### What problem does this PR solve?
Closes#15025
Langfuse-enabled `dialog_service.async_chat()` regressed to
`langfuse_tracer.start_generation(...)` after the earlier Langfuse v4
migration. Langfuse v4 uses `start_observation(as_type="generation")`,
so the remaining `start_generation` call can fail when chat tracing is
enabled.
This restores the migrated `start_observation(as_type="generation")`
call for chat observations while preserving the existing trace context,
model, input payload, and update/end flow. It also adds a regression
test with a fake Langfuse v4-style client that exposes
`start_observation()` but not `start_generation()`.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Tests
- `.venv/bin/pytest
test/unit_test/api/db/services/test_dialog_service_final_answer.py -q`
- `.venv/bin/ruff check api/db/services/dialog_service.py
test/unit_test/api/db/services/test_dialog_service_final_answer.py`
### What problem does this PR solve?
extend restful api suite
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Other (please describe): test
### What problem does this PR solve?
extend restful api suite
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Other (please describe): test
### What problem does this PR solve?
extend restful api suite
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Other (please describe): test
### 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?
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?
1. expose batch_chunk_token_size for configuration
2. retrieve chunks when build subgraph for the doc, not retreive all
docs chunks at the begining
3. get all chunks for a document, used to be hard coded 10000
4. delete not used method run_graphrag
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
Follow on: #14617
### What problem does this PR solve?
Fixes#14746.
Adds tenant access checks for connector-by-id REST routes before reading
connector details, mutating connector config/status, deleting
connectors, rebuilding, or listing sync logs. Unauthorized callers now
receive `RetCode.AUTHENTICATION_ERROR` with `No authorization.` without
reaching the connector/log mutation paths.
Validation:
- `python3 -m pytest
--confcutdir=test/testcases/test_web_api/test_connector_app
test/testcases/test_web_api/test_connector_app/test_connector_routes_unit.py`
- `uvx ruff check api/apps/restful_apis/connector_api.py
api/db/services/connector_service.py
test/testcases/test_web_api/test_connector_app/test_connector_routes_unit.py`
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: dev111-actor <dev111-actor@users.noreply.github.com>
Closes#14753
## What changed
| File | Change |
|---|---|
| `pyproject.toml` | `requires-python` → `>=3.13,<3.15`; remove
`strenum==0.4.15` |
| `Dockerfile` | `uv python install 3.13`, `uv sync --python 3.13` |
| `.github/workflows/tests.yml` | `uv sync --python 3.13` on both matrix
legs |
| `CLAUDE.md` | dev setup command + requirements note updated |
| `deepdoc/parser/mineru_parser.py` | `from strenum import StrEnum` →
`from enum import StrEnum` |
| `agent/tools/code_exec.py` | same |
`StrEnum` has been in the stdlib since Python 3.11 — the `strenum`
backport package is no longer needed once the floor is 3.13.
## Why uv.lock is not regenerated
`uv lock --python 3.13` fails because:
1. The infiniflow/graspologic fork pins `numpy>=1.26.4,<2.0.0`
2. `tensorflow-cpu>=2.20.0` (the first release with cp313 wheels)
depends on `ml-dtypes>=0.5.1`, which requires `numpy>=2.1.0`
3. These two constraints are irreconcilable on Python 3.13
The lockfile regeneration requires loosening the `numpy` upper bound in
the `infiniflow/graspologic` fork. Once that fork commit is updated and
the SHA in `pyproject.toml:49` is bumped, `uv lock --python 3.13` will
succeed.
## RFC corrections
Two claims in the original RFC (#14753) did not hold up under code
review:
- **"graspologic hard-blocks 3.13"** — the infiniflow fork at the pinned
commit has no `<3.13` Python constraint. The blocker is the transitive
`numpy<2.0.0` conflict with tensorflow-cpu's test dependency, not a
direct Python version cap.
- **"free-threading throughput gains for I/O-bound workload"** — Python
3.13 free-threading requires a special `--disable-gil` build and
provides no benefit for async I/O code (the GIL is already released
during I/O). The real motivation is forward compatibility and improved
error messages.
### What problem does this PR solve?
Closes#14853
The `/agents/download` and `/agents/<agent_id>/upload` endpoints in the
agent API are missing `@login_required` and `@add_tenant_id_to_kwargs`
decorators, allowing unauthenticated access. This is a security issue —
any user can upload files to or download files from an agent without
being logged in. Additionally, the upload endpoint bypasses canvas
access control (`@_require_canvas_access_async`).
This PR adds the missing authentication and authorization decorators to
both endpoints and replaces the manual `user_id` / `created_by` lookups
with the `tenant_id` provided by the auth middleware, making these
endpoints consistent with the rest of the agent API.
### 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
v0.25.3 to v0.25.4
- 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
### What problem does this PR solve?
Includes gpt-5.4-mini and gpt-5.4-nano to the OpenAI model list
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
Closes#14774.
Adds free-form tags on agents (UserCanvas) with full UI + API:
- Stored as comma-separated `tags` column on `UserCanvas` with online
migration.
- New endpoints: `GET /v1/agents/tags` (aggregate counts) and `PUT
/v1/agent/<id>/tags` (write). `GET /v1/agents` accepts a `tags=` query.
- "Edit tags" item in agent dropdown opens a chip-style editor dialog;
tags render as badges on each agent card.
- New "Tags" facet in the agents filter bar, with counts.
## Implementation notes
- **Tag matching is exact-token**: the SQL filter wraps stored tags as
`,…,` and matches `,ml,` so `ml` doesn't match `ml-ops`.
- **Server-side normalization** in `UserCanvasService.update_tags`:
dedup (case-insensitive), per-tag cap of 64 chars, total length capped
at 512 chars to fit the column, commas inside tag values are replaced
with spaces.
- **Tenant authorization**: `PUT /v1/agent/<id>/tags` gates on
`UserCanvasService.accessible(canvas_id, tenant_id)`.
- **Tag listing scope**: `UserCanvasService.list_tags` follows the same
own + team-shared rule as `get_by_tenant_ids`.
- **i18n**: keys added to `en.ts` and `zh.ts` only (per project
convention; other locales fall back).
