ctx so nested trace -> span -> generation trees render correctly.
- types.TracingContext + LangfuseTracingCarrier: embed on all 17 asynq
payloads so trace_id / parent_obs_id / user_id / session_id serialise
into every job.
- langfuse.InjectTracing: injected at 28 enqueue sites before json.Marshal
so the HTTP-layer trace survives the Redis hop.
- langfuse.AsynqMiddleware: mux.Use hook that peeks the payload, either
resumes the upstream trace or opens a standalone asynq.<type> trace
for scheduled jobs, and wraps the handler in a SPAN with task metadata
(id / queue / retry / payload_bytes) plus ERROR level on failure.
- GinMiddleware.shouldTrace: whitelist ingestion / knowledge-mutation /
FAQ / wiki / datasource endpoints so the root trace actually starts.
- Tests: tracer_test.go covers span nesting, error status, and
ResumeTrace no-trace-create guarantee; asynq_test.go covers
InjectTracing round-trip, middleware resume path, and standalone
trace fallback.
- Docs: docs/Langfuse\u96c6\u6210.md now lists the covered task types
and documents the cross-process propagation model.
No behavioural change when Langfuse is disabled (all new code paths are
no-ops and carriers serialise to empty strings with omitempty).
The share-time validation forced every agent bound to a knowledge base to
have a rerank model configured, which blocked Wiki-type agents
(`wiki_search` / `wiki_read_page` / …) from being shared even though they
never invoke the reranker at runtime.
Align the check with the runtime logic in `session_agent_qa.go`: only
require a rerank model when the agent's allowed_tools include
`knowledge_search` (or rely on the default tool set, which contains it).
Wiki-only agents can now be shared without a rerank model; hybrid / RAG
agents still enforce the requirement.
Also update the handler error message to reflect the new rule.
Introduce per-model `custom_headers` config (similar to OpenAI Python SDK's
`extra_headers`) so users can inject gateway auth tokens, trace IDs, etc.
into every outbound API request for chat / embedding / rerank / VLLM / ASR
models. Reserved headers like Authorization / Content-Type are always
preserved to avoid breaking auth or signing flows.
Along the way, unify production and "test connection" paths onto a single
`ConfigFromModel(*types.Model, appID, appSecret)` constructor in each
model package. Both `service.modelService.GetXxxModel` and the four
`handler.initialization.Check*Model` / `TestEmbeddingModel` endpoints now
go through the same field mapping, so new parameters only need to be
added in one place.
Backend:
- Add `CustomHeaders map[string]string` to `types.ModelParameters` with
JSON/YAML `omitempty` tags for forward compatibility.
- New `internal/utils/extraheaders.go` providing `ApplyCustomHeaders`
(for hand-rolled HTTP paths) and `WrapHTTPClientWithHeaders` /
`CustomHeadersRoundTripper` (for SDK paths like go-openai). Reserved
headers (Authorization, api-key, Content-Type, Accept, Host,
Content-Length, User-Agent) are filtered out.
- Inject headers in every remote model path: chat (SDK + raw HTTP),
embedding (openai/aliyun/jina/volcengine/nvidia/azure_openai), rerank
(remote_api/aliyun/jina/nvidia/zhipu — via shared `customHeaderSetter`
interface), VLM and ASR (via http.Client wrapping).
- Add `ConfigFromModel` to each of chat/embedding/rerank/vlm/asr,
consolidating field mapping (ExtraConfig, CustomHeaders, InterfaceType
defaulting, WeKnoraCloud credentials, etc.) and cover it with
dedicated unit tests per package.
- Refactor `service/model.go`: replace ~100 lines of hand-written Config
literals with one-line `ConfigFromModel` calls; drop the now-unused
`stringMapToAnyMap` helper.
- Refactor `handler/initialization.go` test-connection endpoints: merge
four ad-hoc request structs into one `ModelTestRequest`, extract
`buildTestModel` and `resolveTenantWeKnoraCloudCreds` helpers, and
route all four endpoints through `ConfigFromModel` + `NewXxx` so the
test path is now behaviorally identical to production.
Frontend:
- Add `custom_headers` to the `ModelConfig` API type.
- `ModelEditorDialog.vue`: new Key-Value header editor (add/remove rows),
auto-converts map <-> array when loading/saving, and passes the
current header set to the `Test Connection` button so the preview
reflects exactly what production will send.
- `ModelSettings.vue`: serialize the header array back into a map for
the backend (drops empty rows).
- i18n: add custom-header labels / descriptions / placeholders to
zh-CN, en-US, ru-RU, ko-KR.
Tests:
- `internal/utils/extraheaders_test.go` covers reserved-header filtering
and round-tripper wrapping (including nil-client fallback).
- New `config_from_model_test.go` in chat / embedding / rerank / vlm /
asr verifies all fields (CustomHeaders, ExtraConfig, InterfaceType
defaulting, AppID/AppSecret) are propagated end-to-end.
