### What problem does this PR solve?
Partially addresses #14362.
This PR enables syncing deleted files for RSS data sources.
Previously, RSS incremental sync only returned feed entries whose
timestamps were inside the poll window. If an entry was removed from the
RSS feed, RAGFlow had no full current RSS snapshot to pass into the
shared stale-document cleanup path, so the deleted remote entry could
remain in the knowledge base.
This PR:
- adds `retrieve_all_slim_docs_perm_sync()` to `RSSConnector`
- reuses the same `rss:<md5(stable_key)>` document ID derivation used by
normal RSS ingest
- returns `(document_generator, file_list)` for incremental RSS sync
when `sync_deleted_files` is enabled
- captures the poll end timestamp before snapshot/poll so cleanup does
not race against the same sync window
- adds start/end logs around RSS slim snapshot collection
- exposes the deleted-file sync toggle for RSS in the data source UI
Per maintainer request on related datasource PRs, this PR contains no
test-case changes. Local verification was run with an external script.
Validation:
- `uv run ruff check common/data_source/rss_connector.py
rag/svr/sync_data_source.py`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
- `git diff --check`
- `uv run python /tmp/verify_rss_deleted_sync.py --repo
/root/74/ragflow`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## What problem does this PR solve?
Incremental WebDAV sync only ingested files whose modification time fell
inside the poll window; documents removed on the WebDAV server were
never removed from the knowledge base. This aligns with
[#14362](https://github.com/infiniflow/ragflow/issues/14362)
(coordinated datasource “sync deleted files” work).
This PR adds a **full-tree slim snapshot**
(`retrieve_all_slim_docs_perm_sync`) that enumerates current remote
paths **without downloading file contents**, using the same logical
document IDs as full ingest (`webdav:{base_url}:{file_path}`). When
**`sync_deleted_files`** is enabled on incremental runs, sync returns
**`(document_generator, file_list)`** so **`SyncBase`** runs
**`cleanup_stale_documents_for_task`** and removes KB rows no longer
present remotely.
Design notes:
- **`_list_files_recursive`** gains **`filter_by_mtime`**: snapshot
passes **`filter_by_mtime=False`** (full tree under **`remote_path`**);
**`poll_source`** keeps mtime-window filtering as before.
- Slim snapshot applies the same **extension** and **`size_threshold`**
rules as **`_yield_webdav_documents`** so retain IDs match what would be
indexed.
- **`end_ts`** is captured before building **`file_list`**, then
**`poll_source`** uses the same upper bound (consistent with
Dropbox-style connectors).
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Files changed
| Area | Change |
|------|--------|
| `common/data_source/webdav_connector.py` |
`SlimConnectorWithPermSync`, `retrieve_all_slim_docs_perm_sync`,
`filter_by_mtime` on `_list_files_recursive` |
| `rag/svr/sync_data_source.py` | WebDAV `_generate`: `file_list` +
tuple return; pass **`batch_size`** from connector config |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`syncDeletedFiles` for WebDAV in `DataSourceFeatureVisibilityMap` |
### What problem does this PR solve?
Refs #14362.
This PR enables syncing deleted files for Zendesk data sources.
Previously, Zendesk incremental sync never returned a slim remote
snapshot to the shared stale-document cleanup path, so deleted remote
Zendesk records could remain in RAGFlow. The existing Zendesk slim
snapshot also included records that ingestion intentionally skips, such
as draft articles, articles without bodies, skipped-label articles,
empty-body articles, and tickets with `status == "deleted"`.
This PR:
- exposes the deleted-file sync option for Zendesk in the data source UI
- returns Zendesk slim snapshots during incremental sync when
`sync_deleted_files` is enabled
- reuses Zendesk indexability rules so cleanup compares against the same
records ingestion can materialize
- adds start/end logs around Zendesk slim snapshot collection for
operational visibility
Per maintainer request, this PR contains no test-case changes. Manual
verification recording will be provided separately.
Validation:
- `uv run ruff check common/data_source/zendesk_connector.py
rag/svr/sync_data_source.py`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
### 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):
### What problem does this PR solve?
Partially addresses #14362.
