### What problem does this PR solve?
- Update version tags in README files (including translations) from
v0.25.0 to v0.25.1
- 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
## Add Astraflow Provider Support
This PR integrates [Astraflow](https://astraflow.ucloud.cn/) (by UCloud
/ 优刻得) as a new AI model provider in RAGFlow, with support for both
global and China endpoints.
### About Astraflow
Astraflow is an OpenAI-compatible AI model aggregation platform
supporting 200+ models from major providers including DeepSeek, Qwen,
GPT, Claude, Gemini, Llama, Mistral, and more.
| Variant | Factory Name | Endpoint | Env Var |
|---------|-------------|----------|---------|
| Global | `Astraflow` | `https://api-us-ca.umodelverse.ai/v1` |
`ASTRAFLOW_API_KEY` |
| China | `Astraflow-CN` | `https://api.modelverse.cn/v1` |
`ASTRAFLOW_CN_API_KEY` |
- **API key signup**: https://astraflow.ucloud.cn/
---
### Files Changed
| File | Change |
|------|--------|
| `rag/llm/__init__.py` | Register `Astraflow` and `Astraflow-CN` in
`SupportedLiteLLMProvider` enum, `FACTORY_DEFAULT_BASE_URL`, and
`LITELLM_PROVIDER_PREFIX` |
| `rag/llm/chat_model.py` | Add `AstraflowChat` and `AstraflowCNChat`
(OpenAI-compatible `Base` subclass) |
| `rag/llm/embedding_model.py` | Add `AstraflowEmbed` and
`AstraflowCNEmbed` (subclasses of `OpenAIEmbed`) |
| `rag/llm/rerank_model.py` | Add `AstraflowRerank` and
`AstraflowCNRerank` (subclasses of `OpenAI_APIRerank`) |
| `rag/llm/cv_model.py` | Add `AstraflowCV` and `AstraflowCNCV`
(subclasses of `GptV4`) |
| `rag/llm/tts_model.py` | Add `AstraflowTTS` and `AstraflowCNTTS`
(subclasses of `OpenAITTS`) |
| `rag/llm/sequence2txt_model.py` | Add `AstraflowSeq2txt` and
`AstraflowCNSeq2txt` (subclasses of `GPTSeq2txt`) |
| `conf/llm_factories.json` | Register `Astraflow` and `Astraflow-CN`
factories with a curated list of popular models |
---
### Supported Model Types
- ✅ **Chat / LLM** — DeepSeek-V3/R1, Qwen3, GPT-4o/4.1, Claude 3.5/3.7,
Gemini 2.0/2.5 Flash, Llama 3.3/4, Mistral, and 200+ more
- ✅ **Text Embedding** — text-embedding-3-small/large
- ✅ **Image / Vision (IMAGE2TEXT)** — GPT-4o, GPT-4.1, Claude, Gemini,
Llama-4, etc.
- ✅ **Text Re-Rank**
- ✅ **TTS** — tts-1
- ✅ **Speech-to-Text (SPEECH2TEXT)** — whisper-1
### Implementation Notes
- Uses the `openai/` LiteLLM prefix — consistent with other
OpenAI-compatible aggregation platforms (SILICONFLOW, DeerAPI, CometAPI,
OpenRouter, n1n, Avian, etc.)
- `Astraflow` (global, rank 250) and `Astraflow-CN` (China, rank 249)
are separate factory entries, allowing users to choose the optimal
endpoint based on their region.
- All model classes cleanly subclass existing base classes (`Base`,
`OpenAIEmbed`, `OpenAI_APIRerank`, `GptV4`, `OpenAITTS`, `GPTSeq2txt`)
with no custom logic needed — the provider is fully OpenAI-compatible.
---------
Co-authored-by: user <user@xzaaaMacBook-Air.local>
### What problem does this PR solve?
Refactor /api/v1/chats to be more RESTful.
### Type of change
- [x] Refactoring
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
- Update version tags in README files (including translations) from
v0.24.0 to v0.25.0
- 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?
