### What problem does this PR solve?
Feat: sync deleted files in Bitbucket
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
**Addresses the Google Drive integration for #14362**
This PR completely overhauls the Google Drive sync logic to accurately
detect remote deletions, while drastically reducing the memory footprint
during the snapshot phase.
### What changed under the hood:
* **Killed the memory bloat:** Swapped out the massive document
dictionary objects for a lightweight `collections.namedtuple` (`SlimDoc
= namedtuple('SlimDoc', ['id'])`). This prevents RAM spikes during
`retrieve_all_slim_docs_perm_sync` on massive enterprise drives.
* **Flawless downstream integration:** The `SlimDoc` object relies on
simple duck typing. It perfectly delivers the `.id` attribute required
by `ConnectorService.cleanup_stale_documents_for_task`, meaning your
core `hash128` vector cleanup logic runs natively without modification.
* **Fixed the Shared Drive blindspot:** The standard API query was
missing team folders. Injected the `corpora="allDrives"` and
`includeItemsFromAllDrives=True` override flags so the connector now
accurately maps state across both personal workspaces and organizational
Shared Drives.
### Testing:
Isolated the Google API retrieval logic locally to prove the `SlimDoc`
mapping works and correctly registers state drops when a file is trashed
remotely.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Performance Improvement
### What problem does this PR solve?
Fix: enable sync deleted file in airtable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
## Summary
Migrate two web API endpoints to REST-style HTTP API endpoints,
following the pattern established in #14222:
| Old Endpoint | New Endpoint |
|---|---|
| `POST /v1/chunk/retrieval_test` | `POST
/api/v1/datasets/<dataset_id>/search` |
| `GET /v1/chunk/knowledge_graph` | `GET
/api/v1/datasets/<dataset_id>/graph` |
### What problem does this PR solve?
Fix: google authentication - gmail && google-drive
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Steps to re-produce (existing bug before API migration):
create a new dataset
upload a file
click on "General" in "Parse" column and then click on "switch or
configure ingestion pipeline"
click on "Settings" (at right of "Auto metadata")
click "Add" to add new metadata
click on "Save"
re-open "Settings" and the newly added metadata is not there
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
agent toolcall null response & schema validation & DeepSeek think
history
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feat: enable sync delted files for connectors
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
Fixes case-asymmetric matching for manual `meta_data_filter` when using
**`in`** / **`not in`** with a **list** `value`. Document metadata
strings were lowercased, but list elements were not, so values like
`"F2"` failed to match `["F2", "F11"]` even though **`=`** behaved
correctly.
Closes#14389
## Changes
- **`common/metadata_utils.py`**: For **`in`** / **`not in`**, normalize
string elements when `value` and/or `input` is a list, consistent with
scalar string lowercasing.
- **`test/unit_test/common/test_metadata_filter_operators.py`**:
Regression tests for list `value` case-insensitivity and **`not in`**.
## Type of change
- [x] Bug fix (non-breaking)
### What problem does this PR solve?
This PR fixes a regression where Manual pipeline + Naive (Plain Text)
PDF parsing crashed with `AttributeError: 'PlainParser' object has no
attribute 'extract_positions'` in `rag/app/manual.py`.
fixes#14411
### Type of change:
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Add methods to volcengine
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Always return success if no such task id to follow existing code logic.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
align chat recommendation and thumbup APIs
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
preserve infinity available_int zero filter
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
1. Refactor server_main
2. Add volcengine
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
[Uploading part_4-13.pdf…]()
### What problem does this PR solve?
In chat, the thumbnails didn't display correctly
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
Steps to reproduce:
1. create dataset and upload a file (see attached)
2. parse the document
3. once parsing completed, create a chat and associate it with the
dataset
4. ask a question (DAP VS DAPE comparison)
