Bump version to v0.5.0 across VERSION, frontend/package.json,
frontend/package-lock.json and helm/Chart.yaml.
Highlights:
- Wiki Mode: agent-driven Wiki knowledge system that distills raw
documents into interlinked markdown pages, with a dedicated
WikiBrowser and an interactive knowledge graph visualizing page
references and relationships.
- Observability: Langfuse tracing across the agent ReAct loop, LLM
token usage, tool calls and the asynq async pipeline.
- Customizable indexing strategy: per-knowledge-base toggles for
vector / keyword / Wiki / knowledge-graph indexing.
- Vector Store UI & per-KB binding.
- Yuque connector with full / incremental sync.
- Agent enhancements: json_repair tool, OpenMAIC Classroom skill,
multi-sheet DuckDB Excel analysis.
- Docs: refreshed READMEs (EN/CN/JA/KO), CHANGELOG, QA, regenerated
Swagger and updated architecture diagram with new Wiki/Langfuse
components.
The existing Langfuse integration covered Chat / Embedding / Rerank / VLM /
ASR generations plus the HTTP + asynq spans, but the agent's own execution
tree was invisible: tool calls never appeared, multi-round ReAct iterations
were flat under the HTTP trace, and there was no single node representing
"one agent run".
This change adds three levels of agent-side spans:
- agent.execute — wraps AgentEngine.Execute, records query preview,
knowledge bases, allowed tools, final-answer length
and totals on finish.
- agent.round.<N> — wraps each ReAct iteration; records finish_reason,
tool-call count, token usage and duration.
- agent.tool.<name> — wraps each tool invocation; records arguments,
success, duration, output preview (rune-safe, 4KB
cap), error, data keys and image count.
To keep the loop's many exit paths (natural stop, stuck loop, empty-content
retry, final_answer, context cancellation) span-safe, the iteration body was
extracted into runReActIteration with a single defer span.Finish() and an
iterOutcome sentinel driving the outer loop. database_query arguments are
redacted (keys only) to avoid leaking raw SQL into the observability
backend, mirroring the existing UI hint policy.
Adds unit tests for the new helpers (truncateForLangfuse, argKeys, dataKeys,
finishToolSpan nil-safety, iterOutcome.String).
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).
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.
- Create structured wiki from docs/ directory
- Add 17 markdown pages organized in 7 categories
- Include standard Markdown relative path links for navigation
- Add Mermaid knowledge graph visualization in Home.md
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.
- 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.
- Deleted references to WeCom document integration in the data source import module documentation.
- Updated the quick start guide and related sections to reflect the removal of WeCom, enhancing clarity and focus on supported platforms.
- 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.
- Introduced comprehensive documentation for the data source import module, detailing integration with external platforms like Feishu, Notion, and Confluence.
- Included a quick start guide, front-end management instructions, architecture overview, and data model specifications.
- Enhanced user understanding of data source configuration, synchronization processes, and error handling.
- add setup guides for both WebSocket and Webhook modes
- document streaming reply mechanisms (editMessage / AI card)
- update architecture diagrams, data model, and config reference
- 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.
- Removed redundant dropdown menu styles from various components and centralized them in a new `dropdown-menu.less` file.
- Updated components to use the unified styles, ensuring consistency in appearance and behavior across the application.
This change simplifies maintenance and enhances the visual coherence of dropdown menus.
- 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.
- 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
- 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 `.env.lite.example` file for the Lite version, providing a minimal configuration template.
- Updated `.env.example` to remove deprecated variables and include new Docreader settings.
- Enhanced Docker configurations to support the Lite version, including a new Dockerfile for the Docreader service.
- Introduced a Makefile target for building and running the Lite version, along with packaging capabilities.
- Created GitHub workflows for building and releasing Lite binaries, including Homebrew formula support.
- Implemented a new service file for managing the Lite version as a system service.
This update enables a streamlined, single-binary deployment of WeKnora, reducing external dependencies and simplifying setup.
- Introduced a new ROADMAP.md file detailing WeKnora's product plans and upcoming features.
- Updated README files in both English and Chinese to include links to the new roadmap, enhancing visibility of future developments.
- Changed references from "工作空间" to "共享空间" for consistency and clarity.
- Expanded the documentation to include details on shared agents and their management within shared spaces.
- Updated sections to reflect the new structure and rules regarding knowledge base and agent sharing, including permissions and user roles.
- Improved overall readability and organization of the document to facilitate better understanding of shared space functionalities.
This update enhances the documentation by ensuring accurate terminology and providing comprehensive guidelines on shared space features.
- Changed the default maximum number of members per organization from 50 to 200 in both the documentation and the application service layer.
- This update allows for greater flexibility in organization membership management, accommodating larger teams.
- Introduced a new member limit feature for organizations, allowing a maximum number of members to be set (default is 50; 0 means unlimited).
- Updated API endpoints to handle member limit during organization creation and updates.
- Implemented validation to prevent adding members when the limit is reached.
- Enhanced UI components to display and manage member limits, including localization updates for English and Chinese.
- Added migration files to support the new member limit column in the organizations table.
This update improves organization management by allowing administrators to control membership size.
- Introduced a new API for organization management, including creation, updating, and deletion of organizations.
- Implemented organization sharing functionality for knowledge bases, allowing users to share knowledge bases with specific organizations and set permissions.
- Added UI components for organization selection and sharing dialogs.
- Enhanced existing components to support organization-related features, including shared knowledge base handling and organization role management.
This update enhances collaboration capabilities within the application by allowing users to manage and share knowledge bases across different organizations.
wizardchen