## Summary
Migrate PaddleOCR integration from the deprecated synchronous HTTP API
to the new asynchronous Job API (`submit → poll → fetch`), aligning with
PaddleOCR 3.6.0+ architecture.
## Changes
### Python (`deepdoc/parser/paddleocr_parser.py`)
- Replace synchronous `requests.post()` with async Job API flow (submit
→ poll → fetch)
- Authentication: `token {token}` → `Bearer {token}`
- File transfer: base64 JSON body → multipart file upload
- Polling: exponential backoff (initial 3s, ×1.5, max 15s, timeout
controlled by `request_timeout`)
- Result: fetch full JSONL from result URL, preserving `prunedResult`
with bbox info for crop functionality
- Rename `api_url` → `base_url` (backward compatible: `api_url` still
accepted as fallback)
### Python (`rag/llm/ocr_model.py`)
- Prefer `paddleocr_base_url` / `PADDLEOCR_BASE_URL`, fallback to
`paddleocr_api_url` / `PADDLEOCR_API_URL`
### Go (`internal/entity/models/paddleocr.go`)
- Add `Client-Platform: ragflow` header to submit and poll requests
- Change polling from fixed 3s to exponential backoff (initial 3s, ×1.5,
max 15s)
### Python (`common/constants.py`)
- Add `PADDLEOCR_BASE_URL` to env keys and default config
## Backward Compatibility
- Old env var `PADDLEOCR_API_URL` still works (used as fallback)
- Frontend field `paddleocr_api_url` still works (backend reads it as
fallback)
- No user-facing configuration changes required for existing setups
## Why not use the `paddleocr` SDK package directly?
RAGFlow's `_transfer_to_sections()` relies on `prunedResult` (containing
`block_bbox`, `block_label`, `parsing_res_list`) from the raw API
response for PDF crop functionality. The SDK's public `parse_document()`
API only returns `DocParsingResult` with `markdown_text`, discarding the
bbox data. Therefore we implement the async Job API flow directly via
HTTP, following the same logic as the SDK internally.
### What problem does this PR solve?
Feat:
- Allow upsert model_type for instance model
Fix:
- Allow create instance with duplicate api_key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15461.
RAGFlow had no way to ingest Salesforce CRM data, so support / sales
teams couldn't ground responses on live Accounts, Contacts,
Opportunities, Cases, or Knowledge articles. This adds a first-class
Salesforce data source connector that authenticates against a Connected
App via OAuth 2.0 client-credentials, queries selected SObjects via
SOQL, and turns each record into an indexable document with incremental
sync.
**Highlights**
- `common/data_source/salesforce_connector.py`: new
`SalesforceConnector` (`CheckpointedConnectorWithPermSync` +
`SlimConnectorWithPermSync`).
- OAuth 2.0 client-credentials flow; canonical `instance_url` from the
token response so multi-pod orgs route correctly.
- Per-object `SystemModstamp` cursor stored in
`SalesforceCheckpoint.cursors` — a failure mid-object doesn't rewind
sibling objects, and re-syncs only fetch changed rows.
- Deterministic record-to-text formatter (sorted keys) so SOQL field
reordering on the server doesn't mark every row "changed" on each poll.
- `_get_json` raises on non-2xx so 429 / 5xx never silently advance the
checkpoint past missing data.
- `Knowledge__kav` is in the default object set but is skipped silently
when the org doesn't have Salesforce Knowledge enabled (404 on
describe).
- Slim-doc IDs are scoped as `<Object>/<Id>` so prune deletes can't
collide across object types.
- `common/constants.py`, `common/data_source/config.py`,
`common/data_source/__init__.py`: register `salesforce` in `FileSource`
/ `DocumentSource` and export `SalesforceConnector`.
- `rag/svr/sync_data_source.py`: new `Salesforce(SyncBase)` class routed
through `load_from_checkpoint` (poll_source would re-walk every object
each run) and added to `func_factory`.
- Frontend:
- `web/src/pages/user-setting/data-source/constant/index.tsx`: new
`DataSourceKey.SALESFORCE`, form fields (instance URL, client ID/secret,
objects, api_version, batch size), `syncDeletedFiles` capability,
default form values, and tile entry with the new icon.
- `web/src/locales/{en,zh}.ts`: description + per-field tooltips.