This fixes two broken internal documentation links in the guides:
- `docs/develop/mcp/launch_mcp_server.md` linked
`./acquire_ragflow_api_key.md`, but the target page lives one level up
as `../acquire_ragflow_api_key.md`.
- `docs/guides/dataset/run_retrieval_test.md` linked
`./construct_knowledge_graph.md`, but the actual page lives under
`./advanced/construct_knowledge_graph.md`.
These broken links make it harder to follow the MCP and retrieval-test
docs from the local docs tree.
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### What problem does this PR solve?
Adds Perplexity contextualized embeddings API as a new model provider,
as requested in #13610.
- `PerplexityEmbed` provider in `rag/llm/embedding_model.py` supporting
both standard (`/v1/embeddings`) and contextualized
(`/v1/contextualizedembeddings`) endpoints
- All 4 Perplexity embedding models registered in
`conf/llm_factories.json`: `pplx-embed-v1-0.6b`, `pplx-embed-v1-4b`,
`pplx-embed-context-v1-0.6b`, `pplx-embed-context-v1-4b`
- Frontend entries (enum, icon mapping, API key URL) in
`web/src/constants/llm.ts`
- Updated `docs/guides/models/supported_models.mdx`
- 22 unit tests in `test/unit_test/rag/llm/test_perplexity_embed.py`
Perplexity's API returns `base64_int8` encoded embeddings (not
OpenAI-compatible), so this uses a custom `requests`-based
implementation. Contextualized vs standard model is auto-detected from
the model name.
Closes#13610
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
### What problem does this PR solve?
This PR adds support for parsing PDFs through an external Docling
server, so RAGFlow can connect to remote `docling serve` deployments
instead of relying only on local in-process Docling.
It addresses the feature request in
[#13426](https://github.com/infiniflow/ragflow/issues/13426) and aligns
with the external-server usage pattern already used by MinerU.
### 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):
### What is changed?
- Add external Docling server support in `DoclingParser`:
- Use `DOCLING_SERVER_URL` to enable remote parsing mode.
- Try `POST /v1/convert/source` first, and fallback to
`/v1alpha/convert/source`.
- Keep existing local Docling behavior when `DOCLING_SERVER_URL` is not
set.
- Wire Docling env settings into parser invocation paths:
- `rag/app/naive.py`
- `rag/flow/parser/parser.py`
- Add Docling env hints in constants and update docs:
- `docs/guides/dataset/select_pdf_parser.md`
- `docs/guides/agent/agent_component_reference/parser.md`
- `docs/faq.mdx`
### Why this approach?
This keeps the change focused on one issue and one capability (external
Docling connectivity), without introducing unrelated provider-model
plumbing.
### Validation
- Static checks:
- `python -m py_compile` on changed Python files
- `python -m ruff check` on changed Python files
- Functional checks:
- Remote v1 endpoint path works
- v1alpha fallback works
- Local Docling path remains available when server URL is unset
### Related links
- Feature request: [Support external Docling server (issue
#13426)](https://github.com/infiniflow/ragflow/issues/13426)
- Compare view for this branch:
[main...feat/docling-server](https://github.com/infiniflow/ragflow/compare/main...spider-yamet:ragflow:feat/docling-server?expand=1)
##### Fixes [#13426](https://github.com/infiniflow/ragflow/issues/13426)
### What problem does this PR solve?
Standardize term capitalization in `deploy_local_llm.mdx` and improve
code block formatting.
### Type of change
- [x] Documentation Update
…ff publishing this guide.
### What problem does this PR solve?
Removed failsure mode checklist per your request. @JinHai-CN
### Type of change
- [x] Documentation Update
### What problem does this PR solve?
This PR adds a new guide: **"RAG failure modes checklist"**.
RAG systems often fail in ways that are not immediately visible from a
single metric like accuracy or latency. In practice, debugging
production RAG applications requires identifying recurring failure
patterns across retrieval, routing, evaluation, and deployment stages.
