Commit Graph

16 Commits

Author SHA1 Message Date
matevip
8bd8a02cd0 feat(agent,ui): nested subagent timeline + always-on plan panel 2026-05-22 09:48:06 +08:00
matevip
82594878a0 refactor(ui): fold the live runtime view into the Employees page 2026-05-16 14:50:58 +08:00
matevip
2ab0bd000b fix(chat): suppress duplicate assistant row on force-recycle 2026-05-05 10:39:54 +08:00
matevip
2e0ff8f90f fix(agent): unblock multi-role parallel delegation + propagate force-stop 2026-05-04 20:54:38 +08:00
matevip
42d406ffc8 fix(agent): drop brittle output policing, add evidence-grounded long-task safeguards 2026-05-04 11:55:44 +08:00
matevip
7d02841d1d fix(chat): per-event SSE ids for safe reconnect dedup + queue race recovery 2026-05-03 19:57:06 +08:00
matevip
66f09a968a feat(chat-stream): streaming UX overhaul + multi-agent stability layer 2026-05-03 17:15:02 +08:00
matevip
e3ab06d57c feat(generative): unified async pipeline + live SSE delivery for music/video/image 2026-05-01 20:15:20 +08:00
matevip
0476447ab6 fix(agent): persist mid-turn narrative, queue follow-ups without dispose, flush on shutdown
A bundle of stability fixes that all surfaced together while running
the same long-form generation task across multiple turns. Each one
addresses a distinct way the previous behavior silently dropped
content the user had already seen on screen.

1. Mid-turn narrative persistence (StateGraphReActAgent +
   SummarizingNode). Intermediate ReasoningNode rounds and
   SummarizingNode broadcast their content_delta directly to the
   SSE channel for live display, but the StreamAccumulator only
   received the final answer. After refresh the assistant message
   showed only tool_call cards with no body text.
   StateGraphReActAgent now also forwards STREAMED_CONTENT (already
   set per round) as a persistOnly StreamDelta whenever it changes,
   so every narrative chunk lands in the accumulator's content
   buffer and gets written to mate_message. SummarizingNode now
   writes its summary into the same key so summarize narratives
   persist too.

2. Follow-up message queue, not dispose (ChatController#interruptStream).
   Sending a new message while a turn was running called
   requestInterrupt, which dispose()d the active Reactor chain mid
   LLM call. That cancelled the in-flight generation, lost partial
   tokens, and left the user staring at a half-finished bubble.
   The endpoint now uses enqueueMessage in all paths, matching
   the "wait for current turn, then run" behavior. The old
   requestInterrupt API is kept for any future force-replace UI
   but no caller routes to it.

3. Queued user message ordering (ChatStreamTracker.QueuedInput +
   ChatController.startQueuedMessage). interruptStream used to save
   the queued user message immediately, before the in-flight
   assistant message finalized in doOnError. listMessages orders
   by create_time ASC, so the queued user message ended up above
   the assistant reply it was supposed to follow. QueuedInput now
   carries contentParts; persistence is delayed to startQueuedMessage,
   which runs only after Asst-N is on disk.

4. JVM shutdown flush (ChatStreamTracker @PreDestroy +
   emergencySaveAccumulator). A mvn spring-boot:run restart used to
   wipe in-flight turns: SSE emitter timed out, ShutdownHook fired,
   HikariPool closed before doOnError could save. ChatStreamTracker
   now exposes an emergency-save callback per RunState; ChatController
   registers one per stream that snapshots the accumulator and
   writes status="interrupted_shutdown". @PreDestroy walks active
   runs, invokes the callback, then disposes. Spring's reverse-order
   bean teardown keeps ConversationService and Hikari alive long
   enough for the save to complete.

5. Observation thresholds for summarize (GraphObservationProperties +
   application.yml). The previous total-chars threshold of 12 KB
   triggered summarize after one or two RFC reads, costing a 40 to
   80 second compaction LLM call per loop. Tuned to: total 200 KB,
   single 16 KB, large-result 32 KB, rounds safety net 25. Java
   field defaults reverted to the conservative original values so
   application.yml stays the source of truth.

6. Frontend thinking segmentation (useChat.ts thinking_delta +
   phase). Multi-round ReAct turns merged every reasoning + summarize
   round's thinking into one segment, accumulating to 9 KB+ in a
   single bubble. thinking_delta now uses findLast(running) so a
   tool_call_started or phase transition closes the previous segment
   and the next delta opens a fresh one. phase event also closes
   running thinking/content segments.

7. Other small things bundled: removed a debug metadata-keys log
   that flooded the log file with one line per stream chunk; fixed
   three stale tests that didn't compile after earlier constructor
   changes (WikiLogServiceTest, WikiOverviewSpliceTest,
   WikiProcessingServiceLazyTest); added rfc-066 documenting the
   unified message queue + priority refactor as the next logical
   step on top of these stabilizations.

Verified end-to-end with multiple full sessions: a four-minute
generation that produced the expected docx and a follow-up enqueue
that ran cleanly after the previous turn naturally completed,
without the old "Disposable unavailable" interrupt path.
2026-04-27 07:51:49 +08:00
matevip
187197e804 fix(sse): preserve done event for late reconnect window 2026-04-27 07:50:35 +08:00
matevip
3f10553186 fix(sse): distinguish stream_not_local vs completed on reconnect 2026-04-26 08:32:44 +08:00
matevip
8a25d723f0 fix(chat): reset stale stopRequested flag on new stream register 2026-04-16 18:13:34 +08:00
matevip
80b7b8e126 feat(agent): multi-agent delegation with parallel execution (RFC-004) 2026-04-12 23:40:27 +08:00
matevip
5fc60ec513 feat(agent): smart truncation, stale stream cleanup, configurable tool timeouts, and new indexes 2026-04-07 06:39:46 +08:00
matevip
a8613fb05c fix(agent): improve execution stability to prevent premature task exits 2026-04-07 01:34:40 +08:00
matevip
579d60125b Initial commit: MateClaw — Java + Vue 3 AI Assistant System
Full-stack AI assistant built on Spring AI Alibaba.
Features: ReAct Agent, Plan-and-Execute, MCP Protocol, Multi-Model, Multi-Channel.

Apache-2.0 License
2026-04-04 19:03:49 +08:00