### What problem does this PR solve?
This PR adds Google BigQuery as a first-class data source connector in
RAGFlow.
It enables users to ingest and sync BigQuery data using the same
row-to-document model used by relational database connectors: selected
content columns become document text, metadata columns become document
metadata, an optional ID column provides stable document IDs, and an
optional timestamp column enables cursor-based incremental sync.
The connector supports service-account JSON credentials, table mode,
custom query mode, GoogleSQL queries, cursor-based incremental sync,
deleted-row pruning support, configurable query limits such as
`maximum_bytes_billed`, dry-run validation, batch loading, stable
document IDs, and BigQuery-aware value serialization.
### What problem does this PR solve?
The `get_ingestion_log` endpoint (both Python
`dataset_api_service.get_ingestion_log` and Go
`DatasetService.GetIngestionLog`) was returning only the
**dataset-level** field set, which omits critical fields such as `dsl`,
`document_id`, `parser_id`, `document_name`, `pipeline_id`, etc.
This caused the front-end **dataflow-result page** to be unable to
render the pipeline timeline and chunks when viewing a single ingestion
log, regardless of whether the log was a dataset-level operation
(graph/raptor/mindmap) or a per-file parse.
### Background
`PipelineOperationLogService` provides two field sets:
| Method | Fields |
|---|---|
| `get_dataset_logs_fields` | Minimal set (progress, status, timestamps,
etc.) |
| `get_file_logs_fields` | Superset — includes `document_id`, `dsl`,
`parser_id`, `document_name`, `pipeline_id`, … |
When listing logs, the API correctly distinguishes dataset-level vs
file-level logs and uses the appropriate converter. However, when
**fetching a single log by ID**, both the Python and Go implementations
were hardcoded to the dataset-level set, dropping the extra fields that
the front-end needs.
### What problem does this PR solve?
Feat:
- Allow upsert model_type for instance model
Fix:
- Allow create instance with duplicate api_key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix: Remove the pagination from the search and retrieval pages.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
Fixes#15532 — `delete_datasets()` crashes with `IndexError` when a
document has no `File2Document` row.
`delete_datasets()` in `dataset_api_service.py` called
`File2DocumentService.get_by_document_id()` and immediately accessed
`f2d[0].file_id` without checking whether the lookup returned any rows.
Documents created via API ingestion or connector sync may exist without
a linked file record, causing dataset deletion to abort with HTTP 500.
This PR mirrors the existing guard already used in `file_service.py` and
`document_api_service.py`.
### What problem does this PR solve?
FIx replicate model provider failing with valid api key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Wang Qi <wangq8@outlook.com>
### What problem does this PR solve?
Fix: The dataset retrieval test returned an incorrect total number.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: balibabu <assassin_cike@163.com>
### What problem does this PR solve?
Support factory models with multiple model types, so visual chat models
can be exposed as both image2text and chat while preserving the database
model-type-per-record design.
This also updates the SILICONFLOW model list and adds a helper script to
refresh SiliconFlow models from the provider API.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
When setting the API key for the BaiduYiyan provider, all model
validations fail with the error "Fail to access model using this api
key. No valid response received".
**Root cause:**
1. `BaiduYiyanChat` in `rag/llm/chat_model.py` does not override
`async_chat_streamly()`. The `verify_api_key()` function uses
`mdl.async_chat_streamly()` to validate, but `BaiduYiyanChat` inherits
`Base.async_chat_streamly()` which uses the OpenAI client, not the Baidu
Qianfan SDK (qianfan). Since BaiduYiyan has no OpenAI-compatible
base_url, validation always fails.
2. `verify_api_key()` in `provider_api_service.py` does not format the
raw API key string into the JSON format (`{"yiyan_ak": "...",
"yiyan_sk": "..."}`) that `BaiduYiyanChat.__init__()` expects via
`json.loads(key)`.
**Fix:**
1. Add `async_chat_streamly()` method to `BaiduYiyanChat` using the
qianfan SDK, consistent with the existing `chat_streamly()` method.
2. Add BaiduYiyan API key formatting in `provider_api_service.py`
`verify_api_key()` to match the format expected by
`BaiduYiyanChat.__init__()`.
