### What problem does this PR solve?
Fix authorization bypass (IDOR) in `/v1/document/web_crawl` allows
Cross-Tenant Dataset Modification.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix LFI vulnerability in document parsing API.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
Fixes MinIO SSL/TLS support in two places: the MinIO **client**
connection and the **health check** used by the Admin/Service Health
dashboard. Both now respect the `secure` and `verify` settings from the
MinIO configuration.
Closes#13158Closes#13159
---
## Problem
**#13158 – MinIO client:** The client in `rag/utils/minio_conn.py` was
hardcoded with `secure=False`, so RAGFlow could not connect to MinIO
over HTTPS even when `secure: true` was set in config. There was also no
way to disable certificate verification for self-signed certs.
**#13159 – MinIO health check:** In `api/utils/health_utils.py`, the
MinIO liveness check always used `http://` for the health URL. When
MinIO was configured with SSL, the health check failed and the dashboard
showed "timeout" even though MinIO was reachable over HTTPS.
---
## Solution
### MinIO client (`rag/utils/minio_conn.py`)
- Read `MINIO.secure` (default `false`) and pass it into the `Minio()`
constructor so HTTPS is used when configured.
- Add `_build_minio_http_client()` that reads `MINIO.verify` (default
`true`). When `verify` is false, return an `urllib3.PoolManager` with
`cert_reqs=ssl.CERT_NONE` and pass it as `http_client` to `Minio()` so
self-signed certificates are accepted.
- Support string values for `secure` and `verify` (e.g. `"true"`,
`"false"`).
### MinIO health check (`api/utils/health_utils.py`)
- Add `_minio_scheme_and_verify()` to derive URL scheme (http/https) and
the `verify` flag from `MINIO.secure` and `MINIO.verify`.
- Update `check_minio_alive()` to use the correct scheme, pass `verify`
into `requests.get(..., verify=verify)`, and use `timeout=10`.
### Config template (`docker/service_conf.yaml.template`)
- Add commented optional MinIO keys `secure` and `verify` (and env vars
`MINIO_SECURE`, `MINIO_VERIFY`) so deployers know they can enable HTTPS
and optional cert verification.
### Tests
- **`test/unit_test/utils/test_health_utils_minio.py`** – Tests for
`_minio_scheme_and_verify()` and `check_minio_alive()` (scheme, verify,
status codes, timeout, errors).
- **`test/unit_test/utils/test_minio_conn_ssl.py`** – Tests for
`_build_minio_http_client()` (verify true/false/missing, string values,
`CERT_NONE` when verify is false).
---
## Testing
- Unit tests added/updated as above; run with the project's test runner.
- Manually: configure MinIO with HTTPS and `secure: true` (and
optionally `verify: false` for self-signed); confirm client operations
work and the Service Health dashboard shows MinIO as alive instead of
timeout.
### What problem does this PR solve?
Fix stored XSS via HTML file upload and inline rendering in
/v1/file/get/<id>
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
When using a chat assistant that has a hardcoded `empty_response`, that
response was not returned correctly in streaming mode when no
information is found in the knowledge base. In this case only one
response with `"content": null` was yielded. If `"references": true`,
then the `empty_response` is still put into the `final_content` so there
is technically some content returned, but when `"references": false` no
content at all is returned.
I update the OpenAI chat completion endpoint to yield an additional
response with the `empty_response` in the content.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Fixes AttributeError in _remove_reasoning_content() when LLM returns
None, and improves JSON parsing regex for markdown code fences in
agent_with_tools.py
### What problem does this PR solve?
This fixes the bug described in #13130. When starting RAGFlow with
Postgres the admin tenant create failed because the rerank model was not
set.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR fixes missing metadata on documents synced from the Moodle
connector, especially for **Book** modules.
Background:
- Moodle Book metadata includes fields like `chapters`, which is a
`list[dict]`.
- During metadata normalization in
`DocMetadataService._split_combined_values`, list deduplication used
`dict.fromkeys(...)`.
- `dict.fromkeys(...)` fails for unhashable values (like `dict`),
causing metadata update to fail.
- Result: documents were imported, but metadata was not saved for
affected module types (notably Books).