- 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.
- Deleted the RebuildKnowledgeBaseIndex function from the knowledge service, which was responsible for reprocessing all documents in a knowledge base.
- Removed the RebuildIndex handler from the KnowledgeBaseHandler, eliminating the endpoint that triggered the rebuild process.
- Updated the router to remove the route for rebuilding the index, streamlining the knowledge base management functionality.
- 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.
- Introduced multiple new documentation files, including a complete documentation index, quick reference guides, and comprehensive analyses of the knowledge base and wiki settings.
- The new files provide detailed insights into the architecture, data models, API endpoints, and implementation strategies for the knowledge base and wiki functionalities.
- Enhanced navigation and usability for developers and team members by summarizing key components, configurations, and data flows related to the knowledge base system.
These additions significantly improve the clarity and accessibility of documentation, supporting better onboarding and reference for current and future team members.
- 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 functionality to enable updates for the `wiki_config` field in the `KnowledgeBaseConfig` struct. This includes:
1. **Type Definition Update**: Added `WikiConfig` field to `KnowledgeBaseConfig` to allow JSON unmarshaling during update requests.
2. **Service Layer Enhancement**: Updated the `UpdateKnowledgeBase` method to persist changes to `wiki_config` when provided in the request.
3. **Frontend TypeScript Adjustments**: Enhanced type definitions in the frontend to support `wiki_config`, improving IDE autocomplete and type checking.
This fix addresses the critical issue where `wiki_config` was not being updated due to its absence in the `KnowledgeBaseConfig` struct, ensuring proper handling of wiki configurations during knowledge base updates.
This commit introduces the complete wiki feature for WeKnora, enabling AI-powered wiki page generation and management. The implementation includes:
**Backend Changes:**
- Wiki data model: WikiPage type with support for multiple page types (Summary, Entity, Concept, Index, Log)
- Database schema: wiki_pages table with full migration support
- WikiPageService: CRUD operations and page management
- WikiPageRepository: GORM-based persistence layer
- Wiki ingest pipeline: Automated generation of wiki pages from knowledge documents
* Summary page generation using LLM
* Entity and concept extraction in a single LLM call
* Synthesis opportunity detection
* Index page rebuilding
* Log page maintenance
- Wiki boost feature: Enhance chat retrieval with wiki context
- Wiki linting: Maintenance and validation utilities
- Agent wiki tools: Enable agents to query and interact with wiki pages
- Wiki prompts: Comprehensive LLM prompt templates for all wiki generation tasks
- Language support: Reuse existing middleware language infrastructure for LLM prompts
**Frontend Changes:**
- Wiki browser UI: View all wiki pages with filtering and search
- Wiki API client: Knowledge base wiki management endpoints
- Knowledge base editor: Configure wiki settings (language, auto-ingest, synthesis model)
- i18n updates: Support for English, Korean, Russian, and Chinese interfaces
**Configuration:**
- Container DI: Wire up all wiki services
- Router: Register wiki API endpoints
- Task handling: Support async wiki ingest tasks
**Testing:**
- Unit tests for wiki page types
- Service layer tests
- Endpoint tests for wiki operations
- Integration tests with LLM mocking
**Documentation:**
- Language refactoring analysis and guides
- Implementation completion reports
- Quick reference guides for developers
**Key Features:**
✅ LLM-powered wiki page generation from documents
✅ Multi-language support (9+ languages)
✅ Automatic extraction of entities and concepts
✅ Synthesis opportunity detection
✅ Index and log page maintenance
✅ Progressive wiki building across multiple documents
✅ Agent-based wiki interaction
✅ Chat retrieval enhancement with wiki context
✅ Full frontend UI for wiki browsing
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Added validation for bucket name format to ensure it meets specified criteria, returning an error message for invalid formats.
- Simplified error messages for MinIO client creation and bucket auto-creation failures, enhancing clarity for users.
- Updated success messages to reflect the correct status of bucket creation without public-read policy settings.
This update improves the robustness of the MinIO integration and enhances user feedback during storage checks.
- Replaced references to Docreader credentials with WeKnoraCloud credentials across multiple services and handlers.
- Introduced a new CredentialsConfig structure to manage third-party provider credentials, specifically for WeKnoraCloud.
- Updated database schema to include a credentials column for storing WeKnoraCloud AppID and AppSecret.
- Enhanced methods for retrieving and utilizing WeKnoraCloud credentials, ensuring proper encryption and decryption during storage and retrieval.
This update improves the management of WeKnoraCloud credentials, streamlining the integration and enhancing security measures.
- Changed 'Audio Processing' to 'Audio & Video' in English, Korean, Russian, and Chinese translations to reflect expanded functionality.
- Updated ASR configuration titles and descriptions to include video processing capabilities, enhancing user understanding of the feature.