5. check result
### What problem does this PR solve?
Before migration
Web API: POST /v1/document/change_parser
HTTP API: PATCH /api/v1/datasets/<dataset_id>/documents
After consolidation, Restful API
PATCH /api/v1/datasets/<dataset_id>/documents
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Add executor shutdown in finally clause to free resources.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Before migration: GET /v1/document/thumbnails
After migration: GET /api/v1/thumbnails
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Before migration: POST /v1/document/run
After migration: POST /api/v1/documents/ingest/
### Type of change
- [x] Refactoring
### What problem does this PR solve?
As title.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
### Summary
PR #14222 consolidated KB (web) API endpoints into RESTful Dataset
(HTTP) API endpoints and deleted the web API test suite under
`test_web_api/test_kb_app/` and `test_web_api/test_document_app/`. While
most test coverage was migrated to the HTTP API test suite, some tests
were not ported over. This PR adds back the missing coverage.
### Route migration reference
| Old Web API | New HTTP API | Missing tests |
|---|---|---|
| `POST /v1/kb/update_metadata_setting` | `PUT
/api/v1/datasets/<id>/metadata/config` | auth & error paths |
| `GET /api/v1/datasets/<id>/auto_metadata` | `GET
/api/v1/datasets/<id>/metadata/config` | auth & CRUD |
| `PUT /api/v1/datasets/<id>/auto_metadata` | `PUT
/api/v1/datasets/<id>/metadata/config` | auth & CRUD |
| `GET /v1/kb/<kb_id>/basic_info` | `GET
/api/v1/datasets/<id>/ingestions/summary` | covered |
| `POST /v1/kb/list_pipeline_logs` | `GET
/api/v1/datasets/<id>/ingestions` | edge cases missing |
### Changes
#### `test_file_management_within_dataset/test_metadata_config.py` (new,
10 tests)
Covers `GET/PUT /datasets/<id>/metadata/config` (migrated from
`test_kb_tags_meta.py`'s `test_update_metadata_setting` and
`test_document_metadata.py`'s negative tests):
- Authorization for dataset metadata config GET/PUT
- Authorization for document metadata config PUT
- Success, invalid dataset, missing payload, not found scenarios
#### `test_dataset_management/test_ingestion_logs.py` (extended, +2
tests)
Covers `GET /datasets/<id>/ingestions` edge cases (migrated from
`test_kb_pipeline_tasks.py`):
- Missing dataset ID
- Abnormal date filter
### Type of change
- [x] Other: Test coverage improvement
---------
Signed-off-by: noob <yixiao121314@outlook.com>
### What problem does this PR solve?
Before migration
Web API: POST /v1/document/change_status
After consolidation, Restful API
POST /api/v1/datasets/<dataset_id>/documents/batch-update-status
### Type of change
- [x] Refactoring
### What problem does this PR solve?
prioritize explore session ID and reset default conversation variables
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Dockerfile v0.25.0 expects nginx conf at path
/etc/nginx/ragflow.conf.python, see
[Dockerfile#L200](ca01c7a745/Dockerfile (L200))
However current helm template mount the conf at path
/etc/nginx/ragflow.conf causing runtime error at startup time.
### Type of change
- [X] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Mauro Gattari <mauro.gattari@infn.it>
### What problem does this PR solve?
Before migration: POST /v1/document/upload_info/
After migration: POST /api/v1/documentss/upload/
### Type of change
- [x] Refactoring
## Summary
- **Lazy img_np loading**: `np.array(img)` is now deferred until the
first OCR text extraction is actually needed, avoiding unnecessary
memory allocation for pages that already have text.
- **Chunked parse_into_bboxes**: Large PDFs (>50 pages, configurable via
`PDF_PARSER_PAGE_BATCH_SIZE`) are processed in batches. Each chunk's
boxes are normalized with `_to_global_boxes` to produce globally
consistent page numbers and position tags.
- **DLA early init**: Move remote-client initialization before model
loading in `LayoutRecognizer.__init__` so `DEEPDOC_URL` (or legacy
`TENSORRT_DLA_SVR`) short-circuits unnecessary model download for parser
containers relying on remote inference.