- `web/src/assets/svg/data-source/salesforce.svg`: 48x48 brand-style
icon to match the other Microsoft / cloud tiles.
**Verification**
- `npm run build` (vite + esbuild) passes (1m 26s).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15465.
RAGFlow supports S3, Google Cloud Storage, R2, and OCI as data sources
but not Azure Blob Storage, leaving Azure users without a way to index
container objects into a knowledge base. This adds a first-class Azure
Blob Storage data-source connector — distinct from RAGFlow's existing
Azure storage *backends* (`rag/utils/azure_sas_conn.py`,
`rag/utils/azure_spn_conn.py`) which store RAGFlow's own files.
**Highlights**
- `common/data_source/azure_blob_connector.py`: new `AzureBlobConnector`
(`CheckpointedConnectorWithPermSync` + `SlimConnectorWithPermSync`).
- Uses the existing `azure-storage-blob` dependency (already in
`pyproject.toml`).
- Three auth modes, tried in order of precedence:
1. **Account key** — `account_name` + `account_key` + `container_name`.
2. **Connection string** — `connection_string` + `container_name`.
3. **SAS token** — `container_url` + `sas_token` (same shape as
`RAGFlowAzureSasBlob`).
- ETag fingerprint stored per blob in `AzureBlobCheckpoint.etags` —
unchanged blobs (same ETag as last run) are skipped without a download.
Only new/modified blobs are fetched.
- Optional `prefix` scopes indexing to a virtual folder.
- `validate_connector_settings()` probes `get_container_properties()`
and maps `AuthenticationFailed / 403 / ContainerNotFound` to typed
connector exceptions.
- Slim-doc IDs are blob names so prune reconciles correctly.
- `common/constants.py`, `common/data_source/config.py`,
`common/data_source/__init__.py`: register `azure_blob` in `FileSource`
/ `DocumentSource` and export `AzureBlobConnector`.
- `rag/svr/sync_data_source.py`: new `AzureBlob(SyncBase)` class routed
through `load_from_checkpoint` (ETag fingerprint owns change-detection)
and added to `func_factory`.
- Frontend:
- `web/src/pages/user-setting/data-source/constant/index.tsx`: new
`DataSourceKey.AZURE_BLOB`, auth-mode selector (account key / connection
string / SAS token), all credential fields, prefix + batch-size,
`syncDeletedFiles` capability, default form values, tile entry with
icon.
- `web/src/locales/{en,zh}.ts`: description + per-field tooltips for all
9 new keys.
- `web/src/assets/svg/data-source/azure-blob.svg`: Azure-branded
stacked-cylinders icon.
**Verification**
- `npm run build` (vite + esbuild) passes (37 s).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15332.
RAGFlow can index Gmail and generic IMAP mailboxes but had no native
connector for Outlook / Microsoft 365 mail. Organisations on Microsoft
365 had no way to bring mailbox content into a knowledge base through
Microsoft Graph.
This PR adds a net-new Outlook data source that:
- Authenticates against Microsoft Graph with the same MSAL
client-credentials flow already used by the SharePoint and Teams
connectors (no new auth primitives).
- Pages over `/users/{id}/mailFolders/{folder}/messages/delta` per
mailbox and persists `@odata.deltaLink` values in
`OutlookCheckpoint.delta_links`, so incremental syncs only fetch changed
messages.
- Supports two scoping modes:
- **Tenant-wide** (default): enumerates every user in the tenant via
`/users` and syncs each mailbox. Requires `User.Read.All`.
- **Targeted**: when `user_ids` is provided (comma-separated UPNs or
object IDs), only those mailboxes are synced. `User.Read.All` is not
needed in this mode.
- Lets the caller pick the mail folder (`inbox`, `sentitems`, `archive`,
...). Defaults to `inbox`.
- Maps each message to a `Document` shaped after the Gmail connector:
one `TextSection` carrying `From/To/Cc/Subject` headers + body, with
HTML bodies stripped to text inline (no extra dependency).
- Surfaces typed errors on the validation probe:
401 → `ConnectorMissingCredentialError`, 403 →
`InsufficientPermissionsError` (with `Mail.Read` / `User.Read.All`
hint), 404 on a configured mailbox → `ConnectorValidationError`, 5xx →
`UnexpectedValidationError`.
