### What problem does this PR solve?
```
RAGFlow(api/default)> list dataset 'ccc' files;
Total: 1
```
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
Migrated the dataset document upload API (`POST
/api/v1/datasets/:dataset_id/documents`) from Python to the Go backend.
It supports local file uploads (`type=local`), web page ingestion
(`type=web`), and empty document creation (`type=empty`).
## Changes
- **Router**: Registered `POST /api/v1/datasets/:dataset_id/documents`
route.
- **Handler**: Implemented `UploadDocuments` handler and its routing
functions (`uploadLocalDocuments`, `uploadWebDocument`,
`uploadEmptyDocument`).
- **Service**: Implemented `UploadLocalDocuments`, `UploadWebDocument`,
and `UploadEmptyDocument` in `DocumentService`.
- **Refactoring**: Moved permission checking logic to a shared helper
for reuse in file and document services.
- **Tests**: Added comprehensive unit tests for the new handler and
service upload paths.
## Verification
Ran and passed the test suite for service and handler packages:
- `go test ./internal/service`
- `go test ./internal/handler`
### What problem does this PR solve?
- added the new dataset search route and handler
- reused the existing shared SearchDatasets service by adapting
single-dataset requests into dataset_ids=[dataset_id]
- aligned handler error responses with Python behavior for argument/data
errors
- aligned key service error messages such as invalid search_id and mixed
embedding models
- added focused handler and service tests for request mapping and error
behavior
### Tests:
`/usr/local/go/bin/go test ./internal/service -run
'TestSearchDatasetRequestToSearchDatasetsRequest|TestDatasetServiceSearchDatasets'`
`/usr/local/go/bin/go test ./internal/handler -run
'TestDatasetsHandlerSearchDataset'`
## Problem
The Wikipedia tool silently swallows all exceptions with `except
Exception: pass`, making it impossible to debug failures when fetching
Wikipedia pages.
## Fix
Replace the bare `except Exception: pass` with specific exception
handling:
- `DisambiguationError`: log available options
- `PageError`: log page not found
- `Exception`: log unexpected errors with full traceback
Co-authored-by: wills <willsgao@163.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.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 problem does this PR solve?
- OpenTelemetry integration
- Checkpoint conformance tests
- State inspector API
- Callbacks
- A series of fault injection tests
- Pregel integration tests
### Type of change
- [x] Refactoring
### What problem does this PR solve?
```
RAGFlow(api/default)> set key 'ragflow-JgnarFSCUiV99oOvvMDei7ZzZg1cVlqGd1AMHrHeKE4';
SUCCESS
RAGFlow(api/default)> unset key;
SUCCESS
RAGFlow(api/default)> list provider 'zhipu-ai' instances;
RAGFlow(api/default)> list providers;
RAGFlow(api/default)> list available providers;
RAGFlow(api/default)> list provider 'zhipu-ai' instance 'test' models;
```
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
- add `GET /api/v1/datasets/:dataset_id/tags`
- add `PUT /api/v1/datasets/:dataset_id/tags`
- implement dataset tag listing and rename flow
- align rename tag validation and response shape with the Python API
- add handler and service tests for dataset tags
## Routes
- `GET /api/v1/datasets/:dataset_id/tags`
- `PUT /api/v1/datasets/:dataset_id/tags`
## Test
- Run specific tests for dataset tags:
```
go test -v ./internal/service ./internal/handler -run 'TestDatasetServiceListTags|TestDatasetServiceRenameTag|TestDatasetsHandlerListTags|TestDatasetsHandlerRenameTag'
```
- Run all tests for service and handler to verify no regressions:
```
go test ./internal/service ./internal/handler
```
- use curl cmd to test
## Summary
Align the Go implementations of these APIs with the Python behavior:
- `POST /api/v1/datasets/:dataset_id/metadata/update`
- `PATCH /api/v1/datasets/:dataset_id/documents/metadatas`
- `POST /api/v1/documents/upload`
## What changed
- Added the Go routes and handlers for the 3 APIs.
