### Summary
Add object as a begin-node parameter type with JSON editor UI, webhook
schema support, and backend parsing in UserFillUp.
Co-authored-by: Cursor <cursoragent@cursor.com>
## Summary
Fixes a page crash when opening the BGPT node configuration in the
canvas.
## Root Cause
BGPT was using the tool-form watcher call pattern in a normal canvas
component form.
Tool forms use:
useWatchFormChange(form)
Canvas component forms use:
useWatchFormChange(node?.id, form)
Tool is not equal to component. The BGPT canvas component imported the
component-level hook but called it like a tool-form hook, so the form
argument became undefined and React Hook Form tried to read control from
a null context.
## Change
Updated the BGPT canvas form to pass the node id and form instance
correctly.
## Validation
Ran ESLint for the changed file:
npx eslint src/pages/agent/form/bgpt-form/index.tsx
<img width="1369" height="1184" alt="image"
src="https://github.com/user-attachments/assets/a40c5202-7394-4f26-9da2-08329dcc7fbf"
/>
## Summary
- Add language-aware Snowball stemmer to `RagTokenizer` supporting 16
languages (Dutch, German, French, Spanish, etc.)
- Thread the KB `language` parameter through the full tokenization
pipeline (14 parser modules + task executor)
- Add Dutch to the frontend language lists and cross-language form
## Problem
RAGFlow uses the English Porter stemmer + WordNet lemmatizer for **all**
BM25 tokenization, regardless of the knowledge base language setting.
This produces incorrect stems for non-English text. For example:
| Dutch word | Dutch stemmer | English Porter |
|---|---|---|
| documenten | document | documenten (unchanged!) |
| gebruikers | gebruiker | gebruik (over-stemmed) |
| instellingen | instell | instellingen (unchanged!) |
This degrades BM25 recall for any non-English knowledge base.
## Solution
NLTK already ships Snowball stemmers for 16 languages. This PR:
1. **`rag/nlp/rag_tokenizer.py`**: Overrides `tokenize()` with
`set_language()` and `_normalize_token()` that selects the correct NLTK
Snowball stemmer. Falls back to Porter for unmapped languages (Chinese,
Japanese, Korean, etc. — these use character-based tokenization anyway).
2. **`rag/nlp/__init__.py`** + **14 `rag/app/*.py` parsers** +
**`rag/svr/task_executor.py`**: Threads the `language` parameter through
`tokenize()`, `tokenize_chunks()`, `tokenize_table()`, and all callers.
3. **Frontend**: Adds Dutch (`Nederlands`) to `LanguageList`,
`LanguageMap`, `LanguageAbbreviationMap`, `LanguageTranslationMap`,
cross-language form field, and `en.ts` locale.
## Backward Compatibility
- Default language is `"English"`, preserving existing behavior for all
current users
- Languages without a Snowball stemmer mapping fall back to Porter (no
change)
- No new dependencies — NLTK Snowball is already bundled
## Summary
Fixes#15215 — attachments uploaded to an agent were not reaching the
LLM.
When a user uploads a file in an agent chat, `canvas.run` parses it into
the `sys.files` global (text content for documents, `data:image/...`
URIs
for images — see `agent/canvas.py:752-768`). But the LLM/Agent
component's
`_prepare_prompt_variables` only substitutes variables the user's prompt
template explicitly references via `{var}` placeholders. The default
prompt is `[{"role": "user", "content": "{sys.query}"}]` with no
`{sys.files}`, so the parsed attachment content never reaches the model.
In the reporter's logs, this is why the agent saw only the bare query
`附件 摘要 attachment summary` and went searching the dataset instead of
reading the uploaded PDF.
## Fix
`agent/component/llm.py` — added `_collect_sys_files()` and an
auto-injection step in `_prepare_prompt_variables`:
- If `sys.files` is non-empty **and** neither `sys_prompt` nor any entry
in `prompts` already contains `{sys.files}` (no double-injection),
split the entries into text vs. `data:image/...` URIs.
- Image URIs are merged into `self.imgs`, which the existing logic uses
to switch the chat model to `IMAGE2TEXT` and pass `images=...` to
`async_chat`.
- Text content is appended to the last `user` role message in `msg`,
mirroring how `dialog_service.async_chat_solo` handles attachments for
the non-agent chat path (`api/db/services/dialog_service.py:318-321`).
Both `LLM._invoke_async` and `Agent._invoke_async` (tool-using) go
through `_prepare_prompt_variables`, so plain LLM nodes and Agent nodes
are fixed in both streaming and non-streaming paths.
## Test plan
- [ ] Upload a PDF attachment to an agent with the default `{sys.query}`
prompt and ask "summarize the attachment" — the model should answer
from the file content rather than searching the knowledge base.
- [ ] Upload an image attachment to an agent and ask about its contents
—
the model should switch to the vision-capable LLM and answer from
the image.
- [ ] Verify that an agent whose prompt **does** include `{sys.files}`
still works and does **not** include the file content twice.
- [ ] Verify that an agent run with no attachments behaves unchanged.
- [ ] Run `uv run pytest` to make sure no existing tests regress.
### 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):
---------
Co-authored-by: yzc <yuzhichang@gmail.com>
### What problem does this PR solve?
This PR adds Google BigQuery as a first-class data source connector in
RAGFlow.
It enables users to ingest and sync BigQuery data using the same
row-to-document model used by relational database connectors: selected
content columns become document text, metadata columns become document
metadata, an optional ID column provides stable document IDs, and an
optional timestamp column enables cursor-based incremental sync.
The connector supports service-account JSON credentials, table mode,
custom query mode, GoogleSQL queries, cursor-based incremental sync,
deleted-row pruning support, configurable query limits such as
`maximum_bytes_billed`, dry-run validation, batch loading, stable
document IDs, and BigQuery-aware value serialization.
### Summary
fix: user-setting modal fixes and DOMPurify cleanup
- HighlightMarkdown: drop post-process DOMPurify pass (ineffective after
preprocessLaTeX; Coderabbit CRITICAL
#3486038798)
- SettingTeam: add invite-only-registered-users hint to add-user modal
- SettingModel: reset provider loading state when add-provider modal
closes
- MCP edit dialog: set maskClosable=false to prevent accidental
dismissal
- Form: switch FormDescription color from text-muted-foreground to
text-text-disabled
closes#14769
### What problem does this PR solve?
_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._
### 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):
---------
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