### 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):
### What problem does this PR solve?
Propagate `tenant_id` from memory task messages into task collection so
refactored task execution can build a valid context.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Display intl `base_url` for Tongyi-Qianwen
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Support model list for VolcEngine.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix LM-Studio provider connection verification so embedding checks await
the async wrapper correctly and log the full traceback on failures.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feat：
- Get model list from remote provider.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix:
- VolcEngine adapt to new api_key format
- Save dict api_key as json
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Not display `success` when check not passed.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix:
- Verify provider with empty llm list in llm_factories.json
- Set search bot's chat_llm_name, use tenant default chat model as
default
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
### Problem
On the Model Providers page, the Embedding Model dropdown in System
Model Settings shows empty (no default selected), even though a default
embedding model is configured in `service_conf.yaml`.
### Root Cause
Two issues were identified:
1. **Backend: `_get_model_info` fails for unregistered providers**
The tenant's `embd_id` is set to `bge-m3@xxxx` during initialization
(from the placeholder config `factory: 'xxxx'`). The `_get_model_info`
function requires the provider to exist in `tenant_model_provider`
table, but `xxxx` is never a real provider. Even after the user adds a
real provider (e.g., ZHIPU-AI), the stale `embd_id` still references the
non-existent one, causing the function to return `None`.
2. **Frontend: default models cache not invalidated after adding
provider**
`useAddProviderInstance` only invalidates `addedProviders` and
`allModels` caches after adding a provider instance, but does **not**
invalidate the `defaultModels` cache. This means the default model list
is not re-fetched until the user manually refreshes the page.
### Fix
**`api/apps/services/models_api_service.py`**
- Added `_resolve_model_from_tenant_providers()` helper: when the
default model's provider doesn't exist (e.g., placeholder `xxxx`), it
searches through the tenant's actually registered providers for a model
of the same type and returns the first match.
- When an instance name doesn't match (e.g., `"default"` vs actual name
`"1"`), the function now auto-resolves to the first real instance under
that provider.
- Falls back to `FACTORY_LLM_INFOS` validation when neither provider nor
instance exists.
**`web/src/hooks/use-llm-request.tsx`**
- Added `queryClient.invalidateQueries({ queryKey:
LlmKeys.defaultModels() })` to `useAddProviderInstance` so that the
default model list is re-fetched immediately after a provider instance
is added, eliminating the need for a manual page refresh.
### Testing
- Verified with a tenant whose `embd_id=bge-m3@xxxx` and only provider
is ZHIPU-AI (instance `1`): `_resolve_model_from_tenant_providers`
correctly resolves to `embedding-2@1@ZHIPU-AI`.
- After adding a provider via the UI, the embedding model dropdown now
immediately shows the resolved default without requiring a page refresh.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: noob <yixiao121314@outlook.com>
## Summary
Fixes#15534 — `update_document_name_only()` crashes with
`AttributeError` when `File2Document` exists but the linked `File` row
was deleted.
`update_document_name_only()` in `document_api_service.py` called
`FileService.get_by_id()` when a `File2Document` row existed, then
accessed `file.id` without checking the lookup result. An orphan
`File2Document` link (file deleted, mapping left behind) caused document
rename via `PATCH /api/v1/datasets/{dataset_id}/documents/{document_id}`
to return HTTP 500.
This PR mirrors guards used in `file2document_api.py` and
`file_api_service.py`: skip the optional file rename when the file is
missing, and still update the document record and search index.
## Changes
- `api/apps/services/document_api_service.py` — check `e and file`
before `FileService.update_by_id`
- `test/unit_test/api/apps/services/test_update_document_name_only.py` —
regression tests (orphan link + happy path)
## Test plan
- [x] `pytest
test/unit_test/api/apps/services/test_update_document_name_only.py -v`
- [ ] Manual: PATCH document `name` when `File2Document` points to a
non-existent `file_id` → 200, document/index renamed, no 500
### What problem does this PR solve?
Fix:
- Handle siliconflow and siliconflow_intl api_key
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix:
- Use @ to avoid split by `_` in model_name.
- Verify api_key when add instance.
- Pop api_key in list intances response.
- Remove useless index.
- Sort providers, instances and models by name.
