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59 Commits

Author SHA1 Message Date
tmimmanuel
5e25e2600b Go: implement Xiaomi chat provider (#15626) 
### What problem does this PR solve?

Implements the Xiaomi MiMo chat provider for the Go model provider
layer.

Reference issue: #14736

Official docs used:
- Xiaomi MiMo OpenAI-compatible chat API:
https://platform.xiaomimimo.com/docs/en-US/api/chat/openai-api
- Xiaomi MiMo model and rate limits:
https://platform.xiaomimimo.com/docs/en-US/quick-start/model
- Xiaomi MiMo model hyperparameters:
https://platform.xiaomimimo.com/docs/en-US/quick-start/model-hyperparameters
 2026-06-08 13:09:36 +08:00
Hz_
47626bbe63 go: add Qiniu model provider (#15280) 
### What problem does this PR solve?

This PR adds Qiniu provider integration for the Go model driver layer in
RAGFlow.

  Supported capabilities:

  - [X] Chat
  - [X] Think Chat
  - [X] Stream Chat
  - [X] Stream Think Chat
  - [X] Model listing
  - [X] Provider configuration and factory registration

  Verified examples from the CLI:

  ```
  login user '***' password '***';

  ADD PROVIDER 'qiniu';

  CREATE PROVIDER 'qiniu' INSTANCE 'test' KEY '***';

chat with 'deepseek/deepseek-v3.1-terminus-thinking@test@qiniu' message
'hello';

think chat with 'deepseek/deepseek-v3.1-terminus-thinking@test@qiniu'
message 'hello';

stream chat with 'deepseek/deepseek-v3.1-terminus-thinking@test@qiniu'
message 'hello, what are you';

stream think chat with
'deepseek/deepseek-v3.1-terminus-thinking@test@qiniu' message 'hello,
what are you';

stream think chat with 'qwen3-max-2026-01-23@test@qiniu' message 'hello,
what are you';

  LIST MODELS FROM 'qiniu' 'test';

```

  ### Type of change

  - [X] New Feature
  - [X] Provider integration
 2026-05-27 13:19:39 +08:00
oktofeesh
5ae41dc1eb fix(go-models): route hosted OCR providers through drivers (#15233) 
## Summary
- route hosted MinerU.Net and PaddleOCR.Net provider names to their
existing Go drivers
- add regression coverage for loading the hosted OCR provider configs
through ProviderManager

## What changed
- Added canonical provider-name aliases for the hosted OCR provider
display names.
- Covered both bundled configs with a focused provider-manager test.

## Why
The hosted provider configs use display names with `.Net`, while model
factory dispatch lowercases the provider name. Without aliases, those
configs fall through to `DummyModel` instead of using the existing
MinerU and PaddleOCR drivers.

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-26 20:40:40 +08:00
Haruko386
3619ceca01 Go: implement provider: OrcaRouter (#15235) 
### What problem does this PR solve?

implement provider `OrcaRouter`
**The following functionalities are now supported:**

**Cohere:**
- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Model listing
- [x] TTS
- [ ] Balance


### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-26 18:20:33 +08:00
dripsmvcp
a48bcf814d Go: implement provider: ModelScope (#15041) 
Closes #15040.

ModelScope was listed unchecked in the Go-rewrite tracker #14736 and
already had an llm_factories.json entry (tags: LLM) but no Go driver, so
the new Go API server could not route ModelScope instances. The Python
side has supported it through the OpenAI-compatible base at
rag/llm/chat_model.py:618 (ModelScopeChat), which requires a
user-supplied base URL and appends /v1.

This adds:
- internal/entity/models/modelscope.go: self-hosted OpenAI-compatible
driver with chat (sync + SSE stream with idle-timeout cancellation),
list_models, and check_connection. Auth header is optional, matching the
xinference pattern, so deployments without auth and auth-enabled
deployments both work. Base URL is normalized so users can configure
either the root endpoint or the /v1 endpoint.
- internal/entity/models/modelscope_test.go: 12 tests covering name, URL
normalization, factory routing, chat happy path / auth header /
reasoning_content extraction, stream happy path / stream=false rejection
/ idle cancellation, list_models + check_connection, missing-base-URL
clear error, and the no-such-method sentinels.
- conf/models/modelscope.json: shipped config (class: "local",
url_suffix v1/chat/completions and v1/models).
- internal/entity/models/factory.go: case "modelscope" →
ModelScopeModel.
- internal/service/llm.go: ModelScope added to the selfDeployed map
alongside Ollama, Xinference, LocalAI, LM-Studio, GPUStack — the Python
side requires user-supplied URL with no default, so the Go side
classifies it the same way.

Follow-on issues will add Embed and Rerank, in line with how Novita,
NVIDIA, TogetherAI, and other providers landed method-by-method.

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-26 18:18:46 +08:00
Hz_
84add43208 Add HuaweiCloud model provider (#15237) 
### What problem does this PR solve?

  This PR adds HuaweiCloud provider integration in RAGFlow.

  Supported capabilities:

  - [x] Chat / Think Chat / Stream Chat / Stream Think Chat
  - [x] Embedding
  - [x] Rerank
  - [x] Model listing
  - [x] Provider connection checking

  Verified examples from the CLI:

  ```
  check instance 'test' from 'HuaweiCloud';

  chat with 'deepseek-v4-flash@test@HuaweiCloud' message 'hello';

  think chat with 'deepseek-v4-flash@test@HuaweiCloud' message 'hello';

  stream chat with 'deepseek-v4-flash@test@HuaweiCloud' message 'hello';

stream think chat with 'deepseek-v4-flash@test@HuaweiCloud' message
'hello';

embed text 'what is rag' 'who are you' with 'bge-m3@test@HuaweiCloud'
dimension 1024;

rerank query 'what is rag' document 'rag is retrieval augmented
generation' 'rag need llm' 'famous rag
project includes ragflow' with 'bge-reranker-v2-m3@test@HuaweiCloud' top
3;

  list supported models from 'HuaweiCloud' 'test';

  LIST MODELS FROM 'HuaweiCloud' 'test';
```
  ### Type of change

  - [x] New Feature
  - [x] Provider integration
 2026-05-26 17:13:15 +08:00
ghost
a7d25391dc fix(tokenhub): wire Go driver and harden requests (#15224) 
## Summary
- Wire the Go TokenHub provider through the model factory.
- Harden TokenHub request handling for chat, streaming, embeddings, and
model listing.
- Add focused TokenHub unit coverage for factory wiring and provider
behavior.

## Notes
- Refs #14736.
- Follows up #15159.

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-26 17:12:37 +08:00
Jake Armstrong
0fb85a66bc feat(go-models): add AWS Bedrock provider driver (#15166) 
## Summary

Closes #15165.

Implements the AWS Bedrock model provider for the Go API server, tracked
under #14736. Adds Converse + Converse-Stream chat and foundation-model
listing, with SigV4 signing over a hand-rolled `net/http` path that
matches the established pattern in `internal/entity/models/` (no new
direct `go.mod` deps).

## Linked tracker

Tracked under #14736 (Implement model providers of RAGFlow API server in
Go). Closes #15165.
 2026-05-26 17:10:06 +08:00
sxxtony
67f7d87dff Go: implement provider: FuturMix (#15013) 
### What problem does this PR solve?

Add a Go driver for **FuturMix** (https://futurmix.ai/docs), one of the
unchecked providers on the umbrella tracking issue #14736. FuturMix is
documented as an "OpenAI-compatible API" aggregator over Claude / GPT /
Gemini / DeepSeek (~22 models per their `/models` page).

Until this PR, a tenant who configured `futurmix` 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.

---------

Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-26 10:51:29 +08:00
Haruko386
5d022d83e8 Go: implement provider: PaddleOCR_Local (#15158) 
### What problem does this PR solve?

