## Summary
Add support for **"New API"** as a model provider, enabling connection
to [New API](https://github.com/QuantumNous/new-api) /
[one-api](https://github.com/songquanpeng/one-api) compatible gateways
that aggregate multiple LLM backends behind a unified OpenAI-compatible
`/v1` endpoint.
### Features
- **All model types**: Chat, Embedding, Rerank, Image2Text, TTS,
Speech2Text
- **List Models discovery**: `NewAPI(OpenAIAPICompatible)` class in
`model_meta.py` queries the gateway's `/v1/models` to auto-discover
available models via the native `GET /api/v1/providers/<name>/models`
endpoint
- **Model parameter editing**: Pencil icon on each discovered model row
to edit `model_type`, `max_tokens`, and `features` (e.g. tool call
support) before submitting
- **Custom model addition**: "Add Custom Model" button at the bottom of
the List Models dropdown for models not returned by the API
- **Gear icon settings**: Enabled the Settings gear button on provider
instances to manage models on existing instances (viewMode)
- **viewMode credential passthrough**: Fixed List Models in viewMode —
merges `initialValues` credentials when `api_key`/`base_url` fields are
hidden by `hideWhenInstanceExists`
### Changes
**Backend** (8 files):
- `rag/llm/chat_model.py` — `NewAPIChat(Base)` class
- `rag/llm/embedding_model.py` — `NewAPIEmbed(OpenAIEmbed)` class (no
auto `/v1` append)
- `rag/llm/rerank_model.py` — `NewAPIRerank(Base)` class (uses `/rerank`
endpoint)
- `rag/llm/cv_model.py` — `NewAPICv(GptV4)` class
- `rag/llm/tts_model.py` — `NewAPITTS(OpenAITTS)` class
- `rag/llm/sequence2txt_model.py` — `NewAPISeq2txt(GPTSeq2txt)` class
- `rag/llm/model_meta.py` — `NewAPI(OpenAIAPICompatible)` class for List
Models discovery
- `conf/llm_factories.json` — New API factory entry with all model type
tags
**Frontend** (8 files + 1 new SVG):
- `web/src/assets/svg/llm/new-api.svg` — New API logo icon
- `web/src/constants/llm.ts` — `LLMFactory.NewAPI` enum + `IconMap`
entry
- `web/src/components/svg-icon.tsx` — `NewAPI` added to `svgIcons`
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/field-config/local-llm-configs.ts`
— New API `buildLocalConfig`
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/constants.ts`
— `LIST_MODEL_PROVIDERS` includes NewAPI
- `web/src/pages/user-setting/setting-model/components/used-model.tsx` —
Enable Settings gear button
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/hooks/use-list-models-picker.ts`
— viewMode credential merge + model editing state/handlers
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/hooks/use-list-models-options.tsx`
— Pencil edit icon per model row
-
`web/src/pages/user-setting/setting-model/modal/provider-modal/index.tsx`
— `AddCustomModelDialog` import + edit dialog rendering
**Note on Go implementation**: A Go model driver (`NewAPIModel`
delegating to `OpenAIModel`) has been prepared but is deferred until the
Go runtime is enabled in a future release (current v0.26.0 images use
`API_PROXY_SCHEME=python` and do not compile Go binaries). Will submit
as a follow-up PR.
## Related
- Depends on: #15996 (provider instance API improvements — server-side
credential lookup, idempotent `add_model`, security fixes — required for
viewMode gear icon and batch model submission)
## Test plan
- [ ] Add New API provider with api_key and base_url pointing to an
OpenAI-compatible gateway
- [ ] Click "List Models" — should discover and display available models
from `/v1/models`
- [ ] Click pencil icon on a model — should open edit dialog to change
model_type, max_tokens, features
- [ ] Select multiple models and click OK — should add all selected
models
- [ ] Click gear icon on the added instance — should open viewMode with
List Models working
- [ ] In viewMode, select new models including pre-existing ones, click
OK — should succeed (requires #15996)
- [ ] Verify all model types work: create a Chat assistant, Embedding
KB, Rerank setting
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Tim Wang <wanghualoong@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Adds Keenable as a web search tool in the agent, alongside the existing
Tavily/DuckDuckGo/SearXNG/Google tools.
