Closes#620#497. Add opt-in Langfuse observability covering all five
model types (chat, embedding, rerank, VLM, ASR) with HTTP-request-scoped
traces and Docker Compose support (both cloud and self-hosted).
Core package internal/tracing/langfuse:
- HTTP client with batched async ingestion (non-blocking in request path)
- Sampling, environment / release tagging, and graceful fallback when
LANGFUSE_* env vars are absent (wrappers become no-ops)
- Gin middleware opens one trace per traced request and finishes it after
the handler chain returns, attaching method / path / user / session
- Trace context is stored under a typed key exported from internal/types
so logger.CloneContext can preserve it across handler / goroutine
boundaries (otherwise each LLM call auto-created an orphan trace,
fragmenting one request into many)
Per-model generation wrappers (opt-in via NewChat/NewEmbedder/...):
- chat: captures prompt, streaming output, token usage + TTFT
- embedding: approximates tokens when the provider omits usage
- rerank: previews query/docs, summarizes results to keep payload small
- vlm: records image count and total bytes, never uploads raw pixels
- asr: records file size and audio duration, never uploads audio bytes
Async title generation (GenerateTitleAsync) now forwards the trace key
into the goroutine so title calls appear under the parent chat trace.
Docker Compose:
- LANGFUSE_* env passthrough on the `app` service for cloud deployments
- Optional `langfuse` profile spins up a self-hosted Langfuse stack that
reuses WeKnora's existing PostgreSQL (separate database via an idempotent
init container that fixes ICU collation drift) and Redis (separate DB
number), adding only ClickHouse, MinIO, web and worker containers
- web/worker entrypoints URL-encode DB_PASSWORD / REDIS_PASSWORD at start
to avoid Prisma P1013 when passwords contain @ / # / etc.
Docs: docs/Langfuse集成.md covers cloud vs self-hosted, per-model usage
strategy, code map, and resource footprint.
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 UpdateAutoLinkedContent method to persist changes from machine-only link decorators without incrementing the version number, ensuring user-facing revisions remain consistent.
- Updated existing methods to utilize the new auto-linked content update logic, enhancing the handling of cross-link injection and dead-link cleanup.
- Enhanced the KnowledgeBase and WikiBrowser components to improve user experience during wiki operations, including status tracking and indexing indicators.
- Added scheduling for wiki status probes to provide timely feedback on indexing processes after user-triggered actions.
- Enhanced the UpdatePage and UpdateMeta methods to differentiate between user-visible content changes and bookkeeping updates, ensuring the version number is only incremented for actual content modifications.
- Updated the WikiPage struct and associated interfaces to reflect the new versioning policy, improving clarity on when the version is modified.
- Improved documentation for methods to clarify their intended use and behavior regarding versioning and metadata updates.
- 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.
- Updated the knowledge base service to ensure WikiConfig is created when wiki indexing is enabled, enhancing backward compatibility.
- Removed legacy syncing of WikiConfig.Enabled to IndexingStrategy.WikiEnabled, streamlining the logic for wiki enablement.
- Introduced a custom retry delay function for asynq tasks to handle wiki ingest lock conflicts more effectively, improving task management.
- Cleaned up the container file by removing unnecessary whitespace, contributing to better code readability.
- Added comments to clarify the purpose of the `builtinAgentIDsOrdered` list, specifically noting the exclusion of `BuiltinWikiFixerID` to prevent clutter in the user-facing agent list.
- This change enhances the understanding of the built-in agent configuration and maintains a cleaner user interface for agent selection.
- 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 SuggestedQuestion interface to include 'wiki' as a new source type, allowing for more diverse question origins.
- Implemented ListRecentForSuggestions method in the wikiPageRepository to retrieve recent wiki pages for fallback suggestions, improving the agent's ability to generate relevant questions when other sources are unavailable.
- Enhanced the customAgentService to utilize the new wiki page suggestions, ensuring a broader range of questions can be generated from wiki content.
These changes significantly improve the agent's suggestion capabilities by incorporating wiki content, enhancing user interaction and information retrieval.
- Enhanced the `cleanupWikiOnKnowledgeDelete` function to pass the full knowledge object, allowing for more accurate title and summary retrieval during wiki cleanup.
- Introduced a tombstone mechanism in Redis to mark recently deleted knowledge, enabling in-flight wiki ingest tasks to skip processing for deleted documents.