Go: implement provider: PaddleOCR_Local

**Verified from CLI**

```
RAGFlow(user)> ocr with 'PaddleOCR-VL@test@paddleocr_local' file './internal/test1.jpg'
+----------------------+
| text                 |
+----------------------+
| ## Parallel to these |
+----------------------+
```

### Type of change

- [X] Bug Fix (non-breaking change which fixes an issue)
- [X] New Feature (non-breaking change which adds functionality)
- [X] Refactoring
 2026-05-25 12:12:57 +08:00
dripsmvcp
8d8ea71877 Go: implement provider: Tencent Hunyuan (#15092) 
## Summary
- Adds a `Hunyuan` Go driver so the new API server can route Tencent
Hunyuan chat instances (registered in `conf/llm_factories.json:3830` as
`Tencent Hunyuan`). Follows the same SaaS-driver shape used for
Astraflow, Avian, Novita, TogetherAI, Replicate, DeepInfra, Upstage, and
LongCat.

Closes #15087

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-25 11:04:39 +08:00
ghost
f9ce07ced1 feat(go-models): add Groq provider driver (#15097) 
### What problem does this PR solve?

Closes #15088.

Adds Groq support to the Go model-provider layer so Groq instances can
be routed through the Go API server with the same OpenAI-compatible
chat, streaming, model listing, and connection-check flow used by other
SaaS providers.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

## Summary

- Added a Groq Go model driver.
- Added the Groq provider catalog and default OpenAI-compatible API URL.
- Registered Groq in the model factory.
- Added focused provider tests.

## What changed

- Implemented chat completions, SSE streaming, ListModels, and
CheckConnection for Groq.
- Covered request shape, stream termination, reasoning fallback, model
listing, custom base URLs, safe transport setup, and unsupported
methods.
- Kept the provider catalog scoped to current Groq chat-capable model
IDs.
- Cleaned up pre-existing Go model package validation blockers so the
package can be tested normally with vet enabled.

## Why

The existing Python/provider catalog path includes Groq, but the Go
model-provider layer did not have a Groq driver, so the Go API server
could not instantiate or use Groq as requested in #15088.

## Notes

The model package now validates without disabling vet.

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-22 15:24:52 +08:00
dripsmvcp
ed04893415 Go: implement provider: TokenPony (#15091) 
## Summary
- Adds a `TokenPony` Go driver so the new API server can route TokenPony
chat instances, matching the existing Python `TokenPonyChat`
(`rag/llm/chat_model.py:1210`). Follows the same SaaS-driver shape used
for Astraflow, Avian, Novita, TogetherAI, Replicate, DeepInfra, Upstage,
and LongCat.

Closes #15086

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-22 15:21:45 +08:00
ghost
b2053cc3c7 feat(go-models): add PPIO provider driver (#15099) 
### What problem does this PR solve?

Closes #15089.

Adds PPIO support to the Go model-provider layer so PPIO instances can
be routed through the Go API server with the same OpenAI-compatible
chat, streaming, model listing, and connection-check flow used by other
SaaS providers.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

## Summary

- Added a PPIO Go model driver.
- Added the PPIO provider catalog and default OpenAI-compatible API URL.
- Registered PPIO in the model factory.
- Added focused provider and provider-manager tests.

## What changed

- Implemented chat completions, SSE streaming, ListModels, and
CheckConnection for PPIO.
- Covered request shape, stream termination, reasoning fallback, model
listing, custom base URLs, safe transport setup, unsupported methods,
and provider config loading.
- Kept the provider catalog aligned with the existing RAGFlow PPIO
factory model set.
- Cleaned up pre-existing Go model package validation blockers so the
scoped provider tests can run normally with vet enabled.

## Why

The existing Python/provider catalog path includes PPIO, but the Go
model-provider layer did not have a PPIO driver, so the Go API server
could not instantiate or use PPIO as requested in #15089.
 2026-05-22 11:52:18 +08:00
BitToby
bd4ce39038 Go: implement provider: Perplexity (#15008) 
## What
- Add Perplexity as a chat and embedding provider backed by its
OpenAI-compatible `/chat/completions` and `/v1/embeddings` APIs
- Register Perplexity in the Go model factory and provider config
- Support non-streaming chat, SSE streaming chat, embeddings, model
listing, and connection checks

Refs #14736

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-21 15:33:02 +08:00
dripsmvcp
d5ba14a128 feat(go): implement provider Astraflow (#15062) (#15064) 
- Adds an `Astraflow` Go driver so the new API server can route
Astraflow (UCloud ModelVerse) chat instances, matching the existing
Python `AstraflowChat` (`rag/llm/chat_model.py:1237`). Follows the same
SaaS-driver shape used for Avian, Novita, TogetherAI, Replicate,
DeepInfra, Upstage, and LongCat.

Closes #15062

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-21 15:32:56 +08:00
dripsmvcp
5a18df0fd0 Go: implement provider: Avian (#15045) 
Closes #15044.

Avian was listed unchecked in the Go-rewrite tracker #14736 and already
had an llm_factories.json entry with 4 preconfigured chat models
(deepseek-v3.2, kimi-k2.5, glm-5, minimax-m2.5), but the Go API server
had no driver to route them. The Python side has supported Avian at
rag/llm/chat_model.py:1220 (AvianChat) via the LiteLLM openai/ provider
with default base https://api.avian.io/v1.

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-21 15:32:49 +08:00
web-dev0521
2d3a1a4483 feat(go-models): add Azure OpenAI model driver (#15022) 
## What problem does this PR solve?

Closes #15021.

The Go model-provider layer had no support for **Azure OpenAI**. Azure
OpenAI is *not* a drop-in base-URL swap of the OpenAI driver — it
differs in authentication, endpoint structure, and how models are listed
— so it needs its own `ModelDriver` implementation.

## Type of change

- [x] New feature (non-breaking change which adds functionality)

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-21 11:52:56 +08:00
Renzo
c7ac9b7171 Go: implement provider: GPUStack (chat) (#15024) 
### What problem does this PR solve?

Fixes #15023

GPUStack is listed as unchecked in the Go-rewrite tracker #14736, and
`internal/service/llm.go:171` already classifies it as a self-deployed
provider alongside Ollama, Xinference, LocalAI, and LM Studio — but
`internal/entity/models/` had no `gpustack.go` driver, so the new Go API
server could not route GPUStack instances. This PR adds the chat surface
for GPUStack so it lines up with the existing self-hosted Go drivers.

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-21 11:49:18 +08:00
sxxtony
63db30f0d9 Go: implement provider: n1n.ai (#15010) 
### What problem does this PR solve?

Add a Go driver for **n1n.ai** (https://docs.n1n.ai), one of the
unchecked providers on the umbrella tracking issue #14736. n1n.ai is an
OpenAI-compatible aggregator hosting a 450+ model catalog (GPT, Claude,
Gemini, DeepSeek, Kimi, Qwen, embedding + reranker families) under
`https://api.n1n.ai/v1`.

Until this PR, a tenant who configured `n1n` 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.

---------

Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
 2026-05-21 10:13:15 +08:00
Haruko386
4a91ca5349 Go: implement provider: MinerU_Local (#15051) 
### What problem does this PR solve?
1. Add model types when add model
---
```
RAGFlow(user)> add model 'pipeline' to provider 'mineru_local' instance 'test' with tokens 131072 doc_parse;
SUCCESS
```

2. implement provider: MinerU_Local
---
**Verified from CLI**
```
RAGFlow(user)> parse with 'pipeline@test@mineru_local' file './internal/test.pdf'
+--------------------------------------+
| task_id                              |
+--------------------------------------+
| c7260e31-b6e2-4b36-955d-e9c60510c669 |
+--------------------------------------+
RAGFlow(user)> show 'test@mineru_local' task 'c7260e31-b6e2-4b36-955d-e9c60510c669'
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| content                                                                                                                                                                                                                                                          | index |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| # Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Bingxin Ke Anton Obukhov Shengyu Huang Nando Metzger Rodrigo Caye Daudt Konrad Schindler Photogrammetry and Remote Sensing, ETH Zurich ¨

![](images/ae256101419715b544d13722...  | 1     |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
```

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
 2026-05-20 19:21:57 +08:00
Haruko386
2836a934b5 Go: implement provider: 302.AI and JieKou-AI (#15034) 
### What problem does this PR solve?