- **`HomeCard`** gets a non-breaking `extra?: ReactNode` slot for the
chip row; no `src/components/ui/` files modified.
## Test plan
- [ ] Backend boot runs `migrate_db` → confirm `user_canvas.tags` column
exists (`DESCRIBE user_canvas`).
- [ ] Agents page renders cards normally (no console error from missing
field).
- [ ] `⋯ → Edit tags` opens a dialog that stays open (regression: dialog
was unmounting with the dropdown).
- [ ] Typing a tag without pressing Enter and clicking Save persists it
(regression: last typed tag was being dropped).
- [ ] Chip input supports Enter/comma to commit, Backspace on empty to
remove, `×` to remove individual chip.
- [ ] Tag containing a comma sent via API is stored with the comma
replaced by a space.
- [ ] 20 long tags sent via API does not error (length cap silently
truncates).
- [ ] "Tags" filter in the filter bar shows counts and narrows the list.
- [ ] Filtering by `ml` does **not** return agents tagged `ml-ops`.
- [ ] UI in Chinese shows 编辑标签 / 添加标签以整理和筛选你的智能体 etc.
- [ ] `PUT /v1/agent/<other-tenant-id>/tags` returns `Agent not found or
no permission.`
# 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>
### 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?
- Update version tags in README files (including translations) from
v0.25.2 to v0.25.3
- 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
### What problem does this PR solve?
The delete /graph is duplicated of
`/datasets/<dataset_id>/<index_type>`, delete it.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
add new testing suite for the new restful api endpoints meant to replace
http and web api tests
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Other (please describe): test
Closes#14768
### What problem does this PR solve?
The `list_chats` and `list_searches` REST API endpoints did not enforce
authorization on the `owner_ids` query parameter. Any authenticated user
could pass arbitrary tenant IDs to `owner_ids` and retrieve chats or
search apps belonging to other tenants they are not a member of.
This PR resolves the issue by:
1. Looking up the current user's authorized tenants via
`TenantService.get_joined_tenants_by_user_id` and rejecting any
`owner_ids` that fall outside that set.
2. When no `owner_ids` are provided, scoping the query to only the
user's authorized tenants instead of returning an unfiltered result.
3. Adding unit tests that verify unauthorized `owner_ids` are rejected
with `OPERATING_ERROR`.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
OpenAI model catalogs used in provider selection flows were missing the
latest GPT models (`gpt-5.5` and `gpt-5.4`).
Because model availability is driven by seeded catalog data
(`conf/llm_factories.json` → DB seed → API response), these models were
not selectable in the UI or `/llm/list` responses.
This PR updates and synchronizes the OpenAI catalog definitions across
configuration sources and ensures the new models are correctly exposed
through the API layer and validated in tests.
---
### Type of change
* [x] New Feature (non-breaking change which adds functionality)
---
### Changes Made
* Added `gpt-5.5` and `gpt-5.4` to OpenAI catalog definitions in:
* `conf/llm_factories.json`
* `conf/models/openai.json` (chat + vision support)
* Ensured consistency between DB-seeded factory config and provider
model configuration
* Updated test coverage in:
* `test_llm_list_unit.py`
* seeded OpenAI catalog entries
* added response-level assertion validating `/llm/list` includes both
new model IDs under OpenAI grouping
---
### Root Cause
OpenAI model listings in selection flows are generated from catalog data
seeded via `conf/llm_factories.json`.
The catalog had not been updated to include the latest GPT models,
resulting in missing availability in UI and API responses.
---
### Testing
* Created isolated test environment:
* `python -m venv .venv-review`
* installed `pytest`
* Ran targeted and full test suite:
* `test_list_app_grouping_availability_and_merge`: ✅ passed
* Full `test_llm_list_unit.py`: ✅ 10 passed
---
### Risks / Limitations
* Adding models to the catalog does not guarantee upstream provider
availability or account entitlement.
* Environments with pre-seeded DB catalogs may require reseed or refresh
to reflect updated configuration.
---
### Notes
* Changes are minimal and scoped strictly to catalog configuration and
related test coverage.
* Ensures `/llm/list` API remains aligned with expected latest OpenAI
model availability.
* Closes#14827
This PR adds focused unit tests for aggregate_by_field in OceanBase
memory utilities to improve behavior coverage for real-world input
shapes.
- Adds test coverage for list-valued aggregation fields, including
whitespace trimming and skipping invalid list entries.
- Adds test coverage for scalar field values to ensure blank/non-string
values are ignored.
- Confirms aggregation output remains correct and stable for
mixed-quality message payloads.
### Why this helps
It strengthens regression protection for aggregation logic used by
memory retrieval flows, with no production code changes and minimal
review risk.
### What problem does this PR solve?
Closes#14674.
This PR improves RAPTOR configuration and tree construction while
preserving the existing RAPTOR behavior as the default.
RAPTOR currently builds summary layers with the original UMAP + GMM
clustering path. This PR keeps that default path, and adds:
- A hidden backend tree-builder option:
- `tree_builder="raptor"`: default, existing RAPTOR behavior.
- `tree_builder="psi"`: rank-aware Psi-style tree builder using original
embedding-space cosine ranking.
- A user-facing clustering method option for the default RAPTOR builder:
- `clustering_method="gmm"`: existing default.
- `clustering_method="ahc"`: agglomerative hierarchical clustering path.
- A RAPTOR UI setting for `Clustering method` and `Max cluster`.
### What changed
#### Backend
- Added `tree_builder` support for RAPTOR/Psi.
- Added `clustering_method` support for GMM/AHC.
- Kept existing RAPTOR + GMM as the default.
- Added Psi tree building from original-space cosine similarity.
- Added bucketed Psi building controls for large inputs:
- `raptor.ext.psi_exact_max_leaves`
- `raptor.ext.psi_bucket_size`
- Added method-aware RAPTOR summary metadata using existing
`extra.raptor_method`.
- Avoided adding a dedicated DB schema field for experimental method
tracking.
- Added cleanup/migration logic to avoid mixing stale RAPTOR summary
trees.
- Added defensive checks for Psi tree construction and summary failures.