- **Fix outline regression**: Restore `self.outlines =
extract_pdf_outlines(fnm)` in `parse_into_bboxes`; this was dropped
during refactoring and is required by downstream `remove_toc` and
metadata handling in `rag/flow/parser/parser.py`.
## Test plan
- [ ] Small PDF (<=50 pages): verify parse succeeds and `self.outlines`
is populated
- [ ] Large PDF (>50 pages): verify chunked processing produces globally
consistent page numbers
- [ ] With `DEEPDOC_URL` set: verify remote DLA client is used and local
model is not downloaded
- [ ] With legacy `TENSORRT_DLA_SVR` set: verify backward compatibility
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
### What problem does this PR solve?
This PR fixes issue #14371 where file parsing failed after upgrading
from v0.24.0 to v0.25.0, because metadata config could be a JSON Schema
object but was handled like a list and later caused `KeyError:
'properties'`.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Before migration: GET /v1/document/artifact/<filename>
After migration: GET /api/v1/documents/artifact/<filename>
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Fixes#14196
## Problem
When using DeepDOC to parse large PDFs (over 1000 pages), the parser
silently truncated processing at 300 pages due to a hardcoded default
`page_to=299` in `RAGFlowPdfParser.__images__()`. This caused:
- **Errors** on pages beyond the limit
- **Poor image quality** as the parser attempted to compensate with
missing page data
- **Inconsistent chunk splitting** between full PDF imports and partial
imports
Additionally, the codebase scattered magic numbers (`299`, `600`,
`10000`, `100000`, `100000000`, `10000000000`, `10**9`) across 22 files
as sentinel values for "parse all pages", making future maintenance
error-prone.
## Root Cause
```python
# deepdoc/parser/pdf_parser.py (before)
def __images__(self, fnm, zoomin=3, page_from=0, page_to=299, callback=None):
# Only the first 300 pages were rendered; everything beyond was silently dropped
```
While most callers in `rag/app/*.py` correctly passed `to_page=100000`,
the base class `RAGFlowPdfParser.__call__()` and `parse_into_bboxes()`
invoked `__images__` **without** forwarding `page_from`/`page_to`,
falling back to the restrictive default of 299.
## Solution
### 1. Define constants in `common/constants.py`
```python
MAXIMUM_PAGE_NUMBER = 100000 # Used by the parsing layer
MAXIMUM_TASK_PAGE_NUMBER = MAXIMUM_PAGE_NUMBER * 1000 # Used by the task/DB layer
```
### 2. Replace all hardcoded sentinel values
| Layer | Files Changed | Old Values | New Value |
|---|---|---|---|
| **Deepdoc parsers** | `pdf_parser.py`, `mineru_parser.py`,
`docling_parser.py`, `opendataloader_parser.py`, `paddleocr_parser.py`,
`docx_parser.py` | `299`, `600`, `10**9`, `100000000` |
`MAXIMUM_PAGE_NUMBER` |
| **Chunk parsers** | `naive.py`, `book.py`, `qa.py`, `one.py`,
`manual.py`, `paper.py`, `presentation.py`, `laws.py`, `resume.py`,
`email.py`, `table.py` | `100000`, `10000`, `10000000000` |
`MAXIMUM_PAGE_NUMBER` |
| **Task/DB layer** | `db_models.py`, `task_service.py`,
`document_service.py`, `file_service.py` | `100000000` |
`MAXIMUM_TASK_PAGE_NUMBER` |
### 3. Fix `parse_into_bboxes()` missing parameters
Added `from_page`/`to_page` parameters to `parse_into_bboxes()` so that
the `rag/flow/parser/parser.py` DeepDOC path no longer falls back to the
restrictive default.
