Feat: model list (#15774)

### What problem does this PR solve?

Support model list for VolcEngine.

### Type of change

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

api/apps/services/provider_api_service.py

@@ -256,7 +256,7 @@ async def create_provider_instance(tenant_id: str, provider_name: str, instance_
     :param region: region
     :param model_info: model info, [{
         "model_type": ["chat"],  # support multiple
-        "model_name": "name"，
+        "model_name": "name",
         "max_tokens": 4096,
         "extra": {
             "field1": "value1",
@@ -352,7 +352,7 @@ async def verify_api_key(provider_name: str, api_key: str|dict, base_url: str=No
     :param region: region
     :param model_info: model info, [{
         "model_type": ["chat"],  # support multiple
-        "model_name": "name"，
+        "model_name": "name",
         "max_tokens": 4096,
         "extra": {
             "field1": "value1",

conf/llm_factories.json

@@ -1175,6 +1175,7 @@
             "logo": "",
             "tags": "LLM, TEXT EMBEDDING, IMAGE2TEXT",
             "status": "1",
+            "url": "https://ark.cn-beijing.volces.com/api/v3",
             "llm": []
         },
         {

rag/llm/model_meta.py

@@ -16,6 +16,8 @@
 import json
 import aiohttp
 from abc import ABC
+from urllib.parse import urlparse
+from json.decoder import JSONDecodeError
 
 from common.constants import LLMType
 
@@ -58,9 +60,61 @@ class Base(ABC):
 class VolcEngine(Base):
     _FACTORY_NAME = "VolcEngine"
 
-    def get_model_list(self):
-        # todo implement access token auth
-        raise NotImplementedError
+    def _get_api_key(self):
+        try:
+            api_key = json.loads(self.api_key).get("ark_api_key", "")
+        except JSONDecodeError:
+            api_key = self.api_key
+        return api_key
+
+    def _get_model_list_url(self):
+        if not self.base_url:
+            self.base_url = "https://ark.cn-beijing.volces.com/api/v3"
+        parsed = urlparse(self.base_url)
+        return f"{parsed.scheme}://{parsed.netloc}/api/v3/models"
+
+    def _format_model_list(self, raw_model_list):
+        serving_model = [model for model in raw_model_list["data"] if model.get("status", "") != "Shutdown"]
+        res = []
+        for model in serving_model:
+
+            model_types = []
+
+            if model.get("domain", "") == "Embedding":
+                model_types.append(LLMType.EMBEDDING.value)
+            else:
+                modalities = model.get("modalities", {})
+                input_modalities = modalities.get("input_modalities", [])
+                output_modalities = modalities.get("output_modalities", [])
+
+                if "text" in output_modalities:
+                    model_types.append(LLMType.CHAT.value)
+                if "embeddings" in output_modalities:
+                    model_types.append(LLMType.EMBEDDING.value)
+                if "image" in input_modalities and "text" in output_modalities:
+                    model_types.append(LLMType.IMAGE2TEXT.value)
+                if "audio" in input_modalities and "text" in output_modalities:
+                    model_types.append(LLMType.SPEECH2TEXT.value)
+                if "audio" in output_modalities:
+                    model_types.append(LLMType.TTS.value)
+
+            if not model_types:
+                continue
+
+            features = []
+            if model.get("features", {}).get("tools", {}).get("function_calling", False):
+                features.append("is_tools")
+            if model.get("token_limits", {}).get("max_reasoning_token_length", 0) > 0:
+                features.append("thinking")
+
+            res.append({
+                "name": model["id"],
+                "model_types": model_types,
+                "features": features,
+                "max_tokens": model.get("token_limits", {}).get("max_input_token_length", 8192),
+                "status": model.get("status")
+            })
+        return res
 
 
 class Ollama(Base):