### 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
### What problem does this PR solve?
Add a Go driver for Novita.ai (https://novita.ai), one of the unchecked
providers on the umbrella tracking issue #14736. Novita exposes an
OpenAI-compatible REST API at `https://api.novita.ai/v3/openai` and
proxies a large catalog of third-party models (DeepSeek, Llama, Qwen3,
Kimi, Gemma, Mistral, MiniMax, GLM, etc.) behind a single OpenAI-shaped
surface — 102 models live at the time of writing.
Until this PR, a tenant who configured `novita` as a model provider in
the Go layer fell through to the default branch of
`internal/entity/models/factory.go` and got the dummy driver.
### What this PR includes
- New `internal/entity/models/novita.go` with a `NovitaModel`
implementing the `ModelDriver` interface (~520 lines).
- New `conf/models/novita.json` with 7 representative chat models
(DeepSeek-V4, Llama-3.3-70B, Qwen3-30B/235B reasoning, Kimi-K2,
Gemma-3-27B, Mistral-Nemo).
- `factory.go`: route `"novita"` to `NewNovitaModel`.
- `internal/entity/models/novita_test.go`: 23 unit tests.
### Notable design point: `<think>...</think>` reasoning extraction
Novita-routed reasoning models like `qwen3-*` and `deepseek-r1-*` embed
their chain-of-thought **inline inside content as `<think>...</think>`
tags**, rather than in a separate `reasoning_content` field. Verified
live by probing `api.novita.ai`:
```
content head 200: <think>
Okay, let's see. I need to find 15% of 80. Hmm, percentages can sometimes be tricky, but I think
content tail 100: h, that works.
Alternatively, 0.15 × 80. If I move the decimal two places to the left for </think>
```
Without handling, a tenant picking qwen3 via Novita would see raw
`<think>` tags in their UI answer — different from every other reasoning
provider in the Go layer.
The driver detects those tags and routes the inner text to
`ChatResponse.ReasonContent` (non-stream) or the sender's second arg
(stream), keeping the visible answer clean of tag clutter:
- **`splitNovitaThink`** — scans a complete content string. Used by the
non-streaming path. Handles multiple `<think>` blocks, unclosed tags
(the model got cut off mid-reasoning), pure-text content with no tags.
- **`novitaThinkExtractor`** — stateful streaming version. Buffers
trailing bytes that might be the start of a tag (e.g. `<thi` held back
when the next chunk completes `nk>`), then emits segments in routing
order so callers can pipe them to a UI. Tested with byte-level chunk
boundaries and tag-spanning scenarios.
### Method coverage
| Method | Behavior |
|---|---|
| `ChatWithMessages` | `POST /v3/openai/chat/completions`, `<think>`
extraction on response |
| `ChatStreamlyWithSender` | SSE stream, stateful `<think>` extraction
across deltas |
| `ListModels` / `CheckConnection` | `GET /v3/openai/models` (102 live)
|
| `Embed` / `Rerank` / `Balance` / `TranscribeAudio` / `AudioSpeech` /
`OCRFile` | `"no such method"` — Novita's OpenAI-compatible surface does
not expose any |
No interface change. No new dependencies.
### How was this tested?
**23 unit tests** in `internal/entity/models/novita_test.go` — all pass:
```
$ go test -vet=off -run "TestNovita|TestSplitNovita" -count=1 ./internal/entity/models/...
ok ragflow/internal/entity/models 0.020s
```
Coverage:
- `splitNovitaThink` (5 cases: pure text, single block, leading text,
multiple blocks, unclosed tag)
- `novitaThinkExtractor` (6 cases: single-chunk, opening tag span,
closing tag span, byte-level chunking, no tags, lone `<` not as tag
start)
- `ChatWithMessages`: pure text, with `<think>` tags, missing API key,
empty messages, HTTP error
- `ChatStreamlyWithSender`: tag-stripping with spanning deltas, pure
content, sender-required, stream-true-required
- `ListModels` / `CheckConnection` (happy paths)
- All sentinel methods
`go build ./internal/entity/models/...` exits 0 on go 1.25.
**Live integration test** against `api.novita.ai/v3/openai`:
```
=== RUN TestNovitaLiveSmoke
[OK] Name() = "novita"
[OK] CheckConnection
[OK] ListModels: 102 models (showing first 6) [deepseek/deepseek-v4-pro deepseek/deepseek-v4-flash deepseek/deepseek-v3.2 xiaomimimo/mimo-v2.5-pro moonshotai/kimi-k2.6 zai-org/glm-5.1]
[OK] Chat (llama-3.3) answer="ok" reason=""
[OK] Chat (qwen3) answer len=0 head=""
ReasonContent len=1657 head="Okay, so I need to figure out what 15% of 80 is. Hmm, percentages can sometimes trip me up, but let ..."
[OK] Stream content: 0 chunks, 0 chars; reasoning: 600 chunks, 1667 chars
[OK] Embed/Rerank/Balance/TranscribeAudio/AudioSpeech/OCRFile all return "novita, no such method"
NOVITA LIVE SMOKE PASSED
--- PASS: TestNovitaLiveSmoke (26.18s)
```
What the live run proves on the wire:
- Auth (`Bearer <key>`) accepted by `api.novita.ai`.
- `/v3/openai/models` parser handles the real 102-model response.
- Non-stream chat against `meta-llama/llama-3.3-70b-instruct`: clean
string answer, empty ReasonContent (non-reasoning model, pure-text
path).