- Aligned batch document metadata updates with Python semantics:
- support `match` in update items
- support list append / replace behavior
- support deleting specific list values
- remove metadata entirely when it becomes empty
- create metadata for documents that previously had none when updates
apply
- count `updated` only when a document actually changes
- Aligned `documents/upload` file uploads with Python-style
`upload_info` behavior:
- store upload-info blobs in the per-user downloads bucket
- return lightweight upload descriptors instead of normal
file-management responses
- Improved URL upload behavior:
- SSRF-guarded fetch with redirect validation
- redirect limit aligned to Python behavior
- normalize filename and MIME type
- add `.pdf` when the fetched content is PDF
- normalize HTML content into readable text instead of storing raw HTML
shells
## Validation
### Unit tests
Passed:
- `go test ./internal/service`
- `go test ./internal/handler`
Also verified targeted cases for:
- batch metadata update semantics
- upload_info URL handling
- upload_info download bucket behavior
### curl checks
Verified the new Go endpoints with `curl` and compared the response
shape and behavior with Python for:
- `POST /api/v1/datasets/{dataset_id}/metadata/update`
- `PATCH /api/v1/datasets/{dataset_id}/documents/metadatas`
- `POST /api/v1/documents/upload`
The Go responses were checked against Python for:
- argument validation
- success response shape
- metadata update results
- upload_info result structure
- file vs URL input handling
### Description
Migrates the datasets tags aggregation API `GET
/api/v1/datasets/tags/aggregation` from Python to Go.
### Changes
- Registered the `GET /api/v1/datasets/tags/aggregation` route.
- Implemented `AggregateTags` in datasets `handler` and `service`.
- Added handler and service `unit tests`.
### Test Verification
- Verified by comparing results between Python (9380) and Go (9384)
services.
- Tested scenarios: single dataset, multiple datasets, empty parameters,
and unauthorized/invalid IDs.
- All tests and Go `unit tests` passed.
### What problem does this PR solve?
```
RAGFlow(api/default)> add admin host '127.0.0.1:9383';
SUCCESS
RAGFlow(api/default)> use admin;
SUCCESS
RAGFlow(admin)> delete api 'default';
SUCCESS
RAGFlow(admin)> delete api 'default';
CLI error: api server: default not found
RAGFlow(admin)> add api 'default' host '127.0.0.1:9384';
SUCCESS
RAGFlow(admin)> use api 'default';
SUCCESS
RAGFlow(api/default)> delete admin
SUCCESS
RAGFlow(api/default)> delete admin;
CLI error: admin server not exists
RAGFlow(api/default)> list api server;
+------------+---------------+-----------------+---------+
| api_server | api_server_ip | api_server_port | auth |
+------------+---------------+-----------------+---------+
| default | 127.0.0.1 | 9384 | no auth |
+------------+---------------+-----------------+---------+
RAGFlow(api/default)> add admin host '127.0.0.1:9383';
SUCCESS
RAGFlow(api/default)> show admin server;
+-------------------+-----------+
| field | value |
+-------------------+-----------+
| admin_server_ip | 127.0.0.1 |
| admin_server_port | 9383 |
| auth | no auth |
+-------------------+-----------+
```
### 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?
When I run RAGFlow_server.py:
```
2026-06-24 10:27:01,938 ERROR 3413485 fetch task exception
Traceback (most recent call last):
File "/home/infiniflow/Documents/development/ragflow/api/db/services/document_service.py", line 948, in _sync_progress
if t.progress_msg.strip():
^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'strip'
```
fixed:
```python
if t.progress_msg.strip():
# fix:
if (t.progress_msg or "").strip():
```
Fix crash in `_sync_progress` when `progress_msg` is `None`.