#### Frontend/UI
- Added `Clustering method` in RAPTOR settings with `GMM` and `AHC`.
- Added/kept `Max cluster` in RAPTOR settings.
- Enlarged max cluster UI limit to `1024`, matching backend validation.
- Kept AHC editable even when a RAPTOR task has already finished.
- Fixed the UI save payload so `clustering_method` and `tree_builder`
are serialized through `parser_config.raptor.ext`, avoiding backend
validation errors for extra top-level RAPTOR fields.
Example saved RAPTOR config:
```json
{
"raptor": {
"max_cluster": 317,
"ext": {
"clustering_method": "ahc",
"tree_builder": "raptor"
}
}
}
Co-authored-by: CaptainTimon <CaptainTimon@users.noreply.github.com>
### What problem does this PR solve?
Fixes#14570. On OpenSearch backends (`DOC_ENGINE=opensearch`) every
document-metadata write failed with `'OSConnection' object has no
attribute 'create_doc_meta_idx'`, so both `PATCH
/api/v1/datasets/{ds}/documents/{doc}` with `meta_fields` and `POST
/api/v1/datasets/{ds}/metadata/update` were unusable while every other
document operation (retrieval, parsing, name update, chunk management)
worked correctly on the same OpenSearch cluster.
The bug runs deeper than the missing method name in the error message
suggests. `DocMetadataService` also reached into
`settings.docStoreConn.es.*` directly for the index refresh, the
scripted partial update, and the count call, which means that even after
adding `create_doc_meta_idx` to `OSConnection` the very next call in the
same metadata flow would still raise `AttributeError` because
`OSConnection` exposes `self.os` rather than `self.es`. Fixing only the
reported symptom would have moved the failure one line down without
restoring the feature.
This PR adds a uniform document-metadata dispatch surface to both
connection classes so they present the same abstract API, and routes the
service layer through that surface via `getattr` guards instead of
poking at backend-specific attributes. The four new methods on
`OSConnection` and `ESConnectionBase` are `create_doc_meta_idx`,
`refresh_idx`, `count_idx`, and `replace_meta_fields`.
`OSConnection.create_doc_meta_idx` reuses the existing
`conf/doc_meta_es_mapping.json` schema in the OpenSearch `body=` form
because OpenSearch and Elasticsearch share the same index-creation
payload, and `replace_meta_fields` emits a full scripted assignment
(`ctx._source.meta_fields = params.meta_fields`) on both backends so
removed keys actually disappear instead of being preserved by deep-merge
semantics.
The `getattr`-guarded dispatch in `DocMetadataService` keeps the
existing fall-through paths intact for Infinity and OceanBase, which
continue to rely on their search-based count fallback and on the
delete-then-insert metadata replacement they used before, so this change
is strictly additive for those two backends.
Verification: `pytest
test/unit_test/rag/utils/test_opensearch_doc_meta.py` runs 16 new unit
tests that pass locally and pin the `OSConnection` dispatch surface, the
`create_doc_meta_idx` short-circuit when the index already exists, the
mapping-file payload routing, the `IndicesClient.create` failure path,
the `refresh_idx` and `count_idx` success and error sentinels, and the
full-assignment script emitted by `replace_meta_fields`. The test module
stubs `common.settings` and `rag.nlp` at import time so the suite runs
without the heavy backend SDKs that the rest of the repository pulls in
transitively.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: tmimmanuel <tmimmanuel@users.noreply.github.com>
### What problem does this PR solve?
fix some comments to improve readability
### Type of change
- [x] Documentation Update
---------
Signed-off-by: box4wangjing <box4wangjing@outlook.com>
### What problem does this PR solve?
The document parse status was set to DONE before the document chunks
were actually retrievable from Elasticsearch/Opensearch because it did
not wait for the index refresh. This meant that it was possible that the
document parse status returned by the API was DONE but when trying to
retrieve chunks there were none. Since the index refreshes every 1
second this was quite likely to happen when wait for document parsing by
polling with a short interval and then immediately trying to retrieve
chunks once the status was DONE.
I fixed this bug and added a test case that would have caught it.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Addresses event-loop blocking under high concurrency reported in #13825.
When multiple requests hit the API simultaneously, synchronous DB/Redis
calls block the async event loop, preventing Quart from handling other
requests and causing cascading 502/504 timeouts.
This PR wraps all remaining blocking DB/Redis calls in `canvas_app.py`,
`chat_api.py`, `session.py`, and `canvas_service.py` with `await
thread_pool_exec()`
- Offload all synchronous `Service.*`, `REDIS_CONN.*`, and
`APIToken.query` calls to the thread pool
- Convert sync endpoint handlers (`list_chats`, `get_chat`, `templates`,
`sessions`, etc.) to `async def`
- Convert sync helper functions (`_ensure_owned_chat`,
`_validate_llm_id`, `_validate_dataset_ids`, etc.) to async - no
duplicate sync/async pairs
- Wrap `CanvasReplicaService` Redis IO calls (`bootstrap`,
`replace_for_set`, `commit_after_run`)
- Use `asyncio.gather()` for concurrent file uploads and chat response
building
**Note:** This fixes the code-level event-loop blocking, which is a
prerequisite for handling concurrent requests. For the full "30
concurrent requests without 502/504" goal described in the issue, users
should also tune deployment config:
- `WS=4` or higher (HTTP worker processes, default 1)
- `MAX_CONCURRENT_CHATS=50` (default 10)
- `SANDBOX_EXECUTOR_MANAGER_POOL_SIZE` for workflow-heavy workloads
### Performance verification
Reviewer asked for a before-vs-after comparison
([comment](https://github.com/infiniflow/ragflow/pull/13941#issuecomment-4393667231)).
I built a self-contained microbenchmark that reproduces the exact
failure mode this PR targets: an async handler that performs blocking
DB/Redis-style calls (50 ms each, 3 per request, 30 concurrent requests)
is run twice — once with the pre-PR pattern (sync call directly inside
the async handler) and once with the post-PR pattern (`await
thread_pool_exec(...)`). The benchmark imports nothing from RAGFlow
except `thread_pool_exec` itself, so it is hermetic and reproducible
(`THREAD_POOL_MAX_WORKERS=128`, Python 3.13.12).