- Skips messages flagged `@removed` by the delta semantics and messages
whose `receivedDateTime` is older than `poll_range_start`.
#### Files
| File | Change |
|------|--------|
| `common/data_source/outlook_connector.py` | **New** —
`OutlookConnector` (`CheckpointedConnectorWithPermSync` +
`SlimConnectorWithPermSync`) + `OutlookCheckpoint` + tiny `_strip_html`
helper. |
| `common/data_source/config.py` | `DocumentSource.OUTLOOK = "outlook"`.
|
| `common/constants.py` | `FileSource.OUTLOOK = "outlook"`. |
| `common/data_source/__init__.py` | Export `OutlookConnector`. |
| `rag/svr/sync_data_source.py` | `Outlook(SyncBase)` with `batch_size`
normalisation, CSV/list parsing of `user_ids`; registered in
`func_factory`. |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`DataSourceKey.OUTLOOK`, visibility map (`syncDeletedFiles: true`), info
entry, form fields (tenant_id, client_id, client_secret, folder,
user_ids, batch_size), default values. |
| `web/src/locales/en.ts`, `web/src/locales/zh.ts` |
`outlookDescription` + 5 tooltip keys (EN + ZH). |
| `test/unit_test/data_source/test_outlook_connector_unit.py` | **New**
— 19 unit tests (`p1`/`p2`/`p3`) covering auth, validation (tenant-wide
vs specific user vs error paths), checkpoint helpers, user enumeration
pagination, message filtering, HTML body stripping. |
#### Required Azure AD permissions
- `Mail.Read` (Application, admin-granted) — always.
- `User.Read.All` (Application, admin-granted) — only when `user_ids` is
left blank so the connector can enumerate mailboxes.
#### Out of scope
- **Attachment indexing.** The current connector emits message body +
headers; binary attachments are flagged via `metadata.has_attachments`
but not pulled. Adding attachment hydration is straightforward but
scoped out per the issue's "decide whether attachments are indexed in
the first version" note.
- **Delegated (per-user) OAuth.** The connector uses app-only
credentials, consistent with the SharePoint / Teams precedent in this
codebase.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15330.
RAGFlow had no connector for OneDrive / OneDrive for Business. Users who
store working documents in OneDrive could not index them into a
knowledge base without manually downloading and re-uploading files.
This PR adds a net-new OneDrive data source that:
- Authenticates against Microsoft Graph with the same MSAL
client-credentials flow already used by the SharePoint and Teams
connectors (no new auth primitives).
- Enumerates every drive visible to the service principal and pages
through `/drives/{id}/root/delta`, persisting `@odata.deltaLink` values
per drive so subsequent syncs only fetch changed items.
- Optionally narrows ingestion to a sub-folder (`folder_path`) without
needing a separate code path.
- Surfaces typed errors on the validation probe (`GET /drives?$top=1`):
401 → `ConnectorMissingCredentialError`, 403 →
`InsufficientPermissionsError` (with a `Files.Read.All` hint), 5xx →
`UnexpectedValidationError`.
- Filters folders, soft-deleted items, and unsupported extensions (`.pdf
.docx .doc .xlsx .xls .pptx .ppt .txt .md .csv`).
#### Files
| File | Change |
|------|--------|
| `common/data_source/onedrive_connector.py` | **New** —
`OneDriveConnector` + `OneDriveCheckpoint`. |
| `common/data_source/config.py` | `DocumentSource.ONEDRIVE =
"onedrive"`. |
| `common/constants.py` | `FileSource.ONEDRIVE = "onedrive"`. |
| `common/data_source/__init__.py` | Export `OneDriveConnector`. |
| `rag/svr/sync_data_source.py` | `OneDrive(SyncBase)` with `batch_size`
normalisation; registered in `func_factory`. |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`DataSourceKey.ONEDRIVE`, visibility map (`syncDeletedFiles: true`),
info entry, form fields (tenant_id, client_id, client_secret,
folder_path, batch_size), default values. |
| `web/src/locales/en.ts`, `web/src/locales/zh.ts` |
`onedriveDescription` + 4 tooltip keys (EN + ZH). |
| `test/unit_test/data_source/test_onedrive_connector_unit.py` | **New**
— 13 unit tests (`p1`/`p2`) covering auth, validation, checkpoint
helpers, and document filtering. |
#### Required Azure AD permission
`Files.Read.All` (Application, admin-granted).