This PR implement implement provider 302.AI and JieKouAI

**The following functionalities are now supported:**

**302.ai**

- [x] chat / think chat / stream chat / stream think chat
- [x] Embedding
- [x] ASR
- [x] ListModels
- [x] Provider connection checking
- [x] Balance
- [x] Rerank
- [x] OCR
- [x] Doc Parse
- [x] Show task 
- [ ]  ~~List Tasks!~~
- [ ] TTS

**JieKouAI**

- [x] chat / think chat / stream chat / stream think chat
- [x] Embedding
- [x] Rerank
- [x] ListModels

**Verified examples from the CLI:**
```palintext
# jiekouAI

RAGFlow(user)> stream think chat with 'zai-org/glm-4.5@test@jiekouai' message 'Hi'
Thinking: Let me think about how to respond to this simple greeting. The user just said "Hi", which is a basic and friendly way to start a conversation. I should respond in a similarly warm and welcoming manner.First, I need to acknowledge their greeting and reciprocate with enthusiasm. Something like "Hello!" or "Hi there!" would work well to create a positive atmosphere right from the start.Next, I should make it clear that I'm ready to help. Since they haven't asked anything specific yet, I'll keep it open-ended and inviting. Perhaps offering assistance with a question or task would encourage them to engage further.I should also maintain a professional yet approachable tone. Being an AI assistant, I want to convey that I'm knowledgeable and capable, but also friendly and easy to talk to.Let me put this all together into a concise response. I'll start with a cheerful greeting, express my readiness to help, and finish with an open invitation for them to share what's on their mind. This should create a welcoming environment for whatever they want to discuss next.
Answer: ! I'm Claude, an AI assistant created by Anthropic. I'm here to help you with information, answer questions, or assist you with tasks. What can I help you with today?

RAGFlow(user)> think chat with 'zai-org/glm-4.5@test@jiekouai' message 'Hi'
Thinking: Let me consider how to respond to this greeting. The user initiated with a simple "Hi," so a friendly and open response would be most appropriate to encourage further conversation. I should maintain a welcoming tone while offering assistance.

The response should accomplish a few key things: return the greeting warmly, show openness to conversation, and offer specific ways I can help. This approach demonstrates both approachability and usefulness.

I'll start with a greeting in return, then express my availability to help, and finish by suggesting some areas where I can provide assistance. This creates a natural flow from acknowledgment to support.

It's important to keep the response concise but inviting. Since the user hasn't specified their needs yet, I'll present a few broad categories of assistance to spark their thinking about what they might want to discuss or ask about.

The response should end with an encouraging note that prompts them to share what's on their mind, keeping the conversational ball in their court while making it clear I'm ready to engage with whatever they need.
Answer: Hello! How can I help you today? Whether you have questions, need information, or just want to chat, I'm here to assist.

RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'text-embedding-3-large@test@jiekouai' dimension 16
+-----------+-------+
| dimension | index |
+-----------+-------+
| 3072      | 0     |
| 3072      | 1     |
+-----------+-------+

RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'baai/bge-reranker-v2-m3@test@jiekouai' top 3
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0     | 0.9830034       |
| 2     | 0.06399203      |
| 1     | 0.04665664      |
+-------+-----------------+


# 302.ai

RAGFlow(user)> think chat with 'kimi-k2.6@test@302.ai' message 'who r u'
Thinking: The user is asking "who r u" which is a casual way of asking "who are you." I need to identify myself as an AI assistant created by Moonshot AI. I should be friendly, concise, and helpful.

Key points to include:
- I am Kimi, an AI assistant made by Moonshot AI
- I can help with various tasks like answering questions, writing, analysis, coding, etc.
- Keep it casual but informative since the user used "r u" (text speak)

I should not:
- Pretend to be human
- Claim to have personal experiences or emotions
- Be overly formal or robotic

Simple, friendly response is best.
Answer: I'm Kimi, an AI assistant made by Moonshot AI. I can help you with answering questions, writing, coding, analysis, or just chatting. What can I do for you?
Time: 17.687750

RAGFlow(user)> stream think chat with 'kimi-k2.6@test@302.ai' message 'who r u'
Thinking:  user asked "who r u" which is a casual way of asking "who are you." I should introduce myself as Kimi, an AI assistant developed by Moonshot AI. I need to be friendly, concise, and accurate. I should mention my capabilities briefly and keep the tone helpful. Since the user used casual text speak ("r u"), I can match that energy with a friendly but still informative tone.Key points:- I'm Kimi, an AI assistant made by Moonshot AI- I can help with various tasks like answering questions, writing, coding, analysis, etc.- Keep it brief but warm- Don't claim to be human- Don't over-explainDraft:"I'm Kimi, an AI assistant created by Moonshot AI. I can help with answering questions, writing, coding, analysis, brainstorming, and lots of other tasks. What can I do for you?"This is good - direct, accurate, and inviting.
Answer:  Kimi, an AI assistant made by Moonshot AI. I can help with answering questions, writing, coding, analysis, brainstorming, and lots of other stuff. What can I do for you?
Time: 14.912576

RAGFlow(user)> asr with 'whisper-v3-turbo@test@302.ai' audio './internal/test.wav' param ''
+---------------------------------------------------------------------------------------------------------------------+
| text                                                                                                                |
+---------------------------------------------------------------------------------------------------------------------+
| The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired |
+---------------------------------------------------------------------------------------------------------------------+

RAGFlow(user)> ocr with 'mistral-ocr-latest@test@302.ai' file './internal/test.pdf'
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| text                                                                                                                                                                                                                                                             |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| # Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Bingxin Ke

Nando Metzger

Anton Obukhov

Rodrigo Caye Daudt

Shengyu Huang

Konrad Schindler

Photogrammetry and Remote Sensing, ETH Zürich

![img-0.jpeg](img-0.jpeg)
Figur...  |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


RAGFlow(user)> parse with 'vlm@test@302.ai' file 'https://arxiv.org/pdf/2505.09358'
+--------------------------------------+
| task_id                              |
+--------------------------------------+
| 6de6eae6-c122-4b67-91e8-b061a0b8c087 |
+--------------------------------------+
RAGFlow(user)> show 'test@302.ai' task '6de6eae6-c122-4b67-91e8-b061a0b8c087'
+----------------------------------------------------------------------------+-------+
| content                                                                    | index |
+----------------------------------------------------------------------------+-------+
| https://file.302.ai/gpt/imgs/20260519/b340fdff4774699c287fe4ee4658b317.zip | 0     |
+----------------------------------------------------------------------------+-------+

RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'jina-embeddings-v3@test@302.ai' dimension 16
+-----------+-------+
| dimension | index |
+-----------+-------+
| 1024      | 0     |
| 1024      | 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@302.ai' top 3;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0     | 0.74167407      |
| 2     | 0.18832397      |
| 1     | 0.15713684      |
+-------+-----------------+
```


### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
 2026-05-20 14:10:15 +08:00
tmimmanuel
243d9ed281 Add TogetherAI chat provider (#14957) 
## What
- Add TogetherAI as a chat provider backed by its OpenAI-compatible
`/v1/chat/completions` API
- Register TogetherAI in the Go model factory and provider config
- Support non-streaming chat, SSE streaming chat, model listing, and
connection checks

## Notes
- Uses the current TogetherAI OpenAI-compatible base URL
`https://api.together.ai/v1`
- Forwards documented chat parameters from `ChatConfig`: `max_tokens`,
`temperature`, `top_p`, `stop`, and GPT-OSS `reasoning_effort`
- Routes Together reasoning traces from `reasoning` /
`reasoning_content` into `ReasonContent`

## Tests
- `go test -vet=off -run TestTogetherAI -count=1
./internal/entity/models`
- `go test -vet=off -count=1 ./internal/entity/models`

Refs #14736
 2026-05-19 15:10:42 +08:00
tmimmanuel
09a06f1b00 Go: implement provider: Xinference (#14938) 
### What problem does this PR solve?

Closes #14808.

Adds a Go model driver for Xinference so self-hosted Xinference chat
models can be used through the Go provider layer instead of falling
through to the dummy driver. Xinference exposes an OpenAI-compatible API
under `/v1`; the driver accepts either a root endpoint such as
`http://127.0.0.1:9997` or an OpenAI-compatible endpoint such as
`http://127.0.0.1:9997/v1` and normalizes it before calling chat or
model-listing routes.