What this PR changes:
- Replaces hash-based list deduplication with `dedupe_list(...)`, which
safely handles unhashable list items while preserving order.
- This allows Book metadata (and other complex list metadata) to be
persisted correctly.
### 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):
Contribution during my time at RAGcon GmbH.
### What problem does this PR solve?
Decouple the memory API into a gateway layer (for routing/param parse)
and a service layer (for business logic).
### Type of change
- [x] Refactoring
When match_expressions contains coroutine objects (from GraphRAG's
Dealer.get_vector()), the code cannot identify this type because it only
checks for MatchTextExpr, MatchDenseExpr, or FusionExpr.
As a result:
score_func remains initialized as an empty string ""
This empty string is appended to the output list
The output list is passed to Infinity SDK's table_instance.output()
method
Infinity's SQL parser (via sqlglot) fails to parse the empty string,
throwing a ParseError
### What problem does this PR solve?
Adjust highlight parsing, add row-count SQL override, tweak retrieval
thresholding, and update tests with engine-aware skips/utilities.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR adds an opt‑in way to include document‑level metadata in
OpenAI‑compatible reference chunks. Until now, metadata could be used
for filtering but wasn’t returned in responses. The change enables
clients to show richer citations (author/year/source, etc.) while
keeping payload size and privacy under control via an explicit request
flag and optional field allowlist.
### 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):
Contribution during my time at RAGcon GmbH.
### Closes: #12921
### What problem does this PR solve?
Previously, multi-file upload was not working correctly across the
application:
- **Chat**: UI displayed "Upload max 5 files" but only the first file
was actually uploaded
- **Agent conversational mode**: Frontend sent multiple files but
backend only processed one
- **Agent task-mode file inputs**: Explicitly limited to single file
only
This PR enables proper multi-file upload support for both chat and agent
workflows, allowing users to upload and process multiple files (up to 5)
as the UI originally suggested.
**Changes:**
- `web/src/pages/next-chats/hooks/use-upload-file.ts`: Process all files
instead of only `files[0]`
- `api/apps/canvas_app.py`: Handle multiple files via
`files.getlist("file")`
- `web/src/pages/agent/debug-content/uploader.tsx`: Allow up to 5 files
with `multiple={true}`
- `agent/component/begin.py` & `fillup.py`: Support file arrays while
maintaining backward compatibility
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix: PDF chunking issue for single-page documents
Refactor: Change the default refresh frequency to 5
Fix: Add a 0-degree threshold; require other rotation angles to exceed
it by at least 0.2
Fix: Put connector name tips to correct place
Fix: incorrect example response in delete datasets.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
### What problem does this PR solve?
Fix "metadata table not exists" when updating a meta data.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Add tenant for default admin, and allow login to ragflow server as
default admin.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
As title.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Liu An <asiro@qq.com>
## Description
This PR focuses on API performance optimization and refining the model
capability detection logic in the Agent/Canvas module.
### 1. Performance Optimization (Backend)
- **Changes**: Removed `cls.model.dsl` from query fields in
`UserCanvasService.get_by_tenant_ids`.
- **Reasoning**: The `dsl` object is large and unnecessary for the Agent
list view. Excluding it reduces the payload size of the
`/v1/canvas/list` API, leading to faster serialization and reduced
network latency.
- **Consistency**: Full DSL data remains accessible via the individual
`/v1/canvas/get/<id>` endpoint used in the detail view.
### 2. Multimodal Detection Refinement (Frontend)
- **Changes**: Replaced `model_type === LlmModelType.Image2text` with
`tags?.includes('IMAGE2TEXT')`.
- **Reasoning**: In RAGFlow, `model_type` defines the primary role of a
model (e.g., `chat`). However, many advanced Chat models are also
vision-capable. Since `model_type` is a single-value field, it cannot
represent these multiple capabilities.
- **Solution**: Utilizing the `tags` field (which supports multiple
attributes) to check for `IMAGE2TEXT` ensures that models like
`gpt-5.2-pro` correctly display multimodal input options.