## Files Changed (22)
- `common/constants.py`
- `deepdoc/parser/pdf_parser.py`
- `deepdoc/parser/mineru_parser.py`
- `deepdoc/parser/docling_parser.py`
- `deepdoc/parser/opendataloader_parser.py`
- `deepdoc/parser/paddleocr_parser.py`
- `deepdoc/parser/docx_parser.py`
- `rag/app/naive.py`
- `rag/app/book.py`
- `rag/app/qa.py`
- `rag/app/one.py`
- `rag/app/manual.py`
- `rag/app/paper.py`
- `rag/app/presentation.py`
- `rag/app/laws.py`
- `rag/app/resume.py`
- `rag/app/email.py`
- `rag/app/table.py`
- `api/db/db_models.py`
- `api/db/services/task_service.py`
- `api/db/services/document_service.py`
- `api/db/services/file_service.py`
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
---------
Signed-off-by: noob <yixiao121314@outlook.com>
### What problem does this PR solve?
As title.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
AI coding agents (Claude, Copilot, etc.) tend to directly edit files in
`src/components/ui/` when asked to tweak styles or add props, treating
them like ordinary feature code. This silently breaks the shared
component library that both shadcn primitives and project-authored
common components live in.
This PR adds a `Shared UI Component Lock` convention to `web/CLAUDE.md`
to instruct AI agents to treat the entire `src/components/ui/` directory
as read-only. Any customization must be done via wrappers or composition
outside the directory; exceptions require explicit user approval.
### Type of change
- [x] Other (please describe): Update `CLAUDE.md`
## Summary
PDF files often contain a bookmark/outline tree (table of contents built
into the file by the authoring tool). RAGFlow's `pdf_parser.outlines`
already extracts these `(title, depth)` tuples via pypdf, but they are
used ephemerally during chunking (`manual` parser uses them for
hierarchy detection) and then discarded.
This PR persists the outline as `doc.meta_fields["outline"]` — a JSON
array of `{"title": str, "depth": int}` objects — so downstream features
can use the structural information.
### Why this matters
- **Complementary to `toc_extraction`** — the existing `toc_extraction`
feature uses LLM calls to generate a TOC and only works for the `naive`
parser. The raw PDF outline is free (already extracted by pypdf), works
for all parsers, and captures the author's original document structure.
- **Document navigation** — frontends can render a clickable TOC from
the outline
- **Entity extraction** — the outline provides a structural map for
identifying document sections and key topics
- **Search result context** — knowing which section a chunk belongs to
helps users evaluate relevance
### Changes
| File | Change | LOC |
|------|--------|-----|
| `rag/app/naive.py` | Attach `pdf_parser.outlines` as `__outline__` on
first chunk dict | ~7 |
| `rag/app/manual.py` | Same for the manual parser | ~5 |
| `rag/svr/task_executor.py` | Extract `__outline__`, persist via
`DocMetadataService.update_document_metadata()` | ~12 |
### Design decisions
- **Transient key pattern**: The outline is passed from parser →
task_executor via `__outline__` on the first chunk dict, then removed
before indexing. This follows the same pattern as `metadata_obj` for
LLM-generated metadata.
- **No schema changes**: Uses the existing `meta_fields` JSON column on
the document table.
- **Graceful degradation**: If a PDF has no outline (common for scanned
docs), nothing is stored. If persistence fails, it logs a warning and
continues — parsing is not interrupted.
### Backward compatibility
- **Fully backward compatible** — no existing fields, behavior, or
schemas changed
- PDFs without outlines are unaffected
- Existing `meta_fields` data is preserved (merged, not overwritten)
## Test plan
- [ ] Parse a PDF with bookmarks (e.g. any multi-chapter document),
verify `meta_fields["outline"]` is populated
- [ ] Parse a PDF without bookmarks, verify no errors and no outline key
in meta_fields
- [ ] Verify existing `meta_fields` data is preserved (not overwritten)
when outline is added
- [ ] Verify `manual` parser also persists outlines
- [ ] Verify outline JSON structure: `[{"title": "Chapter 1", "depth":
0}, ...]`
Related: #9921 (Deterministic Document Access Layer)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: yuch85 <yuch85.1@gmail.com>
Co-authored-by: Wang Qi <wangq8@outlook.com>
