## Summary
Six sites used to read the same `parser_config.delimiter` field with
divergent grammars:
- `rag.nlp.get_delimiters` (PDF/DOCX/HTML/EPUB/JSON/CSV/XLSX/email/book)
- `rag.nlp.naive_merge` (custom-delimiter branch)
- `rag.nlp.naive_merge_with_images`
- `rag.nlp._build_cks`
- `deepdoc.parser.txt_parser.parser_txt` (.txt, code)
-
`deepdoc.parser.markdown_parser.MarkdownElementExtractor.get_delimiters`
The six implementations disagreed on bare-vs-wrapped chars, dedupe, sort
order, CRLF normalization, and `re.I` (#17384). The shipped default ``
`\n!?;。；！？` `` was a no-op for `.md` because the markdown path only
matched backtick-wrapped tokens.
## Changes
- **new:** `rag/nlp/delim.py` with `parse_delimiter_field` and
`compile_delimiter_pattern`. Single source of truth. CRLF normalization
at the top; longest-first stable sort; insertion-ordered dedupe; no
`re.I`.
- **refactor:** all six call sites delegate to the helper.
- `rag/nlp/__init__.py::get_delimiters` becomes a thin shim.
- `deepdoc/parser/txt_parser.py::parser_txt` drops the
`[encode/decode/unicode_escape]` round-trip.
- `deepdoc/parser/markdown_parser.py::get_delimiters` honors bare chars
(fixes [1]).
- **tests:** `test/unit_test/rag/test_delim.py` (85 tests) — helper,
acceptance table, frontend parity, static guard against re-inlining.
- **tests:** `test/unit_test/rag/test_delimiter_case_sensitive.py` (from
#17386) updated to retarget the static check at the new helper +
AST-based broader scan.
## Acceptance criteria
- All six sites produce the same regex pattern for the same input.
- Shipped default keeps working for `.txt` / `.pdf` / `.docx`.
- Shipped default for `.md` now splits (was a silent no-op).
- Tooltip example `` `\n##;` `` produces three effective delimiters
regardless of file type.
- Bare whitespace inputs split on every occurrence.
- Backtick-wrapped whitespace splits only on the exact N-char sequence.
- CRLF-line-ending documents split identically to LF-line-ending
documents.
- 123 tests pass (85 new + 38 existing).
## Rebase protocol
As #17385 and #17386 evolve, this branch will be rebased on top. The
only overlap between this PR's diff and the other two is
`test_delimiter_case_sensitive.py`, where #17383 modifies the static
check to point at the new helper location.
---------
Co-authored-by: kiloconnect[bot] <240665456+kiloconnect[bot]@users.noreply.github.com>
## Summary
Improve the UX of the **"Delimiter for text"** field on the dataset
configuration page. The field is a single string with a backtick-based
mini-syntax, but both the tooltip and the surrounding UI failed to
surface what delimiters the backend would actually derive from a given
value — leaving users to discover by trial-and-error that the same
string produces different splits depending on file type (see #7436,
#4704, #9680).
### Summary
This PR adds **aimlapi.com** as a model provider, so a RAGFlow user can
enter one API key in the model settings and use AIMLAPI's models across
the app. AIMLAPI ([aimlapi.com](https://aimlapi.com)) is an
OpenAI-compatible aggregator that serves 700+ models (LLM, embedding,
vision, TTS, ASR) from many providers behind a single API.
The change mirrors the repo's existing "add provider" pattern (e.g.
FuturMix / OpenRouter): provider logic lives in the same files those
providers use, and shared / UI files get only registration entries.
**Backend**
- `conf/llm_factories.json` — the `aimlapi.com` factory entry.
- `rag/llm/__init__.py`, `rag/llm/{chat,embedding,cv}_model.py` —
LiteLLM adapters (chat, embedding, image2text) with a production base
URL, overridable via `AIMLAPI_API_URL`.
- `rag/llm/model_meta.py` — an `AIMLAPI` model-meta so the provider
lists its full `/v1/models` catalog dynamically (classified by the
endpoint `type`), the same way OpenRouter does.
- `api/apps/restful_apis/aimlapi_api.py` — an optional "Get API key"
flow using AIMLAPI's agent-authorization (OAuth 2.0 Device Authorization
Grant, RFC 8628). The device code is kept server-side (Redis); only the
issued key reaches the browser.
**Frontend (`web/`)**
- Provider registration (constant, icon allowlist, brand logo), the
model picker (`LIST_MODEL_PROVIDERS` + a `buildLocalConfig` entry), and
the "Get API key" button in the provider dialog. Locales added to `en`
and `zh`.
**Configuration** — production defaults are compiled in; endpoints and
the partner id are overridable through `AIMLAPI_*` environment
variables, so the same build works across environments.
**Testing** — the `web` build passes; chat, embedding and dynamic model
listing were smoke-tested against the live API.
