Fix: default model base url extraction logic (#11263)

### What problem does this PR solve?

Fixes an issue where default models which used the same factory but
different base URLs would all be initialised with the default chat
model's base URL and would ignore e.g. the embedding model's base URL
config.

For example, with the following service config, the embedding and
reranker models would end up using the base URL for the default chat
model (i.e. `llm1.example.com`):

```yaml
ragflow:
  service_conf:
    user_default_llm:
      factory: OpenAI-API-Compatible
      api_key: not-used
      default_models:
        chat_model:
          name: llm1
          base_url: https://llm1.example.com/v1
        embedding_model:
          name: llm2
          base_url: https://llm2.example.com/v1
        rerank_model:
          name: llm3
          base_url: https://llm3.example.com/v1/rerank

  llm_factories:
    factory_llm_infos:
    - name: OpenAI-API-Compatible
      logo: ""
      tags: "LLM,TEXT EMBEDDING,SPEECH2TEXT,MODERATION"
      status: "1"
      llm:
        - llm_name: llm1
          base_url: 'https://llm1.example.com/v1'
          api_key: not-used
          tags: "LLM,CHAT,IMAGE2TEXT"
          max_tokens: 100000
          model_type: chat
          is_tools: false

        - llm_name: llm2
          base_url: https://llm2.example.com/v1
          api_key: not-used
          tags: "TEXT EMBEDDING"
          max_tokens: 10000
          model_type: embedding

        - llm_name: llm3
          base_url: https://llm3.example.com/v1/rerank
          api_key: not-used
          tags: "RERANK,1k"
          max_tokens: 10000
          model_type: rerank
```

### Type of change

- [X] Bug Fix (non-breaking change which fixes an issue)

api/db/services/llm_service.py

@@ -19,6 +19,7 @@ import re
 from common.token_utils import num_tokens_from_string
 from functools import partial
 from typing import Generator
+from common.constants import LLMType
 from api.db.db_models import LLM
 from api.db.services.common_service import CommonService
 from api.db.services.tenant_llm_service import LLM4Tenant, TenantLLMService
@@ -32,6 +33,14 @@ def get_init_tenant_llm(user_id):
     from common import settings
     tenant_llm = []
 
+    model_configs = {
+        LLMType.CHAT: settings.CHAT_CFG,
+        LLMType.EMBEDDING: settings.EMBEDDING_CFG,
+        LLMType.SPEECH2TEXT: settings.ASR_CFG,
+        LLMType.IMAGE2TEXT: settings.IMAGE2TEXT_CFG,
+        LLMType.RERANK: settings.RERANK_CFG,
+    }
+
     seen = set()
     factory_configs = []
     for factory_config in [
@@ -54,8 +63,8 @@ def get_init_tenant_llm(user_id):
                     "llm_factory": factory_config["factory"],
                     "llm_name": llm.llm_name,
                     "model_type": llm.model_type,
-                    "api_key": factory_config["api_key"],
+                    "api_key": model_configs.get(llm.model_type, {}).get("api_key", factory_config["api_key"]),
-                    "api_base": factory_config["base_url"],
+                    "api_base": model_configs.get(llm.model_type, {}).get("base_url", factory_config["base_url"]),
                     "max_tokens": llm.max_tokens if llm.max_tokens else 8192,
                 }
             )
@@ -80,8 +89,8 @@ class LLMBundle(LLM4Tenant):
 
     def encode(self, texts: list):
         if self.langfuse:
-            generation = self.langfuse.start_generation(trace_context=self.trace_context, name="encode", model=self.llm_name, input={"texts": texts})        
+            generation = self.langfuse.start_generation(trace_context=self.trace_context, name="encode", model=self.llm_name, input={"texts": texts})
-    
+
         safe_texts = []
         for text in texts:
             token_size = num_tokens_from_string(text)
@@ -90,7 +99,7 @@ class LLMBundle(LLM4Tenant):
                 safe_texts.append(text[:target_len])
             else:
                 safe_texts.append(text)
-                
+
         embeddings, used_tokens = self.mdl.encode(safe_texts)
 
         llm_name = getattr(self, "llm_name", None)