Feat: Support Specifying OpenRouter Model Provider (#10550)

### What problem does this PR solve?
issue:
[#5787](https://github.com/infiniflow/ragflow/issues/5787)
change:
Support Specifying OpenRouter Model Provider

### Type of change

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

api/apps/llm_app.py

@@ -194,6 +194,9 @@ def add_llm():
     elif factory == "Azure-OpenAI":
         api_key = apikey_json(["api_key", "api_version"])
 
+    elif factory == "OpenRouter":
+        api_key = apikey_json(["api_key", "provider_order"])
+
     llm = {
         "tenant_id": current_user.id,
         "llm_factory": factory,

rag/llm/chat_model.py

@@ -1425,6 +1425,9 @@ class LiteLLMBase(ABC):
             self.bedrock_ak = json.loads(key).get("bedrock_ak", "")
             self.bedrock_sk = json.loads(key).get("bedrock_sk", "")
             self.bedrock_region = json.loads(key).get("bedrock_region", "")
+        elif self.provider == SupportedLiteLLMProvider.OpenRouter:
+            self.api_key = json.loads(key).get("api_key", "")
+            self.provider_order = json.loads(key).get("provider_order", "")
 
     def _get_delay(self):
         """Calculate retry delay time"""
@@ -1469,7 +1472,6 @@ class LiteLLMBase(ABC):
             timeout=self.timeout,
         )
         # response = self.client.chat.completions.create(model=self.model_name, messages=history, **gen_conf, **kwargs)
-
         if any([not response.choices, not response.choices[0].message, not response.choices[0].message.content]):
             return "", 0
         ans = response.choices[0].message.content.strip()
@@ -1620,6 +1622,24 @@ class LiteLLMBase(ABC):
                     "aws_region_name": self.bedrock_region,
                 }
             )
+
+        if self.provider == SupportedLiteLLMProvider.OpenRouter:
+            if self.provider_order:
+                def _to_order_list(x):
+                    if x is None:
+                        return []
+                    if isinstance(x, str):
+                        return [s.strip() for s in x.split(",") if s.strip()]
+                    if isinstance(x, (list, tuple)):
+                        return [str(s).strip() for s in x if str(s).strip()]
+                    return []
+                extra_body = {}
+                provider_cfg = {}
+                provider_order = _to_order_list(self.provider_order)
+                provider_cfg["order"] = provider_order
+                provider_cfg["allow_fallbacks"] = False
+                extra_body["provider"] = provider_cfg
+                completion_args.update({"extra_body": extra_body})
         return completion_args
 
     def chat_with_tools(self, system: str, history: list, gen_conf: dict = {}):

rag/llm/cv_model.py

@@ -38,6 +38,7 @@ class Base(ABC):
         self.is_tools = False
         self.tools = []
         self.toolcall_sessions = {}
+        self.extra_body = None
 
     def describe(self, image):
         raise NotImplementedError("Please implement encode method!")
@@ -77,7 +78,8 @@ class Base(ABC):
         try:
             response = self.client.chat.completions.create(
                 model=self.model_name,
-                messages=self._form_history(system, history, images)
+                messages=self._form_history(system, history, images),
+                extra_body=self.extra_body,
             )
             return response.choices[0].message.content.strip(), response.usage.total_tokens
         except Exception as e:
@@ -90,7 +92,8 @@ class Base(ABC):
             response = self.client.chat.completions.create(
                 model=self.model_name,
                 messages=self._form_history(system, history, images),
-                stream=True
+                stream=True,
+                extra_body=self.extra_body,
             )
             for resp in response:
                 if not resp.choices[0].delta.content:
@@ -177,6 +180,7 @@ class GptV4(Base):
         res = self.client.chat.completions.create(
             model=self.model_name,
             messages=self.prompt(b64),
+            extra_body=self.extra_body,
         )
         return res.choices[0].message.content.strip(), total_token_count_from_response(res)
 
@@ -185,6 +189,7 @@ class GptV4(Base):
         res = self.client.chat.completions.create(
             model=self.model_name,
             messages=self.vision_llm_prompt(b64, prompt),
+            extra_body=self.extra_body,
         )
         return res.choices[0].message.content.strip(),total_token_count_from_response(res)
 
@@ -327,10 +332,27 @@ class OpenRouterCV(GptV4):
     ):
         if not base_url:
             base_url = "https://openrouter.ai/api/v1"
-        self.client = OpenAI(api_key=key, base_url=base_url)
+        api_key = json.loads(key).get("api_key", "")
+        self.client = OpenAI(api_key=api_key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
+        provider_order = json.loads(key).get("provider_order", "")
+        self.extra_body = {}
+        if provider_order:
+            def _to_order_list(x):
+                if x is None:
+                    return []
+                if isinstance(x, str):
+                    return [s.strip() for s in x.split(",") if s.strip()]
+                if isinstance(x, (list, tuple)):
+                    return [str(s).strip() for s in x if str(s).strip()]
+                return []
+            provider_cfg = {}
+            provider_order = _to_order_list(provider_order)
+            provider_cfg["order"] = provider_order
+            provider_cfg["allow_fallbacks"] = False
+            self.extra_body["provider"] = provider_cfg
 
 
 class LocalAICV(GptV4):

web/src/pages/user-setting/setting-model/ollama-modal/index.tsx

@@ -15,7 +15,10 @@ import {
 import omit from 'lodash/omit';
 import { useEffect } from 'react';
 
-type FieldType = IAddLlmRequestBody & { vision: boolean };
+type FieldType = IAddLlmRequestBody & {
+  vision: boolean;
+  provider_order?: string;
+};
 
 const { Option } = Select;
 
@@ -128,6 +131,10 @@ const OllamaModal = ({
       { value: 'speech2text', label: 'sequence2text' },
       { value: 'tts', label: 'tts' },
     ],
+    [LLMFactory.OpenRouter]: [
+      { value: 'chat', label: 'chat' },
+      { value: 'image2text', label: 'image2text' },
+    ],
     Default: [
       { value: 'chat', label: 'chat' },
       { value: 'embedding', label: 'embedding' },
@@ -233,6 +240,16 @@ const OllamaModal = ({
             onKeyDown={handleKeyDown}
           />
         </Form.Item>
+        {llmFactory === LLMFactory.OpenRouter && (
+          <Form.Item<FieldType>
+            label="Provider Order"
+            name="provider_order"
+            tooltip="Comma-separated provider list, e.g. Groq,Fireworks"
+            rules={[]}
+          >
+            <Input placeholder="Groq,Fireworks" onKeyDown={handleKeyDown} />
+          </Form.Item>
+        )}
 
         <Form.Item noStyle dependencies={['model_type']}>
           {({ getFieldValue }) =>