#### Out of scope
- Interactive end-user OAuth (delegated permissions) — the connector
uses app-only credentials, consistent with the SharePoint / Teams
precedent.
- Binary download of file contents — the sync layer emits `Document`s
carrying `webUrl` + metadata; bytes are hydrated downstream by the parse
pipeline.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Python implementation of the Go-based model_provider API suite.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
### What problem does this PR solve?
1. Break huge function into smaller pieces
2. Add unit test for the smaller pieces function
3. Layer-ed design
a. infra layer - task_context.py, recording_context.py,
write_operation_interceptor.py, ...
b. service layer - *_service.py
c. business layer - task_handler.py
4. Default behavior: use "refactor-ed version" - can switch to original
version by change env variable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement
---------
Co-authored-by: Liu An <asiro@qq.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### What problem does this PR solve?
This PR improves the connector dashboard task management experience and
adds better visibility into connector execution logs.
### Overview:
#### Before
<img width="700" alt="image"
src="https://github.com/user-attachments/assets/e4a8ed6f-2e18-4f0f-8528-41a514550052"
/>
#### Now:
<img width="700" alt="Screenshot from 2026-05-18 16-31-30"
src="https://github.com/user-attachments/assets/d4ca193b-847a-49ae-9e4f-5fbca60ea627"
/>
### 1. Add a new logging page to the connector dashboard
A new logging page has been added so users can view connector task
execution logs directly from the connector dashboard.
### 2. Merge the Resume button into Confirm
The separate **Resume** button has been removed. The **Confirm** button
now represents different actions depending on the current task state:
- **Save**: Save form changes and reschedule tasks.
- **Stop**: Cancel currently scheduled or running tasks.
- **Resume**: Create new scheduled tasks after the previous tasks have
been stopped.
- **Start**: Start tasks when no task has been started yet.
### 3. Separate syncing and pruning tasks
Connector tasks are now separated into **syncing** and **pruning**.
Pruning is controlled by the **Sync deleted files** option:
- When **Sync deleted files** is disabled, only syncing tasks are shown.
- When **Sync deleted files** is enabled, both syncing and pruning tasks
are shown.
**Now: Sync deleted files disabled**
<img width="700" alt="Sync deleted files disabled"
src="https://github.com/user-attachments/assets/dbd9232e-614a-407f-a0b1-c109e5fa567d"
/>
**Now: Sync deleted files enabled**
<img width="700" alt="Sync deleted files enabled"
src="https://github.com/user-attachments/assets/1f527f48-ccb3-4ee8-97ca-086891489296"
/>
### 4. Update logs in backend
<img width="700" alt="image"
src="https://github.com/user-attachments/assets/10a95a3f-98c1-4e67-8afa-ddf6cda5b0b2"
/>
### 5. Remove connector resume API
- Removed: `POST /v1/connectors/<connector_id>/resume`
- Replaced by: `PATCH /v1/connectors/<connector_id>`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Closes#14753
## What changed
| File | Change |
|---|---|
| `pyproject.toml` | `requires-python` → `>=3.13,<3.15`; remove
`strenum==0.4.15` |
| `Dockerfile` | `uv python install 3.13`, `uv sync --python 3.13` |
| `.github/workflows/tests.yml` | `uv sync --python 3.13` on both matrix
legs |
| `CLAUDE.md` | dev setup command + requirements note updated |
| `deepdoc/parser/mineru_parser.py` | `from strenum import StrEnum` →
`from enum import StrEnum` |
| `agent/tools/code_exec.py` | same |
`StrEnum` has been in the stdlib since Python 3.11 — the `strenum`
backport package is no longer needed once the floor is 3.13.
## Why uv.lock is not regenerated
`uv lock --python 3.13` fails because:
1. The infiniflow/graspologic fork pins `numpy>=1.26.4,<2.0.0`
2. `tensorflow-cpu>=2.20.0` (the first release with cp313 wheels)
depends on `ml-dtypes>=0.5.1`, which requires `numpy>=2.1.0`
3. These two constraints are irreconcilable on Python 3.13
The lockfile regeneration requires loosening the `numpy` upper bound in
the `infiniflow/graspologic` fork. Once that fork commit is updated and
the SHA in `pyproject.toml:49` is bumped, `uv lock --python 3.13` will
succeed.