Adds deleted-file sync support for the Asana data source. Asana already
indexes task attachments as documents, but it did not provide the slim
document snapshot required by stale-document reconciliation, and the
sync wrapper never returned a `file_list` for cleanup.
This PR:
- adds `retrieve_all_slim_docs_perm_sync()` to `AsanaConnector`
- builds slim IDs with the same `asana:{task_id}:{attachment_gid}`
format used by indexed documents
- avoids downloading attachment blobs during the snapshot
- aborts the snapshot if Asana API errors occur, preventing partial
snapshots from deleting valid local docs
- captures the incremental poll end time before snapshotting and makes
`poll_source()` respect that boundary
- exposes the deleted-file sync toggle for Asana in the data source UI
Per maintainer request, this PR contains no test-case changes. Manual
verification recording will be provided separately.
Validation:
- `uv run ruff check common/data_source/asana_connector.py
rag/svr/sync_data_source.py`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
- `git diff --check`
### Type of change
- [x] New Feature
### What problem does this PR solve?
Incremental Seafile sync only ingests files whose modification time
falls in the poll window; documents removed in Seafile were never
removed from the knowledge base. This contributes to
[#14362](https://github.com/infiniflow/ragflow/issues/14362) (datasource
“sync deleted files” coordination).
This PR adds a **slim snapshot** (`retrieve_all_slim_docs_perm_sync`)
that enumerates current remote file IDs **without downloading content**,
using the same logical IDs as full ingest
(`seafile:{repo_id}:{file_id}`). When **`sync_deleted_files`** is
enabled on incremental runs, **`SeaFile._generate`** returns
**`(document_generator, file_list)`** so **`SyncBase`** can run
**`cleanup_stale_documents_for_task`** and remove stale KB documents.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What changed
- **`common/data_source/seafile_connector.py`**: `SeaFileConnector`
implements **`SlimConnectorWithPermSync`**;
**`_list_files_recursive(..., filter_by_mtime=...)`** supports full-tree
listing for snapshots; **`retrieve_all_slim_docs_perm_sync()`** reuses
the same library/root scan as ingest and applies the same **size**
ceiling; logging for snapshot start/end and counts.
- **`rag/svr/sync_data_source.py`**: **`SeaFile._generate`** validates
**`batch_size`**, captures **`end_ts`** before snapshot +
**`poll_source`**, wraps slim retrieval in **`try`/`except`** (
**`file_list = None`** on failure so ingest continues), returns
**`(generator, file_list)`**.
- **`web/src/pages/user-setting/data-source/constant/index.tsx`**:
**`syncDeletedFiles`** for Seafile in
**`DataSourceFeatureVisibilityMap`**.
### What problem does this PR solve?
Both tokenizer (`rag/flow/tokenizer/tokenizer.py`) and
`BuiltinEmbed.encode`
(`rag/llm/embedding_model.py`) currently accumulate embedding batches
via
`np.concatenate` inside the per-batch loop. `np.concatenate` allocates a
new
array and copies all existing data on every call, so accumulating N
batches
is O(N²) in both time and peak memory.
Replacing the incremental concatenate with a list-of-batches + a single
`np.vstack` at the end gives O(N) total work.
For tokenizer the title-vector broadcast `np.concatenate([vts[0]] * N)`
is
also replaced by `np.tile`, which does the same job with a single
contiguous
allocation instead of building a Python list of references.
This is purely a CPU/memory optimisation — output shape and dtype are
unchanged. Measured impact grows with document size:
- 1k chunks (batch 512, 2 iters): ~negligible
- 10k chunks (20 iters): ~10× speedup on this stage
- 100k chunks (195 iters): ~100× speedup, and peak RAM
drops from O(N) extra to near-zero
### Type of change
- [x] Performance Improvement
Co-authored-by: yoan sapienza <Yoan Sapienza yoan.sapienza@orange.fr Yoan Sapienza zappy@macbookpro.home>
### What problem does this PR solve?
Feat: enable sync deleted file for Discord
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Partially addresses #14362 by adding deleted-file sync support for the
Dropbox data source.
Dropbox previously did not provide the slim current-file snapshot
required by stale document reconciliation, and its sync runner returned
only document batches. As a result, enabling deleted-file sync could not
remove local documents that had been deleted from Dropbox.