The main difference from the other search tools is that it doesn't
require an
API key. By default it uses Keenable's keyless public endpoint, so it
works out
of the box. Providing a key (in the tool config) switches to the
authenticated
endpoint and lifts the rate limits.
### Changes
- Backend: `agent/tools/keenable.py` — `KeenableSearch`, follows the
Tavily/DuckDuckGo tool shape (results go through `_retrieve_chunks`).
Auto-registered by `agent/tools/__init__.py`.
- Frontend: wired into the agent builder — operator + icon, config form
(optional API key, search mode, site filter, top N), the search tool
menu,
and the existing api_key export sanitizer.
### Config
- API key: optional. Blank = keyless free tier; set it to lift limits /
enable
`realtime` mode.
- `site`: restrict to a single domain.
- `mode`: `pro` (default) or `realtime`.
### Notes
`KEENABLE_API_URL` can override the API base (HTTPS enforced; defaults
to
`https://api.keenable.ai`). The tool only sends the query (no URL
fetch), so
there's no SSRF surface. Verified the frontend with `vite build` and the
backend search path against the public endpoint.
### What does this PR do?
This PR adds a new DingTalk chat channel integration and hardens the
inbound callback path.
### Summary
- Adds DingTalk as a selectable chat channel in the UI and backend
channel registry.
- Adds the DingTalk chat channel icon asset.
- Acknowledges DingTalk Stream callbacks and deduplicates repeated
inbound messages to avoid duplicate replies.
### What problem does this PR solve?
#15844
Adds a **Chat channels** capability so a RAGFlow assistant (Dialog) can
be exposed as a bot on external messaging platforms (Feishu/Lark,
Discord, Telegram, Slack, WeCom, LINE, etc.). An admin configures a bot
in the UI, connects it to an assistant, and inbound messages are
answered from that assistant's knowledge base — replies are delivered
back on the channel.
**Feishu/Lark is implemented and tested end-to-end.** Discord, Telegram,
LINE, and WeCom are scaffolded against the same interface; the remaining
listed channels are tracked as follow-ups.
### Design
**Backend**
- New `chat_channel` table (`tenant_id`, `name`, `channel`, `config`
JSON holding `{credential: {...}}`, `dialog_id`, `status`) +
`ChatChannelService` and RESTful CRUD under `/api/v1/chat_channels`.
- Channel framework under `api/channels/`: a `core` registry +
per-channel packages that self-register a builder and implement a common
`Channel` interface (`start`/`stop`/`send` + inbound normalization) over
`IncomingMessage`/`OutgoingMessage`.
- Embedded **reconcile loop** in `ragflow_server`
(`api/channels/bootstrap.py`): loads enabled bots, and
starts/stops/restarts them as rows change (no server restart needed).
Inbound messages run the connected dialog via the non-streaming
completion path, keeping per-end-user conversation history.
- Missing optional channel SDKs degrade gracefully (channel skipped with
a warning; others unaffected). Channel-level errors are logged, not
crashed.
- Feishu's WebSocket client runs in a dedicated thread with its own
event loop to avoid cross-loop/contextvars conflicts with the channel
runtime.
**Frontend**
- **Settings → Chat channels** panel: available-channels grid +
configured-bots list with add/edit/delete and a **Connect assistant**
popup that binds a bot to a dialog.
- Brand icons via simple-icons / reused shared data-source assets, with
colored fallbacks for brands not available.
- Route, sidebar entry, i18n (en/zh), and a top-nav segment-boundary fix
so the settings page no longer highlights the Chat tab.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### Notes
- DB: new `chat_channel` table is auto-created; `chat_channel.dialog_id`
is also covered by a `migrate_db` `alter_db_add_column` for existing
installs.