## RFC corrections
Two claims in the original RFC (#14753) did not hold up under code
review:
- **"graspologic hard-blocks 3.13"** — the infiniflow fork at the pinned
commit has no `<3.13` Python constraint. The blocker is the transitive
`numpy<2.0.0` conflict with tensorflow-cpu's test dependency, not a
direct Python version cap.
- **"free-threading throughput gains for I/O-bound workload"** — Python
3.13 free-threading requires a special `--disable-gil` build and
provides no benefit for async I/O code (the GIL is already released
during I/O). The real motivation is forward compatibility and improved
error messages.
# feat: Add Generic REST API Connector
## What problem does this PR solve?
RAGFlow supports many specific data source connectors (MySQL, Slack,
Google Drive, etc.), but there was no way to connect an arbitrary REST
API as a data source. Users with custom or third-party APIs had to write
a new connector class for each one.
This PR adds a **generic, configuration-driven REST API connector** that
lets users connect any REST API as a data source entirely through the UI
— no code changes needed per API.
---
## Features
### Core Connector (`common/data_source/rest_api_connector.py`)
- Implements `LoadConnector` and `PollConnector` interfaces for full and
incremental sync
- **Configurable authentication:** None, API Key (custom header), Bearer
Token, Basic Auth
- **Pluggable pagination:** Page-based, Offset-based, Cursor-based, or
None
- Smart page-size inference from user's query parameters to avoid
duplicate/conflicting params
- Configurable request delay between pages to prevent API rate limiting
- Auto-detection of the items array in JSON responses (`items`,
`results`, `data`, `records`, or first list found)
- **Advanced field mapping** with dot-notation (`country.name`), array
wildcards (`newsType[*].name`), type hints, and default values
- Optional content template rendering (`"Title: {title}\nBody: {body}"`)
- HTML stripping for content fields
- Stable document IDs via `hash128` from a configurable ID field or
auto-generated from item content
- Pydantic configuration schema with automatic coercion of UI string
inputs to dicts/lists
### Backend Registration (`rag/svr/sync_data_source.py`,
`common/constants.py`, `common/data_source/config.py`)
- `REST_API` sync class wired into RAGFlow's `func_factory`
- Full sync (`load_from_state`) and incremental polling (`poll_source`)
support
- Credentials and config passed from task to connector following
existing patterns (MySQL, SeaFile, etc.)
### Test Connection Endpoint (`api/apps/connector_app.py`)
- `POST /v1/connector/<id>/test` validates config schema,
authentication, and API connectivity without triggering a sync
- Clear error messages for auth failures vs. config issues
### Frontend UI (`web/src/pages/user-setting/data-source/constant/`)
- **Postman-style configuration:** Base URL, Query Parameters (key=value
per line), Auth, Content Fields, Metadata Fields, Pagination Type
- Auth-type-aware form: fields for API key header/value, Bearer token,
or Basic username/password appear only when relevant
- **Advanced Settings** toggle for: Custom Headers, Max Pages, Request
Delay, Poll Timestamp Field, Request Body (POST)
- Connector icon (SVG) and i18n strings (English)
- **"Test Connection"** button to validate before syncing
---
## Controls & Safety
- Configurable max pages safety cap (default: 1000, adjustable in UI)
- Configurable request delay between pages (default: 0.5s, adjustable in
UI)
- Auth errors (401/403) fail immediately without retries; transient
errors retry with exponential backoff
- Diagnostic logging: auth setup confirmation, request details on
failure, content field extraction status
---
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
##Visual Screenshots of Features
<img width="482" height="510" alt="Screenshot 2026-03-11 at 5 19 52 PM"
src="https://github.com/user-attachments/assets/dcb7ab4a-1622-44f3-bb02-d6f0527314c4"
/>
(Connector can be configured within the external data sources tab)
Configuration Parameters:
<img width="661" height="682" alt="Screenshot 2026-03-11 at 5 20 46 PM"
src="https://github.com/user-attachments/assets/5e154e71-4ab5-4872-bfb2-04f02b73c18a"
/>
<img width="661" height="682" alt="Screenshot 2026-03-11 at 5 20 54 PM"
src="https://github.com/user-attachments/assets/00cb14b7-0bcf-4b94-9d71-34e93369ecb2"
/>
Connection can be tested before attaching to dataset:
<img width="981" height="681" alt="Screenshot 2026-03-11 at 5 21 40 PM"
src="https://github.com/user-attachments/assets/aaa6eeeb-89a7-4349-bc34-2423bf8be9ee"
/>
Ingestion tested with API connector (works perfectly fine):
<img width="1062" height="705" alt="Screenshot 2026-03-11 at 5 22 30 PM"
src="https://github.com/user-attachments/assets/afcd0d58-cadd-4152-badc-d2f14d96fbec"
/>
Search & Retrieval works as well with metadata flow:
<img width="1062" height="705" alt="Screenshot 2026-03-11 at 5 23 05 PM"
src="https://github.com/user-attachments/assets/d41ee935-dcf7-4456-b317-22a76ca032c0"