- Non-stream chat against `qwen/qwen3-30b-a3b-fp8`: 1657-char reasoning
extracted from `<think>...</think>` and routed to
`ChatResponse.ReasonContent`. Visible answer is 0 chars in this run
because qwen3 spent its 600-token budget entirely on reasoning before
reaching the answer phase — that's the model's behavior, not a driver
bug. The important thing: **no `<think>` tags leaked into the visible
Answer field**.
- Streaming against qwen3: 600 reasoning chunks (1667 chars) emitted via
the sender's 2nd arg across SSE deltas; **no `<think>` tag fragments
leaked into the content channel** despite tag boundaries crossing chunk
boundaries on the wire.
- All 6 sentinel methods return the documented `"no such method"`
strings.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Tracking: #14736
… modal
- Add vertical resizing functionality for the text field
### What problem does this PR solve?
_Fix the issue where the text content of the knowledge base editing
parsing block is too long to scroll._
<img width="701" height="775" alt="image"
src="https://github.com/user-attachments/assets/b258422e-fbc1-466d-abab-062e642c21d5"
/>
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: chenyun <chenyun@chenyundemacbook-pro.local>
### 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?
The Baidu (Qianfan) `Encode` method silently swallowed malformed
responses. If a `data[]` item from the API was missing a field (`index`,
`embedding`, or unexpected shape), the loop did `continue` instead of
returning an error, leaving `nil` entries in the result slice. Callers
got back partial results with no indication anything went wrong, which
then crashes downstream consumers when they try to use a `nil` vector.
Concrete gaps fixed:
- No count-mismatch check between `data` length and input texts (only
checked for empty)
- No duplicate-index detection (a duplicate would silently overwrite)
- No missing-index final scan
- No empty-embedding rejection
- No per-call context timeout
- `EmbeddingConfig.Dimension` (added in #14735) was not propagated
This PR replaces `map[string]interface{}` parsing with a typed
`baiduEmbeddingResponse` struct, applies the standard four-layer
validation (count → out-of-range → duplicate → empty → final
missing-index scan), adds `context.WithTimeout(nonStreamCallTimeout)`,
and forwards `embeddingConfig.Dimension` as the `dimensions` parameter
(Baidu Qianfan v2 uses an OpenAI-compatible API).
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Problem
When parsing DOCX files with many tables, DeepDOC generates chunks
containing only empty HTML table tags, such as:
```html
<table><tr><td></td></tr><tr><td></td></tr><tr><td></td></tr><tr><td></td></tr></table>
```
After the regex cleanup at `task_executor.py:584`, this becomes `" "`
(whitespace only).
The guard at line 585 (`if not c`) only catches empty strings `""`, but
whitespace strings are truthy in Python and pass through. When sent to
Zhipu `embedding-3` API, it rejects them with error 1213:
`未正常接收到prompt参数`.
## Root Cause
```python
c = re.sub(r"</?(table|td|caption|tr|th)( [^<>]{0,12})?>", " ", c)
if not c: # ← only catches "", not " " / "\n" / "\t"
c = "None"
```
Verified with Zhipu `embedding-3`:
| Input | Result |
|---|---|
| `""` | error 1213 |
| `" "` | error 1213 |
| `"\n"` | error 1213 |
| `"None"` | OK |
## Fix
```diff
- if not c:
+ if not c.strip():
c = "None"
```
## Testing
Reproduced with a 678KB DOCX file (166 tables, 270 chunks). Chunk #89 is
the empty table above. After fix, `"None"` is sent instead and embedding
succeeds.
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
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?
In rag/app/audio.py, the supported audio extensions list contains
duplicate entries: .wav appears twice (positions 3 and 5) and .aac
appears twice (positions 6 and 14). While this does not affect runtime
behavior, it is redundant and makes the code harder to maintain.
This PR removes the duplicate entries to keep the list clean and
consistent.
Type of change
- [X] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
```
RAGFlow(user)> chat with 'glm-ocr@test@zhipu-ai' message 'what is this'
CLI error: expect model glm-ocr@zhipu-ai is a chat or multimodal model
```
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Feat: When a Wait Node precedes a Message Node within a Loop Node, the
outgoing message is split into two separate messages.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### 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