### What is changed?

- Add `internal/entity/models/xinference.go` implementing `ModelDriver`
for Xinference chat.
- Route provider name `xinference` in
`internal/entity/models/factory.go`.
- Add `conf/models/xinference.json` as a local provider config.
- Add focused unit tests in `internal/entity/models/xinference_test.go`.

Initial method coverage:

- `ChatWithMessages`: POST `/v1/chat/completions`.
- `ChatStreamlyWithSender`: SSE streaming from `/v1/chat/completions`.
- `ListModels`: GET `/v1/models`.
- `CheckConnection`: lightweight `ListModels` probe.
- Optional auth: send `Authorization: Bearer <api_key>` only when a
non-empty key is configured, matching Xinference no-auth and
auth-enabled deployments.
- `Balance`, `Embed`, `Rerank`, ASR, TTS, and OCR return `no such
method` for this initial chat-provider PR.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Bug Fix (non-breaking change which fixes an issue)

### Tests

- `go test -vet=off -run TestXinference -count=1
./internal/entity/models/...`
- `go test -vet=off -count=1 ./internal/entity/models/...`

### References

- Xinference docs:
https://inference.readthedocs.io/zh-cn/latest/index.html
- OpenAI-compatible chat usage:
https://inference.readthedocs.io/zh-cn/latest/getting_started/using_xinference.html
- API key auth:
https://inference.readthedocs.io/zh-cn/latest/user_guide/auth_system.html

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-19 15:10:13 +08:00
tmimmanuel
4c9529ef36 Add Replicate chat provider (#14958) 
## What
- Add Replicate as a chat provider backed by the documented predictions
API
- Register Replicate in the Go model factory and provider config
- Support non-streaming chat through sync predictions, polling fallback,
streaming through `urls.stream`, model listing, and connection checks

## Notes
- Uses `POST /v1/predictions` with Replicate model identifiers in
`version`, which supports official and community model identifiers
- Maps RAGFlow messages into Replicate prompt-shaped inputs (`prompt`,
optional `system_prompt`) and forwards common documented LLM inputs:
`max_new_tokens`, `temperature`, `top_p`
- Preserves whitespace in SSE output chunks and emits RAGFlow `[DONE]`
at stream completion

## Tests
- `go test -vet=off -run TestReplicate -count=1
./internal/entity/models`
- `go test -vet=off -count=1 ./internal/entity/models`

Refs #14736
 2026-05-19 11:10:36 +08:00
Haruko386
db9e782747 Go: implement provider: MinerU (#14990) 
### What problem does this PR solve?

Implement MinerU Provider

**The following functionalities are now supported:**

**MinerU**
----
- [x] Parse file
- [x] Show task
- [ ] ~~List tasks~~

**Verified examples from the CLI:**
```plaintext
RAGFlow(user)> parse with 'vlm@test@mineru' file 'https://arxiv.org/pdf/2505.09358'
+--------------------------------------+
| task_id                              |
+--------------------------------------+
| 142ac8ea-d9d0-4a68-a2d1-d3af67635dc9 |
+--------------------------------------+

RAGFlow(user)> show 'test@mineru' task '142ac8ea-d9d0-4a68-a2d1-d3af67635dc9'
+--------------------------------------------+-------+
| content                                    | index |
+--------------------------------------------+-------+
| Task is running... Progress: 17 / 18 pages | 0     |
+--------------------------------------------+-------+

RAGFlow(user)> show 'test@mineru' task '142ac8ea-d9d0-4a68-a2d1-d3af67635dc9'
+--------------------------------------------------------------------------------------------+-------+
| content                                                                                    | index |
+--------------------------------------------------------------------------------------------+-------+
| https://cdn-mineru.openxlab.org.cn/pdf/2026-05-18/142ac8ea-d9d0-4a68-a2d1-d3af67635dc9.zip | 0     |
+--------------------------------------------------------------------------------------------+-------+

```


### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
 2026-05-19 10:49:33 +08:00
Haruko386
92145dc764 Go: implement provider: DeepInfra, XunFei (#14978) 
### What problem does this PR solve?

This PR implement implement provider  and Mistral, DeepInfra, XunFei

**The following functionalities are now supported:**

**DeepInfra**

- [x] chat / think chat / stream chat / stream think chat
- [x] Embedding
- [x] ASR
- [x] TTS
- [x] ListModels
- [x] Provider connection checking
- [x] Balance
- [ ] ~~Rerank~~

**XunFei**

- [x] chat / think chat / stream chat / stream think chat

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
 2026-05-18 16:57:42 +08:00
tmimmanuel
b09da6e347 Go: implement provider: CometAPI (#14930) 
### What problem does this PR solve?

Adds the Go model provider driver for CometAPI, which is listed as
unchecked in the Go provider tracking issue #14736 and requested in
#14804. Without this, the Go layer falls back to the dummy driver for
the `cometapi` provider.

Fixes #14804

### What this PR includes

- New `internal/entity/models/cometapi.go` implementing `ModelDriver`
for CometAPI.
- New `conf/models/cometapi.json` with CometAPI base URLs and
representative chat / embedding models from the public catalog.
- `factory.go`: route `"cometapi"` to `NewCometAPIModel`.
- Unit tests in `internal/entity/models/cometapi_test.go`.

### Method coverage

- `ChatWithMessages`: `POST /v1/chat/completions`.
- `ChatStreamlyWithSender`: SSE streaming on the same endpoint.
- `Embed`: `POST /v1/embeddings`, including optional `dimensions`.
- `ListModels`: `GET /api/models` public catalog.
- `Balance`: `GET https://query.cometapi.com/user/quota?key=...`.
- `CheckConnection`: delegates to the quota query to verify the key.
- `Rerank`, ASR, TTS, OCR: return `no such method` for now.

No ModelDriver interface change. No new dependencies.

### How was this tested?

```bash
go test -vet=off -run TestCometAPI -count=1 ./internal/entity/models/...
go test -vet=off -count=1 ./internal/entity/models/...
```

---------

Signed-off-by: dependabot[bot] <support@github.com>
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Signed-off-by: majiayu000 <1835304752@qq.com>
Co-authored-by: 加帆 <Jiafan@users.noreply.github.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: bulexu <baiheng527@gmail.com>
Co-authored-by: xubh <xubh@wikiflyer.cn>
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: Carve_ <75568342+Rynzie02@users.noreply.github.com>
Co-authored-by: Paul Y Hui <paulhui@seismic.com>
Co-authored-by: LIRUI YU <128563231+LiruiYu33@users.noreply.github.com>
Co-authored-by: yun.kou <koopking@gmail.com>
Co-authored-by: Yun.kou <yunkou@deepglint.com>
Co-authored-by: Ahmad Intisar <168020872+ahmadintisar@users.noreply.github.com>
Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
Co-authored-by: chanx <1243304602@qq.com>
Co-authored-by: Syed Shahmeer Ali <syedshahmeerali196@gmail.com>
Co-authored-by: Octopus <liyuan851277048@icloud.com>
Co-authored-by: lif <1835304752@qq.com>
 2026-05-18 14:31:16 +08:00
carlos4s
2eba2c4d75 Add Anthropic Go model provider (#14940) 
### What problem does this PR solve?

Adds the missing Anthropic provider implementation for the Go model
provider layer.

Closes #14939

### What changed

- Add `conf/models/anthropic.json` with Anthropic Claude chat/vision
models and API endpoints.
- Add `internal/entity/models/anthropic.go` implementing non-streaming
Messages API chat, model listing, and connection checking.
- Register `anthropic` in the Go model factory.
- Add httptest coverage for headers, payload mapping, response parsing,
validation errors, provider errors, model listing, connection checking,
factory registration, and unsupported methods.

### Notes

Streaming chat is left as an explicit `no such method` follow-up because
this initial provider focuses on non-streaming chat and connection
checking.

### Tests

- `docker run --rm -v
/home/ubuntu/Documents/gitTensor_repos/carlos/ragflow:/work -v
/tmp/ragflow-go-cache:/go/pkg/mod -v
/tmp/ragflow-go-build:/root/.cache/go-build -w /work golang:1.25 go test
-vet=off ./internal/entity/models -run Anthropic -count=1 -v`
- `docker run --rm -v
/home/ubuntu/Documents/gitTensor_repos/carlos/ragflow:/work -v
/tmp/ragflow-go-cache:/go/pkg/mod -v
/tmp/ragflow-go-build:/root/.cache/go-build -w /work golang:1.25 go test
-vet=off ./internal/entity -count=1`
- `git diff --check`
- `jq . conf/models/anthropic.json >/dev/null`

Plain `go test ./internal/entity/models` currently hits pre-existing
unrelated vet findings in other provider files (`baidu.go`, `cohere.go`,
`fishaudio.go`, `openrouter.go`).