- Updated the `isKnowledgeGone` function to check for deleted knowledge using the tombstone, improving efficiency in handling concurrent delete and ingest operations.
- Added logic in the `wikiIngestService` to filter out updates for deleted knowledge, ensuring that no ghost references are created during the ingest process.
- Implemented lint checks for stale source references in the `WikiLintService`, allowing for automatic cleanup of pages referencing deleted knowledge.
These changes significantly enhance the robustness of the wiki system, ensuring consistency and accuracy in handling knowledge deletions and related wiki pages.
- 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 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.
- Introduced a new test file for JSON handling to ensure that unmarshalling correctly copies input data.
- Validated that the stored JSON remains intact after input mutation, and confirmed successful marshalling of the output.
- This test enhances the reliability of JSON operations within the types package.
- Introduced `pending_tasks` and `is_active` fields in the `WikiStats` interface to track the number of tasks waiting for processing and the current activity status of the wiki ingestion.
- Updated localization files for English, Korean, Russian, and Chinese to include a new message for displaying the number of pending tasks in the wiki queue.
- Enhanced the `WikiBrowser` component to display the queue status, improving user awareness of ongoing tasks.
- Implemented polling logic to refresh wiki stats periodically when there are pending tasks or active ingestion, ensuring up-to-date information is presented.
These changes enhance the functionality and user experience of the wiki management system by providing real-time insights into task status.
- Introduced a new method to list all wiki pages without pagination, facilitating index rebuilding and cross-linking.
- Updated the wiki ingest and retract processes to include document summaries, improving clarity in log entries and index updates.
- Enhanced the cleanup process for deleted knowledge, ensuring accurate summary retention and index regeneration.
- Streamlined the handling of page updates and deletions, optimizing the overall wiki management workflow.
These changes improve the efficiency and reliability of wiki content management and indexing.
Implemented new features in the WikiBrowser component to allow users to toggle the visibility of arrows on graph edges. This includes:
1. **Localization Updates**: Added translations for "Show arrows" and "Hide arrows" in English, Korean, Russian, and Chinese.
2. **Graph Rendering Enhancements**: Introduced logic to dynamically update SVG line attributes based on the toggle state, improving visual clarity in graph representations.
3. **State Management**: Added reactive state management for arrow visibility, ensuring seamless user interaction.
These changes enhance the user experience by providing more control over graph visualizations.
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.
Implemented three critical fixes to enable wiki_config persistence during
KB creation and updates:
1. Add WikiConfig field to KnowledgeBaseConfig struct
- File: internal/types/knowledgebase.go
- Enables JSON unmarshaling of wiki_config from update requests
- Previously the field was silently dropped during deserialization
2. Update UpdateKnowledgeBase service to persist wiki_config
- File: internal/application/service/knowledgebase.go
- Added logic to apply wiki_config changes when config parameter is provided
- Mirrors existing logic for FAQConfig persistence
3. Update frontend TypeScript types for IDE completion
- File: frontend/src/api/knowledge-base/index.ts
- Added WikiConfig type to createKnowledgeBase function signature
- Added structured WikiConfig type to updateKnowledgeBase config parameter
- Enables TypeScript IDE autocomplete and type checking
Flow:
- Frontend: updateKnowledgeBase({name, description, config: {wiki_config}})
- Handler: Parses into UpdateKnowledgeBaseRequest via KnowledgeBaseConfig
- Service: Applies wiki_config to KB entity via UpdateKnowledgeBase
- Database: Persists wiki_config JSONB field to postgres
Root cause analysis:
Previously, the KnowledgeBaseConfig struct was missing the WikiConfig field,
causing JSON unmarshaling to silently drop any wiki_config data sent in
PUT requests. KB creation worked because it used the full KnowledgeBase struct.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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>
Syncs personal and team Yuque knowledge bases into WeKnora as Markdown,
with full and incremental sync + deletion detection. Mirrors the Feishu
connector pattern; fetches V2Doc.body directly (no async export step).
Config: api_token (required) + base_url (optional, defaults to public cloud).
401/403 wraps ErrInvalidCredentials; 429 honors Retry-After; filters to
status=1 Doc-type at list stage to conserve API quota. Body is trusted as
Markdown for format=markdown|lake — empirically verified against the v2
API: Lake XML lives separately in body_lake, body itself is the Markdown
view. Other formats (e.g. html) are skipped with a placeholder item so
they surface in sync logs instead of silently feeding HTML to the parser.
v2Doc.status accepts both int and string shapes — Yuque's OpenAPI spec
declares string but the live API returns int. sanitizeFileName truncates
at a UTF-8 rune boundary so multi-byte titles don't emit invalid UTF-8
that downstream filename validation rejects.