#### Root Cause
`progress_msg` from task records can be `None`, causing:
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### 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)
### What problem does this PR solve?
Fix release
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
This PR fixes two issues discovered during testing of the PaddleOCR
async API refactoring:
### 1. PP-OCRv6 returns `ocrResults` instead of `layoutParsingResults`
Models like PP-OCRv6 are pure text recognition models that return
results in `ocrResults.prunedResult.rec_texts` format rather than the
`layoutParsingResults.prunedResult.parsing_res_list` format used by
layout-aware models (PaddleOCR-VL series).
**Changes:**
- `deepdoc/parser/paddleocr_parser.py`: Extract `ocrResults` alongside
`layoutParsingResults` in `_send_request()`, add fallback logic in
`_transfer_to_sections()` and `parse_image()`
- `internal/entity/models/paddleocr.go`: Add `ocrResults` struct and
fallback extraction in Go OCR handler
### 2. Image parsing not integrated into picture chunker
The `parse_image()` method existed in PaddleOCRParser but was never
called from `rag/app/picture.py` (the module that handles image file
uploads). Users configuring PaddleOCR as their layout recognizer would
still get local deepdoc OCR for images.
**Changes:**
- `rag/app/picture.py`: When `layout_recognize` is set to PaddleOCR, use
`PaddleOCROcrModel.parse_image()` instead of local OCR. Falls back
gracefully to local OCR on failure.
## Testing
Verified end-to-end in Docker:
- PaddleOCR-VL-1.6 PDF parsing: ✅ (10 text blocks with bbox)
- PaddleOCR-VL-1.6 image parsing: ✅ (219 chars)
- PP-OCRv6 PDF parsing with ocrResults fallback: ✅ (10 text blocks)
- PP-OCRv6 image parsing with ocrResults fallback: ✅ (136 chars)
## Related PRs
- #15967 (merged) - PaddleOCR async Job API refactoring + new models
- #16086 (merged) - PaddleOCR image parsing support
### What problem does this PR solve?
`_get_optimal_clusters` in `rag/raptor.py` had two edge-case issues in
GMM cluster-count selection:
1. It used `np.arange(1, max_clusters)`, which never evaluates the
upper-bound candidate (`max_clusters`).
2. When effective `max_clusters` becomes `1`, the candidate list was
empty and `argmin` crashed.
This PR makes candidate evaluation inclusive (`1..max_clusters`) and
guards the single-cluster case by returning `1` directly.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Validation
- `pytest test/unit_test/rag/test_raptor_psi_tree_builder.py
--config-file pyproject.toml -q`
- `ruff check rag/raptor.py
test/unit_test/rag/test_raptor_psi_tree_builder.py`
### Tests added
- Regression test for `max_cluster == 1` path (no crash, returns 1)
- Regression test verifying upper-bound candidate is evaluated and can
be selected
_AI-assistance disclosure: parts of this change (bug triage and test
scaffolding) were drafted with AI assistance and fully reviewed and
verified by me._
---------
Co-authored-by: Harsh Kashyap <harshkashyap@Harshs-MacBook-Pro.local>
Co-authored-by: Cursor <cursoragent@cursor.com>
### What problem does this PR solve?
- Tools management
- Pregel engine wrapper for better usage
- UT race
- Coding style
### Type of change
- [x] Refactoring
### What problem does this PR solve?
```
RAGFlow(admin)> show model 'abc';
+------------+----------------------------------------------------------------+
| field | value |
+------------+----------------------------------------------------------------+
| command | get_model_by_model_name |
| error | 'get model by model name' is implemented in enterprise edition |
| model_name | abc |
+------------+----------------------------------------------------------------+
RAGFlow(admin)> list models;
+-----------------+--------------------------------------------------------+
| command | error |
+-----------------+--------------------------------------------------------+
| list_all_models | 'list all models' is implemented in enterprise edition |
+-----------------+--------------------------------------------------------+
```
### Type of change
- [x] Refactoring
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Implement:
1. `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks GET`
2.
`/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks/<chunk_id>
PATCH`
3. `/api/v1/datasets/<dataset_id>/documents/<document_id>/chunks PATCH`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
