### What problem does this PR solve?
Issue [#16758](https://github.com/infiniflow/ragflow/issues/16758) —
clicking a chunk whose data references a single-line variable from an
Await-Response (UserFillUp) component, the Agent's `user_prompt` is
being resolved against the **previous** canvas run's captured value
instead of the current run's value. The system-prompt path works only
because the system prompt is computed upstream and re-reads the value on
the new run.
### Root cause
`Canvas._run_impl` reset every path component with `only_output=True`,
so `_param.inputs` was never cleared between runs.
`ComponentBase.get_input()` calls `set_input_value(var, resolved)` at
line 482, which writes the resolved variable into
`self._param.inputs[var]["value"]`. On the next canvas run, that input
was never cleared, so the previous run's resolved value stuck around.
The Agent's `kwargs.get("user_prompt")` then read the stale string and
forwarded it to the LLM, which produced the "Understood. Please provide
the text..." fallback because the prompt looked empty.
### What changed?
- `agent/canvas.py` — differentiate `begin` (still `only_output=True`,
since it has no inputs and the webhook payload branch below populates
`request` explicitly) from non-begin path components (reset with
`only_output=False`, which clears both `inputs` and `outputs`).
- `test/unit_test/agent/test_canvas_input_reset.py` — new pytest module.
Pinned the contract: non-begin path components receive
`only_output=False`. The fix is small enough to verify with a stub
canvas rather than a full canvas-runtime test (the existing agent
conftest hits an unrelated `scholarly` import on Python 3.13, so a real
canvas import would require fixing that first).
### Backward compatibility
- `Begin` behaviour unchanged.
- All non-begin path components: previously persisted inputs across runs
(the bug); now reset between runs. Components that were relying on stale
inputs (none found in the existing test suite) would lose that as a side
effect, but that is the entire point of the fix.
- No API surface change. No backend change.
### Testing
```
$ uv run pytest test/unit_test/agent/test_canvas_input_reset.py -v
collected 4 items
test/unit_test/agent/test_canvas_input_reset.py::test_begin_is_reset_with_only_output_true PASSED
test/unit_test/agent/test_canvas_input_reset.py::test_non_begin_path_components_are_reset_with_only_output_false PASSED
test/unit_test/agent/test_canvas_input_reset.py::test_only_path_components_are_reset PASSED
test/unit_test/agent/test_canvas_input_reset.py::test_inputs_reset_flag_is_passed_to_non_begin_components PASSED
4 passed in 0.14s
```
`python3 -m py_compile agent/canvas.py` clean. Existing agent test files
(`test_switch.py`, `test_llm_prompt.py`) hit a pre-existing `scholarly`
import error on Python 3.13 (unrelated to this PR), so I couldn't run
the full agent suite. Recommend fixing the `scholarly` import
separately.
### Files changed
- `agent/canvas.py` (+9 / −1)
- `test/unit_test/agent/test_canvas_input_reset.py` (new, +104)
Fixes#16758
---------
Co-authored-by: Harsh Kashyap <harshkashyap@Harshs-MacBook-Pro.local>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Fixes#7316.
## Problem
`deepdoc/vision/operators.py` defines the image-standardize
preprocessing op as `class StandardizeImag` (missing the final `e`), but
every caller — including
`deepdoc/vision/recognizer.py::Recognizer.preprocess` — looks the class
up by the canonical string `"StandardizeImage"` via:
```python
op_type = new_op_info.pop("type") # "StandardizeImage"
preprocess_ops.append(getattr(operators, op_type)(**new_op_info))
```
So `getattr(operators, "StandardizeImage")` raised `AttributeError`, and
the "StandardizeImage" preprocessing step silently never ran for any
image pipeline that used the dynamic dispatch (LayoutLMv3 and friends).
The user-visible symptom is that the standardize step is missing
entirely from the preprocessing chain, so the model gets un-normalized
images.