- Get `is_tools` from llm_factories.json
### 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>
### Related issues
Closes#14922
### What problem does this PR solve?
`POST /memories` already resolves `tenant_llm_id` and `tenant_embd_id`
through `ensure_tenant_model_id_for_params`, but `PUT
/memories/<memory_id>` accepted client-supplied `tenant_llm_id` /
`tenant_embd_id` without checking that those `tenant_llm` rows belong to
the memory owner’s tenant. A caller could persist another tenant’s row
IDs and later trigger extraction or embedding that loaded foreign model
credentials via `get_model_config_by_id(tenant_model_id)` with no tenant
allow-list.
This change aligns the update path with create: updates that change
models must go through `llm_id` / `embd_id` and
`ensure_tenant_model_id_for_params` scoped to the **memory’s**
`tenant_id` (not only the current user, so team-access cases stay
correct). Direct `tenant_*` fields in the body without `llm_id` /
`embd_id` are rejected. As defense in depth, `memory_message_service`
passes `allowed_tenant_ids` / `requester_tenant_id` into
`get_model_config_by_id` for LLM and embedding resolution so mismatched
IDs cannot be used even if bad data existed. A regression test rejects
payloads that set only `tenant_llm_id` / `tenant_embd_id`.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: jony376 <jony376@gmail.com>
### What problem does this PR solve?
Fixes#14866.
Previously, `DocumentService.increment_chunk_num` and
`decrement_chunk_num` updated the `Document` row and its parent
`Knowledgebase` row in two separate, non-transactional statements. If
the second update failed (DB error, connection drop, etc.) after the
first one succeeded, the document and knowledge base chunk/token
counters would drift apart and stay inconsistent.
There was also a behavioral asymmetry between the two methods:
- `increment_chunk_num` only logged a warning when the document row was
missing and returned a value that callers usually treated as success.
- `decrement_chunk_num` raised `LookupError` in the same situation.
This PR makes the counter updates atomic and aligns the missing-document
behavior between the two methods:
- Wrap the `Document` and `Knowledgebase` updates in
`increment_chunk_num` / `decrement_chunk_num` inside a `DB.atomic()`
block so both succeed or both roll back together.
- Raise `LookupError` from `increment_chunk_num` when the target
document no longer exists, matching `decrement_chunk_num`.
- Update `reset_document_for_reparse` in `document_api_service.py` to
catch the new `LookupError` and return a proper "Document not found!"
API error instead of propagating the exception.
No schema changes, no API contract changes for the success path; only
the failure mode for a missing document during reparse is now a clean
error response instead of an uncaught exception.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Related issues
Closes#14781
### What problem does this PR solve?
Some retrieval endpoints accepted caller-supplied `tenant_rerank_id` and
resolved it through `get_model_config_by_id(...)`. That helper loaded
`TenantLLM` rows by global database id and returned decoded model
configuration without checking whether the model belonged to the
authenticated tenant or the dataset owner tenant.
This meant dataset access was validated, but rerank-model selection was
not. A caller who knew or could guess another tenant's
`tenant_rerank_id` could attempt retrieval with a foreign rerank model
config, creating a cross-tenant authorization gap for model usage.
This PR closes that gap by making `tenant_rerank_id` resolution
tenant-aware across the retrieval paths that accept it.
### 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):
### Solution
- Extend `get_model_config_by_id(...)` to accept an optional
`allowed_tenant_ids` set and reject `TenantLLM` rows whose `tenant_id`
is outside that set.
- Pass the allowed tenant scope from retrieval endpoints that accept
`tenant_rerank_id`:
- `api/apps/sdk/doc.py`
- `api/apps/sdk/session.py`
- `api/apps/services/dataset_api_service.py`
- Use the authenticated tenant plus dataset-owner tenant ids already
derived by each retrieval flow as the authorization boundary for rerank
model selection.
- Add focused unit coverage to assert unauthorized `tenant_rerank_id`
values are rejected and that the allowed tenant set is propagated
correctly.
### Testing
- `python -m py_compile` on:
- `api/db/joint_services/tenant_model_service.py`
- `api/apps/services/dataset_api_service.py`
- `api/apps/sdk/doc.py`
- `api/apps/sdk/session.py`
- Added unit tests in:
-
`test/testcases/test_http_api/test_file_management_within_dataset/test_doc_sdk_routes_unit.py`
-
`test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
### Notes for reviewers
- This change is intentionally narrow: it affects only the
`tenant_rerank_id` path, not the normal `rerank_id` name-based
resolution path.