This PR:
- Adds `retrieve_all_slim_docs_perm_sync()` to `DropboxConnector`.
- Reuses Dropbox metadata traversal to collect current remote file IDs
without downloading file contents.
- Wires incremental Dropbox sync to return `(document_generator,
file_list)` when `sync_deleted_files` is enabled.
- Enables the deleted-file sync toggle for Dropbox in the data source
settings UI.
- Adds regression coverage for slim snapshots, nested folders, paginated
listings, duplicate filenames, and full reindex behavior.
Tests:
- `uv run pytest test/unit_test/common/test_dropbox_connector.py -q`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `uv run pytest test/unit_test/common/test_dropbox_connector.py
test/unit_test/rag/test_sync_data_source.py -q`
- `uv run ruff check common/data_source/dropbox_connector.py
rag/svr/sync_data_source.py
test/unit_test/common/test_dropbox_connector.py
test/unit_test/rag/test_sync_data_source.py`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Feat: enable sync deleted files in gitlab
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Feat: enable sync deleted files for Gmail && fix google drive issues
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
Co-authored-by: balibabu <assassin_cike@163.com>
### 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?
Feat: enable sync delted files for connectors
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### 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?
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)
## 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>
## Summary
RAPTOR's recursive clustering builds a `layers` list tracking
`(start_idx, end_idx)` boundaries per level, but currently discards this
information — only the flat `chunks` list is returned. This makes it
impossible to distinguish leaf-level summaries from top-level ones.
This PR:
- Returns `(chunks, layers)` tuple from `raptor.py`'s `__call__`
- Annotates each RAPTOR summary chunk with `raptor_layer_int` (1 = first
summary level, 2 = summary-of-summaries, etc.)
- Adds `raptor_layer_int` to `infinity_mapping.json` (Elasticsearch
handles it via existing `*_int` dynamic template)
### Why this matters
Downstream features need to know which RAPTOR layer a summary belongs
to:
- **Retrieving the top-level document summary** for entity extraction,
search snippets, or document comparison
- **Filtering by abstraction level** — users may want only high-level
summaries or only leaf-level cluster summaries
- **RAPTOR recall quality** — #10951 reports summaries not being
recalled for definition queries; layer metadata enables targeted
retrieval
### Changes
| File | Change | LOC |
|------|--------|-----|
| `rag/raptor.py` | Return `(chunks, layers)` tuple | ~3 |
| `rag/svr/task_executor.py` | Build `chunk_layer` mapping, set
`raptor_layer_int` | ~12 |
| `conf/infinity_mapping.json` | Add `raptor_layer_int` integer field |
~1 |
### Backward compatibility
- **Additive only** — no existing fields or behavior changed
- Existing RAPTOR chunks continue to work (they'll have
`raptor_layer_int = 0` by default)
- New RAPTOR chunks get layer metadata automatically
## Test plan
- [ ] Parse a document with RAPTOR enabled, verify `raptor_layer_int` is
set on indexed chunks
- [ ] Verify `raptor_layer_int` values increase with abstraction level
(layer 1 < layer 2 < ...)
- [ ] Verify existing RAPTOR deletion (`delete by raptor_kwd`) still
works
- [ ] Verify Infinity backend accepts the new field
Fixes#7488
Related: #4104, #11191, #10951🤖 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>
### What problem does this PR solve?
Blob storage sync was downloading unsupported files first and rejecting
them later, which wasted bandwidth and made sync slower. This PR skips
unsupported extensions before download and applies `allow_images` in
blob sync. fixes#14338
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Get metadata configuration from union of custom metadata and
built_in_metadata.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Addresses review feedback on #14074 (Checkpoint mechanism for
long-running workflow jobs, issue #12494).
**Changes based on @yuzhichang's review:**
1. **Renamed `checkpoint_service.py` → `task_checkpoint.py`** as
suggested.
2. **Replaced Redis with direct docEngine queries** as suggested — the
subgraph already gets persisted to the doc store by
`generate_subgraph()`, so we just query for it instead of maintaining a
separate checkpoint in Redis. This is simpler, has no extra dependency,
and uses a single source of truth.
