* fix(pipeline): stop emitting empty final assistant messages
An empty final streaming chunk was appended as an empty message chain when no sandbox outbox attachment was present, so platforms received an empty reply. Only append the chain when attachments were actually collected, and gate the "Call ..." tool notice behind output.misc.track-function-calls so it no longer becomes the final chunk of a stream.
* fix(wecombot): do not close stream on a blank final snapshot
A blank final snapshot closed the WeCom stream with an empty bubble and pushed the real answer into a separate reply_text message. Skip blank final snapshots and keep the session open so the following non-blank chunk can finalize it. Covered by new regression tests.
* fix(runner): align SDK pin and workspace-aware integration fixtures
* fix(ci): format sources and resolve current migration head
* test(persistence): align standalone migration fixtures with current models
* test(web): align smoke fixtures with current processor UI
---------
Co-authored-by: dadachann <[email protected]>
* fix(wecombot): align media upload protocol
* fix(wecombot): deliver outbox media in reply and fix tool call recording
- Integrate _send_media into reply_message and reply_message_chunk so
sandbox outbox images/voices/files are uploaded and sent instead of
being silently dropped.
- Add missing import base64 that caused _send_media to fail with a
NameError swallowed by its except clause.
- Change yiri2target to return component dicts (text/image/voice/file)
so callers can distinguish text from media.
- Fix _get_message_for_tool_context using result.first()/row[0] which
returned a raw string instead of the ORM object, causing
"'str' object has no attribute 'pipeline_id'" in tool call recording.
Use result.scalars().first() per SQLAlchemy 2.0 convention.
* fix(pipeline): collect outbox attachments on final chunk with empty content
When the last streaming chunk has is_final=True but empty content
(e.g. the LLM sends all text in earlier chunks), the 'if result.content'
branch is skipped entirely, so _append_outbound_attachments never runs
and sandbox outbox images are silently dropped.
Add an elif branch for _is_final_assistant_message that creates an
empty MessageChain and still collects outbox attachments, so images
are delivered even when the final chunk carries no text.
* fix(box): bypass stdout truncation when reading outbox via exec
_read_outbox_via_exec used execute_tool which returns _serialize_result
where stdout is truncated to output_limit_chars (4000). A 7KB JPEG
encodes to ~9400 base64 chars, so the JSON payload was truncated and
json.loads failed silently, returning an empty list.
Call client.execute directly to get the raw BoxExecutionResult with
untruncated stdout, so base64 file data is preserved.
* fix(tests): adapt box and wrapper tests for client.execute and strict is_final check
- wrapper.py: restrict outbox collection on empty-content chunks to
actual MessageChunk instances with is_final=True, not generic Mock
objects that happen to have role='assistant'
- test_box_service.py: update _read_outbox_via_exec tests to mock
client.execute (returning BoxExecutionResult) instead of
execute_tool, matching the implementation change
* chore(wecombot): remove temporary upload log
* test(box): preserve direct outbox read and cleanup coverage
---------
Co-authored-by: fdc310 <[email protected]>
Co-authored-by: dadachann <[email protected]>
* feat: Implement workflow form handling for paused workflows
- Added module-level storage for pending forms to manage state across sessions.
- Introduced functions to set, get, and clear pending forms with expiration handling.
- Enhanced DifyServiceAPIRunner to support resuming paused workflows via form actions.
- Implemented logic to yield human input requests and display appropriate messages.
- Updated workflow submission methods to handle paused states and resume actions.
- Ensured proper merging of pending form actions with user inputs for seamless interaction.
* feat: Add '_routed_by_rule' variable to form action in Lark and Telegram adapters
* feat: Enhance Lark and Telegram adapters with new form handling for paused workflows
* feat: Enhance TelegramAdapter to handle form action buttons and message threading
* feat: Improve TelegramAdapter message handling with enhanced error management and draft message support
* feat: Add the function for formatting human input text to support adapters without rich UI.
* feat(dingtalk): implement human input card support and card action handling
- Add a new module `card_callback.py` to handle card action button clicks from DingTalk.