## Production fix
```diff
-class StandardizeImag:
+class StandardizeImage:
"""normalize image
Args:
mean (list): im - mean
std (list): im / std
is_scale (bool): whether need im / 255
norm_type (str): type in ['mean_std', 'none']
"""
```
That's the entire production change — a one-character class rename. The
misnamed `StandardizeImag` had no other references in the codebase
(verified via `git grep`), so removing it is safe; every caller uses the
canonical `"StandardizeImage"` string and will now resolve correctly.
## Tests
New `test/unit_test/deepdoc/vision/test_operators_standardize_image.py`
with six regression tests, all green locally:
```
test_standardize_image_class_resolves_by_canonical_name PASSED
test_standardize_image_callable_matches_legacy_alias_name PASSED
test_standardize_image_normalizes_input_with_mean_std_and_is_scale PASSED
test_standardize_image_skips_scaling_when_is_scale_false PASSED
test_standardize_image_norm_type_none_passes_image_through PASSED
test_standardize_image_via_module_getattr_dispatch_path PASSED
6 passed in 0.18s
```
The tests:
1. **Pin the dispatch contract** (`hasattr(operators,
"StandardizeImage")`) — this is the exact check the recognizer's
`getattr` would do, so any future regression fails the same way the
runtime would.
2. **Pin that the misspelled name is gone** — if a downstream caller
ever relied on it, this fails loudly.
3–5. **Behavioural coverage** of the three documented code paths:
`is_scale=True, norm_type="mean_std"`, `is_scale=False,
norm_type="mean_std"`, and `norm_type="none"`.
6. **End-to-end via the same `getattr(operators, "StandardizeImage")`
call** the recognizer uses, with a real numpy image, so any rename or
removal surfaces as `AttributeError` instead of silently skipping the
step.
Verified both ways:
- Without the fix → **all 6 tests fail** (Python even suggests
`'StandardizeImag' → 'StandardizeImage'`)
- With the fix → all 6 pass in 0.15s
The test file follows the project's existing pattern
(`test/unit_test/deepdoc/parser/test_html_parser.py`): load the target
module via `importlib.util.spec_from_file_location`, stub the only
project-internal import (`rag.utils.lazy_image`), and assert against the
loaded module — no full RAGFlow runtime required.
## Risk
Very low. The class is renamed; no public Python API was using the
misnamed class. The only reference path is the `"StandardizeImage"`
string in `recognizer.py:270`, which now resolves correctly.
## Out of scope
- No other ops in `operators.py` are affected; checked all the others
(DecodeImage, NormalizeImage, Permute, etc.) and they all use correct
names.
- The dynamic-dispatch lookups in `recognizer.py` for `LinearResize`,
`StandardizeImage`, `Permute`, `PadStride` all use the same dispatch
path; only the `StandardizeImage` key was broken. No other keys need
fixing.
Made with [Cursor](https://cursor.com)
---------
Co-authored-by: Taranum01 <Taranum01@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
## What
An **Await Response** (`UserFillUp`) node placed inside a **Loop** now
pauses and waits for a fresh user response on **every** iteration,
instead of only on the first one.
## Problem
When a `UserFillUp` node lives inside a `Loop`, it only paused for input
on the first iteration. On subsequent iterations the loop ran straight
through, silently reusing the answer the user gave the first time.