Follow-up to #16468.
PR #16468 fixed the Python
(`api/db/joint_services/tenant_model_service.py
split_model_name`) and Go (`internal/service/model_service.go
parseModelName`)
parsers to right-anchor the '@'-split on the composite
"model_name@instance@provider" key, but the matching helper in the
front-end
(`web/src/utils/llm-util.ts parseModelValue`) was missed.
parseModelValue used `split('@')` (anchored on the first '@'), which
silently
mangles composite keys whose model_name itself contains '@' (e.g. LM
Studio
embedding IDs like `text-embedding-nomic-embed-text-v1.5@q8_0`):
text-embedding-nomic-embed-text-v1.5@q8_0@lmstudio@LM-Studio
became
model_name: text-embedding-nomic-embed-text-v1.5
model_instance: q8_0@lmstudio
model_provider: LM-Studio
`PATCH /api/v1/models/default` then sent those to the server, which
returned
HTTP 200 with body `{"code": 102, "message": "Instance 'q8_0@lmstudio'
not
found for provider 'LM-Studio'"}`. The UI swallowed the body-level error
code, so the failure was silent: no toast, the Embedding field stayed
empty.
This change makes parseModelValue do a right-anchored split that mirrors
the
Python `split_model_name` `rsplit('@', 2)` exactly:
- 3-part form: model_name@instance@provider -> same three fields.
- 2-part form: model_name@provider -> model_instance defaults to
"default"
(matching Python).
- 4+-part form (embedded '@' in model name): last two fields are
anchored
as instance and provider, everything to the left is the bare model name.
`parseModelUuid` is updated to the same right-anchored split so the
factoryId portion of `model_name@factory_id[#instance]` keeps the last
'@'
as the separator even when the model name itself contains '@'.
Adds jest cases under `web/src/utils/tests/llm-util.test.ts` (12 total)
covering the plain 3-part, 2-part, 4-part, multi-'@' name,
buildModelValue
round-trip and parseModelUuid variants.
Note for reviewers: the front-end is bundled into the published docker
image, so a release containing this fix will need `docker compose build
--no-cache ragflow-cpu` (or equivalent) for it to be visible to end
users.
Refs #16468, #16467
Both backends serve GET /api/v1/language. Frontend calls it once and
caches. By this way, front end can know the backend is go or python and
thus can determine which part of logic to load.
---------
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
Fixes#14985 — clicking the **Thinking** button in a shared/embedded
chat returns 401 and bounces the user to the login page, even though
the same share page can chat with the agent just fine.
## Root cause
In shared chat, `useGetSharedChatSearchParams` binds `conversationId`
to the URL's `shared_id` query param — which is the **beta APIToken**,
not the real agent id. That `conversationId` propagates through the
component tree:
```tsx
<WorkFlowTimeline canvasId={conversationId}>
→ useFetchMessageTrace(canvasId)
→ GET /api/v1/agents/<sharedId>/logs/<messageId>
```
But `/agents/<agent_id>/logs/<message_id>` is decorated with
`@login_required` (`api/apps/restful_apis/agent_api.py:842-846`).
The share page only holds the beta token — there is no session JWT
— so the request 401s and quart-auth redirects to the login page.
The reporter's server log matches exactly:
```
load_user from jwt got exception No b'.' found in value
load_user: No APIToken found for token=ULG10SWG3E...
Unauthorized request (quart_auth)
GET /api/v1/agents/394013f8d42211f0bad6123fa55e8ed9/logs/96fd72e2-... 1.1 401
```
The `394013f8...` segment in the URL is the `shared_id` (beta
token), not an actual agent id. `_load_user` already accepts the
regular `APIToken.token` field, but not `APIToken.beta`, by design
— beta is a much weaker share-link credential than a personal API
key.
The sibling endpoints `/agentbots/<id>/completions` and
`/agentbots/<id>/inputs` already use the right auth pattern for
this scope (beta-token via `_get_sdk_authorization_token` →
`APIToken.query(beta=token)`). Trace just didn't have a parallel.
## Fix
### Backend (`api/apps/restful_apis/bot_api.py`)
Added a beta-token sibling endpoint:
```
GET /api/v1/agentbots/<shared_id>/logs/<message_id>
```
- Same auth shape as the existing `agentbots` endpoints.
- The `<shared_id>` path segment is a client-supplied label only.
The real `agent_id` used to build the Redis key
(`<agent_id>-<message_id>-logs`) is taken from
`APIToken.dialog_id` on the looked-up token, so the endpoint
never trusts client-supplied identifiers for the data lookup.
- Returns the same `{data: ...}` shape as the existing
`/agents/<id>/logs/<message_id>` endpoint, so the frontend
doesn't need to reshape the response.
### Frontend
- `web/src/utils/api.ts`: added `sharedTrace(sharedId, messageId)`
URL builder.