### What problem does this PR solve?
This PR completes the Jina provider
**The following functionalities are now supported:**
**Jina:**
- [ ] Chat / Stream Chat (Not available for now: [(Jina chat API
docs)](https://api.jina.ai/docs#/Search%20Foundation%20Models/chat_completions_v1_chat_completions_post))
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] ~~Balance~~
**Verified examples from the CLI:**
```plaintext
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'jina-embeddings-v2-base-en@test@jina' dimension 16
+-----------+-------+
| dimension | index |
+-----------+-------+
| 768 | 0 |
| 768 | 1 |
+-----------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'jina-reranker-v2-base-multilingual@test@jina' top 3;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 0.74316794 |
| 2 | 0.18713269 |
| 1 | 0.15817434 |
+-------+-----------------+
RAGFlow(user)> list supported models from 'jina' 'test'
+---------------------------------------------+
| model_name |
+---------------------------------------------+
| Jina AI: Jina VLM |
| Jina AI: Jina Reranker v3 |
| Jina AI: Jina Code Embeddings 0.5b |
| Jina AI: Jina Code Embeddings 1.5b |
| Jina AI: Jina Embeddings v4 |
| Jina AI: Jina Reranker M0 |
| Jina AI: ReaderLM v2 |
| Jina AI: Jina Clip v2 |
| Jina AI: Jina Embeddings v3 |
| Jina AI: Jina Colbert v2 |
| Jina AI: Reader LM 0.5b |
| Jina AI: Reader LM 1.5b |
| Jina AI: Jina Reranker v2 Base Multilingual |
| Jina AI: Jina Clip v1 |
| Jina AI: Jina Reranker v1 Tiny EN |
| Jina AI: Jina Reranker v1 Turbo EN |
| Jina AI: Jina Reranker v1 Base EN |
| Jina AI: Jina Colbert v1 EN |
| Jina AI: Jina Embeddings v2 Base ES |
| Jina AI: Jina Embeddings v2 Base Code |
| Jina AI: Jina Embeddings v2 Base DE |
| Jina AI: Jina Embeddings v2 Base ZH |
| Jina AI: Jina Embeddings v2 Base EN |
| Jina AI: Jina Embedding B EN v1 |
| Jina AI: Jina Embeddings v5 Text Small |
| Jina AI: Jina Embeddings v5 Omni Small |
| Jina AI: Jina Embeddings v5 Omni Nano |
| Jina AI: Jina Embeddings v5 Text Nano |
+---------------------------------------------+
RAGFlow(user)> check instance 'test' from 'jina'
SUCCESS
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
The Mistral Go driver landed in #14805 with chat, list models, and check
connection. `Embed` was left as a stub that returns `"not implemented"`.
This PR fills the gap.
`conf/models/mistral.json` did not list any embedding model out of the
box, so a tenant who wanted to use Mistral end to end (chat +
embeddings) could not run an embedding call. This PR adds
`mistral-embed` to the config and a real `/v1/embeddings`
implementation.
### What this PR includes
- `conf/models/mistral.json`: add `"embedding": "embeddings"` under
`url_suffix` so the driver can build the URL from config (matches the
`URLSuffix.Embedding` field already used by openai, siliconflow,
zhipu-ai), and add a `mistral-embed` entry under `models`
(1024-dimensional vectors, 8192 max input tokens).
- `internal/entity/models/mistral.go`: replace the `Embed` stub with a
real implementation that POSTs to `/v1/embeddings`. Adds local response
types `mistralEmbeddingData` and `mistralEmbeddingResponse`.
No factory change. No interface change.
### How the implementation works
- Validate `apiConfig`, the API key, and the model name. Use the
existing `baseURLForRegion` helper so an unknown region fails fast with
a clear error.
- Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so
the call has a clear deadline. Same pattern as `ChatWithMessages` and
`ListModels` already use in this file.
- Send all input texts in one request. The Mistral API accepts the
`input` field as an array.
- Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData`
indexed by `data[*].index` so the output order matches the input order
even if the API returns items in a different order.
- An empty input slice returns `[]EmbeddingData{}` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks that every input slot got a vector. If any slot is
still empty, return a clear error so the caller does not silently use a
zero vector.
### Note on stacking
This PR builds on #14805 (the Mistral driver). Until #14805 merges, this
PR's diff on GitHub will include both that PR's commits and this one.
After #14805 lands on `main`, GitHub will auto-reduce this PR to only
the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8
lines in `mistral.json`).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the
`go.mod` minimum).
- The full method set on `MistralModel` still matches the `ModelDriver`
interface.
- Pattern parity with the existing OpenAI Embed implementation
(`internal/entity/models/openai.go`).
Closes#14806
Depends on #14805
Tracking: #14736
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
```
RAGFlow(user)> asr with 'glm-asr-2512@test@zhipu-ai' audio './speech.wav';
CLI error: zhipu, no such method
RAGFlow(user)> stream asr with 'glm-asr-2512@test@zhipu-ai' audio './speech.wav';
CLI error: zhipu, no such method
RAGFlow(user)> tts with 'glm-tts@test@zhipu-ai' text 'how are you';
CLI error: zhipu, no such method
RAGFlow(user)> stream tts with 'glm-tts@test@zhipu-ai' text 'how are you';
CLI error: zhipu, no such method
RAGFlow(user)> ocr with 'glm-ocr@test@zhipu-ai' file './test.log';
CLI error: zhipu, no such method
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The Upstage Go driver landed in #14817 with chat, list models, and check
connection. `Embed` was left as a stub that returns `"not implemented"`.
This PR fills the gap.
Upstage exposes an OpenAI-compatible embeddings endpoint at
`https://api.upstage.ai/v1/solar/embeddings` via the
`solar-embedding-1-large` family (`solar-embedding-1-large-query` for
queries, `solar-embedding-1-large-passage` for passages), and the Python
side has had `UpstageEmbed(OpenAIEmbed)` in `rag/llm/embedding_model.py`
for a long time targeting this same path. The existing
`conf/models/upstage.json` did not list any embedding model out of the
box, so a tenant who wanted to use Upstage end to end could not run an
embedding call. This PR fills the gap.
### What this PR includes
- `conf/models/upstage.json`: add `"embedding": "embeddings"` under
`url_suffix` so the driver can build the URL from config (matches the
`URLSuffix.Embedding` field already used by openai, mistral,
siliconflow, zhipu-ai), and add `solar-embedding-1-large-query` and
`solar-embedding-1-large-passage` entries under `models`.
- `internal/entity/models/upstage.go`: replace the `Embed` stub with a
real implementation that POSTs to `/v1/solar/embeddings`. Adds local
response types `upstageEmbeddingData` and `upstageEmbeddingResponse`.
No factory change. No interface change.
