### 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>
### 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
### What problem does this PR solve?
```
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 'rerank@test@zhipu-ai' top 2;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 1 |
| 2 | 0.99999976 |
+-------+-----------------+
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Update mapping.json to treat id as a keyword.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Implement `HuggingFace` provider
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
The Aliyun Go driver has a stub `Rerank` method that always returns
`"Aliyun, Rerank not implemented"`. DashScope exposes an
OpenAI-compatible rerank endpoint (`compatible-mode/v1/rerank`) and
hosts dedicated bilingual rerankers (`gte-rerank-v2`, `gte-rerank`) that
are a natural pairing with the embedding models already in
`aliyun.json`. Without this, Aliyun users cannot use reranking within
RAGFlow.
Closes#14675
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
1. **Fix Global State Pollution in Local Providers (Critical Bug):** -
Resolved a severe concurrency and architecture issue in
`model_service.go`. Previously, `ListSupportedModels` would permanently
overwrite the global provider singleton with a localized URL instance
(`driver.NewInstance`). This caused cross-request contamination in
multi-tenant environments.
- Fixed `CheckProviderConnection` for local models (LM Studio, vLLM,
Ollama). It now properly creates a localized driver copy and injects the
`base_url` before testing the connection, entirely eliminating the
false-positive `missing base URL` error without polluting the global
state.
2. **Implement `VolcEngine` Embeddings:** - Fully implemented the
`Encode` method for the `volcengine` provider, enabling text embedding
capabilities for VolcEngine models.
3. **Enhance Region Validation in `SiliconFlow`:** - Added a strict
empty string check (`*apiConfig.Region != ""`) alongside the existing
`nil` check when parsing regions. This ensures that if an empty string
is passed, the system safely falls back to the `"default"` region,
preventing malformed URL requests and `unsupported protocol scheme`
errors.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
The Aliyun Go driver shipped with a stub \`Encode\` method that returned
\`no such method\`, even though \`conf/models/aliyun.json\` already
wires the OpenAI-compatible embeddings URL suffix at
\`compatible-mode/v1/embeddings\`. The same config also did not list any
embedding models, so the picker had nothing to select.
So an Aliyun tenant who wanted to use Tongyi text-embedding-v3 or v4 in
the Go layer could not, even though the upstream endpoint is public and
uses the standard \`POST /v1/embeddings\` shape that the SiliconFlow and
ZhipuAI
drivers already support.
This PR fills the gap.
### What this PR includes
- \`conf/models/aliyun.json\`: add \`text-embedding-v4\` and
\`text-embedding-v3\` to the \`models\` array.
- \`internal/entity/models/aliyun.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 \`apiConfig\` and the API key, validate the model name,
resolve the region with a default fallback, build the
URL from \`BaseURL[region] + URLSuffix.Embedding\`.
- Send all input texts in one request as the \`input\` array, the same
OpenAI-compatible shape the SiliconFlow \`Encode\`
uses.
- 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.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks every input slot got a vector and returns a clear
error if any slot is still nil.
### 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 \`AliyunModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Encode implementation.
Closes#14646
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
1. implement `rerank`, `embedding`, `balance`, `checkConnet` method for
`OpenRouter`
2. delete `chat` method in `internal/entity/models/volcengine.go`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
The Gitee AI Go driver shipped with a stub \`Rerank\` method that
returned \`Rerank not implemented\`, even though
\`conf/models/gitee.json\` already wires the rerank URL suffix at
\`\"rerank\": \"rerank\"\`. The same config did not list any
rerank model, so the picker had nothing to select.
So a Gitee tenant could not use BAAI/bge-reranker-v2-m3 as a reranker
through the Go layer today, even though the
infrastructure was one config entry and one method body away.
### What this PR includes
- \`conf/models/gitee.json\`: add \`BAAI/bge-reranker-v2-m3\` to the
\`models\` array.
- \`internal/entity/models/gitee.go\`: replace the \`Rerank\` stub with
a real implementation. Adds two small local types
that match the OpenAI-compatible \`/rerank\` shape already used by the
SiliconFlow and ZhipuAI drivers.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, validate the model name,
resolve the region with a default fallback, build the
URL from \`BaseURL[region] + URLSuffix.Rerank\`.
- Use a per-call \`context.WithTimeout(30s)\` and
\`http.NewRequestWithContext\`, matching the pattern the
recently merged Aliyun Encode and the OpenAI driver already use.
- Send \`{model, query, documents, top_n, return_documents:false}\` in
the body.