- Introduce `DingTalkCardActionHandler` to process card action callbacks and extract parameters.
- Update `DingTalkAdapter` to manage card state and handle form input through a single card template.
- Add configuration for `human_input_card_template_id` in `dingtalk.yaml` to specify the template for human input.
- Create a new card template `dingtalk_human_input_card.json` for rendering human input prompts and buttons.
* feat(dingtalk): enhance human input card functionality with streaming support and active turn management
- Updated the DingTalk card template to enable streaming mode and multi-update configuration.
- Removed the obsolete delete_card method from DingTalkClient to streamline card management.
- Enhanced DingTalkAdapter to manage active turn cards and accumulated streaming text, ensuring a seamless user experience during human input prompts.
- Modified the create_message_card method to utilize existing active cards for resumed workflows, preventing duplication.
- Improved the _paint_form_on_card method to update existing cards with human input prompts and buttons dynamically.
- Updated the dingtalk_human_input_card.json template to reflect the new streaming capabilities and configuration options.
* feat(wecom): implement Dify human input pause handling with button interaction support
* feat(qqofficial): implement Dify human input button interaction handling and markdown keyboard support
* feat(qqofficial): implement one-click QR binding and enhance localization support
* feat(discord): implement Discord form view with button interactions for Dify actions
* fix(telegram): correct group chat type check and handle oversized callback data for Telegram actions
fix(difysvapi): ensure safe access to remove-think configuration in pipeline settings
* feat(dify): add support for chatflow app type and enhance human input handling
* feat(telegram): add action title feedback for user selections in Telegram messages
* feat(lark): enhance LarkAdapter to store form content for resume notices
* feat(dingtalk): update display formatting for card content with HTML line breaks
* feat(dingtalk): add feedback functionality to cards with 👍/👎 buttons
- Implemented feedback state management for cards, allowing users to provide feedback via thumbs up/down buttons.
- Enhanced card rendering to include feedback buttons when appropriate.
- Registered feedback listeners to handle feedback events and update card states accordingly.
- Updated the card template to support dynamic button rendering for feedback actions.
- Improved error handling and logging for feedback actions and card updates.
* fix: add Avatar component to dingtalk_human_input_card.json for enhanced user interaction
* feat(wecom): add optional source block to interactive template cards for enhanced branding
* feat(wecom): add functions for template card action extraction and update, enhance button interaction handling
* feat(qqofficial): synchronize passive-reply counter with inbound message sequence
* feat(qqofficial): add method to identify invisible form placeholder chunks in messages
* feat(dingtalk): add download link for human input card template and enhance dynamic form configuration
* feat(telegram): enhance message handling with group stream deletion and form placeholder detection
* Add unit tests for DingTalk, Lark, WeComBot, and Dify service API runners
- Implement tests for DingTalk adapter helper functions including form content cleaning, input extraction, and completed input lines.
- Create unit tests for Lark adapter helper functions focusing on input extraction and completed input lines.
- Add tests for WeComBot template card functionalities, including event extraction and payload building for human input.
- Enhance Dify service API runner tests to cover human input forms, including input collection, action handling, and form snapshot extraction.
* feat: Enhance Telegram and QQ Official adapters with select field handling and form action processing
- Added support for select fields in Telegram adapter, including option extraction and callback handling.
- Implemented form action processing for Telegram callbacks, improving user interaction feedback.
- Introduced new helper functions for building keyboards and resolving select button actions in QQ Official adapter.
- Enhanced DifyServiceAPIRunner to handle cumulative streaming responses and improve error handling during workflow resumes.
- Added unit tests for new functionalities in Telegram and QQ Official adapters, ensuring robust behavior for select fields and form actions.
* feat(lark): add functions for current input definitions and visible form content handling
feat(qqofficial): update fallback text handling for non-streaming scenarios
feat(difysvapi): enhance form content processing for interactive fields and actions
test: add unit tests for Lark and QQ Official adapter functionalities
* Add tests for DingTalk adapter content processing and markdown formatting
- Updated the assertion in `test_dingtalk_completed_input_lines_include_text_and_select_values` to remove unnecessary markdown formatting.