**Throughput — wall-clock for 30 concurrent requests (lower is better)**
| flavour | wall(s) | p50(s) | p95(s) | max(s) |
|---|---:|---:|---:|---:|
| before | 4.986 | 0.158 | 0.207 | 0.269 |
| after | 0.248 | 0.181 | 0.230 | 0.231 |
The pre-PR handler serializes the entire load on the event-loop thread,
so 30 × 3 × 50 ms ≈ 4.5 s shows up as the wall time. The post-PR handler
parallelizes the blocking work across the thread pool and finishes the
same load in 248 ms — a **~20× speedup** on this workload.
**Event-loop responsiveness — latency of an unrelated probe coroutine
while the 30 slow requests are running (lower is better)**
| flavour | samples | probe p50 (ms) | probe p95 (ms) | probe max (ms) |
|---|---:|---:|---:|---:|
| before | 1 | 5442.26 | 5442.26 | 5442.26 |
| after | 28 | 0.88 | 11.53 | 98.02 |
This is the metric that maps directly to "the API still answers other
requests while one is busy". A 5 ms-interval probe was scheduled while
the 30 slow handlers ran. With the pre-PR code the event loop was frozen
for the entire duration of the blocking work, so only one probe sample
was ever picked up and it waited **5,442 ms**. After the PR, 28 probe
samples landed with **p50 0.88 ms / p95 11.53 ms**, meaning unrelated
requests are no longer starved by the slow ones. That is the regression
mode behind the cascading 502/504s reported in #13825.
<details>
<summary>Raw benchmark output</summary>
```
config: 30 concurrent requests, 3 blocking calls of 50ms each per request, THREAD_POOL_MAX_WORKERS=128
=== Throughput (lower wall is better) ===
flavour wall(s) p50(s) p95(s) max(s)
before 4.986 0.158 0.207 0.269
after 0.248 0.181 0.230 0.231
=== Event-loop responsiveness (lower probe latency is better) ===
flavour samples probe p50(ms) probe p95(ms) probe max(ms)
before 1 5442.26 5442.26 5442.26
after 28 0.88 11.53 98.02
```
</details>
The benchmark script is included as a comment on the PR for
reproducibility.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Performance Improvement
Closes [#13825](https://github.com/infiniflow/ragflow/issues/13825)
---------
Co-authored-by: tmimmanuel <tmimmanuel@users.noreply.github.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
## What problem does this PR solve?
The Dify-compatible `/dify/retrieval` endpoint recently gained stricter
parsing and validation for its request payload, including:
- Normalized `retrieval_setting.top_k` and
`retrieval_setting.score_threshold` types.
- Clear separation between malformed arguments vs missing required
fields.
Previously, there was no unit test explicitly guarding the exact error
code and message contract for these cases.
## What does this PR change?
- **Add guard-style unit test** in `test_dify_retrieval_routes_unit.py`:
- `test_retrieval_argument_error_messages`:
- Sends a request with malformed numeric options:
- `retrieval_setting = {"top_k": "not-int", "score_threshold":
"not-float"}`
- Asserts `code == RetCode.ARGUMENT_ERROR` and message contains
`"invalid or malformed arguments:"`.
- Sends a request with required fields missing:
- Empty payload (`{}`)
- Asserts `code == RetCode.ARGUMENT_ERROR` and message contains
`"required arguments are missing:"`.
This test encodes the intended behavior of the Dify retrieval API so
future refactors cannot silently regress error handling.
## Type of change
- [x] Tests (add coverage and guardrails for existing behavior)
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
GraphRAG feature - Part 1 - add spacy to extract entity and relation
<img width="1621" height="1288" alt="image"
src="https://github.com/user-attachments/assets/aadeddad-94da-46c6-adad-9c3784181f61"
/>
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
This PR fixes the `message_fit_in()` truncation bug reported in #13607.
Changes:
- fix the user-message truncation branch to reserve room for the system
prompt token budget
- guard the zero-token edge case to avoid dividing by zero in the
truncation ratio check
- add focused regression tests covering both the user-dominant
truncation path and the zero-token boundary case
## Validation
```bash
pytest -q --noconftest test/unit_test/rag/prompts/test_generator_message_fit_in.py
```
Result: `2 passed`
Closes#13607
### What problem does this PR solve?
The table file parser (CSV/Excel) currently treats all columns
identically — every column is both vectorized (embedded in chunk text)
and stored as filterable metadata. There's no way for users to control
which columns should be searchable by semantic meaning versus which
should only be filterable attributes.
For example, when ingesting a news articles CSV with columns like title,
content, country, category, source, etc., the embedding includes
metadata fields like country: Brazil and source: Reuters in the chunk
text, which dilutes the semantic quality of the embedding without adding
retrieval value.
The RDBMS connector (MySQL/PostgreSQL) already supports content_columns
/ metadata_columns, but this capability was missing for file-based table
ingestion.
This PR adds column-level control (vectorize / metadata / both) for the
table file parser, following RAGFlow's existing patterns.
Backward compatible: Datasets without table_column_roles or with
table_column_mode: auto behave exactly as before (all columns = both).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
Closes#13663.
OAuth / OIDC callbacks call `login_user(user)` which writes `_user_id`
into the session cookie, but `_load_user()` in `api/apps/__init__.py`
only ever looked at the `Authorization` header. The SPA's response
interceptor wipes the Authorization value from `localStorage` on the
first 401 it sees — meaning that during the post-redirect window after
an OAuth login, a single transient 401 sends every subsequent request
back to the login page even though `login_user()` had already
established a perfectly good server-side session.
The reporter's analysis traces this all the way through the redirect →
`navigate('/')` → first request → empty header → 401 → `removeAll()` →
infinite-redirect-to-login chain.
## What changed
- New `_load_user_from_session()` helper that reads
`session["_user_id"]`, looks up the user in `UserService` (with the same
`StatusEnum.VALID` and `access_token` checks already used elsewhere),
and assigns `g.user`.