- Parse \`results[*].relevance_score\` and copy each score into the
output slice indexed by \`results[*].index\`, so the
output order matches the input order even if the API returns items in a
different order.
- Empty input returns \`[]float64{}\` with no HTTP call.
- An out-of-range result index returns a clear error rather than
silently skipping the entry.
- Non-200 responses propagate the upstream status line and body.
### 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 \`GiteeModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Rerank and the recently
merged ZhipuAI Rerank (#14608).
Closes#14655
Closes#14631
### What problem does this PR solve?
The DeepSeek Go driver shipped with a stub \`Balance\` method that
returned \`no such method\`, even though DeepSeek exposes a public \`GET
/user/balance\` endpoint that works with the same Bearer token used for
chat.
So the "Balance" panel in the model provider UI always shows an error
for DeepSeek tenants, while it already works for Moonshot and Gitee.
This PR fills the gap.
### What this PR includes
- \`conf/models/deepseek.json\`: add \`\"balance\": \"user/balance\"\`
under \`url_suffix\` so the driver can build the URL from config the
same way the other endpoints do.
- \`internal/entity/models/deepseek.go\`: replace the \`Balance\` stub
with a real implementation. Adds a small local response type
\`deepseekBalanceResponse\` that matches the upstream shape.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, resolve the region (with a
\`default\` fallback), and build the URL from \`BaseURL[region] +
URLSuffix.Balance\`.
- GET the URL with \`Authorization: Bearer <api_key>\`.
- Parse the upstream response:
\`\`\`json
{
\"is_available\": true,
\"balance_infos\": [
{\"currency\": \"USD\", \"total_balance\": \"10.00\", ...},
{\"currency\": \"CNY\", \"total_balance\": \"70.00\", ...}
]
}
\`\`\`
\`total_balance\` is a string in the upstream API, so the driver parses
it with \`strconv.ParseFloat\`.
- Return the first balance entry as \`{\"balance\": <float>,
\"currency\": <string>}\`, the same shape the Moonshot driver returns.
The UI can render it with no provider-specific code.
### Edge cases
- Missing or empty API key returns a clear local error before any HTTP
call.
- Empty \`balance_infos\` returns a clear \"no balance info in
response\" error rather than a zero-value silent success.
- Non-numeric \`total_balance\` returns a clear parse error.
- Non-200 responses propagate the upstream status line and body so the
user can see why the call failed.
### 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 \`DeepSeekModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing Moonshot and Gitee Balance
implementations.
### 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)
### What problem does this PR solve?
The SiliconFlow Go driver shipped with a stub \`Balance\` method that
returned \`no such method\`, even though SiliconFlow exposes a public
\`GET /v1/user/info\` endpoint that returns the account balance per
currency.
So the "Balance" panel in the model provider UI always shows an error
for SiliconFlow tenants, while it already works for
Moonshot and Gitee. This PR fills the gap.
### What this PR includes
- \`conf/models/siliconflow.json\`: add \`\"balance\": \"user/info\"\`
under \`url_suffix\` so the driver builds the URL from config.
- \`internal/entity/models/siliconflow.go\`: replace the \`Balance\`
stub with a real implementation. Adds a small local response type that
matches the upstream shape.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, resolve the region with a
default fallback, and build the URL from \`BaseURL[region] +
URLSuffix.Balance\`.
- GET the URL with \`Authorization: Bearer <api_key>\`.
- Parse the upstream response. SiliconFlow returns balance fields as
strings, so the driver parses them with \`strconv.ParseFloat\`. It
prefers \`totalBalance\` over \`balance\` when both are present.
- Return \`{\"balance\": <float>, \"currency\": \"CNY\"}\`, the same
shape the Moonshot driver returns. The UI can render it
with no provider-specific code.
### Edge cases
- Missing or empty API key returns a clear local error before any HTTP
call.
- An unknown region falls back to the default base URL.
- Empty \`balance\` and \`totalBalance\` returns a clear "no balance
info in response" error rather than a zero-value silent success.
- Non-numeric balance string returns a clear parse error.
- Non-200 responses propagate the upstream status line and body.
### 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 \`SiliconflowModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing Moonshot and Gitee Balance
implementations.