### How the implementation works
- Validate `apiConfig`, the API key, and the model name. Use the
existing `baseURLForRegion` helper so an unknown region fails fast with
a clear error.
- Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so
the call has a clear deadline. Same pattern as `ChatWithMessages` and
`ListModels` already use in this file.
- Send all input texts in one request. The Upstage API accepts the
`input` field as an array.
- Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData`
indexed by `data[*].index` so the output order matches the input order
even if the API returns items in a different order.
- An empty input slice returns `[]EmbeddingData{}` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks that every input slot got a vector. If any slot is
still empty, return a clear error so the caller does not silently use a
zero vector.
### Note on stacking
This PR builds on #14817 (the Upstage driver). Until #14817 merges, this
PR's diff on GitHub will include both that PR's commits and this one.
After #14817 lands on `main`, GitHub will auto-reduce this PR to only
the `Embed` changes (one commit, ~119 line diff in `upstage.go` plus ~15
lines in `upstage.json`).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the
`go.mod` minimum).
- The full method set on `UpstageModel` still matches the `ModelDriver`
interface.
- Pattern parity with the existing Mistral Embed
(`internal/entity/models/mistral.go`) and OpenAI Embed
(`internal/entity/models/openai.go`) implementations.
Closes#14818
Depends on #14817
Tracking: #14736
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR completes the Baichuan provider
**The following functionalities are now supported:**
**Baichuan:**
- [x] Chat / Stream Chat
- [x] Embedding
- [ ] ~~Rerank~~
- [ ] ~~Model listing~~
- [ ] ~~Provider connection checking~~
- [ ] ~~Balance~~
**Verified examples from the CLI:**
```plaintext
# Baichuan
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'Baichuan-Text-Embedding@test@baichuan' dimension 16;
+-----------+-------+
| dimension | index |
+-----------+-------+
| 1024 | 0 |
| 1024 | 1 |
+-----------+-------+
AGFlow(user)> chat with 'Baichuan-M2@test@baichuan' message 'who r u'
Answer: I'm BaiChuan, a helpful AI assistant created by Baichuan-AI. I'm designed to be a knowledgeable, friendly, and reliable assistant for various tasks like answering questions, explaining concepts, writing content, and more. Feel free to ask me anything! 😊
Time: 1.637975
RAGFlow(user)> stream chat with 'Baichuan-M2@test@baichuan' message 'who r u'
Answer: I'm BaiChuan-m2, an AI assistant developed by Baichuan-AI. My purpose is to help you with a wide range of tasks by providing information, answering questions, solving problems, and assisting with creative projects. Think of me as a helpful digital companion! If you have any questions or need assistance, just let me know.😊
Time: 1.692321
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
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?
fix:
update null checks to use 'is None' for better clarity
replace RAGFlowSelect with SelectWithSearch in DebugContent
add max height and overflow to DialogContent in ParameterDialog
remove unused types from DataOperationsForm
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Fixes#13817
### What problem does this PR solve?
The "knowledge graph construction" link on line 21 of
`docs/guides/dataset/run_retrieval_test.md` points to
`./construct_knowledge_graph.md`, which doesn't exist. The actual file
is at `./advanced/construct_knowledge_graph.md`.
### Type of change
- [x] Documentation Update
Signed-off-by: majiayu000 <1835304752@qq.com>
### What problem does this PR solve?
Add a Go driver for StepFun (阶跃星辰), one of the unchecked providers on
the umbrella tracking issue #14736.
Until this PR, a tenant who configured `stepfun` as a model provider in
the Go layer fell through to the default branch of
`internal/entity/models/factory.go` and got the dummy driver. Chat, list
models, and check connection all returned `"not implemented"` instead of
reaching the StepFun API.
The Python side has had StepFun registered in `rag/llm/__init__.py` as a
`SupportedLiteLLMProvider` with base URL `https://api.stepfun.com/v1`,
plus `StepFunCV` for vision and `StepFunSeq2txt` for ASR, but no Go
path. StepFun's chat API is OpenAI-compatible, so the implementation
pattern is the same as the merged Moonshot driver (#14433) and OpenAI
driver (#14605).
### What this PR includes
- New file `internal/entity/models/stepfun.go` with a `StepFunModel`
that implements the `ModelDriver` interface.
- `factory.go`: route the `"stepfun"` provider name to
`NewStepFunModel`.
- New `conf/models/stepfun.json` with the public StepFun chat models
(step-2-16k, step-1 family in 8k/32k/128k/256k context lengths,
step-1-flash, and the step-1v / step-1o vision models) and `url_suffix`
entries for `chat` and `models`.
### How the driver works
- StepFun exposes the OpenAI-compatible API at
`https://api.stepfun.com/v1`.
- `ChatWithMessages` and `ChatStreamlyWithSender` post to
`/chat/completions` in the same shape as the merged moonshot,
openrouter, and openai drivers.
- `ListModels` and `CheckConnection` call `/models` to list available
ids and confirm the API key works.
- `Embed` is left as `"not implemented"`. StepFun has not advertised a
public embeddings endpoint in the API reference linked from the umbrella
issue
(`https://platform.stepfun.com/docs/en/api-reference/chat/chat-completion-create`
is the chat endpoint), so any real implementation belongs in a separate
follow-up only after the endpoint is verified.
- `Rerank` and `Balance` return `"no such method"` because StepFun does
not expose either.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 with no errors
on go 1.25 (the `go.mod` minimum).