## Type of Change
- [x] Bug fix (logic correction for multimodal detection)
- [x] Optimization (performance improvement for list API)
## Main Changes
- `api/db/services/canvas_service.py`: Optimized DB query by excluding
heavy DSL fields.
- `web/src/pages/agent/form/agent-form/index.tsx`: Enhanced capability
detection using the tags system.
## Verification
- [x] Verified Agent list loads faster with reduced response payload.
- [x] Confirmed that `chat` models with the `IMAGE2TEXT` tag now
correctly enable the multimodal input UI.
### What problem does this PR solve?
- Remove unused imports (Mock, patch, MagicMock, json, os,
RAGFLOW_COLUMNS, VECTOR_FIELD_PATTERN) from multiple files
- Replace f-string formatting with regular strings for console output
messages in cli.py
- Clean up unnecessary imports that were no longer being used in the
codebase
### Type of change
- [x] Refactoring
## Summary
This PR adds Peewee ORM support for OceanBase as the primary database in
RAGFlow, as requested in issue #12769.
## Changes
### Core Implementation
1. **RetryingPooledOceanBaseDatabase Class**
- Inherits from `PooledMySQLDatabase` (OceanBase is MySQL-compatible)
- Implements retry mechanism for connection issues
- Handles MySQL-specific error codes (2013, 2006 for connection loss)
- Provides connection pool management
2. **PooledDatabase Enum**
- Added `OCEANBASE = RetryingPooledOceanBaseDatabase`
3. **DatabaseLock Enum**
- Added `OCEANBASE = MysqlDatabaseLock`
- OceanBase uses MySQL-style locking
4. **TextFieldType Enum**
- Added `OCEANBASE = "LONGTEXT"`
- OceanBase uses same text field type as MySQL
5. **DatabaseMigrator Enum**
- Added `OCEANBASE = MySQLMigrator`
- OceanBase uses MySQL migration tools
### Usage
```bash
# Set environment variable to use OceanBase
export DB_TYPE=oceanbase
# Configure connection (in docker/.env or environment)
OCEANBASE_HOST=localhost
OCEANBASE_PORT=2881
OCEANBASE_USER=root
OCEANBASE_PASSWORD=password
OCEANBASE_DATABASE=ragflow
```
### Technical Details
- **Location**: `api/db/db_models.py`
- **Dependencies**: No new dependencies (uses existing Peewee MySQL
support)
- **Code Size**: ~90 lines
- **Difficulty**: Simple
### Testing
- Added comprehensive unit tests in
`tests/unit/test_oceanbase_peewee.py`
- Tests cover:
- OceanBase database class existence and inheritance
- Enum values for PooledDatabase, DatabaseLock, TextFieldType
- Initialization with custom retry settings
- Environment variable configuration
### Acceptance Criteria
✅ Can switch to OceanBase database via `DB_TYPE=oceanbase` environment
variable
✅ All database operations work normally in OceanBase environment
✅ OceanBase uses MySQL compatibility mode (no additional dependencies)
### Background
This is part of the RAGFlow + OceanBase Hackathon to allow users to
choose OceanBase as RAGFlow's primary database, leveraging OceanBase's
high availability and scalability.
---
## Related Issues
- **Primary**: https://github.com/infiniflow/ragflow/issues/12769
- **Context**: https://github.com/oceanbase/seekdb/issues/123 (OceanBase
Developer Challenge)
---
Closesinfiniflow/ragflow#12769
### What problem does this PR solve?
close#12770
This PR adds OceanBase as a storage backend for the Table Parser. It
enables dynamic table schema storage via JSON and implements OceanBase
SQL execution for text-to-SQL retrieval.
### 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):
### Changes
- Table Parser stores row data into `chunk_data` when doc engine is
OceanBase. (table.py)
- OceanBase table schema adds `chunk_data` JSON column and migrates if
needed.
- Implemented OceanBase `sql()` to execute text-to-SQL results.