Closes#14642
### What problem does this PR solve?
Feat: support local provider for code exec component & remove some
outdated models
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### 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)
### 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
### What problem does this PR solve?
implement `lm-studio` provider
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
implement `Ollama` provider
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
implement MiniMax provider
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
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>
### What problem does this PR solve?
implement `volcengine` provider
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
1. Refactor server_main
2. Add volcengine
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
As title.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
As title.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Summary
RAPTOR's recursive clustering builds a `layers` list tracking
`(start_idx, end_idx)` boundaries per level, but currently discards this
information — only the flat `chunks` list is returned. This makes it
impossible to distinguish leaf-level summaries from top-level ones.
This PR:
- Returns `(chunks, layers)` tuple from `raptor.py`'s `__call__`
- Annotates each RAPTOR summary chunk with `raptor_layer_int` (1 = first
summary level, 2 = summary-of-summaries, etc.)
- Adds `raptor_layer_int` to `infinity_mapping.json` (Elasticsearch
handles it via existing `*_int` dynamic template)
### Why this matters
Downstream features need to know which RAPTOR layer a summary belongs
to:
- **Retrieving the top-level document summary** for entity extraction,
search snippets, or document comparison
- **Filtering by abstraction level** — users may want only high-level
summaries or only leaf-level cluster summaries
- **RAPTOR recall quality** — #10951 reports summaries not being
recalled for definition queries; layer metadata enables targeted
retrieval
### Changes
| File | Change | LOC |
|------|--------|-----|
| `rag/raptor.py` | Return `(chunks, layers)` tuple | ~3 |
| `rag/svr/task_executor.py` | Build `chunk_layer` mapping, set
`raptor_layer_int` | ~12 |
| `conf/infinity_mapping.json` | Add `raptor_layer_int` integer field |
~1 |
### Backward compatibility
- **Additive only** — no existing fields or behavior changed
- Existing RAPTOR chunks continue to work (they'll have
`raptor_layer_int = 0` by default)
- New RAPTOR chunks get layer metadata automatically
## Test plan
- [ ] Parse a document with RAPTOR enabled, verify `raptor_layer_int` is
set on indexed chunks
- [ ] Verify `raptor_layer_int` values increase with abstraction level
(layer 1 < layer 2 < ...)
- [ ] Verify existing RAPTOR deletion (`delete by raptor_kwd`) still
works
- [ ] Verify Infinity backend accepts the new field
Fixes#7488
Related: #4104, #11191, #10951🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: yuch85 <yuch85.1@gmail.com>
Co-authored-by: Wang Qi <wangq8@outlook.com>
### What problem does this PR solve?
As title
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
1. Add new provider minimax
2. Add new command: CHECK INSTANCE 'instance_name' FROM 'provider_name';
```
RAGFlow(user)> check instance 'test' from 'minimax';
SUCCESS
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
As title
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
## Add Astraflow Provider Support
This PR integrates [Astraflow](https://astraflow.ucloud.cn/) (by UCloud
/ 优刻得) as a new AI model provider in RAGFlow, with support for both
global and China endpoints.
### About Astraflow
Astraflow is an OpenAI-compatible AI model aggregation platform
supporting 200+ models from major providers including DeepSeek, Qwen,
GPT, Claude, Gemini, Llama, Mistral, and more.
| Variant | Factory Name | Endpoint | Env Var |
|---------|-------------|----------|---------|
| Global | `Astraflow` | `https://api-us-ca.umodelverse.ai/v1` |
`ASTRAFLOW_API_KEY` |
| China | `Astraflow-CN` | `https://api.modelverse.cn/v1` |
`ASTRAFLOW_CN_API_KEY` |
- **API key signup**: https://astraflow.ucloud.cn/
---
### Files Changed
| File | Change |
|------|--------|
| `rag/llm/__init__.py` | Register `Astraflow` and `Astraflow-CN` in
`SupportedLiteLLMProvider` enum, `FACTORY_DEFAULT_BASE_URL`, and
`LITELLM_PROVIDER_PREFIX` |
| `rag/llm/chat_model.py` | Add `AstraflowChat` and `AstraflowCNChat`
(OpenAI-compatible `Base` subclass) |
| `rag/llm/embedding_model.py` | Add `AstraflowEmbed` and
`AstraflowCNEmbed` (subclasses of `OpenAIEmbed`) |
| `rag/llm/rerank_model.py` | Add `AstraflowRerank` and
`AstraflowCNRerank` (subclasses of `OpenAI_APIRerank`) |
| `rag/llm/cv_model.py` | Add `AstraflowCV` and `AstraflowCNCV`
(subclasses of `GptV4`) |
| `rag/llm/tts_model.py` | Add `AstraflowTTS` and `AstraflowCNTTS`
(subclasses of `OpenAITTS`) |
| `rag/llm/sequence2txt_model.py` | Add `AstraflowSeq2txt` and
`AstraflowCNSeq2txt` (subclasses of `GPTSeq2txt`) |
| `conf/llm_factories.json` | Register `Astraflow` and `Astraflow-CN`
factories with a curated list of popular models |
---
### Supported Model Types
- ✅ **Chat / LLM** — DeepSeek-V3/R1, Qwen3, GPT-4o/4.1, Claude 3.5/3.7,
Gemini 2.0/2.5 Flash, Llama 3.3/4, Mistral, and 200+ more
- ✅ **Text Embedding** — text-embedding-3-small/large
- ✅ **Image / Vision (IMAGE2TEXT)** — GPT-4o, GPT-4.1, Claude, Gemini,
Llama-4, etc.