- Method set of `StepFunModel` matches the `ModelDriver` interface:
`NewInstance`, `Name`, `ChatWithMessages`, `ChatStreamlyWithSender`,
`Embed`, `Rerank`, `ListModels`, `Balance`, `CheckConnection`.
- Pattern parity with the merged moonshot (#14433), openai (#14605),
openrouter (#14652), and xai (#14550) drivers.
Closes#14814
Tracking: #14736
Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.6.3 to 2.7.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/urllib3/urllib3/releases">urllib3's
releases</a>.</em></p>
<blockquote>
<h2>2.7.0</h2>
<h2>🚀 urllib3 is fundraising for HTTP/2 support</h2>
<p><a
href="https://sethmlarson.dev/urllib3-is-fundraising-for-http2-support">urllib3
is raising ~$40,000 USD</a> to release HTTP/2 support and ensure
long-term sustainable maintenance of the project after a sharp decline
in financial support. If your company or organization uses Python and
would benefit from HTTP/2 support in Requests, pip, cloud SDKs, and
thousands of other projects <a
href="https://opencollective.com/urllib3">please consider contributing
financially</a> to ensure HTTP/2 support is developed sustainably and
maintained for the long-haul.</p>
<p>Thank you for your support.</p>
<h2>Security</h2>
<p>Addressed high-severity security issues. Impact was limited to
specific use cases detailed in the accompanying advisories; overall user
exposure was estimated to be marginal.</p>
<ul>
<li>
<p>Decompression-bomb safeguards of the streaming API were bypassed:</p>
<ol>
<li>When <code>HTTPResponse.drain_conn()</code> was called after the
response had been read and decompressed partially. (Reported by <a
href="https://github.com/Cycloctane"><code>@Cycloctane</code></a>)</li>
<li>During the second <code>HTTPResponse.read(amt=N)</code> or
<code>HTTPResponse.stream(amt=N)</code> call when the response was
decompressed using the official <a
href="https://pypi.org/project/brotli/">Brotli</a> library. (Reported by
<a
href="https://github.com/kimkou2024"><code>@kimkou2024</code></a>)</li>
</ol>
<p>See GHSA-mf9v-mfxr-j63j for details.</p>
</li>
<li>
<p>HTTP pools created using
<code>ProxyManager.connection_from_url</code> did not strip sensitive
headers specified in <code>Retry.remove_headers_on_redirect</code> when
redirecting to a different host. (GHSA-qccp-gfcp-xxvc reported by <a
href="https://github.com/christos-spearbit"><code>@christos-spearbit</code></a>)</p>
</li>
</ul>
<h2>Deprecations and Removals</h2>
<ul>
<li>Used <code>FutureWarning</code> instead of
<code>DeprecationWarning</code> for better visibility of existing
deprecation notices. Rescheduled the removal of deprecated features to
version 3.0. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3763">urllib3/urllib3#3763</a>)</li>
<li>Removed support for end-of-life Python 3.9. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3720">urllib3/urllib3#3720</a>)</li>
<li>Removed support for end-of-life PyPy3.10. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4979">urllib3/urllib3#4979</a>)</li>
<li>Bumped the minimum supported pyOpenSSL version to 19.0.0. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3777">urllib3/urllib3#3777</a>)</li>
</ul>
<h2>Bugfixes</h2>
<ul>
<li>Fixed a bug where <code>HTTPResponse.read(amt=None)</code> was
ignoring decompressed data buffered from previous partial reads. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3636">urllib3/urllib3#3636</a>)</li>
<li>Fixed a bug where <code>HTTPResponse.read()</code> could cache only
part of the response after a partial read when
<code>cache_content=True</code>. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4967">urllib3/urllib3#4967</a>)</li>
<li>Fixed <code>HTTPResponse.stream()</code> and
<code>HTTPResponse.read_chunked()</code> to handle <code>amt=0</code>.
(<a
href="https://redirect.github.com/urllib3/urllib3/issues/3793">urllib3/urllib3#3793</a>)</li>
<li>Updated <code>_TYPE_BODY</code> type alias to include missing
<code>Iterable[str]</code>, matching the documented and runtime behavior
of chunked request bodies. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3798">urllib3/urllib3#3798</a>)</li>
<li>Fixed <code>LocationParseError</code> when paths resembling
schemeless URIs were passed to
<code>HTTPConnectionPool.urlopen()</code>. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3352">urllib3/urllib3#3352</a>)</li>
<li>Fixed <code>BaseHTTPResponse.readinto()</code> type annotation to
accept <code>memoryview</code> in addition to <code>bytearray</code>,
matching the <code>io.RawIOBase.readinto</code> contract and enabling
use with <code>io.BufferedReader</code> without type errors. (<a
href="https://redirect.github.com/urllib3/urllib3/issues/3764">urllib3/urllib3#3764</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/urllib3/urllib3/blob/main/CHANGES.rst">urllib3's
changelog</a>.</em></p>
<blockquote>
<h1>2.7.0 (2026-05-07)</h1>
<h2>Security</h2>
<p>Addressed high-severity security issues.