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-18 12:03:33 +08:00
Haruko386
bf41d35729 Go: implement PaddleOCR provider and implement ASR for CoHere (#14954) 
### What problem does this PR solve?

This PR implement implement OCR for Baidu and Mistral, implement
PaddleOCR provider and implement ASR for CoHere

**Verified examples from the CLI:**

```
RAGFlow(user)> ocr with 'mistral-ocr-2512@test@mistral' file './internal/text.jpg'
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| text                                                                                                                                                                                                                                                             |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Parallel to these organizational innovations there were significant complementary technical innovations (e.g., improved methods of manufacturing cast-iron pipe and of coating interiors for pressure maintenance, and newer paving and construction material... |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


RAGFlow(user)> ocr with 'paddleocr-vl-0.9b@test@baidu' file './internal/text.jpg'
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| text                                                                                                                                                                                                                                                             |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Parallel to these organizational innovations there were significant complementary technical innovations (e.g., improved methods of manufacturing cast-iron pipe and of coating interiors for pressure maintenance, and newer paving and construction material... |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

# PaddleOCR
RAGFlow(user)> ocr with 'PaddleOCR-VL-1.5@test@paddleocr' file './internal/test.pdf'
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| text                                                                                                                                                                                                                                                             |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| # Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Bingxin Ke

Nando Metzger

Photogra

Anton Obukhov

Rodrigo Caye Daudt

netry and Remote Sensing,

Shengyu Huang

Konrad Schindler

ETH Zürich





<div style="text-align: c...  |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

# Cohere

RAGFlow(user)> asr with 'cohere-transcribe-03-2026@test@cohere' audio './internal/test.wav' param '{"language": "en"}'
+-----------------------------------------------------------------------------------------------------------------------+
| text                                                                                                                  |
+-----------------------------------------------------------------------------------------------------------------------+
|  The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired. |
+-----------------------------------------------------------------------------------------------------------------------+
```

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
 2026-05-15 18:41:43 +08:00
tmimmanuel
cb01529d8b Go: implement provider: Voyage AI (#14811) 
### What problem does this PR solve?

Add a Go driver for Voyage AI (https://voyageai.com), one of the
unchecked providers on the umbrella tracking issue #14736. Voyage AI is
**embed + rerank only** — no chat, no streaming, no `/v1/models`
endpoint. It's the first provider in the Go layer of this shape.

Until this PR, a tenant who configured `voyage` 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/voyage.go` with a `VoyageModel`
implementing the `ModelDriver` interface.
- New `conf/models/voyage.json` with 6 embedding models (`voyage-3.5`,
`voyage-3.5-lite`, `voyage-3-large`, `voyage-code-3`, `voyage-law-2`,
`voyage-finance-2`) and 2 rerank models (`rerank-2`, `rerank-2-lite`).
- `factory.go`: route `"voyage"` to `NewVoyageModel`.
- `internal/entity/models/voyage_test.go`: 19 unit tests.

### How the driver works

- **Embed**: `POST /v1/embeddings`. Response is OpenAI-shaped (`{data:
[{embedding, index, object, text}], model, usage}`). Driver reorders by
`index`, rejects duplicate / out-of-range / missing slots, and
short-circuits empty input without an HTTP call.
- **Rerank**: `POST /v1/rerank`. Voyage uses **`top_k`** as the request
param name (not `top_n` like Aliyun/SiliconFlow); the driver translates
`RerankConfig.TopN` → `top_k`. Response is Cohere-shaped (`{data:
[{relevance_score, index}], model}`), so the existing
`RerankResponse{Data: []RerankResult{Index, RelevanceScore}}` shape fits
cleanly.
- **`ListModels`**: returns a hardcoded list of `voyageKnownModels`.
Voyage does **not** expose `/v1/models` (probed live, returns 404), so
the driver synthesizes the list from the same set the config ships. New
upstream models are added by extending one slice.
- **`CheckConnection`**: pings a 1-input embed call against
`voyage-3.5`. Without `/v1/models`, this is the cheapest way to verify
the API key + network path before a tenant tries a real workload.
- **`ChatWithMessages` / `ChatStreamlyWithSender` / `Balance` /
`TranscribeAudio` / `AudioSpeech` / `OCRFile`**: all return `"no such
method"`. Voyage does not host any of these surfaces.

No interface change. No new dependencies.

### How was this tested?

**19 unit tests** in `internal/entity/models/voyage_test.go` — all pass
on go 1.25:

```
$ go test -vet=off -run TestVoyage -count=1 ./internal/entity/models/...
ok      ragflow/internal/entity/models  0.036s
```

Coverage: Name; Embed (happy path, reorder, empty-input, missing
key/model, duplicate index, out-of-range index, missing slot); Rerank
(happy path with `top_k` assertion, default-to-len-documents, empty
documents, out-of-range index); ListModels (static list, missing key);
CheckConnection (happy, 401); chat methods sentinels; Balance sentinel;
audio/OCR sentinels.

`go build ./internal/entity/models/...` exits 0.

**Live integration test** against `api.voyageai.com`:

```
=== RUN   TestVoyageLiveSmoke
    [OK] Name() = "voyage"
    [OK] ListModels (static): 8 models -> [voyage-3.5 voyage-3.5-lite voyage-3-large voyage-code-3 voyage-law-2 voyage-finance-2 rerank-2 rerank-2-lite]
    [OK] CheckConnection
    [OK] Embed vectors=3 dim=1024 indices=[0 1 2]
    [OK] Embed(empty) -> 0 vectors
    [OK] Rerank results=3 scores=[0.8125 0.59765625 0.39453125]
    [OK] ChatWithMessages returns voyage, no such method
    [OK] Balance returns voyage, no such method

VOYAGE LIVE SMOKE PASSED
--- PASS: TestVoyageLiveSmoke (0.81s)
```

What the live run proves on the wire:

- Auth (`Bearer <key>`) accepted by `api.voyageai.com`.
- Embed `voyage-3.5` on 3 inputs returns 3 vectors at dim 1024 with
`index` field preserved as `[0, 1, 2]` — the reorder-by-index code is
exercised on real data.
- Empty input short-circuits without an HTTP call (mock server would
have been hit if it did).
- Rerank `rerank-2` on 3 docs returns 3 real `relevance_score` floats
`[0.8125, 0.598, 0.395]`. The `top_k` translation works on the live
wire.
- All sentinel methods return the documented `"no such method"` strings.

### Note on PR history

This branch was previously named for LocalAI Embed work which is now
consolidated into PR #14813. The branch was reset to `upstream/main` and
rebuilt for Voyage. Diff against `main` is a clean +838 lines across 4
files.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

Tracking: #14736

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-14 09:46:54 +08:00
tmimmanuel
0a4b733b2a Go: implement Rerank in LocalAI driver (#14813) 
### What problem does this PR solve?

The LocalAI Go driver landed in #14809 and Embed landed in #14811.
`Rerank` was left as a stub that returns `"not implemented"`. This PR
fills the gap.

LocalAI exposes a public rerank endpoint at `<tenant-url>/v1/rerank`
with a Cohere-shaped request and response (`{model, query, documents,
top_n}` → `{results: [{index, relevance_score}]}`). The Python side has
had `LocalAIRerank` in `rag/llm/rerank_model.py` for a long time. Until
this PR, a tenant who wanted to use LocalAI for reranking in the Go
layer got `"not implemented"`.

### What this PR includes

- `conf/models/localai.json`: add `"rerank": "rerank"` under
`url_suffix` so the driver can build the URL from config. This matches
the `URLSuffix.Rerank` field already used by aliyun and siliconflow.
- `internal/entity/models/localai.go`: replace the `Rerank` stub with a
real implementation that POSTs to `/v1/rerank`. Adds local
request/response types `localAIRerankRequest` and
`localAIRerankResponse`.