- Channel SDKs (`lark-oapi`, `discord.py`, `python-telegram-bot`,
`line-bot-sdk`, `wechatpy`, `aiohttp`) added to dependencies.
- Screenshots / per-channel credential docs to follow.
<img width="1338" height="1290" alt="Image"
src="https://github.com/user-attachments/assets/042cb2f9-0dad-4e6a-bcf7-43ced4bbd704"
/>
<img width="1344" height="738" alt="Image"
src="https://github.com/user-attachments/assets/373cd08e-ec40-4c67-9c51-4d948b1ba617"
/>
<img width="672" height="887" alt="Image"
src="https://github.com/user-attachments/assets/5a34953f-a9a3-4c1e-869e-5eff0dc64c84"
/>
---------
### 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#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?
Closes#15191.
RAGFlow shipped a Microsoft Teams connector stub
(`common/data_source/teams_connector.py`) whose document-loading methods
all returned `[]`, `Teams._generate()` was a `pass`, and Teams was
commented out of the data-source settings UI. As a result there was no
way to index Teams channel conversations into a knowledge base.
This PR implements the connector end to end on top of Microsoft Graph
(Office365-REST-Python-Client). It shares the MSAL client-credentials
auth shape with the SharePoint connector.
**Backend**
- `common/data_source/teams_connector.py`
- `load_credentials()` now builds the Graph client using an MSAL
client-credentials **token callback** — the form `GraphClient` actually
expects. (The previous stub passed a raw access-token string to
`GraphClient(...)`, which is not how that client is driven.) Token
acquisition is lazy, so credential loading performs no network call.
- `validate_connector_settings()` lists teams via Graph.
- `load_from_checkpoint()` is now a generator that pages teams →
channels → messages, flattens each top-level post together with its
replies into one blob-based `Document` (`extension` `.txt`/`.html`,
`blob`, `size_bytes`, `doc_updated_at`). Incremental syncs are bounded
by message `lastModifiedDateTime` (falling back to `createdDateTime`).
Per-message errors surface as `ConnectorFailure` instead of aborting the
run.
- `retrieve_all_slim_docs_perm_sync()` yields id-only `SlimDocument`
batches and the checkpoint helpers return proper `TeamsCheckpoint`s.
- ACL → `ExternalAccess` mapping is intentionally left best-effort
(`load_from_checkpoint_with_perm_sync` delegates to the standard load)
because the sync pipeline does not currently persist `ExternalAccess`.
- `rag/svr/sync_data_source.py`
- Implemented `Teams._generate()` using the existing
`CheckpointOutputWrapper` pattern (same shape as Confluence/Jira/Google
Drive), supporting full reindex and incremental polling from
`poll_range_start`.
- `TeamsConnector` is already exported from
`common/data_source/__init__.py`.
**Frontend (`web/`)**
- Enabled the `TEAMS` data-source enum and added its form fields
(`tenant_id`, `client_id`, `client_secret`), default values, display
metadata, and a Teams icon.
- Added `teamsDescription` / `teamsTenantIdTip` to `en.ts` and `zh.ts`.
**Tests**
- `test/unit_test/data_source/test_teams_connector_unit.py`: mock-based
unit tests covering credential loading (incomplete creds raise, happy
path sets the Graph client, fetch-without-creds raises), post/reply
flattening (incl. the HTML vs text extension), incremental
`lastModifiedDateTime` filtering, and slim-doc listing. All 6 pass;
`ruff check` is clean.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15187.
RAGFlow shipped a Slack connector
(`common/data_source/slack_connector.py`) but it was never usable:
`Slack._generate()` in the sync worker was a `pass` stub, the
connector's document-generating code was incompatible with the current
data model,
and Slack was commented out of the data-source settings UI. As a result,
teams had no way to index Slack channels/threads into a knowledge base.
This PR completes the connector end to end.
