Add three new optional filters to the document list under a knowledge base
detail page — parse status, source/channel, and updated time range — and
rework multi-select to no longer cause the card title to jitter on hover.
Backend
- Introduce types.KnowledgeListFilter to aggregate optional filter dimensions
(tag, keyword, file_type, parse_status, source, updated_from/to) and switch
ListPagedKnowledgeByKnowledgeBaseID (repository/service/interface) to accept
it instead of a growing positional parameter list.
- The ListKnowledge HTTP handler accepts new parse_status, source, start_time
and end_time query params; time params accept RFC3339, "YYYY-MM-DD HH:MM:SS"
and "YYYY-MM-DD". The repository routes source="manual"/"url" onto the type
column to stay consistent with file_type semantics; other source values match
the channel column.
- Update the four other callers (agent_service, initialization) to pass an
empty filter struct, preserving prior behavior.
Frontend
- Add three controls in the doc-filter-bar (status select, source select,
date-range picker with future-date disabled) wired through getKnowled /
listKnowledgeFiles into the new backend params.
- Replace the hover-triggered card checkbox with an explicit "批量管理" mode
(mirrors the session list UX): in card view the checkbox only renders while
batch mode is on, entered via the per-card "..." menu; the list view keeps
its leading checkbox column. Switching from list to grid auto-enables batch
mode when something is already selected, so the selection stays visible.
- DocumentBatchBar now stays open whenever batch mode or selection > 0, and
its "取消选择" button both clears the selection and exits batch mode.
API surface sync
- Regenerate Swagger artifacts (docs/docs.go / swagger.json / swagger.yaml).
- Update docs/api/knowledge.md with the new query parameters.
- Add backward-compatible ListKnowledgeWithFilter + KnowledgeListFilter to the
Go SDK; the existing ListKnowledge keeps its signature.
i18n
- New filter labels in zh-CN / en-US / ko-KR / ru-RU; reuse existing
menu.batchManage / batchManage.cancel for the multi-select strings.
Add scripts and docs for packaging WeKnora into cloud images (AMI,
custom images, snapshots) so users can distribute one-click deployable
templates on any cloud provider.
- scripts/cloud-image/: cloud-agnostic prepare/cleanup/firstboot scripts
plus systemd units. Downloads only the 4 runtime files needed by the
compose stack (~100KB) instead of cloning the full repo, and pins to
any git ref via WEKNORA_REF for reproducible builds.
- firstboot.sh randomizes DB/Redis/JWT/AES secrets on first boot,
writes credentials to /root/weknora-credentials.txt and self-removes.
- docs/cloud-image/: per-platform packaging guides. Includes a guide
for Tencent Cloud Lighthouse / CVM covering image creation, sharing,
and marketplace listing.
Default-on services match the unprofiled compose stack (frontend, app,
docreader, postgres, redis); optional services (qdrant, milvus,
neo4j, langfuse, etc.) remain opt-in via compose profiles to keep the
image size small.
Add an opt-in human approval gate so Agent runs pause before executing
MCP tools that operators flag as dangerous, surface an approval card in
the chat UI, and only resume after the user approves (optionally with
edited args) or rejects.
Backend
- New mcp_tool_approvals table + repo/service to mark per-tool approval
required (PG migration 000042 + sqlite init).
- approval.Gate coordinates RequestAndWait / Resolve with sync.Once
delivery, configurable timeout, and Redis Pub/Sub fan-out so multi-
replica deployments work without sticky sessions.
- MCPTool.Execute integrates the gate; uses a round-level ApprovalCtx
(without the per-tool 60s timeout) for the wait, and re-derives a
fresh 60s exec ctx after approval so CallTool keeps a full window.
- New SSE response types (tool_approval_required / _resolved) and
EventBus events plumb approval state to AgentStreamDisplay.
- REST: list/set per-tool approval flag, resolve pending approval.
- Configurable via agent.tool_approval_timeout_seconds (yaml) or
WEKNORA_AGENT_TOOL_APPROVAL_TIMEOUT env (accepts seconds or Go
duration).
Frontend
- MCP settings: per-tool "require approval" switch on the test panel.
- Chat: ToolApprovalCard renders the pause point with editable JSON
args, validation feedback, mm:ss countdown that turns warning/danger
near deadline, and a resolved state that retains context.
- i18n strings added for zh-CN / en-US / ko-KR / ru-RU.
Docs
- docs/zh/mcp-approval.md covering behavior, config, API, deployment
considerations (Redis cross-instance, restart limitations).
Three small follow-ups from the QA review of 13f57ca:
1. KBChunkingDebug error surfacing
Previously a 200 OK response with { success: false, error: "..." }
would have been swallowed under a generic "unexpected response shape"
message. The strict-shape check now distinguishes empty response,
success=false (surfaces resp.error directly), and missing data — so
any future backend-side validation message reaches the user.
2. Token approximation in docs/CHUNKING.md
Child Chunk Size default 384 ≈ 95 EN tokens (was rounded to 80).
384 / 4 chars-per-token = 96; "~95" is the honest figure.
3. API surface in docs/CHUNKING.md
The example only documented PUT /initialization/config/:kbId
(camelCase, documentSplitting envelope). Added explicit notes that
POST/PUT /knowledge-bases use snake_case under chunking_config, and
that POST /chunker/preview also uses the snake_case form plus a text
field. Readers picking the wrong endpoint won't be surprised by the
case mismatch anymore.
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
Three documentation passes around the adaptive chunking work:
UI
- Frontend ChunkOverlap default consolidated to 80 (was 100), matching
chunker.DefaultChunkOverlap on the backend. Both DEFAULT_CHUNKING_PRESET
and initFormData updated. The KB-load fallback also uses 80 when a
loaded KB has no chunk_overlap stored.
- All four locales (en-US, zh-CN, ko-KR, ru-RU) get rewritten chunking
setting descriptions: each now states the validated range, the default,
and the situations where you'd deviate (FAQ vs narrative, embedder
token limits, language-specific corpora).
Source code
- splitter.go: DefaultChunkSize / DefaultChunkOverlap constants get a
longer block-comment explaining the per-language token math and the
use-case sweet spots, plus the migration note on what the old
inconsistent defaults were.
- KBChunkingSettings.vue: new comment block above ChunkingConfig
documents the slider min/max for each setting, why those bounds
exist, and the recommended TokenLimit values per embedding model.
Repo docs
- New docs/CHUNKING.md: end-to-end guide covering why chunking matters,
the adaptive 3-tier architecture, per-setting reference with ranges
and sweet spots, parent-child explanation, the token-limit table per
embedder (OpenAI / Voyage / Cohere / BGE / MiniLM / Jina), 7 use-case
presets, the debug panel workflow, the API surface, and known
trade-offs (recursive strategy hidden from UI, no auto-reindex on
strategy switch, OCR limitations).
- CHANGELOG.md gets a new [Unreleased] section consolidating all the
adaptive-chunking work shipped on this branch: 5 features, 8
improvements, 6 fixes, 1 docs entry. The entry references
docs/CHUNKING.md for deeper explanation.
https://claude.ai/code/session_01XADhx6mtu2ZYW3DE9Lun6k
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.
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