No factory change. No interface change.

### How the implementation works

- Validate the model name and resolve the tenant-supplied base URL with
the existing `resolveBaseURL` helper.
- Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so
the call has a clear deadline. Same pattern `ChatWithMessages`,
`ListModels`, and `Embed` already use in this file.
- Only set the `Authorization` header when a non-empty API key was
supplied. LocalAI accepts an empty key by default, so this preserves the
optional-auth contract.
- Default `top_n` to `len(documents)` when `rerankConfig.TopN == 0`,
matching the existing Aliyun and SiliconFlow rerank implementations.
- Validate every `results[].index` against `len(documents)`. If the
upstream returns an out-of-range index, fail clearly instead of silently
writing past the slice.
- An empty `documents` slice returns `&RerankResponse{}` with no HTTP
call.
- Non-200 responses propagate the upstream status line and body.

### Note on stacking

This PR builds on #14809 (LocalAI driver) and #14811 (LocalAI Embed).
Until both merge, this PR's diff on GitHub will include all three
commits. After #14809 and #14811 land on `main`, GitHub will auto-reduce
this PR to only the `Rerank` changes (one commit, ~99 line diff in
`localai.go` plus 1 line in `localai.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 `LocalAIModel` still matches the `ModelDriver`
interface.
- Pattern parity with the existing Aliyun Rerank
(`internal/entity/models/aliyun.go`) and SiliconFlow Rerank
(`internal/entity/models/siliconflow.go`) implementations.

Closes #14812
Depends on #14809, #14811
Tracking: #14736

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-13 19:35:19 +08:00
tmimmanuel
8b53960819 Go: implement provider: LongCat (#14809) 
### What problem does this PR solve?

Add a Go driver for LongCat (Meituan, https://longcat.chat), one of the
unchecked providers on the umbrella tracking issue #14736. LongCat
exposes an OpenAI-compatible REST API at
`https://api.longcat.chat/openai/v1` with three public chat models
including `LongCat-Flash-Thinking`, a reasoning model that returns
chain-of-thought in `reasoning_content` (OpenAI o-series shape).

Until this PR, a tenant who configured `longcat` 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/longcat.go` with a `LongCatModel`
implementing the `ModelDriver` interface.
- New `conf/models/longcat.json` with the 3 public chat models
(Flash-Chat, Flash-Lite, Flash-Thinking) and `url_suffix` for `chat` and
`models`.
- `factory.go`: route `"longcat"` to `NewLongCatModel`.

Method coverage:

- `ChatWithMessages`: `POST /openai/v1/chat/completions`, non-streaming
- `ChatStreamlyWithSender`: SSE stream against the same endpoint
- `ListModels` / `CheckConnection`: `GET /openai/v1/models`
- **Reasoning extraction**: `message.reasoning_content` (non-stream) and
`delta.reasoning_content` (stream) flow into
`ChatResponse.ReasonContent` / the sender's second arg. Matches the
OpenAI o-series convention also used by kimi-k2.6 and DeepSeek-R1.
- **`reasoning_effort` propagation**: `ChatConfig.Effort` → request body
`reasoning_effort` (LongCat-Flash-Thinking honors it; non-reasoning
models ignore it).
- `Embed` / `Rerank` / `Balance` / `TranscribeAudio` / `AudioSpeech` /
`OCRFile` return `"no such method"` (LongCat does not expose any of
these surfaces).

No interface change. No new dependencies.

### How was this tested?

**21 unit tests** in `internal/entity/models/longcat_test.go` — all
pass:

```
$ go test -vet=off -run TestLongCat -count=1 -v ./internal/entity/models/...
=== RUN   TestLongCatName
--- PASS: TestLongCatName (0.00s)
=== RUN   TestLongCatChatHappyPath
--- PASS: TestLongCatChatHappyPath (0.00s)
=== RUN   TestLongCatChatExtractsReasoningContent
--- PASS: TestLongCatChatExtractsReasoningContent (0.00s)
=== RUN   TestLongCatChatPropagatesReasoningEffort
--- PASS: TestLongCatChatPropagatesReasoningEffort (0.00s)
=== RUN   TestLongCatChatOmitsReasoningEffortWhenUnset
--- PASS: TestLongCatChatOmitsReasoningEffortWhenUnset (0.00s)
=== RUN   TestLongCatChatRequiresAPIKey
--- PASS: TestLongCatChatRequiresAPIKey (0.00s)
=== RUN   TestLongCatChatRequiresMessages
--- PASS: TestLongCatChatRequiresMessages (0.00s)
=== RUN   TestLongCatChatRejectsHTTPError
--- PASS: TestLongCatChatRejectsHTTPError (0.00s)
=== RUN   TestLongCatStreamHappyPath
--- PASS: TestLongCatStreamHappyPath (0.00s)
=== RUN   TestLongCatStreamExtractsReasoningContent
--- PASS: TestLongCatStreamExtractsReasoningContent (0.00s)
=== RUN   TestLongCatStreamRejectsExplicitFalse
--- PASS: TestLongCatStreamRejectsExplicitFalse (0.00s)
=== RUN   TestLongCatStreamRequiresSender
--- PASS: TestLongCatStreamRequiresSender (0.00s)
=== RUN   TestLongCatStreamFailsWithoutTerminal
--- PASS: TestLongCatStreamFailsWithoutTerminal (0.00s)
=== RUN   TestLongCatListModelsHappyPath
--- PASS: TestLongCatListModelsHappyPath (0.00s)
=== RUN   TestLongCatListModelsRequiresAPIKey
--- PASS: TestLongCatListModelsRequiresAPIKey (0.00s)
=== RUN   TestLongCatCheckConnectionDelegatesToListModels
--- PASS: TestLongCatCheckConnectionDelegatesToListModels (0.00s)
=== RUN   TestLongCatEmbedReturnsNoSuchMethod
--- PASS: TestLongCatEmbedReturnsNoSuchMethod (0.00s)
=== RUN   TestLongCatRerankReturnsNoSuchMethod
--- PASS: TestLongCatRerankReturnsNoSuchMethod (0.00s)
=== RUN   TestLongCatBalanceReturnsNoSuchMethod
--- PASS: TestLongCatBalanceReturnsNoSuchMethod (0.00s)
=== RUN   TestLongCatAudioOCRReturnNoSuchMethod
--- PASS: TestLongCatAudioOCRReturnNoSuchMethod (0.00s)
PASS
ok      ragflow/internal/entity/models  0.020s
```

`go build ./internal/entity/models/...` exits 0 on go 1.25.

**Live integration test** against `api.longcat.chat`:

```
=== RUN   TestLongCatLiveSmoke
    [OK] Name() = "longcat"
    [OK] CheckConnection
    [OK] ListModels: 5 models -> [LongCat-Flash-Lite LongCat-Flash-Chat LongCat-Flash-Thinking-2601 LongCat-Flash-Omni-2603 LongCat-2.0-Preview]
    [OK] Chat (Flash-Chat) answer="Got it! Let me know if you" reason=""
    [OK] Chat (Flash-Thinking) answer len=443 head="To find 15 % of 80, follow these steps:\n\n1. **Convert the percentage to a frac..."
                  ReasonContent len=557 head="The user asks: \"15% of 80?\" They want step by step reasoning and final answer in \\boxed{}. So we need to compute 15% of ..."
    [OK] Stream content: 78 chunks, 351 chars
    [OK] Stream reasoning: 107 chunks, 537 chars
    [OK] Balance returns longcat, no such method
    [OK] Embed returns longcat, no such method
    [OK] Rerank returns longcat, no such method

LONGCAT LIVE SMOKE PASSED
--- PASS: TestLongCatLiveSmoke (31.01s)
```

What the live run proves on the wire:

- Auth header (`Bearer <key>`) is accepted by `api.longcat.chat`.
- `/openai/v1/models` parser handles the real 5-model response (note:
live API returns versioned aliases `LongCat-Flash-Thinking-2601`,
`LongCat-Flash-Omni-2603`, `LongCat-2.0-Preview` plus the un-versioned
`LongCat-Flash-Chat` and `LongCat-Flash-Lite`).
- Non-stream chat against `LongCat-Flash-Chat`: visible answer parses
correctly, `ReasonContent` correctly empty.
- Non-stream chat against `LongCat-Flash-Thinking`: 443-char answer
flows into `Answer`, 557-char chain-of-thought flows into
`ReasonContent` via the new `message.reasoning_content` extraction.
- Streaming chat against `LongCat-Flash-Thinking`: 107 reasoning chunks
(537 chars) reach the sender's second arg via `delta.reasoning_content`;
78 content chunks (351 chars) reach the first arg. Before this code, the
reasoning chunks would have been silently dropped.
- All sentinel methods (Balance, Embed, Rerank, audio/OCR) return the
documented `"no such method"` strings.