**Backend**
- `common/data_source/slack_connector.py`
- Rewrote `thread_to_doc` to produce a blob-based `Document`
(`extension`/`blob`/`size_bytes`). The previous implementation built the
doc with a `sections=[...]` argument and omitted the now-required
`blob`/`extension`/ `size_bytes` fields, so it raised a validation error
against the current `Document` model. Thread messages are now cleaned
and flattened into a single UTF-8 text blob.
- Added `load_from_state()` / `poll_source(start, end)` generators. The
connector's checkpoint interface is a no-op stub, so both full and
incremental syncs run through a single channel-iterating generator built
on the existing module helpers (`get_channels`, `filter_channels`,
`get_channel_messages`, `_process_message`), with per-channel thread
de-duplication.
- `rag/svr/sync_data_source.py`
- Implemented `Slack._generate()`. Credentials are loaded via
`StaticCredentialsProvider` (the connector requires `slack_bot_token`
and does not support `load_credentials`). Supports full reindex and
incremental polling from `poll_range_start`, plus the optional channel
filter. Modeled on the Confluence/Dropbox wrappers.
- `SlackConnector` was already exported from
`common/data_source/__init__.py`.
**Frontend (`web/`)**
- Enabled the `SLACK` data-source enum and added its form fields (Slack
bot token + optional channel filter), default values, display metadata,
and a Slack icon.
- Added `slackDescription` / `slackBotTokenTip` / `slackChannelsTip`
strings to `en.ts` and `zh.ts`.
**Tests**
- `test/unit_test/data_source/test_slack_connector_unit.py`: unit tests
covering credential loading (`load_credentials` raises,
`set_credentials_provider` initializes clients, missing credentials
raises) and document generation (standalone message + flattened thread,
blob/extension/size_bytes/metadata, and the incremental poll time
window). All 5 pass; `ruff check` is clean.
Required Slack scopes: `channels:read`, `channels:history`,
`users:read`.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15189.
RAGFlow shipped a SharePoint connector stub
(`common/data_source/sharepoint_connector.py`) whose document-loading
methods all returned `[]`, `SharePoint._generate()` was a `pass`, and
SharePoint was commented out of the data-source settings UI. As a result
there was no way to index files stored in SharePoint document libraries.
This PR implements the connector end to end on top of Microsoft Graph
(Office365-REST-Python-Client).
**Backend**
- `common/data_source/sharepoint_connector.py`
- `load_credentials()` now builds the Graph client using an MSAL
client-credentials **token callback** — the form `GraphClient` actually
expects. (The previous stub passed a raw access-token string to
`GraphClient(...)`, which is not how that client is driven.) Token
acquisition is lazy, so credential loading does no network call.
- `validate_connector_settings()` resolves the configured site via
Graph.
- `load_from_checkpoint()` is now a generator that enumerates every
document library under the site, walks folders depth-first, downloads
each file, and yields blob-based `Document` objects (`extension` /
`blob` / `size_bytes` / `doc_updated_at`). Incremental syncs are bounded
by file `lastModifiedDateTime`. Per-file errors are surfaced as
`ConnectorFailure` rather than aborting the run.
- `retrieve_all_slim_docs_perm_sync()` yields id-only `SlimDocument`
batches (no downloads) and the checkpoint helpers return proper
checkpoints.
- ACL → `ExternalAccess` mapping is intentionally left best-effort
(`load_from_checkpoint_with_perm_sync` delegates to the standard load)
because the sync pipeline does not currently persist `ExternalAccess`;
this can be extended once that plumbing exists.
- `rag/svr/sync_data_source.py`
- Implemented `SharePoint._generate()` using the existing
`CheckpointOutputWrapper` pattern (same shape as Confluence/Jira/Google
Drive), supporting full reindex and incremental polling from
`poll_range_start`.
- `SharePointConnector` is already exported from
`common/data_source/__init__.py`.
**Frontend (`web/`)**
- Enabled the `SHAREPOINT` data-source enum and added its form fields
`site_url`, `tenant_id`, `client_id`, `client_secret`), default values,
display metadata, and a SharePoint icon.
- Added `sharepointDescription` / `sharepointSiteUrlTip` to `en.ts` and
`zh.ts`.