- Every `return None` path in `_load_user()` now routes through that
helper before giving up:
- missing `Authorization` header
- malformed `bearer ` prefix
- empty / too-short JWT payload
- JWT signature failure
- JWT-resolved user not found / has no `access_token`
- `APIToken.query()` fallback exhausted
The JWT and API-token paths still take precedence — the session is only
consulted when those can't authenticate the request. So existing
local-login and SDK callers see no behaviour change; only OAuth / OIDC
users that hit the original race now stay logged in.
The Bearer-prefix issue called out in #13663 (lines 103-110) is already
handled in the current code, so this PR only addresses the second half
of the report.
## Test plan
- [ ] Configure OIDC under `oauth` in `service_conf.yaml`
- [ ] Click the OIDC login button, complete auth at the IdP
- [ ] Confirm that navigating between pages no longer bounces back to
`/login`
- [ ] Confirm local email/password login still issues + accepts JWTs
- [ ] Confirm SDK/API key callers still authenticate via `Authorization:
Bearer <api-token>`
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
S3-family connector syncs currently re-download every in-window object
just so we can compute `xxhash128(blob)` and compare against
`Document.content_hash`. Anything that bumps `LastModified` without
changing bytes (`aws s3 cp` touches, bucket re-encryption, etc.) pays
full bandwidth and re-parses files that didn't actually change. #14628
covers the broader incremental-ingestion redesign; this PR is the first
slice.
The fix is a pre-listing short-circuit. `BlobStorageConnector` (S3 / R2
/ GCS / OCI / S3-compat) now implements a new `FingerprintConnector`
interface: `list_keys()` paginates `list_objects_v2` and yields
`KeyRecord(key, fingerprint)` where `fingerprint = xxhash128(ETag)`. The
orchestrator joins those against the connector's existing `{doc_id:
content_hash}` map and only calls `get_value(key)` when the fingerprint
differs. Unchanged keys are skipped entirely — no `GetObject`, no
re-parse.
No DDL. xxhash128(ETag) is 32 hex chars and reuses the existing
`Document.content_hash` column per @yingfeng's suggestion; the connector
decides at listing time whether to populate it. Local uploads and
connectors that don't opt in fall through to the existing post-download
`xxhash128(blob)` path with no behavior change.
This is PR-1 of a 4-PR series — full design lives on #14628. Subsequent
PRs extend tier 1 to local FS / WebDAV / Dropbox / Seafile / RDBMS
(PR-2), wire up tier 2 cursor connectors with `SyncLogs.next_checkpoint`
(PR-3), and unify deletion via `KeyRecord(deleted=True)` reconciliation
(PR-4). Holding those back keeps this PR additive and reviewable on its
own.
#### Files touched
- `common/data_source/models.py` — new `KeyRecord`; optional
`fingerprint` on `Document`
- `common/data_source/interfaces.py` — `IncrementalCapability` enum,
`FingerprintConnector` ABC
- `common/data_source/blob_connector.py` — `BlobStorageConnector`
implements `FingerprintConnector`; per-object download factored into
`_build_document_from_obj()` so `_yield_blob_objects`, `list_keys`,
`get_value` all share it
- `rag/svr/sync_data_source.py` —
`_BlobLikeBase._fingerprint_filtered_generator` does the bypass loop;
`_run_task_logic` plumbs `doc.fingerprint` into the upload dict
- `api/db/services/document_service.py` —
`list_id_content_hash_map_by_kb_and_source_type()` helper
- `api/db/services/connector_service.py` + `file_service.py` —
fingerprint flows through `duplicate_and_parse → upload_document` and
lands in `content_hash`
- `test/unit_test/common/test_blob_connector_fingerprint.py` — 14 tests
covering ETag normalization (single-part, multipart, quoted, empty),
`list_keys()` not calling `GetObject`, `get_value()` materializing with
fingerprint, deterministic/stable fingerprints, and the bypass loop
asserting `GetObject` is *not* called on a match
#### Worth flagging for review
Old `_BlobLikeBase._generate` called `poll_source(start, now)` with a
`LastModified` window when `poll_range_start` was set. New code uses
`_fingerprint_filtered_generator` (full bucket listing + fingerprint
compare) outside of explicit `reindex=1`. Strictly better for
unchanged-bucket cases since it skips `GetObject`, but it does mean
every sync now does a full `list_objects_v2` paginate. Should still be
cheap for most buckets — flagging in case anyone has a very large bucket
where the time-window filter was meaningful.
On migration: existing rows have `content_hash = xxhash128(blob)` from
the old code. The first sync after this lands sees ETag-derived
fingerprints that don't match, re-fetches every object once, and writes
the new fingerprint. From the second sync onward the bypass works as
expected. "Slow day one, fast every day after." A `fingerprint_backfill:
trust` opt-out is sketched in the design doc but not in this PR.
#### Test plan
- [x] `uv run ruff check` — clean on all 8 touched files
- [x] `uv run pytest
test/unit_test/common/test_blob_connector_fingerprint.py -v` — 14 passed
- [x] Broader unit-test suite — no regressions in anything I touched
- [ ] Manual smoke against a real S3 bucket — configure a connector, run
sync twice, expect the second sync to log `bypassed=N, fetched=0` and no
`GetObject` calls in CloudTrail / bucket access logs
- [ ] Manual smoke with `reindex=1` — confirm the full re-download path
still works
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
### What problem does this PR solve?
- Update version tags in README files (including translations) from
v0.25.1 to v0.25.2
- 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
### 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>
### Related issues
Closes#14644
### What problem does this PR solve?
This PR fixes an authorization bug where datasets marked with
`permission = me` could still be accessed by other members of the same
tenant through APIs that relied on `KnowledgebaseService.accessible()`
or `DocumentService.accessible()`.
Before this change, those shared access helpers only checked tenant
membership and did not enforce the dataset's permission mode. As a
result, a non-owner who knew a private `dataset_id` could still reach
downstream document and chunk operations even though the dataset was
intended to be owner-only.
This change updates the central access checks so that:
- dataset owners always retain access
- joined tenant members only get access when the dataset permission is
`TEAM`
- private datasets with `permission = me` remain inaccessible to
non-owners
- document-level access follows the same dataset permission rules
The PR also adds regression coverage for private-vs-team dataset access
behavior.