- Added new tests to verify that `_dingtalk_clean_form_content` maintains the order of prompts and completed values in various scenarios.
- Introduced `test_dingtalk_card_markdown_preserves_internal_line_breaks` to ensure internal line breaks are correctly converted to HTML line breaks.
* feat: Refactor input handling and feedback messages across multiple adapters
* feat: Update the human-computer interaction template cards, and optimize the prompt information and content display.
* feat: Refactor pending form handling to isolate by bot and pipeline
* feat: Enhance error handling and caching for Dify and WeCom interactions
* feat: Enhance select input handling and validation in Dify API runner and Telegram adapter
* feat: Add missing completed input lines handling in DingTalk adapter
* feat: Add pipeline_uuid handling across multiple adapters and update related tests
The multipart tuple form (None, user) is httpx 'files=' syntax for a part
with no filename; placed under 'data=' it expanded into a stray user=None
field, so Dify associated the uploaded file with the wrong user and the
workflow never received the image. Send 'user' as a plain string.
* add conversation expire config
* add user query text to card
* fix(pipeline): move session limit to AI config
* test(pipeline): cover AI session limit config
* refactor(pipeline): merge session expire-time into AI runner stage
Move the session validity duration field out of the standalone
session-limit stage into the runner stage so it actually renders in the
AI tab (the tab only shows the runner stage and the stage matching the
selected runner — any other stage is filtered out). Read path, default
config, metadata description, and tests updated accordingly.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* fix(pipeline): expire conversations from last update time
* fix(n8n): sync generated conversation id into payload
---------
Co-authored-by: RockChinQ <[email protected]>
Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
* feat(monitoring): link feedback to LangBot message ID and add feedback export
- Add pipeline→adapter notification hook so monitoring message ID is
passed back to WecomBotAdapter after creation
- Store stream_id→monitoring_message_id mapping with 10-min TTL cleanup
- Replace feedback record stream_id with LangBot monitoring message ID
so feedback can be linked to actual message records
- Rename streamId label to "Related Query ID" in all 7 i18n locales
- Remove non-functional message ID jump button from FeedbackList
- Add feedback export option to ExportDropdown (backend already implemented)
Co-Authored-By: Claude Opus 4.6 <[email protected]>
* feat(monitoring): add combined refresh handler for monitoring and feedback data
* fix(wecombot): improve stream ID mapping and error logging in WecomBotAdapter
* feat(lark): add monitoring message ID mapping for feedback correlation
* feat(lark): rename monitoring message ID mappings for clarity and consistency
feat(feedback): add button to view conversation for feedback items
* feat(bot-session-monitor): add feedback handling for bot messages with visual indicators
* feat(bot-session-monitor): enhance feedback display with hover content for like/dislike indicators
* fix(dingtalk): use voice recognition text instead of raw audio binary
When DingTalk sends a voice message to the bot, the callback JSON contains
a 'recognition' field with the speech-to-text result (powered by Qwen).
Previously, LangBot only extracted the 'downloadCode' to download the raw
audio binary and passed it as 'file_base64' to LLM APIs, which caused
400 errors since most models don't support this content type.
This patch:
- Extracts the 'recognition' field from DingTalk audio message content
- Uses it as plain text input to the LLM instead of raw audio
- Falls back to audio binary only when no recognition text is available
- Fixes duplicate text issue for audio messages with recognition
Fixes voice messages returning 'Request failed' on all LLM models.
* fix: add filereader for dingtalk,lark (#2122)
* fix: add filereader for dingtalk
* feat: add lark
* feat: update uv.lock
* chore: update version to 4.9.6 in pyproject.toml, __init__.py, and uv.lock
* fix: update langbot-plugin version to 0.3.8
* fix: update langbot-plugin version to 0.3.8
* fix(wecombot): extend StreamSession TTL for feedback sessions to prevent context data loss
StreamSessionManager.cleanup() removes sessions after 60s TTL, but feedback
events (like → cancel → dislike) can arrive later. When the session expires
before the dislike event, all context fields (session_id, user_id, message_id,
stream_id) are lost because get_session_by_feedback_id() returns None.