- ✅ **Text Re-Rank**
- ✅ **TTS** — tts-1
- ✅ **Speech-to-Text (SPEECH2TEXT)** — whisper-1
### Implementation Notes
- Uses the `openai/` LiteLLM prefix — consistent with other
OpenAI-compatible aggregation platforms (SILICONFLOW, DeerAPI, CometAPI,
OpenRouter, n1n, Avian, etc.)
- `Astraflow` (global, rank 250) and `Astraflow-CN` (China, rank 249)
are separate factory entries, allowing users to choose the optimal
endpoint based on their region.
- All model classes cleanly subclass existing base classes (`Base`,
`OpenAIEmbed`, `OpenAI_APIRerank`, `GptV4`, `OpenAITTS`, `GPTSeq2txt`)
with no custom logic needed — the provider is fully OpenAI-compatible.
---------
Co-authored-by: user <user@xzaaaMacBook-Air.local>
### What problem does this PR solve?
1. Supports stream and non-stream chat
2. Supports think and non-think chat
3. List supported models from DeepSeek service. (This command can be
used to verify the API validity)
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Now each model support region with different URL
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
https://bailian.console.aliyun.com/cn-beijing?tab=api#/api/?type=model&url=2780056
### What problem does this PR solve?
_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._
### Type of change
- [x] Other (please describe): add gte-rerank-v2、qwen3-rerank
### What problem does this PR solve?
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
---------
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
Introduce 5 new tables, including model groups and provider instance.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
When using Infinity as DOC_ENGINE with parent-child chunker enabled,
vector insertion fails because the "mom" field is missing from the index
mapping. This fix adds the required field to resolve the issue.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Go cli
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Adds Perplexity contextualized embeddings API as a new model provider,
as requested in #13610.
- `PerplexityEmbed` provider in `rag/llm/embedding_model.py` supporting
both standard (`/v1/embeddings`) and contextualized
(`/v1/contextualizedembeddings`) endpoints
- All 4 Perplexity embedding models registered in
`conf/llm_factories.json`: `pplx-embed-v1-0.6b`, `pplx-embed-v1-4b`,
`pplx-embed-context-v1-0.6b`, `pplx-embed-context-v1-4b`
- Frontend entries (enum, icon mapping, API key URL) in
`web/src/constants/llm.ts`
- Updated `docs/guides/models/supported_models.mdx`
- 22 unit tests in `test/unit_test/rag/llm/test_perplexity_embed.py`
Perplexity's API returns `base64_int8` encoded embeddings (not
OpenAI-compatible), so this uses a custom `requests`-based
implementation. Contextualized vs standard model is auto-detected from
the model name.
Closes#13610
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
## Summary
Upgrade MiniMax model configuration to include the latest M2.7 model.
## Changes
- Add `MiniMax-M2.7` and `MiniMax-M2.7-highspeed` to the model selection
list in `conf/llm_factories.json`
- Place M2.7 models at the top of the list as the recommended default
- Retain all previous models (M2.5, M2.5-highspeed, M2.1, M2) as
available alternatives
## Why
MiniMax-M2.7 is the latest flagship model with enhanced reasoning and
coding capabilities. This update ensures RAGFlow users can access the
newest model while maintaining backward compatibility with existing
configurations.
## Testing
- JSON config validated (well-formed)
- No existing MiniMax-specific unit tests affected
- Model entries follow the same structure as existing entries
Co-authored-by: PR Bot <pr-bot@minimaxi.com>
### What problem does this PR solve?
1. Change go admin server port from 9385 to 9383 to avoid conflicts
2. Start go server after python servers are started completely, in
entrypoint.sh
3. Fix some database migration issue
4. Add more API routes in web to compliant with EE.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