Impact was limited to specific use cases detailed in the accompanying
advisories; overall user exposure was estimated to be marginal.</p>
<ul>
<li>
<p>Decompression-bomb safeguards of the streaming API were bypassed:</p>
<ol>
<li>When <code>HTTPResponse.drain_conn()</code> was called after the
response had been
read and decompressed partially.</li>
<li>During the second <code>HTTPResponse.read(amt=N)</code> or
<code>HTTPResponse.stream(amt=N)</code> call when the response was
decompressed
using the official <code>Brotli
<https://pypi.org/project/brotli/></code>__ library.</li>
</ol>
<p>See <code>GHSA-mf9v-mfxr-j63j
<https://github.com/urllib3/urllib3/security/advisories/GHSA-mf9v-mfxr-j63j></code>__
for details.</p>
</li>
<li>
<p>HTTP pools created using
<code>ProxyManager.connection_from_url</code> did not strip
sensitive headers specified in
<code>Retry.remove_headers_on_redirect</code> when
redirecting to a different host.
(<code>GHSA-qccp-gfcp-xxvc
<https://github.com/urllib3/urllib3/security/advisories/GHSA-qccp-gfcp-xxvc></code>__)</p>
</li>
</ul>
<h2>Deprecations and Removals</h2>
<ul>
<li>Used <code>FutureWarning</code> instead of
<code>DeprecationWarning</code> for better
visibility of existing deprecation notices. Rescheduled the removal of
deprecated features to version 3.0.
(<code>[#3763](https://github.com/urllib3/urllib3/issues/3763)
<https://github.com/urllib3/urllib3/issues/3763></code>__)</li>
<li>Removed support for end-of-life Python 3.9.
(<code>[#3720](https://github.com/urllib3/urllib3/issues/3720)
<https://github.com/urllib3/urllib3/issues/3720></code>__)</li>
<li>Removed support for end-of-life PyPy3.10.
(<code>[#4979](https://github.com/urllib3/urllib3/issues/4979)
<https://github.com/urllib3/urllib3/issues/4979></code>__)</li>
<li>Bumped the minimum supported pyOpenSSL version to 19.0.0.
(<code>[#3777](https://github.com/urllib3/urllib3/issues/3777)
<https://github.com/urllib3/urllib3/issues/3777></code>__)</li>
</ul>
<h2>Bugfixes</h2>
<ul>
<li>Fixed a bug where <code>HTTPResponse.read(amt=None)</code> was
ignoring decompressed
data buffered from previous partial reads.
(<code>[#3636](https://github.com/urllib3/urllib3/issues/3636)
<https://github.com/urllib3/urllib3/issues/3636></code>__)</li>
<li>Fixed a bug where <code>HTTPResponse.read()</code> could cache only
part of the
response after a partial read when <code>cache_content=True</code>.</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="9a950b92d9"><code>9a950b9</code></a>
Release 2.7.0</li>
<li><a
href="5ec0de499b"><code>5ec0de4</code></a>
Merge commit from fork</li>
<li><a
href="2bdcc44d1e"><code>2bdcc44</code></a>
Merge commit from fork</li>
<li><a
href="f45b0df09d"><code>f45b0df</code></a>
Fix a misleading example for <code>ProxyManager</code> (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4970">#4970</a>)</li>
<li><a
href="577193ca02"><code>577193c</code></a>
Switch to nightly PyPy3.11 in CI for now (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4984">#4984</a>)</li>
<li><a
href="e90af45bb0"><code>e90af45</code></a>
Avoid infinite loop in <code>HTTPResponse.read_chunked</code> when
<code>amt=0</code> (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4974">#4974</a>)</li>
<li><a
href="67ed74fdae"><code>67ed74f</code></a>
Bump dev dependencies (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4972">#4972</a>)</li>
<li><a
href="3abd481097"><code>3abd481</code></a>
Upgrade mypy to version 1.20.2 (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4978">#4978</a>)</li>
<li><a
href="2b8725dfca"><code>2b8725d</code></a>
Drop support for EOL PyPy3.10 (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4979">#4979</a>)</li>
<li><a
href="2944b2a0a6"><code>2944b2a</code></a>
Upgrade <code>setup-chrome</code> and <code>setup-firefox</code> to fix
warnings (<a
href="https://redirect.github.com/urllib3/urllib3/issues/4973">#4973</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/urllib3/urllib3/compare/2.6.3...2.7.0">compare
view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
- `@dependabot recreate` will recreate this PR, overwriting any edits
that have been made to it
- `@dependabot show <dependency name> ignore conditions` will show all
of the ignore conditions of the specified dependency
- `@dependabot ignore this major version` will close this PR and stop
Dependabot creating any more for this major version (unless you reopen
the PR or upgrade to it yourself)
- `@dependabot ignore this minor version` will close this PR and stop
Dependabot creating any more for this minor version (unless you reopen
the PR or upgrade to it yourself)
- `@dependabot ignore this dependency` will close this PR and stop
Dependabot creating any more for this dependency (unless you reopen the
PR or upgrade to it yourself)
You can disable automated security fix PRs for this repo from the
[Security Alerts
page](https://github.com/infiniflow/ragflow/network/alerts).
</details>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
### 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>
## Summary
- Add GET method handler to `/api/v1/dify/retrieval` endpoint for Dify
external knowledge base connectivity verification
- GET requests return a simple success response; POST requests retain
existing retrieval logic unchanged
## Problem
When Dify integrates with RAGFlow as an external knowledge base, it
sends periodic GET requests to the retrieval endpoint for
health/connectivity checks. The endpoint only accepted POST, causing
werkzeug to return `405 Method Not Allowed`. After several successful
POST retrievals, the failing GET health checks trigger Dify's circuit
breaker, causing all subsequent requests to fail.
Traceback from the issue:
```
werkzeug.exceptions.MethodNotAllowed: 405 Method Not Allowed: The method is not allowed for the requested URL.