### 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):
### Testing
- Added
`test/unit_test/api/db/services/test_dataset_access_permissions.py`
- Attempted to run: `python -m pytest
test\\unit_test\\api\\db\\services\\test_dataset_access_permissions.py
-q`
- Local execution in this workspace is currently blocked during test
collection because the environment is missing the `strenum` dependency
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: jony376 <jony376@gmail.com>
Co-authored-by: Wang Qi <wangq8@outlook.com>
Co-authored-by: d 🔹 <liusway405@gmail.com>
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: Magicbook1108 <newyorkupperbay@gmail.com>
Co-authored-by: chanx <1243304602@qq.com>
Co-authored-by: sxxtony <166789813+sxxtony@users.noreply.github.com>
Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
Co-authored-by: Baki Burak Öğün <63836730+bakiburakogun@users.noreply.github.com>
Co-authored-by: bakiburakogun <bakiburakogun@users.noreply.github.com>
Co-authored-by: Panda Dev <56657208+pandadev66@users.noreply.github.com>
Co-authored-by: Haruko386 <tryeverypossible@163.com>
Co-authored-by: D2758695161 <13510221939@163.com>
Co-authored-by: Hunter <hunter@yitong.ai>
Co-authored-by: Lynn <lynn_inf@hotmail.com>
Co-authored-by: buua436 <sz_buua@foxmail.com>
Co-authored-by: web-dev0521 <jasonpette1783@gmail.com>
Co-authored-by: Tim Wang <38489718+wanghualoong@users.noreply.github.com>
Co-authored-by: wanghualoong <wanghualoong@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: qinling0210 <88864212+qinling0210@users.noreply.github.com>
Co-authored-by: dale053 <star05223@outlook.com>
### What
19 methods across `rag/llm/chat_model.py` and `rag/llm/cv_model.py`
declare `gen_conf={}` (or `gen_conf: dict = {}`) as a parameter default
and then mutate `gen_conf` in place — typically `del
gen_conf["max_tokens"]`, `gen_conf["penalty_score"] = ...`, or
`gen_conf.pop(...)` as part of provider-specific normalization.
### The two bugs in this pattern
**1. Mutable default argument (Python footgun).** Python evaluates
default values **once** at function-definition time, so the single `{}`
dict is *shared* across every caller that doesn't pass `gen_conf`. The
first such call's mutations leak into the default seen by every
subsequent call.
```python
# Before
def chat_streamly(self, system, history, gen_conf={}, **kwargs):
if "max_tokens" in gen_conf:
del gen_conf["max_tokens"] # mutates the SHARED default dict
...
```
After call N with `max_tokens` set, call N+1 that omits `gen_conf` no
longer sees `max_tokens` — even though the caller never touched it.
**2. Caller-dict pollution.** When the caller *does* pass a `gen_conf`
dict, the same in-place mutations modify the caller's dict. A reused
`gen_conf` (very common for chat-loop callers that build the config once
and pass it on every turn) silently loses `max_tokens`,
`presence_penalty`, etc. after the first round.
### The fix
In every affected method:
- Change `gen_conf={}` (or `gen_conf: dict = {}`) → `gen_conf=None`.
- Add `gen_conf = dict(gen_conf or {})` as the first statement of the
body so all subsequent mutations operate on a fresh local copy.
```python
# After
def chat_streamly(self, system, history, gen_conf=None, **kwargs):
gen_conf = dict(gen_conf or {})
if "max_tokens" in gen_conf:
del gen_conf["max_tokens"] # local copy — safe
...
```
This is byte-for-byte identical provider-side behavior for callers that
already pass a fresh `gen_conf` per call. The new `dict(...)` copy is
O(small constant) per call.
### Files changed
- `rag/llm/chat_model.py` — 17 methods
- `rag/llm/cv_model.py` — 2 methods
### Tests
Adds `test/unit_test/rag/llm/test_gen_conf_no_mutable_default.py` — an
`ast`-based regression guard that walks both modules and asserts no
parameter named `gen_conf` ever has a mutable literal (`{}` or `[]`) as
its default. The test caught **five additional `gen_conf: dict = {}`
sites** that an initial `gen_conf={}` text grep had missed (annotated
parameters with whitespace), and would fail again if the pattern is ever
reintroduced.
```
$ pytest test/unit_test/rag/llm/test_gen_conf_no_mutable_default.py -v
============================== 3 passed in 0.04s ===============================
```
`ruff check` passes on all touched files.
### Notes
- This PR is intentionally focused on **just** the `gen_conf` default +
copy fix. There's a related (but separate) `history.insert(0, ...)`
pattern in the same files that mutates the caller's history list in 12
places — left for a follow-up so this PR stays mechanical and easy to
review.
### Latest revision (`700bb54a7`) — addresses CodeRabbit review
- Type annotation: `gen_conf: dict = None` → `gen_conf: dict | None =
None` (5 occurrences in `chat_model.py`). The old annotation was a
static-checker mismatch since `None` isn't a `dict`.
- Regression test: the AST check accessed `default.keys` directly.
`ast.List` has no `.keys` attribute — a future `gen_conf=[]` would crash
with `AttributeError` instead of being caught. Use `getattr` for both
`.keys` (Dict) and `.elts` (List). Manually verified the updated check
correctly catches both `gen_conf={}` and `gen_conf=[]` while ignoring
`gen_conf=None` and non-empty literals.
---------
Co-authored-by: Ricardo <ricardo@example.com>
### What problem does this PR solve?
The use_sql() function in dialog_service.py constructed SQL WHERE
clauses and Infinity table names by directly interpolating kb_id values
using Python f-strings, with no validation of the input values. A
malformed or maliciously crafted kb_id (introduced via a compromised
admin account or a separate injection vector) could alter the structure
of the generated SQL query, potentially leading to unauthorized data
access or data manipulation.