- Renamed file type labels from 'Audio Files' to 'Audio & Video' across multiple languages for consistency and clarity.
- Enhanced document upload handling in the KnowledgeBase to filter unsupported file types, providing user feedback for filtered images, videos, and audio files.
This update improves the internationalization of the application and clarifies the capabilities of the ASR feature.
- Improved error handling in the attachment processing logic by providing detailed XML-like error messages for text, audio, and document processing failures.
- Updated the audio file content format to use XML tags for better structure, replacing the previous placeholder format.
This update enhances the clarity of error reporting and improves the overall structure of attachment content.
- Introduced a new WeKnoraCloudSettings component for managing APPID and APPSECRET.
- Added functionality to save WeKnoraCloud credentials without automatically creating models.
- Implemented credential status checks across various components to provide user feedback on configuration status.
- Updated API endpoints to handle credential saving and status retrieval.
- Enhanced UI elements to guide users in configuring WeKnoraCloud settings.
This update improves the integration of WeKnoraCloud by allowing users to manage their credentials more effectively and receive real-time feedback on their configuration status.
Wire VectorStoreService to HTTP with 8 endpoints: types metadata, CRUD
(create/list/get/update/delete), and connection testing (raw + by ID).
Register routes, DI container bindings, and add API documentation.
- Extended the IMChannel interface to include WeChat as a platform, supporting long-polling mode and full output mode.
- Implemented WeChat QR code login functionality, allowing users to authenticate via a QR code.
- Added new API endpoints for generating and polling the status of WeChat QR codes.
- Updated the frontend to handle WeChat-specific UI elements and interactions, including dynamic form adjustments based on the selected platform.
- Enhanced localization files to include WeChat-related translations.
- Introduced a WeChat adapter for message handling and file downloading via the iLink Bot API.
Go 1.24 has an internal compiler race condition on Windows that causes
'package without types' errors during parallel compilation. Setting
GOFLAGS="-p=1" serializes the build and avoids triggering the bug.
- Introduced a new audio file, asr_test.wav, for ASR testing purposes.
- Created embed.go to embed the ASR test audio file as a byte slice.
- Updated initialization.go to utilize the embedded ASR test audio for model transcription checks.
- Implemented a new function to check ASR model connectivity via the /v1/audio/transcriptions endpoint.
- Updated ModelEditorDialog.vue to integrate ASR model checks and adjust UI elements accordingly.
- Enhanced localization files to reflect changes in ASR terminology and descriptions.
- Added ASR configuration options to the knowledge base and updated related migration scripts.
- Refactored model handling to support ASR as a distinct model type in the application.
- Updated UpdateKnowledgeTagBatch method to include an authorizedKBID parameter for restricting updates to specific knowledge items.
- Implemented validation to ensure that all requested knowledge IDs belong to the authorized knowledge base.
- Enhanced GetKnowledgeBatch and UpdateKnowledgeTagBatch methods to incorporate agent-specific knowledge base access restrictions.
- Added utility functions to resolve allowed knowledge base IDs for agents based on their configuration.
- Keep strict mode blocking all direct IPs (IPv4 and IPv6 uniformly)
- Unify all SSRF call sites to use ValidateURLForSSRF (whitelist-aware)
- Add Teredo (2001:0000::/32) and 6to4 (2002::/16) tunnel detection
- Make redirect handler and DNS pinning respect SSRF_WHITELIST
- Unexport isSSRFSafeURL to prevent future callers bypassing whitelist
- Add scheme validation for whitelisted redirect targets
- Document IPv6 whitelist syntax in .env.example
- Add comprehensive IPv6 test coverage
- Updated relevant files to include provider registration, implementation, and metadata.
- Enhanced frontend components to support provider management and configuration.
- Added localization for new provider settings and messages.
- Implemented backend repository methods for CRUD operations on web search providers.
- Added checks to ensure that the tenant ID matches between data sources and knowledge bases during creation and updates.
- Implemented ownership verification for data sources and knowledge bases in the DataSourceHandler, enhancing security and access control.
- Refactored data source retrieval methods to centralize ownership validation, improving code maintainability and clarity.
- Updated error handling to provide more informative responses when access is denied or when knowledge bases are not found.
- Deleted WeCom document connector and callback handling code to streamline the datasource management.
- Removed associated localization entries and UI elements related to WeCom integration.
- Updated data source management logic to eliminate references to WeCom, enhancing maintainability and clarity.
- Adjusted error handling and logging to reflect the removal of WeCom-specific functionality.
- Implemented WeCom document connector to sync smart documents and WeDrive files into WeKnora.
- Added detailed documentation for WeCom integration, including setup instructions for callback URL and API access.
- Enhanced frontend localization to support WeCom-specific messages and instructions.
- Updated data source management UI to include WeCom configuration options and callback URL handling.
- Improved error handling and validation for WeCom API interactions.
Mao Meng