```
## Changes
- `api/apps/sdk/dify_retrieval.py`: Added a separate GET route handler
(`retrieval_health_check`) that returns `get_json_result(data=True)`
## Test plan
- [ ] Verify `GET /api/v1/dify/retrieval` returns `{"code": 0,
"message": "success", "data": true}`
- [ ] Verify `POST /api/v1/dify/retrieval` with valid API key and body
still works as before
- [ ] Verify Dify external knowledge base integration no longer returns
405 errors
Closes#13788🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Asksksn <Asksksn@noreply.gitcode.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
1. Add region check in zhipu-ai embed method
2. Fix retrieval test
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR completes the Cohere provider integration (upgrading to the new
Cohere V2 API) and enhances the Fish Audio provider in RAGFlow.
**The following functionalities are now supported:**
**Cohere:**
- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] Balance
**Fish Audio:**
- [x] Model listing (`ListModels`)
- [x] Balance (`Balance`)
-----
**Verified examples from the CLI:**
```plaintext
# Cohere
RAGFlow(user)> think chat with 'command-a-reasoning-08-2025@test3@cohere' message 'jumperwho'
Thinking: Okay, the user wrote "jumperwho". Let me try to figure out what they might be asking. First, I'll check if it's a misspelling. "Jumper" ...... Hmm. Since the query is unclear, the best approach is to ask the user to provide more context or correct any possible typos.
Answer: It seems there might be a typo or missing context in your query "jumperwho." Could you clarify what you're referring to? For example:
- Are you asking about a **jumper** (a type of sweater, a person who jumps, or a component in electronics)?
- Is this related to a specific context, like a movie (e.g., the 2008 film *Jumper*) or a game?
- Did you mean to ask about a person ("who") associated with jumping (e.g., a parachutist)?
Let me know so I can provide a helpful response! 😊
Time: 6.710331
RAGFlow(user)> stream think chat with 'command-a-reasoning-08-2025@test3@cohere' message 'jumperwho'
Thinking: , the user mentioned "jumperwho". Let me try to figure out what they're referring to. First, I'll check if it's a misspelling. "Jumper" could be a typo for "jumper" or maybe a username. Alternatively, it might be a combination of words like "jumper who",....... the best approach is to inform the user that I don't recognize the term and ask if they can provide more context or clarify what they mean by "jumperwho". That way, I can assist them better once I have more information.
Answer: seems "jumperwho" isn't a widely recognized term, proper noun, or acronym in common usage. Could you provide more context or clarify what you mean by "jumperwho"? This will help me understand your question or request better!
Time: 4.513596
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'embed-v4.0@test3@cohere' dimension 16;
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| embedding | index |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| [-0.016643638 -0.001957038 0.0055713872 0.009027058 0.05275187 -0.024542313 -0.044006906 0.024119169 0.0014192933 0.006558722 0.0019129605 -0.021016119 -0.026516981 -0.017489925 0.021298215 0.017772019 0.04569948 0.008886009 0.012059584 -0.0014721862 0.... | 0 |
| [0.018778935 -0.0063459855 -0.0006839742 0.0046623563 0.0067668925 -0.018001877 -0.03963003 0.035744734 -0.014246088 -0.0020721585 -0.006313608 0.025124922 -0.010749322 0.01217393 -0.010231283 -0.025254432 0.021498645 -0.028880708 0.019167464 -0.0058279... | 1 |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'rerank-v4.0-pro@test@cohere' top 3;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 0.91744334 |
| 1 | 0.7458429 |
| 2 | 0.68729424 |
+-------+-----------------+
RAGFlow(user)> list supported models from 'cohere' 'test'
+-------------------------------------+
| model_name |
+-------------------------------------+
| c4ai-aya-expanse-32b |
| c4ai-aya-vision-32b |
| cohere-transcribe-03-2026 |
| command-a-03-2025 |
| command-a-reasoning-08-2025 |
| command-a-translate-08-2025 |
| command-a-vision-07-2025 |
| command-r-08-2024 |
| command-r-plus-08-2024 |
| command-r7b-12-2024 |
| command-r7b-arabic-02-2025 |
| embed-english-light-v3.0 |
| embed-english-light-v3.0-image |
| embed-english-v3.0 |
| embed-english-v3.0-image |
| embed-multilingual-light-v3.0 |
| embed-multilingual-light-v3.0-image |
| embed-multilingual-v3.0 |
| embed-multilingual-v3.0-image |
| embed-v4.0 |
+-------------------------------------+
RAGFlow(user)> check instance 'test' from 'cohere'
SUCCESS
# FishAudio
RAGFlow(user)> list supported models from 'fishaudio' 'test'
+----------------------------------------+
| model_name |
+----------------------------------------+
| Valentino Narración Biblica Fer |
| Super Smash Bros. 4/Ultimate Announcer |
| Farid Dieck |
| عصام الشوالي |
| ALEX_CHIKNA |
| Energetic Male |
| voz de locutor k |
| يي |
| ELITE |
| Mortal Kombat |
+----------------------------------------+
RAGFlow(user)> show balance from 'fishaudio' 'test'
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
| _id | created_at | credit | has_free_credit | has_phone_sha256 | updated_at | user_id |
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
| 82ffec12cf984d88a30ec504d7909812 | 2026-05-09T07:52:16.119000Z | 0 | | false | 2026-05-09T07:52:16.119000Z | 2578ab1126804d6eaa630552400d7ff3 |
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
## Summary
- Replaces the `"no such method"` stub on `NvidiaModel.Rerank`
(`internal/entity/models/nvidia.go`) with a real implementation against
NVIDIA NIM's `/ranking` endpoint.