(ob_conn.py)
- Add `DOC_ENGINE_OCEANBASE` flag for engine detection (setting.py)
### Test
1. Set `DOC_ENGINE=oceanbase` (e.g. in `docker/.env`)
<img width="1290" height="783" alt="doc_engine_ob"
src="https://github.com/user-attachments/assets/7d1c609f-7bf2-4b2e-b4cc-4243e72ad4f1"
/>
2. Upload an Excel file to Knowledge Base.(for test, we use as below)
<img width="786" height="930" alt="excel"
src="https://github.com/user-attachments/assets/bedf82f2-cd00-426b-8f4d-6978a151231a"
/>
3. Choose **Table** as parsing method.
<img width="2550" height="1134" alt="parse_excel"
src="https://github.com/user-attachments/assets/aba11769-02be-4905-97e1-e24485e24cd0"
/>
4.Ask a natural language query in chat.
<img width="2550" height="1134" alt="query"
src="https://github.com/user-attachments/assets/26a910a6-e503-4ac7-b66a-f5754bbb0e91"
/>
### What problem does this PR solve?
Close#12768.
This PR adds OceanBase support to RAGFlow’s Text-to-SQL (ExeSQL)
component.
OceanBase is integrated via MySQL compatibility mode, and the UI
`db_type` options are updated accordingly.
### 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):
### Changes
**Backend**
- Add `oceanbase` `db_type` validation and connection logic in
`exesql.py` and reuse existing MySQL compatibility mode
**Frontend**
- Add OceanBase option to the ExeSQL `db_type` selector
### How to test
1. Configure OceanBase connection in ExeSQL node
(host/port/user/password/database)
2. Input: “Show 10 rows from test table”
3. Generated SQL: `SELECT * FROM test LIMIT 10;`
4. Query executes successfully and results are returned
### Screenshots
- ExeSQL db_type includes OceanBase
<img width="649" height="1015" alt="2"
src="https://github.com/user-attachments/assets/e0a5f7b9-e282-402a-8639-64c1aef8fce6"
/>
- ExeSQL test OceanBase connection
<img width="2247" height="1140" alt="test_ob"
src="https://github.com/user-attachments/assets/f16ebd93-b48e-4d18-b53f-8496581e755d"
/>
- Query results from OceanBase shown in UI
<img width="2550" height="1351" alt="1"
src="https://github.com/user-attachments/assets/b44163dc-baab-420d-b31e-b644bdcb77a9"
/>
### What problem does this PR solve?
test_update_document.py failed as metadata is not included in the
response of get_list(), fix the issue.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Description
This PR implements comprehensive OceanBase performance monitoring and
health check functionality as requested in issue #12772. The
implementation follows the existing ES/Infinity health check patterns
and provides detailed metrics for operations teams.
## Problem
Currently, RAGFlow lacks detailed health monitoring for OceanBase when
used as the document engine. Operations teams need visibility into:
- Connection status and latency
- Storage space usage
- Query throughput (QPS)
- Slow query statistics
- Connection pool utilization
## Solution
### 1. Enhanced OBConnection Class (`rag/utils/ob_conn.py`)
Added comprehensive performance monitoring methods:
- `get_performance_metrics()` - Main method returning all performance
metrics
- `_get_storage_info()` - Retrieves database storage usage
- `_get_connection_pool_stats()` - Gets connection pool statistics
- `_get_slow_query_count()` - Counts queries exceeding threshold
- `_estimate_qps()` - Estimates queries per second
- Enhanced `health()` method with connection status
### 2. Health Check Utilities (`api/utils/health_utils.py`)
Added two new functions following ES/Infinity patterns:
- `get_oceanbase_status()` - Returns OceanBase status with health and
performance metrics
- `check_oceanbase_health()` - Comprehensive health check with detailed
metrics
### 3. API Endpoint (`api/apps/system_app.py`)
Added new endpoint:
- `GET /v1/system/oceanbase/status` - Returns OceanBase health status
and performance metrics
### 4. Comprehensive Unit Tests
(`test/unit_test/utils/test_oceanbase_health.py`)
Added 340+ lines of unit tests covering:
- Health check success/failure scenarios
- Performance metrics retrieval
- Error handling and edge cases
- Connection pool statistics
- Storage information retrieval
- QPS estimation
- Slow query detection
## Metrics Provided
- **Connection Status**: connected/disconnected
- **Latency**: Query latency in milliseconds
- **Storage**: Used and total storage space
- **QPS**: Estimated queries per second
- **Slow Queries**: Count of queries exceeding threshold
- **Connection Pool**: Active connections, max connections, pool size
## Testing
- All unit tests pass
- Error handling tested for connection failures
- Edge cases covered (missing tables, connection errors)
- Follows existing code patterns and conventions
## Code Statistics
- **Total Lines Changed**: 665+ lines