/>
---------
Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
### 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?
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?
This PR adds support for parsing PDFs through an external Docling
server, so RAGFlow can connect to remote `docling serve` deployments
instead of relying only on local in-process Docling.
It addresses the feature request in
[#13426](https://github.com/infiniflow/ragflow/issues/13426) and aligns
with the external-server usage pattern already used by MinerU.
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### What is changed?
- Add external Docling server support in `DoclingParser`:
- Use `DOCLING_SERVER_URL` to enable remote parsing mode.
- Try `POST /v1/convert/source` first, and fallback to
`/v1alpha/convert/source`.
- Keep existing local Docling behavior when `DOCLING_SERVER_URL` is not
set.
- Wire Docling env settings into parser invocation paths:
- `rag/app/naive.py`
- `rag/flow/parser/parser.py`
- Add Docling env hints in constants and update docs:
- `docs/guides/dataset/select_pdf_parser.md`
- `docs/guides/agent/agent_component_reference/parser.md`
- `docs/faq.mdx`
### Why this approach?
This keeps the change focused on one issue and one capability (external
Docling connectivity), without introducing unrelated provider-model
plumbing.
### Validation
- Static checks:
- `python -m py_compile` on changed Python files
- `python -m ruff check` on changed Python files
- Functional checks:
- Remote v1 endpoint path works
- v1alpha fallback works
- Local Docling path remains available when server URL is unset
### Related links
- Feature request: [Support external Docling server (issue
#13426)](https://github.com/infiniflow/ragflow/issues/13426)
- Compare view for this branch:
[main...feat/docling-server](https://github.com/infiniflow/ragflow/compare/main...spider-yamet:ragflow:feat/docling-server?expand=1)
##### Fixes [#13426](https://github.com/infiniflow/ragflow/issues/13426)
### 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?
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?
Add PaddleOCR as a new PDF parser.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Feat: Bitbucket connector NOT READY TO MERGE
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
issue:
#12313
change:
add Zendesk data source integration with configuration and sync
capabilities
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
issue:
#12217 [#12313](https://github.com/infiniflow/ragflow/issues/12313)
change:
add IMAP data source integration with configuration and sync
capabilities
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Use async task to save memory.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Feat: Gitlab connector
Fix: submit button in darkmode
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
change: Add Asana data source integration and configuration options
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
Fix several text issues.
### 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?
change:
add Airtable connector and integration for data synchronization
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Manage and display memory datasets.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Treat MinerU as an OCR model.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
This Pull Request introduces native support for Google Cloud Storage
(GCS) as an optional object storage backend.
Currently, RAGFlow relies on a limited set of storage options. This
feature addresses the need for seamless integration with GCP
environments, allowing users to leverage a fully managed, highly
durable, and scalable storage service (GCS) instead of needing to deploy
and maintain third-party object storage solutions. This simplifies
deployment, especially for users running on GCP infrastructure like GKE
or Cloud Run.
The implementation uses a single GCS bucket defined via configuration,
mapping RAGFlow's internal logical storage units (or "buckets") to
folder prefixes within that GCS container to maintain data separation.
This architectural choice avoids the operational complexities associated
with dynamically creating and managing unique GCS buckets for every
logical unit.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
To solve the problem of error reporting caused by type errors when
various types of exception returns are triggered
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR adds webdav storage as data source for data sync service.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
This PR adds a native Moodle connector to sync content (courses,
resources, forums, assignments, pages, books) into RAGFlow.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