**Tests**
- `test/unit_test/data_source/test_sharepoint_connector_unit.py`:
mock-based unit tests covering credential loading (incomplete creds
raise, happy path sets the Graph client, fetch-without-creds raises),
drive traversal + file download, incremental `lastModifiedDateTime`
filtering, and slim-doc listing. All 6 pass; `ruff check` is clean.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
# 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?
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 aims to extend the list of possible providers. Adds new Provider
"RAGcon" within the Ollama Modal. It provides all model types except OCR
via Openai-compatible endpoints.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Jakob <16180662+hauberj@users.noreply.github.com>
### What problem does this PR solve?
This PR adds [Avian](https://avian.io) as a new LLM provider to RAGFlow.
Avian provides an OpenAI-compatible API with competitive pricing,
offering access to models like DeepSeek V3.2, Kimi K2.5, GLM-5, and
MiniMax M2.5.
**Provider details:**
- API Base URL: `https://api.avian.io/v1`
- Auth: Bearer token via API key
- OpenAI-compatible (chat completions, streaming, function calling)
- Models:
- `deepseek/deepseek-v3.2` — 164K context, $0.26/$0.38 per 1M tokens
- `moonshotai/kimi-k2.5` — 131K context, $0.45/$2.20 per 1M tokens
- `z-ai/glm-5` — 131K context, $0.30/$2.55 per 1M tokens
- `minimax/minimax-m2.5` — 1M context, $0.30/$1.10 per 1M tokens
**Changes:**
- `rag/llm/chat_model.py` — Add `AvianChat` class extending `Base`
- `rag/llm/__init__.py` — Register in `SupportedLiteLLMProvider`,
`FACTORY_DEFAULT_BASE_URL`, `LITELLM_PROVIDER_PREFIX`
- `conf/llm_factories.json` — Add Avian factory with model definitions
- `web/src/constants/llm.ts` — Add to `LLMFactory` enum, `IconMap`,
`APIMapUrl`
- `web/src/components/svg-icon.tsx` — Register SVG icon
- `web/src/assets/svg/llm/avian.svg` — Provider icon
- `docs/references/supported_models.mdx` — Add to supported models table
This follows the same pattern as other OpenAI-compatible providers
(e.g., n1n #12680, TokenPony).
cc @KevinHuSh @JinHai-CN
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
### 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?
Fix: Some bugs
- Issues and style fixes related to the 'Memory' page
- Data source icon replacement
- Build optimization
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### 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?
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?
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?
Fix:Bugs fix
- Configure memory and metadata (in Chinese)
- Add indexing modal
- Reduce metadata saving steps
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
Feat: Add mineru as a model manufacturer to the system. #10621
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: balibabu <assassin_cike@163.com>
### What problem does this PR solve?
Features: Memory page rendering and other bug fixes
- Rendering of the Memory list page
- Rendering of the message list page in Memory
- Fixed an issue where the empty state was incorrectly displayed when
search criteria were applied
- Added a web link for the API-Key
- modifying the index_mode attribute of the Confluence data source.
### 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?
Fix: Added styles for empty states on the page.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### 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
- [x] New Feature (non-breaking change which adds functionality)
### 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>
### 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
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Jason <ggbbddjm@gmail.com>
### What problem does this PR solve?
Fixes: Fixed some bugs #10703
- Removed S3 upload from the file upload component
- Updated the dropdown menu style on the model provider page
- Updated some model provider icons
- Fixed other style issues
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fixes: Fixed model provider issues and improved some features
- Removed the old login page
- Updated model provider icons
- Added RAPTOR modification range parameter
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix: Improve some functional issues with the data source. #10703
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feature: Added data source functionality
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix: bug fixes and icon replacement #10703
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
- Update version tags in README files (including translations) from
v0.20.5 to v0.21.0
- Modify Docker image references and documentation to reflect new
version
- Update version badges and image descriptions
- Maintain consistency across all language variants of README files
### Type of change
- [x] Documentation Update