- `web/src/services/agent-service.ts`: added
`fetchSharedTrace({ shared_id, message_id })`.
- `web/src/hooks/use-agent-request.ts`: `useFetchMessageTrace`
takes an optional `isShare` argument. When set, it calls
`fetchSharedTrace`; `isShare` is also folded into the
`queryKey` so the two modes never share cached results.
- `web/src/pages/agent/log-sheet/workflow-timeline.tsx`:
forwards the already-existing `isShare` prop into the hook.
All other existing call sites of `useFetchMessageTrace` (webhook
timeline, pipeline log, dataflow result) pass no `isShare`
argument → undefined → falsy → unchanged behavior.
## Test plan
- [ ] In the regular Agent UI (logged-in user): open the trace /
log sheet for any message and click into "Thinking" — the
timeline should still load via `/agents/<id>/logs/<msg>`,
same as before.
- [ ] From the Agent page, click **Chat in new tab** to open
`/chat/share?shared_id=<token>&from=agent`. Send a message,
wait for a response, then click **Thinking** on the
assistant turn. The trace panel should load instead of
redirecting to the login page.
- [ ] Same flow but with the agent embedded in an iframe ("Embed
into webpage") — confirm there is no login redirect.
- [ ] In DevTools → Network, confirm the share-chat trace request
goes to `/api/v1/agentbots/<sharedId>/logs/<msgId>` and
returns 200 with the same JSON shape as the logged-in path.
- [ ] Confirm the chat completions, inputs, and upload flows in
the share page still work — they were not touched.
- [ ] Send a bogus / expired beta token to the new endpoint and
confirm it returns the standard "Authentication error: API
key is invalid!" response (no traceback, no 500).
- [ ] Run `uv run pytest` to make sure no existing tests regress.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
---------
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### What problem does this PR solve?
This PR follows up on
[#15863](https://github.com/infiniflow/ragflow/pull/15863) (Korean i18n)
with translation refinements and i18n coverage for hardcoded strings
found in the UI.
- Refine awkward Korean phrasing (e.g. 'Chunk 만들기' → 'Chunk 생성', '유형' →
'타입', etc.)
- Apply i18n to hardcoded strings in `message-item`,
`next-message-item`, `multi-select`, `chat-prompt-engine`, and various
filter hooks
- Rename `use-selelct-filters.ts` → `use-select-filters.ts` (typo fix)
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Replaces the Python agent canvas runtime with a Go implementation that
runs inside `cmd/server_main`.
The canvas compiles into an eino Workflow that pauses on wait-for-user
via native Interrupt/Resume (no sentinel flag) and resumes from a
Redis-backed CheckPointStore.
All 21 Python agent components and ~35 tools are ported with functional
parity.
Sandbox providers now read their JSON config from the admin-panel
system_settings table with env fallback.
234 files / +35,413 / -6,111. All Go files are gofmt-clean (CI gate
added); drops the v2 DSL E2E step and the gap-analysis plan (both
redundant after the port ships).
## Type of change
- [x] Refactoring
- [x] New feature
- [x] Bug fix
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
### 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?
feat：Unify the 'Add Model Provider' modal
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
Fix: Model provider add verify and fixed form in modal not resetting
issue
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### 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?
Fix: Uploading TSV format documents to the knowledge base did not
generate any error messages.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Follow on PR #15146 to reslove the backwad compatability issue.
1. /agents/<attachment_id>/download ->
/agents/attachments/<attachment_id>/download
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR improves the table upload flow for CSV/Excel files by allowing
table column role configuration at upload time.
Previously, users had to:
1. Upload and parse a table file.
2. Open parser settings and manually set table column roles.
3. Re-parse the file for the roles to take effect.
This was inefficient and required an unnecessary second parse.
With this change:
1. When the knowledge base uses table parsing, the upload dialog
extracts CSV/Excel headers client-side.
2. Users can choose Auto mode or Manual mode.
3. In Manual mode, users can assign per-column roles before upload.
4. The selected parser config is sent with the upload request and
applied server-side during document creation.
Result: configured table column roles are applied from the first parse.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
### What problem does this PR solve?
move agent attachment download api to the correct route and update
frontend callers
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Notes
- Move the attachment download endpoint from document routes to agent
routes.
- Update frontend download callers to use the agent attachment endpoint.
- Reuse the shared file response header helper instead of duplicating it
in `agent_api.py`.
### 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)
### What problem does this PR solve?
```
RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png'
+----------------------------------------------------------+
| text |
+----------------------------------------------------------+
| 生活不是等待风暴过去，而是学会在雨中翩翩起舞。
——佚名 |
+----------------------------------------------------------+
RAGFlow(user)> list 'test@gitee' tasks;
+---------+----------------------------------+
| status | task_id |
+---------+----------------------------------+
| success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 |
+---------+----------------------------------+
RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5';
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| content | index |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| # PDF 1: Purpose of RAGFlow
RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