Fix: Sessions with registered feedback_ids now use a 10-minute TTL, aligned
with the adapter's _stream_to_monitoring_msg TTL in wecombot.py.
Co-Authored-By: Claude Opus 4.6 <[email protected]>
---------
Co-authored-by: 6mvp6 <[email protected]>
Co-authored-by: Claude Opus 4.6 <[email protected]>
Co-authored-by: fdc310 <[email protected]>
Co-authored-by: haiyangbg <[email protected]>
Co-authored-by: Guanchao Wang <[email protected]>
Co-authored-by: Rock Chin <[email protected]>
When DingTalk sends a voice message to the bot, the callback JSON contains
a 'recognition' field with the speech-to-text result (powered by Qwen).
Previously, LangBot only extracted the 'downloadCode' to download the raw
audio binary and passed it as 'file_base64' to LLM APIs, which caused
400 errors since most models don't support this content type.
This patch:
- Extracts the 'recognition' field from DingTalk audio message content
- Uses it as plain text input to the LLM instead of raw audio
- Falls back to audio binary only when no recognition text is available
- Fixes duplicate text issue for audio messages with recognition
Fixes voice messages returning 'Request failed' on all LLM models.
* fix(monitoring): fix WeChat Work feedback recording bugs
- Fix feedback events silently dropped when stream session expires:
dispatch feedback handlers regardless of session availability
- Fix IntegrityError on repeated feedback (like→dislike) for same
message: implement UPSERT logic in record_feedback()
- Fix cancel feedback (type=3) not removing records: add delete logic
- Fix inaccurate_reasons validation error: convert int reason codes
to strings before creating FeedbackEvent (Pydantic expects List[str])
- Fix feedback timestamps 8 hours off in frontend: use parseUTCTimestamp
instead of new Date() for UTC timestamp parsing
- Fix StreamSessionManager.cleanup missing _feedback_index cleanup
Co-Authored-By: Claude Opus 4.6 <[email protected]>
* fix(monitoring): apply ruff format to wecom feedback files
Co-Authored-By: Claude Opus 4.6 <[email protected]>
---------
Co-authored-by: 6mvp6 <[email protected]>
Co-authored-by: Claude Opus 4.6 <[email protected]>
* feat(wecom): add user feedback support for WeChat Work AI Bot
This commit implements user feedback functionality (like/dislike) for
WeChat Work AI Bot conversations, including:
Backend changes:
- Add feedback_id and stream_id fields to WecomBotEvent
- Implement feedback event handling in WecomBotClient (api.py)
- Add StreamSessionManager._feedback_index for feedback_id lookup
- Add on_feedback decorator for custom feedback handlers
- Create MonitoringFeedback entity for database persistence
- Add dbm025 migration for monitoring_feedback table
- Implement FeedbackMonitor helper class
- Update all platform adapters with ap parameter support
- Update botmgr to pass bot_info for monitoring context
Frontend changes:
- Add FeedbackCard and FeedbackList components
- Add useFeedbackData hook for feedback data fetching
- Add feedback tab to monitoring page
- Add feedback types and interfaces
- Add i18n translations (zh-Hans, en-US)
Other changes:
- Update Dockerfile with Chinese mirror for faster builds
- Update docker-compose.yaml with network configuration
- Update .gitignore for docker data and backup files
Note: Known issues that need future improvement:
- feedback_type=3 (cancel) is recorded but not properly handled
- Duplicate feedback records are not deduplicated
* chore: remove unnecessary migration for new table will be created automatically
* chore: ruff format
* chore: prettier
* feat: add feedback handling support across multiple platform adapters
* fix(web): remove unused imports and variables in monitoring module
---------
Co-authored-by: 6mvp6 <[email protected]>
Co-authored-by: Junyan Qin <[email protected]>
Hyu