## Summary
- **Backend**: `_iter_session_completion_events` in `agent_api.py` was
filtering out `user_inputs` and `workflow_finished` SSE events, causing
agents with UserFillUp components to silently fail in explore mode — the
interactive form never appeared, while the same agent worked correctly
in run (editor) mode.
- **Frontend**: `SessionChat` component in explore mode was missing
`DebugContent` children rendering inside `MessageItem`, so even if the
backend forwarded the events, the form UI would not render. Added
`DebugContent`, `MarkdownContent`, `useAwaitCompentData` hook, and
input-disabling logic to match the run mode's `chat/box.tsx` behavior.
## What was changed
### Backend (`api/apps/restful_apis/agent_api.py`)
- Line 266: Added `"user_inputs"` and `"workflow_finished"` to the
allowed event filter in `_iter_session_completion_events`
### Frontend (`web/src/pages/agent/explore/components/session-chat.tsx`)
- Added imports: `DebugContent`, `MarkdownContent`,
`useAwaitCompentData`, `useParams`
- Added `sendFormMessage` from `useSendSessionMessage()` hook
- Added `useAwaitCompentData` hook for form state management
- Added `DebugContent` as `MessageItem` children for the latest
assistant message (renders UserFillUp form)
- Added `MarkdownContent` + submitted values display for previous
assistant messages
- Updated `NextMessageInput` disabled states to respect `isWaitting`
(form submission in progress)
## Test plan
- [x] Agent with UserFillUp component (e.g., email draft with
send/edit/cancel options) shows interactive form in **explore mode**
- [x] Same agent continues to work correctly in **run (editor) mode**
- [x] Form submission sends data back to the agent and workflow
continues
- [x] Input field is disabled while waiting for form submission
- [ ] Agents without UserFillUp components are unaffected in explore
mode
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### What problem does this PR solve?
Closes#14773.
Today, Pipeline (`rag/flow/`) chunking strategies only run as part of a
dataset ingestion that always embeds and indexes the result. There is no
way to drive Pipeline-style chunking from an Agent workflow without
paying that vectorization/persistence cost.
This PR adds a single new Agent component, `PipelineChunker`, that:
- Takes one or more file references (from `Begin` / `UserFillUp`
uploads) as input.
- Runs the existing `rag.app.*` chunking strategies (`naive`, `paper`,
`qa`, `manual`, `book`, `presentation`, `laws`, `table`, `one`, `email`,
`picture`, `audio`, `resume`, `tag`) against each file.
- Emits the resulting chunks as `chunks: list[str]` and `chunks_full:
list[dict]` for downstream Agent nodes.
- Performs **no embedding and no persistence** — chunks live only in
canvas variables for the duration of the run, exactly as requested in
the issue.
The component is auto-discovered by `agent/component/__init__.py`; no
registry edits required. Chunker functions are imported lazily so the
component itself does not pull `deepdoc` / OCR / VLM at
component-discovery time. File resolution mirrors the existing
`ExcelProcessor` convention.
Out of scope for this PR (potential follow-ups):
- Vectorization / KB persistence (explicit ask in the issue).
- Frontend canvas UI for the new component.
- Bridging to the newer Pydantic-based `rag/flow/chunker/TokenChunker`
(consumes a parser node's structured output rather than a raw file — a
separate, larger feature).
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---
## Files changed
- `agent/component/pipeline_chunker.py` — new component (~180 lines)
- `test/unit_test/agent/test_pipeline_chunker.py` — unit tests (~120
lines)
## Test plan
- [x] `ruff check` on changed files — clean.
- [x] `ruff format` applied to the new component file.
- [x] `python -m py_compile` on both new files — both compile.
- [x] New unit test file carries `pytestmark = pytest.mark.p2` so it
runs under marker-filtered CI.
- [x] Every new function, method, and class has a docstring (CodeRabbit
80% docstring-coverage gate).
- [x] `python -m pytest test/unit_test/agent/test_pipeline_chunker.py -x
-q` — **7 passed in 1.95s** locally. Tests stub
`api.db.services.file_service` and `rag.app.*` so they exercise the
parameter validation and parser-id lookup table without requiring the
full backend / model stack.
## Manual integration plan (post-merge)
1. Drop the component into an Agent canvas after a `Begin` node with a
file input.
2. Set `parser_id = "naive"` (or any other strategy) and reference the
file input in `inputs`.
3. Wire the `chunks` output into a downstream `LLM` / `Message` /
`Iteration` node — chunks are available as plain text without any
embedding or KB write.
Co-authored-by: John Baillie <johnbaillie2007@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