**Changes based on CodeRabbit review:**
3. **Fixed `source_id` query format mismatch** — subgraphs are stored
with `source_id: [doc_id]` (list), but the original query used
`source_id: doc_id` (string). Now follows the same pattern as
`does_graph_contains()` in `rag/graphrag/utils.py`: filter by
`knowledge_graph_kwd` only, then match `source_id` in Python. This
avoids ambiguity across Elasticsearch / Infinity / OceanBase backends.
### Changes
| File | Change |
|---|---|
| `api/db/services/task_checkpoint.py` (new) |
`load_subgraph_from_store()` and `has_raptor_chunks()` — docEngine-based
checkpoint queries |
| `rag/graphrag/general/index.py` | `build_one()` calls
`load_subgraph_from_store()` before running LLM extraction |
| `rag/svr/task_executor.py` | RAPTOR per-doc loop calls
`has_raptor_chunks()` before processing |
| `test/unit_test/rag/graphrag/test_checkpoint_resume.py` (new) | 10
unit tests covering subgraph loading, source_id filtering, edge cases |
### How it works
- **GraphRAG:** Before running expensive LLM entity/relation extraction
for a doc, checks the doc store for an existing subgraph (saved by a
previous interrupted run). If found, loads it directly and skips LLM
calls.
- **RAPTOR:** Before processing a doc, checks if RAPTOR chunks
(`raptor_kwd="raptor"`) already exist for it. If yes, skips.
### Testing
- 10 new unit tests — all passing
- Full existing suite: 617 passed
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
Visit
`http://127.0.0.1:9381/?__debugger__=yes&cmd=resource&f=debugger.js`
will expose the flask code:
```
docReady(() => {
if (!EVALEX_TRUSTED) {
initPinBox();
}
// if we are in console mode, show the console.
if (CONSOLE_MODE && EVALEX) {
createInteractiveConsole();
}
const frames = document.querySelectorAll("div.traceback div.frame");
if (EVALEX) {
addConsoleIconToFrames(frames);
}
addEventListenersToElements(document.querySelectorAll("div.detail"), "click", () =>
document.querySelector("div.traceback").scrollIntoView(false)
);
addToggleFrameTraceback(frames);
addToggleTraceTypesOnClick(document.querySelectorAll("h2.traceback"));
addInfoPrompt(document.querySelectorAll("span.nojavascript"));
wrapPlainTraceback();
});
function addToggleFrameTraceback(frames) {
frames.forEach((frame) => {
frame.addEventListener("click", () => {
frame.getElementsByTagName("pre")[0].parentElement.classList.toggle("expanded");
});
})
}
```
### Type of change
- [x] Other (please describe): Fix security risk
fix: support dense_vector from ES fields response (ES 9.x compatibility)
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Configuration Chore (non-breaking change which updates
configuration)
## Summary by CodeRabbit
* **Bug Fixes**
* More accurate handling and unwrapping of dense-vector fields so
returned values have correct shapes.
* Field selection reliably limits returned data and falls back to
alternate result locations when needed.
* Use of consistent result IDs and tolerant handling when score values
are missing.
* **Chores / Configuration**
* Increased build memory and adjusted build-time flags for the frontend
build.
* Simplified runtime model/GPU checks and removed an automated runtime
GPU-install attempt.
* **Build Fixes**
* `web/vite.config.ts`: make `build.minify` and `build.sourcemap`
respect `VITE_MINIFY` and `VITE_BUILD_SOURCEMAP` env vars from
Dockerfile instead of hardcoding `terser` and `true`.
* **Environment**
* Allow stack version override and default the runtime image tag to
"latest".
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Bug Fixes**
* Correct unwrapping of dense-vector fields and reliable field selection
with fallback locations.
* Consistent use of hit-level IDs and tolerant handling when score
values are missing.
* **Chores / Configuration**
* Increased frontend build memory and added build-time minify/sourcemap
flags; build minification and sourcemap now configurable.
* Removed runtime GPU detection for model initialization; force CPU
initialization.
* **Environment**
* Allow stack version override and default runtime image tag to
"latest".
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
Feat: enable sync deleted files for connector
1. first comes with github
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added "sync deleted files" feature for data sources, enabling
automatic removal of files deleted from the source system.
* Added multilingual support for the new sync deleted files setting
across multiple languages.
* **UI Improvements**
* Improved checkbox form field rendering and layout.