This guide introduces a structured, pattern-based checklist (P01–P12) to
help users interpret traces, evaluation results, and dataset behavior
within RAGFlow. The goal is to provide a practical way to classify
incidents (e.g., retrieval hallucination, chunking issues, index
staleness, routing misalignment) and reason about minimal structural
fixes rather than ad-hoc prompt changes.
The change is documentation-only and does not modify any code or
configuration.
Refs #13138
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### What problem does this PR solve?
- Update version tags in README files (including translations) from
v0.23.1 to v0.24.0
- 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?
Proofread the Sandbox Specification document and moved it to a dedicated
folder outside of the original docs.
### Type of change
- [x] Documentation Update
### What problem does this PR solve?
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [x] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### What problem does this PR solve?
Refact: update description for max_token in embedding #12792
### Type of change
- [x] Refactoring
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
```
$ python admin/client/ragflow_cli.py -t user -u aaa@aaa.com -p 9380
ragflow> list datasets;
ragflow> list default models;
ragflow> show version;
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
This PR extends the RAGFlow Admin API and CLI with comprehensive user
API token management capabilities. Administrators can now generate,
list, and delete API tokens for users through both the REST API and the
Admin CLI interface.
## Changes
### Backend API (`admin/server/`)
#### New Endpoints
- **POST `/api/v1/admin/users/<username>/new_token`** - Generate a new
API token for a user
- **GET `/api/v1/admin/users/<username>/token_list`** - List all API
tokens for a user
- **DELETE `/api/v1/admin/users/<username>/token/<token>`** - Delete a
specific API token for a user
#### Service Layer Updates (`services.py`)
- Added `get_user_api_key(username)` - Retrieves all API tokens for a
user
- Added `save_api_token(api_token)` - Saves a new API token to the
database
- Added `delete_api_token(username, token)` - Deletes an API token for a
user
### Admin CLI (`admin/client/`)
#### New Commands
- **`GENERATE TOKEN FOR USER <username>;`** - Generate a new API token
for the specified user
- **`LIST TOKENS OF <username>;`** - List all API tokens associated with
a user
- **`DROP TOKEN <token> OF <username>;`** - Delete a specific API token
for a user
### Testing
Added comprehensive test suite in `test/testcases/test_admin_api/`:
- **`test_generate_user_api_key.py`** - Tests for API token generation
- **`test_get_user_api_key.py`** - Tests for listing user API tokens
- **`test_delete_user_api_key.py`** - Tests for deleting API tokens
- **`conftest.py`** - Shared test fixtures and utilities
## Technical Details
### Token Generation
- Tokens are generated using `generate_confirmation_token()` utility
- Each token includes metadata: `tenant_id`, `token`, `beta`,
`create_time`, `create_date`
- Tokens are associated with user tenants automatically
### Security Considerations
- All endpoints require admin authentication (`@check_admin_auth`)
- Tokens are URL-encoded when passed in DELETE requests to handle
special characters
- Proper error handling for unauthorized access and missing resources
### API Response Format
All endpoints follow the standard RAGFlow response format:
```json
{
"code": 0,
"data": {...},
"message": "Success message"
}
```
## Files Changed
- `admin/client/admin_client.py` - CLI token management commands
- `admin/server/routes.py` - New API endpoints
- `admin/server/services.py` - Token management service methods
- `docs/guides/admin/admin_cli.md` - CLI documentation updates
- `test/testcases/test_admin_api/conftest.py` - Test fixtures
- `test/testcases/test_admin_api/test_user_api_key_management/*` - Test
suites
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Alexander Strasser <alexander.strasser@ondewo.com>
Co-authored-by: Hetavi Shah <your.email@example.com>
### What problem does this PR solve?
Trailing white-spaces in commit 6814ace1aa
got automatically trimmed by code editor may causes documentation
typesetting broken.
Mostly for double spaces for soft line breaks.
### Type of change
- [x] Documentation Update
### What problem does this PR solve?
Update icons for docs.
Trailing spaces are auto truncated by the editor, does not affect real
content.
### Type of change
- [x] Documentation Update
### What problem does this PR solve?
update for 'list configs' and 'list envs'
### Type of change
- [x] Documentation Update
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Refactor setting type
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