### Note on PR history

This branch was previously named for LocalAI work which is now
consolidated into PR #14813. The branch was reset to `upstream/main` and
rebuilt for LongCat. The diff against `main` is a clean +969 lines
across 4 files.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

Tracking: #14736

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-13 16:27:56 +08:00
tmimmanuel
d63d3bb7d2 Go: implement provider: Novita.ai (#14850) 
### 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
 2026-05-13 14:10:50 +08:00
Haruko386
45ee5ca9cd Go: implement provider: Jina (#14838) 
### 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)
 2026-05-12 18:03:05 +08:00
tmimmanuel
7d3836907a Go: implement Embed (embeddings) in Mistral driver (#14807) 
### 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>
 2026-05-12 17:45:48 +08:00
tmimmanuel
eaa2e46b1e Go: implement Embed (embeddings) in Upstage driver (#14819) 
### 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>
 2026-05-12 16:11:06 +08:00
Haruko386
ebab3513c4 Go: implement provider: Baichuan (#14832) 
### 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
 2026-05-12 16:10:32 +08:00
tmimmanuel
558ea51a0f Go: implement provider: StepFun (#14815) 
### 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
 2026-05-12 13:49:35 +08:00
Haruko386
3e90d303e0 Go: implement provider: CoHere and FishAudio (#14790) 
### 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
 2026-05-11 20:18:38 +08:00
Jack Storment
8ff623fbc4 Go: implement Encode (embeddings) in Ollama driver (#14664) 
### What problem does this PR solve?

The Ollama Go driver shipped with a stub \`Encode\` method that returned
\`no such method\`, even though Ollama is one of the most common local
LLM runners and exposes an OpenAI-compatible embeddings endpoint at
\`/v1/embeddings\`.

Ollama users routinely run local embedding models such as
\`nomic-embed-text\`, \`mxbai-embed-large\`, or \`bge-m3\`.
Pulled with \`ollama pull <model>\` and served on the same \`/v1\`
namespace as chat. The existing \`ListModels\` already
discovers them, but because \`Encode\` was a stub, a tenant who picked
one of these models in the Go layer could not
actually run an embedding call.

### What this PR includes

- \`conf/models/ollama.json\`: add \`\"embedding\": \"embeddings\"\`
under \`url_suffix\` so the
  driver can build the URL from config.
- \`internal/entity/models/ollama.go\`: replace the \`Encode\` stub with
a real implementation. Adds a small local response
  type that matches the OpenAI-compatible shape.

No factory change. No interface change.

### How the driver works

- Validate the model name. The API key is optional for local Ollama, so
the Authorization header is only set when both
\`apiConfig\` and \`ApiKey\` are non-nil and non-empty, the same pattern
the recently merged CheckConnection PR (#14614) uses.
- Resolve the region with a default fallback. Return a clear "missing
base URL" error when the user has not configured
  the local access address yet.
- Use a per-call \`context.WithTimeout(30s)\` and
\`http.NewRequestWithContext\`, the same pattern the merged
  Aliyun Encode (#14647) uses.
- Send \`{model, input: [texts]}\` in one request.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\`, so the output
  order matches the input order.
- Handle both \`float64\` and \`float32\` element types.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Length mismatch between input and result, out-of-range index, and any
missing slot all return clear errors instead
  of silent zero vectors.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

### How was this tested?

- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`OllamaModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the merged Aliyun Encode (#14647) and the existing
SiliconFlow Encode.

Closes #14662
 2026-05-11 12:50:15 +08:00
Panda Dev
6bfe0f9a10 Go: implement Encode (embeddings) in OpenAI driver (#14630) 
### What problem does this PR solve?

The OpenAI Go driver landed in #14605 with chat, list models, and check
connection. Encode was left as a stub that returns \`not implemented\`.

\`conf/models/openai.json\` already lists three embedding models out of
the box:

- text-embedding-ada-002
- text-embedding-3-small
- text-embedding-3-large

So a tenant who picked one of these in the Go layer could not actually
run an embedding call. This PR fills the gap.

### What this PR includes

- \`conf/models/openai.json\`: add \`\"embedding\": \"embeddings\"\`
under \`url_suffix\` so the driver can build the URL from config. This
matches the \`URLSuffix.Embedding\` field used by other drivers
(siliconflow, zhipu-ai).
- \`internal/entity/models/openai.go\`: replace the Encode stub with a
real implementation that POSTs to \`/v1/embeddings\`. Adds a small local
response type \`openaiEmbeddingResponse\`.

No factory change. No interface change.

### How the implementation works

- Validate \`apiConfig\` and the API key, validate 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 OpenAI API accepts the
\`input\` field as an array.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\` so the output order matches the input order
even if the API returns items in a different order.
- Handle both \`float64\` and \`float32\` element types, the way the
SiliconFlow driver does.
- An empty input slice returns \`[][]float64{}\` 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 nil, return a clear error so the caller does not silently use a
zero vector.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

### How was this tested?

- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
(the go.mod minimum) returns exit 0.
- The full method set on \`OpenAIModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Encode implementation
(\`internal/entity/models/siliconflow.go\`).

Closes #14629

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-10 10:31:37 +08:00
Haruko386
7931b693dc Go: implement provider: Baidu (#14741) 
### What problem does this PR solve?

This PR completes the Baidu Qianfan provider integration in RAGFlow.

**The following functionalities are now supported:**

- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] Balance

-----

**Verified examples from the CLI:**

```plaintext
RAGFlow(user)> embed text 'what is rag' 'who are you' with 'embedding-3@test@zhipu-ai' dimension 16;
+-----------+-------+
| dimension | index |
+-----------+-------+
| 16        | 0     |
| 16        | 1     |
+-----------+-------+

RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'qwen3-reranker-4b@test@baidu' top 2;
+-------+---------------------+
| index | relevance_score     |
+-------+---------------------+
| 0     | 0.974821150302887   |
| 1     | 0.14223189651966095 |
| 2     | 0.08632347732782364 |
+-------+---------------------+

RAGFlow(user)> think chat with 'deepseek-v3.2@test@baidu' message 'who r u'
Thinking: Hmm, the user is asking for a simple introduction. This is straightforward – no need for overcomplication. 

I should give a clear, friendly response that covers my basic identity as an AI assistant, my purpose, and my capabilities. Keeping it concise but informative is key here. 

Mentioning my creator Anthropic adds credibility, and ending with an offer to help invites further interaction. No need for technical details unless the user asks later.
Answer: Hello! I'm an AI assistant created by Anthropic, designed to help with a wide variety of tasks. You can think of me as a helpful digital companion—I can answer questions, assist with writing, help solve problems, provide explanations, and engage in conversation on many topics. I'm here to help with whatever you need! How can I assist you today?
Time: 8.103902

RAGFlow(user)> stream think chat with 'deepseek-v3.2@test@baidu' message 'who r u'
Thinking: mm, the user is asking "who r u" with casual spelling. This is a straightforward identity question. should give a clear, friendly introduction without overcomplicating it. Can start with my core function as an AI assistant, mention my creator, and briefly state my key capabilities. response should be welcoming and invite further interaction since this seems like an introductory question. Keeping it concise but covering the essentials: who I am, what I do, and how I can help.
Answer: ! I am DeepSeek, an AI assistant created by DeepSeek Company. I'm designed to help answer questions, provide information, assist with various tasks, and engage in conversations on a wide range of topics. I'm here to assist you with whatever you need - whether it's answering questions, helping with analysis, writing, coding, or just having a friendly chat!Is there anything specific I can help you with today? 😊
Time: 7.219703

RAGFlow(user)> list supported models from 'baidu' 'test'
+--------------------------------------+
| model_name                           |
+--------------------------------------+
| ernie-3.5-8k-preview                 |
| ernie-4.0-8k                         |
| ernie-4.0-turbo-8k-latest            |
| ernie-4.0-turbo-8k-preview           |
| ernie-4.0-8k-preview                 |
| ernie-speed-pro-128k                 |
| ernie-char-fiction-8k                |
| ernie-3.5-8k                         |
| ernie-3.5-128k                       |
| ernie-lite-pro-128k                  |
| ernie-novel-8k                       |
| ernie-4.0-turbo-8k                   |
| ernie-4.0-turbo-128k                 |
| ernie-4.0-8k-latest                  |
| irag-1.0                             |
| ...........                          |
| glm-5.1                              |
| ernie-image-turbo                    |
| deepseek-v4-pro                      |
| deepseek-v4-flash                    |
| ernie-5.1                            |
+--------------------------------------+

RAGFlow(user)> check instance 'test' from 'baidu'
SUCCESS
```