* Enhanced full-width display for authentication token input fields.
### What problem does this PR solve?
The MySQL and PostgreSQL sync classes in `sync_data_source.py` were not
passing `id_column`, `timestamp_column`, and `metadata_columns` to
`RDBMSConnector`,
making incremental sync and document update impossible even when
configured.
- Without `id_column`: updated records generate new documents instead of
overwriting existing ones (doc ID is derived from content hash, so any
change produces a new ID).
- Without `timestamp_column`: `poll_source` always falls back to full
sync,
ignoring the configured time range.
- The three fields existed in the frontend default values but had no
form
inputs, so users had no way to fill them in.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### Changes
- **Backend** (`rag/svr/sync_data_source.py`): pass `id_column`,
`timestamp_column`, and `metadata_columns` from `self.conf` to
`RDBMSConnector` for both `MySQL` and `PostgreSQL` sync classes.
- **Frontend**
(`web/src/pages/user-setting/data-source/constant/index.tsx`):
add `ID Column`, `Timestamp Column`, and `Metadata Columns` form fields
to MySQL and PostgreSQL data source configuration UI with tooltips.
Signed-off-by: lixintao <lixintao@uniontech.com>
Co-authored-by: lixintao <lixintao@uniontech.com>
### What problem does this PR solve?
This PR fixes WebDAV sync behavior for unsupported file types
([#13795](https://github.com/infiniflow/ragflow/issues/13795)).
Previously, the WebDAV connector selected files primarily by modified
time (and size threshold) and could still pass unsupported extensions
into the download/document-generation path. This caused unnecessary
processing and inconsistent behavior compared with connectors that
validate file type earlier.
This change adds extension validation in two places:
1. **Early filter during recursive listing** to skip unsupported files
before they enter the download flow.
2. **Defensive filter before download/document creation** to prevent
unsupported files from being processed if any listing edge case slips
through.
It also wires `allow_images` into the WebDAV sync path so image
extension handling follows connector policy.
Scope is intentionally limited to WebDAV for a focused bug-fix PR.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### How was this tested?
- Manual verification with mixed file types under the configured WebDAV
path:
- supported: `.pdf`, `.txt`, `.md`
- unsupported: `.exe`, `.bin`, `.dat`
- Triggered full sync and polling sync.
- Confirmed unsupported files are skipped before download.
- Confirmed supported files are still indexed normally.
- Confirmed image handling follows `allow_images` setting.
Fixes: #13795
### What problem does this PR solve?
Supporting public RSS/Atom feed URLs as data sources for RagFlow.
link https://github.com/infiniflow/ragflow/issues/12313
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
CI isn't stable, try to fix it.
### 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?
Fixes [#13505](https://github.com/infiniflow/ragflow/issues/13505): Jira
incremental sync could miss updated issues after initial sync,
especially near time boundaries.
Root cause:
- Jira JQL uses minute-level precision for `updated` filters.
- Incremental windows had no overlap buffer, so boundary updates could
be skipped.
- Sync log cursor tracking used a backward-facing update for
`poll_range_start`.
- Existing-doc updates in `upload_document` lacked a KB ownership guard
for doc-id collisions.
What changed:
- Added Jira incremental overlap buffer (`time_buffer_seconds`,
defaulting to `JIRA_SYNC_TIME_BUFFER_SECONDS`) when building JQL
lower-bound time.
- Preserved second-level post-filtering to avoid duplicate reprocessing
while still catching boundary updates.
- Improved Jira sync logging to include start/end window and overlap
configuration.
- Updated sync cursor tracking in `increase_docs` to keep
`poll_range_start` moving forward with max update time.
- Added KB ID safety check before updating existing document records in
`upload_document`.
Verification performed:
- Python syntax compile checks passed for modified files.
- Manual verification flow:
1. Run full Jira sync.
2. Edit an already-indexed Jira issue.
3. Run next incremental sync.
4. Confirm updated content is re-ingested into KB.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
### What problem does this PR solve?
Add DingTalk AI Table connector and integration for data synchronization
Issue #13400
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: wangheyang <wangheyang@corp.netease.com>
### What problem does this PR solve?
Add id for table tenant_llm and apply in LLMBundle.