- **New Code**: ~600 lines
- **Test Coverage**: 340+ lines of comprehensive tests
- **Files Modified**: 3
- **Files Created**: 1 (test file)
## Acceptance Criteria Met
✅ `/system/oceanbase/status` API returns OceanBase health status
✅ Monitoring metrics accurately reflect OceanBase running status
✅ Clear error messages when health checks fail
✅ Response time optimized (metrics cached where possible)
✅ Follows existing ES/Infinity health check patterns
✅ Comprehensive test coverage
## Related Files
- `rag/utils/ob_conn.py` - OceanBase connection class
- `api/utils/health_utils.py` - Health check utilities
- `api/apps/system_app.py` - System API endpoints
- `test/unit_test/utils/test_oceanbase_health.py` - Unit tests
Fixes#12772
---------
Co-authored-by: Daniel <daniel@example.com>
### What problem does this PR solve?
Put document metadata in ES/Infinity.
Index name of meta data: ragflow_doc_meta_{tenant_id}
### Type of change
- [x] Refactoring
## Description
This PR fixes issue #12805 by adding validation to handle
whitespace-only questions in the `/retrieval` endpoint.
## Problem
Sending a single space `" "` as the `question` parameter to `/retrieval`
crashes the request with an `AssertionError`. This happens because:
1. The endpoint doesn't trim or validate the question parameter
2. A whitespace-only string is treated as valid input
3. The retrieval logic only checks for empty strings (which are falsy),
but `" "` is truthy
4. Invalid match expressions are constructed, causing an assertion
failure in the Elasticsearch layer
## Solution
- Trim whitespace from the question parameter before processing
- Return an empty result for whitespace-only or empty questions
- Prevents the AssertionError and provides expected behavior
## Changes
- Added whitespace trimming and validation in `api/apps/sdk/doc.py`
- Returns empty result early if question is empty after trimming
## Testing
- Tested with single space input - now returns empty result instead of
crashing
- Tested with empty string - returns empty result
- Tested with normal questions - works as expected
Fixes#12805
Co-authored-by: Daniel <daniel@example.com>
### What problem does this PR solve?
If no `metadata_condition` parameter is given then don't load the
metadata of all documents into memory. Instead just pass `doc_ids` as
`None` to the `retrieval()` method, which means to use all documents of
the given datasets.
This is relevant if you have *a lot* of documents!
### Type of change
- [x] Performance Improvement
### What problem does this PR solve?
When deleting the knowledge base, the records in the Document and
Knowledgebase tables are immediately deleted
But there are still a large number of pending task messages in the Redis
queue (asynchronous queue) if you did not click on stopping tasks before
deleting knowledge base.
TaskService.get_task() uses a JOIN query to associate three tables (Task
← Document ← Knowledgebase)
Since Document/Knowledgebase have been deleted, the JOIN returns an
empty result, even though the Task records still exist
task-executor considers the task does not exist ("collect task xxx is
unknown"), can only skip and warn
log：2026-01-23 16:43:21,716 WARNING 1190179 collect task
110fbf70f5bd11f0945a23b0930487df is unknown
2026-01-23 16:43:21,818 WARNING 1190179 collect task
11146bc4f5bd11f0945a23b0930487df is unknown
2026-01-23 16:43:21,918 WARNING 1190179 collect task
111c3336f5bd11f0945a23b0930487df is unknown
2026-01-23 16:43:22,021 WARNING 1190179 collect task
112471b8f5bd11f0945a23b0930487df is unknown
2026-01-23 16:43:26,719 WARNING 1190179 collect task
112e855ef5bd11f0945a23b0930487df is unknown
2026-01-23 16:43:26,734 WARNING 1190179 collect task
1134380af5bd11f0945a23b0930487df is unknown
2026-01-23 16:43:26,834 WARNING 1190179 collect task
1138cb2cf5bd11f0945a23b0930487df is unknown
As a consequence, a large number of such tasks occupy the queue
processing capacity, causing new tasks to queue and wait
<img width="1910" height="947"
alt="9a00f2e0-9112-4dbb-b357-7f66b8eb5acf"
src="https://github.com/user-attachments/assets/0e1227c2-a2df-4ef3-ba8f-e04c3f6ef0e1"
/>
Solution
Add logic to stop all ongoing tasks before deleting the knowledge base
and Tasks
### Type of change
- Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Add tokenized content es field to query zh message.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
The OpenAI-compatible chat endpoint
(`/chats_openai/<chat_id>/chat/completions`) was not returning accurate
token
usage in streaming responses. The token counts were either missing or
inaccurate because the underlying LLM API
responses weren't being properly parsed for usage data.