Additionally, this PR fixes an incorrect error message typo:

Before:

```go
fmt.Errorf("API requestssss failed with status %d: %s : %s", ...)
```

After:

```go
fmt.Errorf("API request failed with status %d: %s", ...)
```

This PR mainly improves provider compatibility, API completeness, and
runtime stability.

### Type of change

* [x] Bug Fix (non-breaking change which fixes an issue)
* [x] New Feature (non-breaking change which adds functionality)
* [x] Refactoring
 2026-05-09 19:21:13 +08:00
Haruko386
ee0de58204 Go: implement provider: HuggingFace (#14722) 
### What problem does this PR solve?

Implement `HuggingFace` provider

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
 2026-05-09 13:36:03 +08:00
Haruko386
a377512110 Go: implement provider: OpenRouter (#14652) 
### What problem does this PR solve?

1. **Implement `OpenRouter` Provider:** Fully support OpenRouter AI
models (e.g., `gemma`, `minimax`). Includes robust handling of
Server-Sent Events (SSE) streams, error event interception, and proper
parsing of both `reasoning_content` and standard `content`.
2. **Fix BaseURL Resolution Bug:** Fixed a critical edge case in region
configuration parsing. Added a strict empty string check
(`*apiConfig.Region != ""`) alongside the `nil` check. This ensures that
if the UI passes an empty string, the system correctly falls back to the
`"default"` region, preventing `unsupported protocol scheme ""` errors
during HTTP requests.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
 2026-05-08 12:02:37 +08:00
Haruko386
078ea3bf4a Go: implement provider: Nvidia (#14623) 
### What problem does this PR solve?

1. **Implement `Nvidia` Provider:** Fully support NVIDIA NIM APIs with
robust parameter handling (including the `thinking` parameter) and safe
URL merging in `NewInstance`.
2. **Fix Misleading CLI Errors:** Corrected a bug in `common_command.go`
where failed chat requests inaccurately reported `failed to list
instance models`.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
 2026-05-07 14:17:57 +08:00
Panda Dev
b8b741555f Go: implement provider: OpenAI (#14605) 
### What problem does this PR solve?

Add a Go driver for OpenAI (GPT models).

The config file conf/models/openai.json has been in the repo for a while
with the full GPT-5 model list, but
internal/entity/models/factory.go had no case for "openai". So any
tenant that configured OpenAI as a model provider in the Go layer fell
through to the default branch and got the dummy driver. Chat, list
models, and check connection all returned dummy responses instead of
reaching the API.

OpenAI is the most commonly requested provider and the JSON config
already ships with the repo, so this gap is high impact even though the
JSON has been there for some time.

### What this PR includes

- New file internal/entity/models/openai.go with an OpenAIModel that
implements the ModelDriver interface.
- factory.go: route the "openai" provider name to NewOpenAIModel.
- conf/models/openai.json: add "models": "models" under url_suffix so
ListModels can hit /v1/models with no hardcoded fallback.

### How the driver works

- OpenAI exposes the canonical OpenAI-compatible API at
https://api.openai.com/v1.
- ChatWithMessages and ChatStreamlyWithSender post to /chat/completions
in the same shape the moonshot, vllm, and xai drivers use.
- ListModels and CheckConnection call /models to list available ids and
confirm the API key works.
- reasoning_content is passed through for the o-series and other
reasoning models, in both the non-stream and stream paths.
- Encode (embeddings) is left as "not implemented" for now, the same way
the other recent provider drivers do it. Rerank and Balance are not part
of OpenAI's public API surface in this layer and return a clear "not
implemented" or "no such method" error.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

### How was this tested?

- go build ./internal/entity/models/... in a clean go 1.25 image (the
go.mod minimum) returns exit 0 with no errors.
- Method set of OpenAIModel matches the ModelDriver interface:
NewInstance, Name, ChatWithMessages, ChatStreamlyWithSender, Encode,
Rerank, ListModels, Balance, CheckConnection.
- Pattern parity with the merged moonshot (#14433), volcengine (#14460),
minimax (#14478), vllm (#14532), xai (#14550), and lm-studio (#14586)
PRs.

Closes #14604
 2026-05-07 13:09:51 +08:00
Haruko386
dd7a0ce1d3 Go: implement provider: lm-studio (#14586) 
### What problem does this PR solve?

implement `lm-studio` provider

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
 2026-05-06 19:23:11 +08:00
Jack Storment
c2ad672c09 Go: implement provider: xAI (#14550) 
Closes #14552 

### What problem does this PR solve?

Add a Go driver for xAI (Grok models).

The config file conf/models/xai.json has been in the repo since the
early Go provider work, but internal/entity/models/factory.go had no
case for "xai". So any xAI request fell through to the dummy driver
and never reached the API. This PR adds the missing driver and wires it
up.

### What this PR includes

- New file internal/entity/models/xai.go with an XAIModel that
implements the ModelDriver interface.
- factory.go: route the "xai" provider name to NewXAIModel.

### How the driver works

- xAI exposes an OpenAI-compatible API at https://api.x.ai/v1.
- ChatWithMessages and ChatStreamlyWithSender post to /chat/completions
in the same shape the moonshot and deepseek drivers use.
- ListModels and CheckConnection call /models to confirm the API key
works and to list available model ids.
- reasoning_content is passed through for grok-3-mini and other xAI
reasoning models, both in the non-stream and stream paths.
- Encode, Rerank, and Balance are not part of the public xAI API at the
moment, so they return a clear "not implemented" or "no such method"
error.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

### How was this tested?

- go build ./internal/entity/models/... in a clean go 1.25 image (the
  go.mod minimum) returns exit 0 with no errors.
- Method set of XAIModel matches the ModelDriver interface: NewInstance,
  Name, ChatWithMessages, ChatStreamlyWithSender, Encode, Rerank,
  ListModels, Balance, CheckConnection.
- Pattern parity with the merged moonshot (#14433), volcengine (#14460),
  minimax (#14478), and vllm (#14532) PRs.

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
 2026-05-06 12:16:37 +08:00
Jin Hai
bb05a8bd7e Update create model instance command (#14441) 
### What problem does this PR solve?

1. support command:

```
RAGFlow(user)> create provider 'vllm' instance 'test' key 'test-key' url 'base-url' region 'abc';
SUCCESS
RAGFlow(user)> list instances from 'vllm';
+----------+----------------------------------------+----------------------------------+--------------+----------------------------------+--------+
| apiKey   | extra                                  | id                               | instanceName | providerID                       | status |
+----------+----------------------------------------+----------------------------------+--------------+----------------------------------+--------+
| test-key | {"base_url":"base-url","region":"abc"} | 40213c89430311f1a7cf38a74640adcc | test         | b4d40e6142d311f1a4f938a74640adcc | enable |
+----------+----------------------------------------+----------------------------------+--------------+----------------------------------+--------+
```
2. support add vllm model
```
RAGFlow(user)> add model 'Qwen/Qwen2-0.5B' to provider 'vllm' instance 'test' with tokens 131072 chat;
SUCCESS
```
3. add vllm chat

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring

---------

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
 2026-04-29 17:05:08 +08:00