### Type of change
- [x] Refactoring
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
The SeaFile connector currently synchronises the entire account — every
library
visible to the authenticated user. This is impractical for users who
only need
a subset of their data indexed, especially on large SeaFile instances
with many
shared libraries.
This PR introduces granular sync scope support, allowing users to choose
between
syncing their entire account, a single library, or a specific directory
within a
library. It also adds support for SeaFile library-scoped API tokens
(`/api/v2.1/via-repo-token/` endpoints), enabling tighter access control
without
exposing account-level credentials.
### 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):
### Test
```
from seafile_connector import SeaFileConnector
import logging
import os
logging.basicConfig(level=logging.DEBUG)
URL = os.environ.get("SEAFILE_URL", "https://seafile.example.com")
TOKEN = os.environ.get("SEAFILE_TOKEN", "")
REPO_ID = os.environ.get("SEAFILE_REPO_ID", "")
SYNC_PATH = os.environ.get("SEAFILE_SYNC_PATH", "/Documents")
REPO_TOKEN = os.environ.get("SEAFILE_REPO_TOKEN", "")
def _test_scope(scope, repo_id=None, sync_path=None):
print(f"\n{'='*50}")
print(f"Testing scope: {scope}")
print(f"{'='*50}")
creds = {"seafile_token": TOKEN} if TOKEN else {}
if REPO_TOKEN and scope in ("library", "directory"):
creds["repo_token"] = REPO_TOKEN
connector = SeaFileConnector(
seafile_url=URL,
batch_size=5,
sync_scope=scope,
include_shared = False,
repo_id=repo_id,
sync_path=sync_path,
)
connector.load_credentials(creds)
connector.validate_connector_settings()
count = 0
for batch in connector.load_from_state():
for doc in batch:
count += 1
print(f" [{count}] {doc.semantic_identifier} "
f"({doc.size_bytes} bytes, {doc.extension})")
print(f"\n-> {scope} scope: {count} document(s) found.\n")
# 1. Account scope
if TOKEN:
_test_scope("account")
else:
print("\nSkipping account scope (set SEAFILE_TOKEN)")
# 2. Library scope
if REPO_ID and (TOKEN or REPO_TOKEN):
_test_scope("library", repo_id=REPO_ID)
else:
print("\nSkipping library scope (set SEAFILE_REPO_ID + token)")
# 3. Directory scope
if REPO_ID and SYNC_PATH and (TOKEN or REPO_TOKEN):
_test_scope("directory", repo_id=REPO_ID, sync_path=SYNC_PATH)
else:
print("\nSkipping directory scope (set SEAFILE_REPO_ID + SEAFILE_SYNC_PATH + token)")
```
Actual behavior
When using OceanBase as storage, the list_chunk sorting is abnormal. The
following is the SQL statement.
SELECT id, content_with_weight, important_kwd, question_kwd, img_id,
available_int, position_int, doc_type_kwd, create_timestamp_flt,
create_time, array_to_string(page_num_int, ',') AS page_num_int_sort,
array_to_string(top_int, ',') AS top_int_sort FROM
rag_store_284250730805059584 WHERE doc_id = '' AND kb_id IN ('') ORDER
BY page_num_int_sort ASC, top_int_sort ASC, create_timestamp_flt DESC
LIMIT 0, 20
<img width="1610" height="740" alt="image"
src="https://github.com/user-attachments/assets/84e14c30-a97f-4e8f-8c8c-6ccac915d97d"
/>
Co-authored-by: Aron.Yao <yaowei@yaoweideMacBook-Pro.local>
### What problem does this PR solve?
Fix: correct llm_id for graphrag #13030
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR adds MySQL and PostgreSQL as data source connectors, allowing
users to import data directly from relational databases into RAGFlow for
RAG workflows.
Many users store their knowledge in databases (product catalogs,
documentation, FAQs, etc.) and currently have no way to sync this data
into RAGFlow without exporting to files first. This feature lets them
connect directly to their databases, run SQL queries, and automatically
create documents from the results.