This PR adds proper token counting using tiktoken (cl100k_base encoding)
as a fallback when the LLM API doesn't provide usage data in streaming
chunks. This ensures clients always receive token usage information in
the
response, which is essential for billing and quota management.
**Changes:**
- Add tiktoken-based token counting for streaming responses in
OpenAI-compatible endpoint
- Ensure `usage` field is always populated in the final streaming chunk
- Add unit tests for token usage calculation
Fixes#7850
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feat: Add a web search button to the chat box on the chat page.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
**Backend**
\rag\nlp\search.py
*Before the fix*
The top_k parameter was not applied to limit the total number of chunks,
and the rerank model also uses the exact whole valid_idx rather than
assigning valid_idx = valid_idx[:top] firstly.
*After the fix*
The top_k limit is applied to the total results before pagination, using
a default value of top = 1024 if top_k is not modified.
session.py
*Before the fix:*
When the frontend calls the retrieval API with `search_id`, the backend
only reads `meta_data_filter` from the saved `search_config`. The
`rerank_id`, `top_k`, `similarity_threshold`, and
`vector_similarity_weight` parameters are only taken from the direct
request body. Since the frontend doesn't pass these parameters
explicitly (it only passes `search_id`), they always fall back to
default values:
- `similarity_threshold` = 0.0
- `vector_similarity_weight` = 0.3
- `top_k` = 1024
- `rerank_id` = "" (no rerank)
This means user settings saved in the Search Settings page have no
effect on actual search results.
*After the fix:*
When a `search_id` is provided, the backend now reads all relevant
configuration from the saved `search_config`, including `rerank_id`,
`top_k`, `similarity_threshold`, and `vector_similarity_weight`. Request
parameters can still override these values if explicitly provided,
allowing flexibility. The rerank model is now properly instantiated
using the configured `rerank_id`, making the rerank feature actually
work.
**Frontend**
\web\src\pages\next-search\search-setting.tsx
*Before the fix*
search-setting.tsx file, the top_k input box is only displayed when
rerank is enabled (wrapped in the rerankModelDisabled condition). If the
rerank switch is turned off, the top_k input field will be hidden, but
the form value will remain unchanged. In other words: - When rerank is
enabled, users can modify top_k (default 1024). - When rerank is
disabled, top_k retains the previous value, but it's not visible on the
interface. Therefore, the backend will always receive the top_k
parameter; it's just that the frontend UI binds this configuration item
to the rerank switch. When rerank is turned off, top_k will not
automatically reset to 1024, but will retain its original value.
*After the fix*
On the contrary, if we switch off the button rerank model, the value
top-k will be reset to 1024. By the way, If we use top-k in an
individual method, rather than put it into the method retrieval, we can
control it separately
Now all methods valid
Using rerank
<img width="2378" height="1565" alt="Screenshot 2026-01-21 190206"
src="https://github.com/user-attachments/assets/fa2b0df0-1334-4ca3-b169-da6c5fd59935"
/>
Not using rerank
<img width="2596" height="1559" alt="Screenshot 2026-01-21 190229"
src="https://github.com/user-attachments/assets/c5a80522-a0e1-40e7-b349-42fe86df3138"
/>
Before fixing they are the same
### Type of change
- Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
API adds audio to text and text to speech functions
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
As title.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