Root cause is in `UserFillUp._invoke` / the canvas wait-check
(`agent/canvas.py`). The wait-check decides whether to pause by calling
`Canvas._is_input_field_satisfied` on the node's form fields — a field
counts as satisfied as soon as its `value` is not `None`:
```python
@staticmethod
def _is_input_field_satisfied(field):
...
if value is None:
return False
return True
```
The same component object is reused across loop iterations, and
`UserFillUp._invoke` writes the answer into
`self._param.inputs[...]["value"]` via `set_input_value`. Nothing
cleared those values when the node was re-entered for the next
iteration, so:
| Iteration | Entry (no answer yet) | Field value | Satisfied? | Result
|
|---|---|---|---|---|
| 1 | fresh | `None` | no | pauses ✅ |
| 1 | resume w/ answer | `answer` | yes | continues ✅ |
| 2 | fresh | `answer` (**stale**) | yes | continues ❌ (should pause) |
## Fix
When a `UserFillUp` is entered without a fresh user answer
(`merged_inputs` is empty), clear the retained form values so the
wait-check treats the form as unsatisfied and pauses again:
```python
merged_inputs = self._merge_runtime_inputs(kwargs.get("inputs", {}))
if not merged_inputs:
self._clear_form_values()
```
- Fresh entry / new loop iteration → no answer supplied → values cleared
→ node pauses and waits.
- Resume with an answer → `merged_inputs` is non-empty → values applied
normally, nothing cleared.
- Non-loop behavior is unchanged: the first entry already had `None`
values, so clearing is a no-op there.
`Begin` overrides `_invoke` and is unaffected.
## Tests
Added to
`test/testcases/test_web_api/test_canvas_app/test_fillup_unit.py`:
- `test_user_fillup_clears_stale_values_on_reentry_without_answer` — a
retained value is cleared on a fresh entry with no answer (loop
re-entry).
- `test_user_fillup_keeps_values_when_answer_supplied` — a supplied
answer is applied and not cleared.
All unit tests pass and `ruff check` is clean.
## Scope
This targets the Python agent runtime (`agent/`). It is independent of
any other in-flight Await Response change.
### Summary
- Make `BLOB_STORAGE_SIZE_THRESHOLD` configurable through an environment
variable.
- Preserve the existing 20 MiB default.
- Add tests for the default and configured values.
### Why
Blob storage, Seafile, and WebDAV connectors currently use a hardcoded
20 MiB limit. Self-hosted users
cannot raise this limit without modifying the source code inside the
container.
### Testing
- `test/unit_test/data_source/test_config.py`: 2 passed
- `ruff check common/data_source/config.py
test/unit_test/data_source/test_config.py`
Fixes#16634
### What problem does this PR solve?
- Clear stale pipeline IDs and generated data when updating documents
without `pipeline_id`.
- Support tree compilation results in pipeline workflows.
- Update compilation templates in place while preserving existing
template IDs.
- Improve duplicate-template validation messages.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### Summary
```
RAGFlow(admin)> show users plan quota 100;
+---------+------------------------------------------+
| field | value |
+---------+------------------------------------------+
| quota | 100 |
| command | show_users_plan_quota |
| error | 'Show users plan quota' is not supported |
+---------+------------------------------------------+
```
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## 🚨 Urgent: this breaks the Docker image build for **every** open PR
The `ragflow_tests_elasticsearch` and `ragflow_tests_infinity` jobs —
which build the image before running tests — currently **fail on all
pull requests**, not just this one. CI is effectively red repo-wide
until this lands. **Please review and merge ASAP.**
Example failing runs (from an unrelated PR):
-
https://github.com/infiniflow/ragflow/actions/runs/29086020168/job/86341032173
-
https://github.com/infiniflow/ragflow/actions/runs/29086020168/job/86341032196
## Symptom
```
Error: Cannot find module '/ragflow/web/scripts/prepare.js'
npm error command sh -c node scripts/prepare.js
ERROR: process "/bin/bash -c cd web && NODE_OPTIONS=..." did not complete successfully: exit code: 1
```
## Root cause
`web/package.json` defines:
```json
"prepare": "node scripts/prepare.js"
```
npm runs the `prepare` lifecycle script during `npm install`. But the
frontend-deps layer in the `Dockerfile` only copies the package
manifests before installing:
```dockerfile
COPY web/package.json web/package-lock.json web/.npmrc ./web/
RUN ... cd web && npm install # runs `node scripts/prepare.js` → file not present yet
```
Since `web/scripts/prepare.js` is not in the image at that point, `npm
install` aborts and the whole build fails.