Closes#763Closes#11560
### 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):
### What this PR does
**New capabilities:**
- Connect to MySQL and PostgreSQL databases
- Run custom SQL queries to extract data
- Map database columns to document content (vectorized) and metadata
(searchable)
- Support incremental sync using a timestamp column
- Full frontend UI with connection form and tooltips
**Files changed:**
Backend:
- `common/constants.py` - Added MYSQL/POSTGRESQL to FileSource enum
- `common/data_source/config.py` - Added to DocumentSource enum
- `common/data_source/rdbms_connector.py` - New connector (368 lines)
- `common/data_source/__init__.py` - Exported the connector
- `rag/svr/sync_data_source.py` - Added MySQL and PostgreSQL sync
classes
- `pyproject.toml` - Added mysql-connector-python dependency
Frontend:
- `web/src/pages/user-setting/data-source/constant/index.tsx` - Form
fields
- `web/src/locales/en.ts` - English translations
- `web/src/assets/svg/data-source/mysql.svg` - MySQL icon
- `web/src/assets/svg/data-source/postgresql.svg` - PostgreSQL icon
### Testing done
Tested with MySQL 8.0 and PostgreSQL 16:
- Connection validation works correctly
- Full sync imports all query results as documents
- Incremental sync only fetches rows updated since last sync
- Custom SQL queries filter data as expected
- Invalid credentials show clear error messages
- Lint checks pass (`ruff check` returns no errors)
---------
Co-authored-by: mkdev11 <YOUR_GITHUB_ID+MkDev11@users.noreply.github.com>
### What problem does this PR solve?
This PR adds **Seafile** as a new data source connector for RAGFlow.
[Seafile](https://www.seafile.com/) is an open-source, self-hosted file
sync and share platform widely used by enterprises, universities, and
organizations that require data sovereignty and privacy. Users who store
documents in Seafile currently have no way to index and search their
content through RAGFlow.
This connector enables RAGFlow users to:
- Connect to self-hosted Seafile servers via API token
- Index documents from personal and shared libraries
- Support incremental polling for updated files
- Seamlessly integrate Seafile-stored documents into their RAG pipelines
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### Changes included
- `SeaFileConnector` implementing `LoadConnector` and `PollConnector`
interfaces
- Support for API token
- Recursive file traversal across libraries
- Time-based filtering for incremental updates
- Seafile logo (sourced from Simple Icons, CC0)
- Connector configuration and registration
### Testing
- Tested against self-hosted Seafile Community Edition
- Verified authentication (token)
- Verified document ingestion from personal and shared libraries
- Verified incremental polling with time filters
### What problem does this PR solve?
Put document metadata in ES/Infinity.
Index name of meta data: ragflow_doc_meta_{tenant_id}
### Type of change
- [x] Refactoring
## Summary
This PR fixes a `KeyError` crash when running RAPTOR tasks on documents
that don't have the expected vector field.
## Related Issue
Fixes https://github.com/infiniflow/ragflow/issues/12675
## Problem
When running RAPTOR tasks, the code assumes all chunks have the vector
field `q_<size>_vec` (e.g., `q_1024_vec`). However, chunks may not have
this field if:
1. They were indexed with a **different embedding model** (different
vector size)
2. The embedding step **failed silently** during initial parsing
3. The document was parsed before the current embedding model was
configured
This caused a crash:
```
KeyError: 'q_1024_vec'
```
## Solution
Added defensive validation in `run_raptor_for_kb()`:
1. **Check for vector field existence** before accessing it
2. **Skip chunks** that don't have the required vector field instead of
crashing
3. **Log warnings** for skipped chunks with actionable guidance
4. **Provide informative error messages** suggesting users re-parse
documents with the current embedding model
5. **Handle both scopes** (`file` and `kb` modes)
## Changes
- `rag/svr/task_executor.py`: Added validation and error handling in
`run_raptor_for_kb()`
## Testing
1. Create a knowledge base with an embedding model
2. Parse documents
3. Change the embedding model to one with a different vector size
4. Run RAPTOR task
5. **Before**: Crashes with `KeyError`
6. **After**: Gracefully skips incompatible chunks with informative
warnings
---
<!-- Gittensor Contribution Tag: @GlobalStar117 -->
Co-authored-by: GlobalStar117 <GlobalStar117@users.noreply.github.com>
### What problem does this PR solve?
1) Create dataset using table parser for infinity
2) Answer questions in chat using SQL
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