- Local lint/syntax checks passed.
- Full pytest execution could not be completed in this environment
because the local test runtime is missing `strenum`, so the route-test
files fail during collection before exercising the updated cases.
---------
Co-authored-by: jony376 <jony376@gmail.com>
### What problem does this PR solve?
Fix delete graphrag not take effect in UI
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
## Problem
During the REST API refactoring (#13690), the
`/api/v2/kb/check_embedding` endpoint was removed and never migrated to
the new RESTful structure. The frontend was pointed to the
`/api/v1/datasets/{id}/embedding` endpoint (which is `run_embedding` — a
completely different function). Additionally, a hard guard was
introduced that rejects any `embd_id` change when `chunk_num > 0`,
making it impossible to switch embedding models on datasets with
existing chunks.
## Root Cause
1. **Missing endpoint**: The old `check_embedding` logic (sample random
chunks, re-embed with the new model, compare cosine similarity) was not
carried over to the new REST API service layer.
2. **Wrong frontend URL**: `checkEmbedding` in `api.ts` pointed to
`/datasets/{id}/embedding` (`run_embedding`) instead of a dedicated
check endpoint.
3. **Overly restrictive guard**: `dataset_api_service.py` line 310
blocked all `embd_id` updates when `chunk_num > 0`. This check did not
exist in the pre-refactor code — it was incorrectly introduced during
the refactor.
## Changes
### Backend
- **`api/apps/services/dataset_api_service.py`**
- Remove the `chunk_num > 0` hard guard on `embd_id` updates
- Add `check_embedding()` service function: samples random chunks,
re-embeds them with the candidate model, computes cosine similarity,
returns compatibility result (avg ≥ 0.9 = compatible)
- Add `import re` for the `_clean()` helper
- **`api/apps/restful_apis/dataset_api.py`**
- Add `POST /datasets/<dataset_id>/embedding/check` endpoint following
the new REST API conventions
- Clean up unused top-level imports (`random`, `re`, `numpy`)
### Frontend
- **`web/src/utils/api.ts`**
- Fix `checkEmbedding` URL from `/datasets/${datasetId}/embedding` →
`/datasets/${datasetId}/embedding/check`
### Tests
-
**`test/testcases/test_http_api/test_dataset_management/test_update_dataset.py`**
- Update `test_embedding_model_with_existing_chunks` to assert success
(`code == 0`) instead of expecting the old `102` error
-
**`test/testcases/test_web_api/test_dataset_management/test_dataset_sdk_routes_unit.py`**
- Update `test_update_route_branch_matrix_unit` to assert
`RetCode.SUCCESS` when updating `embd_id` on a chunked dataset,
replacing the old `chunk_num` error assertion
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: noob <yixiao121314@outlook.com>
Close#14292
## Issue
File ancestry endpoints return folder metadata without validating tenant
permissions, allowing any authenticated user to query arbitrary
`file_id` values across tenant boundaries.
## Affected Endpoints
- `GET /v1/file/parent_folder?file_id={file_id}`
- `GET /v1/file/all_parent_folder?file_id={file_id}`
- `GET /api/v1/files/{id}/ancestors`
## Root Cause
These endpoints **skip the permission check** that other file operations
(Delete, Download, Move) perform.
## Expected Permission Check
All file operations should follow this 3-step validation:
- Check file.tenant_id
- Check if user_id belongs to this tenant (via user_tenant join table)
- Check KB permission type (team permission)
**Code reference:** This is implemented in `checkFileTeamPermission()`
and used by Delete/Download/Move, but **missing** from
GetParentFolder/GetAllParentFolders.
## Reproduction
```bash
# User B (tenant: BBB) accessing User A's file (tenant: AAA)
curl -H "Authorization: Bearer USER_B_TOKEN" \
"http://localhost:9384/v1/file/parent_folder?file_id=AAA_FILE_123"
# Result: Returns User A's folder metadata ❌
# Expected: "No authorization." ✅
Fix
Pass userID from handler to service and call checkFileTeamPermission() — same as Download/Delete/Move handlers.
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