## Fix
Copy `web/scripts` before `npm install` so the `prepare` script is
present:
```dockerfile
COPY web/package.json web/package-lock.json web/.npmrc ./web/
COPY web/scripts ./web/scripts
RUN ... cd web && npm install
```
- Minimal and safe: `scripts/prepare.js` only installs lefthook git
hooks and already no-ops (wrapped in try/catch) inside the image where
there is no Git repo.
- Preferred over `--ignore-scripts`, which would also disable
dependencies' legitimate install scripts.
- `web/scripts` changes rarely, so build-cache impact on this layer is
negligible.
## Verification
Build reaches the frontend `npm install` step without the
`MODULE_NOT_FOUND` error. No application code is touched — this is a
build-infrastructure fix only.
## Summary
- Register Wikipedia component + tool alias
`wikipedia`/`wikipedia_search`
- Use `generator=search` to get title/summary/url in one request (was
N+1)
- Node params `top_n`/`language` with validation
- Return `formalized_content` for downstream
- tests pass
<img width="1817" height="972" alt="image"
src="https://github.com/user-attachments/assets/f6d79599-6d1f-4ea6-84f7-ac06d0de13b0"
/>
Two refactors on the Go port (agent-go-port):
- Remove the dead per-tenant canvas runtime selector (write-only Redis
scaffolding with no runtime callers) and its dependent metrics/admin
code.
- Move the tokenizer embedding-model id from the shared ingestion
globals into a Tokenizer-scoped setup, and wire the production embedder
resolver in the ingestion task package.
32 files changed, 861 insertions, 1228 deletions.
## Summary
- align the Go Google Scholar component with the Python-side config
pattern
- merge node-level params with runtime inputs so canvas defaults are
preserved and per-run inputs can override them
- add tests covering node param fallback and runtime override behavior
## Verification
- `bash build.sh --test ./internal/agent/component/... -run
TestGoogleScholar`
<img width="1873" height="1165" alt="image"
src="https://github.com/user-attachments/assets/67198c6f-6a0e-43bf-a500-8e88d82b8751"
/>
### What problem does this PR solve?
Closes#8175.
The Agent OpenAI-compatible streaming path uses `get_data_openai(...,
stream=True)`, but that helper currently returns a minimal chunk shape.
The main OpenAI-compatible chat endpoint already includes chunk metadata
such as `created`, `system_fingerprint`, `usage`, `logprobs`, and
assistant role/tool placeholders.
This PR aligns the Agent stream helper with that existing
OpenAI-compatible chunk shape while keeping the current `delta.content`
behavior and existing reference injection path intact.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### Verification
- `./.venv/bin/python -m pytest
test/unit_test/api/utils/test_api_utils.py -q`
- `python3 -m py_compile api/utils/api_utils.py
test/unit_test/api/utils/test_api_utils.py`
- `uvx ruff check api/utils/api_utils.py
test/unit_test/api/utils/test_api_utils.py`
---------
Co-authored-by: Harsh Kashyap <harshkashyap@Harshs-MacBook-Pro.local>
feat(ingestion): mirror Go pipeline progress into the document table;
harden resume guards
- pipeline: bind the owning document via WithDocumentID; after each
TrackProgress event aggregate ingestion_task_log progress and mirror
progress/run/progress_msg back into the document table, so GET
/api/v1/datasets/{dataset_id}/documents reflects live Go pipeline
progress without a bespoke endpoint.
- canvas: extend the S3 resume guard to reject legacy no-op nodes (e.g.
ExitLoop) so component_total equals the count of progress-reporting
components and the aggregate percent can reach 100%.
- runtime/canvas: route progress through TrackProgress; add interrupt
test coverage (r3_interrupt_test.go).