This PR adds strict UUID format validation for all kb_id values before
they are interpolated into any SQL string, causing requests with invalid
IDs to fail fast with a ValueError rather than executing a tampered
query.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
### What problem does this PR solve?
Refactor : Allow search multiple datasets
1. support /datasets/search
2. get rid of /graph/search, use /graph
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
Closes#14590
## Self Checks
- [x] I have searched for existing issues [search for existing
issues](https://github.com/infiniflow/ragflow/issues), including closed
ones.
- [x] I confirm that I am using English to submit this report ([Language
Policy](https://github.com/infiniflow/ragflow/issues/5910)).
- [x] Non-english title submitions will be closed directly (
非英文标题的提交将会被直接关闭 ) ([Language
Policy](https://github.com/infiniflow/ragflow/issues/5910)).
- [x] Please do not modify this template :) and fill in all the required
fields.
## RAGFlow workspace code commit ID
`a1b2c3d4e5f67890123456789abcdef12345678`
## RAGFlow image version
`0.13.1`
## Other environment information
- Hardware parameters: N/A
- OS type: Linux 6.17.0-22-generic
- Others: API key authentication via `Authorization: Bearer <token>`
## Actual behavior
The chatbot API endpoints:
- `POST /chatbots/<dialog_id>/completions`
- `GET /chatbots/<dialog_id>/info`
validate only that the bearer token exists in `APIToken`, but do not
verify that `dialog_id` belongs to the same tenant as that token.
Current flow (simplified):
1. Route extracts bearer token and checks `APIToken.query(beta=token)`.
2. If token exists, request is accepted.
3. Downstream service resolves dialog globally by ID
(`DialogService.get_by_id(dialog_id)` in `conversation_service.py`).
4. No tenant ownership check is enforced for `dialog_id`.
Impact: Any user with a valid API key can attempt arbitrary `dialog_id`
values and access/invoke chatbots outside their own tenant boundary if
IDs are known/guessed/leaked.
Security classification:
- Vulnerability class: Broken Access Control (IDOR, OWASP Top 10 A01)
- Severity recommendation: Critical
- Exploit prerequisite: any valid API key + discoverable target
`dialog_id`
## Expected behavior
Requests to `/chatbots/<dialog_id>/completions` and
`/chatbots/<dialog_id>/info` must be authorized only when:
1. bearer token is valid, and
2. `dialog_id` belongs to the same `tenant_id` as the token.
Otherwise, reject with authorization failure (e.g., 403 or
404-equivalent policy).
## Steps to reproduce
1. Prepare two tenants:
- Tenant A with API key `TOKEN_A`
- Tenant B with chatbot `dialog_id = DIALOG_B`
2. Send request from Tenant A to Tenant B chatbot completion endpoint:
```bash
curl -X POST "https://<host>/chatbots/DIALOG_B/completions" \
-H "Authorization: Bearer TOKEN_A" \
-H "Content-Type: application/json" \
-d '{"question":"hello","stream":false}'
```
3. Observe request is processed (or reaches dialog resolution) without
tenant ownership rejection.
4. Repeat against info endpoint:
```bash
curl -X GET "https://<host>/chatbots/DIALOG_B/info" \
-H "Authorization: Bearer TOKEN_A"
```
5. Observe the same missing ownership enforcement.
## Additional information
Affected code paths:
- `api/apps/sdk/session.py`
- `chatbot_completions(dialog_id)`
- `chatbots_inputs(dialog_id)`
- `api/db/services/conversation_service.py`
- `async_iframe_completion(...)` uses global dialog lookup
Suggested fix:
1. In both chatbot endpoints:
- Resolve `tenant_id = objs[0].tenant_id` from validated token.
- Fetch dialog with tenant-scoped query
(`DialogService.query(id=dialog_id, tenant_id=tenant_id)`).
- Reject if dialog is not found/owned by tenant.
2. Defense in depth:
- Require and enforce `tenant_id` in service-layer dialog resolution for
external flows.
- Avoid global `get_by_id(dialog_id)` where user-controlled dialog IDs
are reachable.
3. Add regression tests:
- Positive: same-tenant token + dialog succeeds.
- Negative: cross-tenant token + dialog fails for both endpoints.
### What problem does this PR solve?
Fixes#14651.
`kb_prompt()` in `rag/prompts/generator.py` crashes with
`AttributeError: 'NoneType' object has no attribute 'items'` during
agent citation generation when a retrieved chunk carries
`document_metadata: null`.
**Root cause.** The crash happens at `rag/prompts/generator.py:132-133`:
```python
meta = ck.get("document_metadata", {})
for k, v in meta.items():
```
`dict.get(key, default)` only returns the default when the key is
*missing*. When the key is present with an explicit `None` value,
`.get()` returns `None`, and `.items()` crashes.
**How the chunk gets `None`.** It's a round-trip inside RAGFlow itself,
not bad input from retrieval:
1. The agent stores retrieved chunks via `agent/canvas.py:814`, which
routes them through `chunks_format()`.
2. `rag/prompts/generator.py:61` canonicalizes the field with
`chunk.get("document_metadata")` (no default), so chunks without
metadata become `{"document_metadata": None, ...}`.
3. `agent/component/agent_with_tools.py:314` feeds those canonicalized
chunks back into `kb_prompt()` for citation generation, and
`.get("document_metadata", {})` no longer protects us.
**Fix.** One-line change at `rag/prompts/generator.py:132`: use
`ck.get("document_metadata") or {}` so an explicit `None` is also
coerced to `{}`.
The line-61 `None` is intentionally part of the API/UI contract — the
frontend handles it via optional chaining
(`web/src/components/markdown-content/index.tsx:184`,
`web/src/pages/next-search/search-view.tsx:217`) — so the fix belongs at
the consumer, not the producer.
### 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?
Closes#14618.
The `GET /v1/document/get/<doc_id>` endpoint in
`api/apps/document_app.py` was protected only by `@login_required` and
called `DocumentService.get_by_id(doc_id)` without verifying that the
document's knowledge base belonged to the requesting user's tenant. Any
authenticated user who knew (or guessed) a document ID could download
files belonging to any other tenant — a cross-tenant IDOR.