- Mirrors the existing Python `NvidiaRerank` class at
`rag/llm/rerank_model.py:149-190` for behavior parity: same
`passages`/`query.text`/`logit` payload shape; `top_n` set to
`len(documents)` so every input gets a score returned in original order
(the issue body's spec omitted `top_n`, which would cause silent data
loss).
- Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model
entries (`nvidia/nv-rerankqa-mistral-4b-v3`,
`nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so
the picker exposes them.
- Follows the same shape as the recently merged Aliyun (#14676), Gitee
(#14656), and ZhipuAI (#14608) Rerank implementations: lowercase
per-driver request/response types, conversion to the project-wide
`RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout`
of 30s.
Closes#14720
## Test plan
- [x] `gofmt -l internal/entity/models/nvidia.go` — clean
- [x] `go vet ./internal/entity/models/...` — no new errors introduced
(the two pre-existing vet errors in `baidu.go:642` and
`openrouter.go:566` are unrelated to this PR)
- [x] `go build ./internal/entity/models/...` — succeeds
- [x] `python3 -c "import json;
json.load(open('conf/models/nvidia.json'))"` — JSON valid
- [ ] Live smoke test against NVIDIA NIM with a real API key (requires
reviewer with NIM credentials)
## Notes for reviewers
- The issue body suggested omitting `top_n`. The Python reference
includes it (`top_n: len(texts)`), and without it NVIDIA returns only
the default top-K rankings rather than scores for every input. This PR
follows the Python.
- The URL host is `integrate.api.nvidia.com` (kept consistent with the
existing chat/embeddings BaseURL in `nvidia.go`), not the legacy
`ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts
the model names as-is, so no per-model URL transform is needed.
### What problem does this PR solve?
As the title suggests.
### Type of change
- [x] Documentation Update
Signed-off-by: Jin Hai <haijin.chn@gmail.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>
Fixes#13851
## Problem
`OCR.detect()` in `deepdoc/vision/ocr.py` returns `None, None,
time_dict` (a truthy 3-tuple) when the text detector fails or receives a
`None` image. However, the caller in `pdf_parser.py:__ocr()` checks:
```python
bxs = self.ocr.detect(np.array(img), device_id)
if not bxs: # False! (None, None, time_dict) is a non-empty tuple → truthy
self.boxes.append([])
return
bxs = [(line[0], line[1][0]) for line in bxs] # iterates (None, None, time_dict)
# line = None → None[0] → TypeError: 'NoneType' object is not subscriptable
```
This causes the `NoneType object is not subscriptable` error that
appears after "OCR started" in the chunking pipeline when using PDF +
General parser.
## Solution
Simplified `OCR.detect()` to return `None` (falsy) instead of `None,
None, time_dict` on failure. The `time_dict` was unused by the only
caller of this method. The early-return guard `if not bxs:` in
`pdf_parser.py` then correctly catches it.
## Testing
- The method's only caller (`pdf_parser.py:__ocr`) already has a `if not
bxs:` guard that handles the `None` return correctly.
- No other callers of `OCR.detect()` exist in the codebase.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Refactor**
* Modified OCR detection function return behavior to streamline output.
The function now returns detection results only, without timing
metadata. Error cases now return `None` instead of empty tuple values.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
This PR fixes a UI issue where the .docx document preview was displayed
incompletely when clicking on a citation/reference link during a
knowledge base conversation.
### What problem does this PR solve?
The Issue:
In the chat interface, when a user clicks the source citation at the end
of an answer, the DocPreviewer opens. However, for .docx files, if the
content exceeded the window height, it was truncated and unscrollable,
preventing users from reading the full referenced text.
Changes:
web/src/components/document-preview/doc-preview.tsx: Added the
overflow-auto Tailwind class to the DocPreviewer root container to
ensure scrollbars appear automatically when content overflows.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: nie.weiyang <nie.weiyang@embedway.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?
Added a private helper _visibility_and_status_filter(joined_tenant_ids,
user_id) that returns the Peewee condition: visible to user (team or
own) and status is VALID.
### Type of change
- [x] Refactoring
---------
Co-authored-by: Serobabov Aleksandr <40SerobabovAS@region.cbr.ru>
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
### 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?
- Moved if not all([email, new_pwd, new_pwd2]) guard to the top, before
any decryption that could crash on None value
- Removed the redundant REDIS_CONN.get() call — one call is sufficient
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
### What problem does this PR solve?
Provide embedding index according to the input text
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
- Wrap 2 `ThreadPoolExecutor` instances in `file_service.py` with `with`
statement
- Ensures threads are properly shut down after all futures complete
## Problem
`parse_docs()` (line 532) and the file processing method (line 694)
create `ThreadPoolExecutor` instances that are never shut down. In a
long-running server process, this leaks thread resources on every
invocation — threads remain alive consuming memory even after all
submitted work is complete.
## Fix
Replace bare `ThreadPoolExecutor()` with `with ThreadPoolExecutor() as
exe:` context manager, which calls `executor.shutdown(wait=True)` on
exit.
## Test plan
- [x] Verified both call sites use `with` statement after fix
- [x] No remaining bare `ThreadPoolExecutor` in `file_service.py`
- [x] `document_service.py:1066` is a module-level executor (different
pattern, not changed in this PR)
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