- dao/entity: add IngestionTask.DocumentID column and AggregateProgress
support used by the mirror; IngestionTaskLog keeps a Checkpoint column
alongside the progress fields.
feat(deepdoc): cache DocAnalyzer inference results in Redis (1h TTL)
- Redis-backed DocAnalyzerCache decorator over inference.Client; cache
key = "ddoc:cache:<method>:" + sha256 of the JPEG-encoded image bytes
(deterministic).
- TTL = 1h; hits skip the inner HTTP call and return cached JSON; inner
errors are not cached.
refactor(deepdoc): align figure cropping with Python cropout + bounded
page caches
- CropSectionByDLA mirrors Python cropout: best-overlap DLA
figure/equation region, fallback to section bbox per page, vertical
concat on gray background.
- sliding-window page-image cache bounds peak memory to the recent
window instead of the whole PDF.
- rename DLADebug -> DLARegions across parser/chunker/tests.
refactor(parser): drop lib_type selector; align NewXxxParser with
NewPDFParser
- remove config["lib_type"] lookup and the libType param/field/switch
from all nine constructors; surface the CGO-required error at
ParseWithResult time instead of construction time; drop resolveLibType,
its test, and the four lib_type constants.
feat(utility): add a reusable workerpool for bounded concurrent
execution
- internal/utility/workerpool.go (+ tests).
refactor: translate Chinese prose comments to English in non-harness Go
files.
chore: upgrade github.com/cloudwego/eino from v0.9.9 to v0.9.12.
## Summary
- register the Go `DuckDuckGo` canvas component and restore its dynamic
input form metadata
- align the Go component input/output surface with the current canvas
usage for `query`, `channel`, and `top_n`
- fix DuckDuckGo news search in Go by fetching the required `vqd` token
before calling `news.js`, and add targeted regression tests
## Testing
Passed:
- `bash build.sh --test ./internal/agent/tool/... -run 'DuckDuckGo'`
- `bash build.sh --test ./internal/agent/component/... -run
'DuckDuckGo|TestVerifyRegistration_P1'`
- `bash build.sh --test ./internal/agent/component/... -run
'DuckDuckGo'`
Not run:
- frontend tests
- frontend build
- full Go test suite
<img width="1776" height="1092" alt="image"
src="https://github.com/user-attachments/assets/9f3f8e4b-f6b4-4915-b96c-3c5b8c7b8b30"
/>
### Summary
- Implemented googleComponent wrapper to bridge the canvas component
contract with Eino's SerpApi-backed GoogleTool.
- Added parameter alias mapping (query to q, max_results to num) and
content formatting logic to match Python search result representation.
- Registered the "Google" component and the "google" tool factory in the
Go agent runtime to support web search nodes.
<img width="1776" height="1092" alt="image"
src="https://github.com/user-attachments/assets/e295ab88-e48c-4fe2-bcb7-47ca5b977c9b"
/>
### Summary
- TavilySearch now stores api_key from component params and injects it
into tool calls when runtime inputs omit it.
- TavilyExtract and BGPT now follow the same stored api_key behavior.
- Canvas decoding now recovers api_key from graph.nodes[].data.form when
components[].obj.params.api_key is empty, matching frontend payload
behavior without changing frontend data.
- Added regression tests for graph form key recovery and stored key
injection / caller key precedence.
Tests: build.sh --test ./internal/agent/component ./internal/service —
all pass.
<img width="1476" height="850" alt="image"
src="https://github.com/user-attachments/assets/0be31587-c1ba-4f3e-b43a-4fe0fca5a44c"
/>
<img width="1476" height="850" alt="image"
src="https://github.com/user-attachments/assets/e3edd92c-c62e-4db4-abe2-772bdf4fe1b2"
/>
### Summary
Handle searching dataset without embedding model
In this PR, Searching datasets with different embedding models or
searching dataset with/without embedding models are not allowed. We will
improve the behavior later.