This PR adds a `DocumentService.accessible(doc_id, current_user.id)`
check before serving the file. The helper already exists and joins
`Document` → `Knowledgebase` → `UserTenant` to verify the requesting
user belongs to the tenant that owns the document's KB. The same pattern
is already used by `api/apps/restful_apis/document_api.py` and mirrors
the tenant scoping in the SDK route at `api/apps/sdk/doc.py`.
The check returns the existing `"Document not found!"` error for both
non-existent and inaccessible documents, so attackers cannot use the
response to enumerate valid doc IDs across tenants.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Other (please describe): Security fix (cross-tenant IDOR /
authorization bypass)
### Related issues
Closes#14648
### What problem does this PR solve?
This PR fixes an authorization flaw in `POST /files/link-to-datasets`.
Before this change, the endpoint only checked whether the supplied
`file_ids` and `kb_ids` existed. It did not verify whether the
authenticated user was actually allowed to access those files or target
datasets. As a result, an authenticated user who knew valid IDs could
relink another user's files to arbitrary datasets.
This was especially risky because the relinking flow is state-changing:
the background worker removes existing file-document mappings and then
recreates documents under the attacker-supplied dataset IDs.
This change makes the route enforce the same permission model already
used by nearby file and document operations:
- each resolved file must pass `check_file_team_permission(...)`
- each target dataset must pass `check_kb_team_permission(...)`
- authorization is enforced before scheduling background relinking work
### 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):
### Testing
- Added regression coverage in
`test/testcases/test_web_api/test_file_app/test_file2document_routes_unit.py`
- Covered:
- unauthorized file access is rejected
- unauthorized dataset access is rejected
- existing success path still returns immediately after scheduling
background work
- Attempted to run:
- `python -m pytest
test\\testcases\\test_web_api\\test_file_app\\test_file2document_routes_unit.py
-q`
- Local execution in this workspace is currently blocked by missing test
dependencies during bootstrap, including `ragflow_sdk`
---------
Co-authored-by: jony376 <jony376@gmail.com>
### What problem does this PR solve?
Fixes#14412.
`common.metadata_utils.meta_filter` evaluates user-defined metadata
conditions in Python after `DocMetadataService.get_flatted_meta_by_kbs`
loads the entire `meta_fields` table into memory. Past a few thousand
documents per knowledge base this becomes a memory bottleneck and a
wasted ES round-trip — every filter request currently fetches up to
10000 metadata rows even when the resulting `doc_ids` list is tiny.
This PR adds an ES push-down path that translates the same filter
language into a `bool` query and returns just the matching document IDs.
**Changes**
- `common/metadata_es_filter.py` *(new)*: pure-Python translator from
the RAGflow filter list to ES DSL. Covers every operator the in-memory
path supports (`=`, `≠`, `>`, `<`, `≥`, `≤`, `in`, `not in`, `contains`,
`not contains`, `start with`, `end with`, `empty`, `not empty`) with
`case_insensitive: true` on `prefix` and `wildcard` for parity with the
existing lower-cased Python comparisons. User wildcard metacharacters
are escaped before being injected into `wildcard` patterns. Negative
operators (`≠`, `not in`, `not contains`, ranges) are wrapped with an
`exists` guard so they do not accidentally match documents missing the
key, matching the legacy `if k not in metas` behaviour.
- `api/db/services/doc_metadata_service.py`: new
`DocMetadataService.filter_doc_ids_by_meta_pushdown(kb_ids, filters,
logic)` that returns the doc IDs ES matched, or `None` to signal the
caller should fall back to the in-memory path. Returns `None` when the
active doc store is Infinity (`meta_fields` is a JSON column, not a
dotted-object mapping), when any filter cannot be expressed in DSL
(`UnsupportedMetaFilter`), or when the ES request or metadata index
lookup errors.
- `common/metadata_utils.py`: `apply_meta_data_filter` accepts an
optional `kb_ids` argument. When supplied, conditions go through
push-down first via a new `_try_meta_pushdown` helper; on `None` the
function falls back to the original `meta_filter` call. Default
behaviour is unchanged for callers that don't pass `kb_ids`.
- Updated all four callers (`agent/tools/retrieval.py`,
`api/db/services/dialog_service.py` ×2,
`api/apps/services/dataset_api_service.py`, `api/apps/sdk/session.py`)
to forward `kb_ids` so the push-down path is exercised in production.
- `test/unit_test/common/test_metadata_es_filter.py` *(new)*: 35 unit
tests covering every operator's DSL shape, value coercion
(`ast.literal_eval`, lowercasing, ISO-date pass-through), wildcard
escaping, OR-logic wrapping that protects negative clauses, and the
doc-ID extractor.
**Behaviour preserved**
- The in-memory `meta_filter` is untouched and still services every
fallback case (Infinity backend, unknown operators, ES outages).
- The eligibility / credibility / issue-multiplier semantics described
in the LLM-driven `auto` and `semi_auto` modes still hand the LLM the
full in-memory `metas` dict to choose conditions from. Only the
*evaluation* of those generated conditions is pushed down.
- Existing tests in
`test/unit_test/common/test_metadata_filter_operators.py` continue to
pass (14/14).
**Test plan**
- `pytest test/unit_test/common/test_metadata_es_filter.py` — 35 passed.
- `pytest test/unit_test/common/test_metadata_filter_operators.py` — 14
passed.
- `ruff check` clean on every modified file.
- Reviewer please validate the ES query shapes against a live cluster —
particularly `case_insensitive` on `wildcard` and `prefix` (requires ES
7.10+) and the `exists` + `must_not` pairing for `≠`.
**Notes**
- The first cut caps each push-down request at 10000 results, matching
the existing `get_flatted_meta_by_kbs` limit, and logs a warning when
the cap is hit. A `search_after` follow-up would let us drop the cap
entirely once the push-down path is validated.
- Operator parity with the in-memory path is exact for the canonical
unicode operators (`≥`, `≤`, `≠`) used internally; the ASCII aliases
(`>=`, `<=`, `!=`) are normalised by `convert_conditions` before they
reach the translator.
### Type of change
- [x] Performance Improvement
---------
Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
### What problem does this PR solve?
Feat: support local provider for code exec component & remove some
outdated models
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