Also adds logger.SetOutput test hook, used by the token-redaction test.
- Extend RetrieveEngineRegistry with byStoreID map for DB store engines
- Add StoreRegistry interface and EngineFactory function type
- Create engine factory with per-driver SDK client construction
- Load VectorStore records from DB into registry at startup
- Wire Create/Delete to dynamically register/unregister engines
- Add comprehensive unit tests for registry and service
- Added a new logger for LLM calls, enabling detailed logging of model interactions, including request and response data.
- Introduced configuration options for enabling and specifying the log directory for LLM debug logs.
- Implemented cleanup for old log files to manage disk space effectively.
- Wrapped existing chat implementations to log calls when debug logging is enabled, enhancing traceability for model interactions.
Introduce KnowledgePostProcess task to decouple image multimodal
processing from downstream operations like summary, question
generation, and knowledge graph extraction.
This allows unifying the processing logic across documents and
images and ensures all text content, including from images,
is properly extracted before post-processing.
Multiple fixes for WeKnora Lite mode (SQLite, no Redis):
1. Redis nil pointer panic on FAQ import (knowledge.go)
- Add in-memory fallback (sync.Map) for FAQ import progress tracking
- Add nil guards for running task locks and progress queries
2. seq_id not auto-incrementing in SQLite (chunk.go, tag.go)
- GORM autoIncrement on non-PK columns doesn't work in SQLite
- Add AssignChunkSeqIDs() to pre-assign seq_ids before batch insert
- Add BeforeCreate hook for KnowledgeTag
- Use Unscoped() to include soft-deleted records in MAX(seq_id) query
3. NOW() function not available in SQLite (chunk.go)
- Replace raw SQL NOW() with time.Now() or datetime('now')
- Use dialector check for raw SQL that must differ between PG and SQLite
4. FAQ KB should reject file uploads (knowledge.go)
- Add kb.Type == "faq" check in CreateKnowledgeFromFile
5. Linux/macOS build compatibility (Makefile)
- Conditionally apply macOS-only linker flag based on uname
6. Add SQLite integration tests (chunk_sqlite_test.go)
- TestCreateChunks_SQLite_SeqIDAutoAssigned
- TestCreateChunks_SQLite_SeqIDContinuesFromExisting
- TestCreateChunks_SQLite_SeqIDUniqueAcrossKBs
- TestCreateChunks_SQLite_SeqIDAfterSoftDelete
- TestKnowledgeTag_SQLite_SeqIDAutoAssigned
- TestUpdateChunk_SQLite_NoNOWError
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- 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.
- Added tenantService to modelService for retrieving WeKnoraCloud credentials when not provided in model parameters.
- Implemented resolveWeKnoraCloudCredentials method to automatically fetch AppID and AppSecret from tenant configuration.
- Updated GetEmbeddingModel, GetEmbeddingModelForTenant, GetRerankModel, and GetChatModel methods to utilize the new credential resolution logic.
- Modified ModelParameters to encrypt and decrypt both APIKey and AppSecret for improved security during database operations.
This update streamlines credential management for WeKnoraCloud models, enhancing security and usability.
- Removed support for video file uploads across the application, including updates to the knowledge base and document processing services.
- Updated internationalization files to reflect the removal of video-related terminology, changing labels and descriptions to focus solely on audio processing.
- Adjusted file type handling to exclude video formats, ensuring users receive accurate feedback regarding supported file types.
- Enhanced error handling to provide clear messages when users attempt to upload unsupported video files.
This update streamlines the application by focusing on audio processing capabilities and improving user experience through clearer messaging.
- 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.
- Updated the query content to include metadata for uploaded images and attachments, improving the context provided to the assistant.
- Modified the attachment prompt format to use XML-like tags for better structure and readability, including metadata for file type and size.
- Enhanced error handling for unsupported file content extraction, providing clearer feedback to users.
This update improves the assistant's ability to process and respond to queries involving multimedia content.
- 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.
- 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.
Add VectorStoreService with CRUD validation, duplicate checking (DB + env
stores), and engine-specific health checks for 6 vector database types.
Include VectorStoreResponse DTO, env store builder, engine type metadata,
and comprehensive unit tests.
wizardchen