chore: change default embedding model and default LLM (from 4o to 5.1)

Author: vemonet

src/sparql_llm/config.py

@@ -131,12 +131,8 @@ class Settings(BaseSettings):
     # vectordb_url: str = "http://vectordb:6334/"
     vectordb_url: str = "data/vectordb"
     # https://qdrant.github.io/fastembed/examples/Supported_Models/#supported-text-embedding-models
-    # embedding_model: str = "BAAI/bge-large-en-v1.5"
-    # embedding_dimensions: int = 1024
-    # embedding_model: str = "BAAI/bge-base-en-v1.5"
-    # embedding_dimensions: int = 768
-    embedding_model: str = "BAAI/bge-small-en-v1.5"
-    embedding_dimensions: int = 384
+    embedding_model: str = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
+    embedding_dimensions: int = 768
     force_index: bool = False
     # Automatically initialize the vector store client, should be False when deploying in prod with multiple workers
     auto_init: bool = True
@@ -148,7 +144,7 @@ class Settings(BaseSettings):
     entities_collection_name: str = "entities"
 
     # Default settings for the agent that can be changed at runtime
-    default_llm_model: str = "openrouter/openai/gpt-4o"
+    default_llm_model: str = "openrouter/openai/gpt-5.1"
     # default_llm_model: str = "openai/gpt-4o"
     # TODO: default_llm_model_cheap: str = "openai/gpt-4o-mini"
 

src/sparql_llm/utils.py

@@ -225,10 +225,10 @@ def _ensure_loaded(self) -> None:
 
         logger.info(f"Fetching metadata for {len(self._endpoints)} endpoints...")
         for endpoint in self._endpoints:
+            logger.info(f"Fetching {endpoint['endpoint_url']} metadata...")
             self._void_dict[endpoint["endpoint_url"]] = get_schema_for_endpoint(
                 endpoint["endpoint_url"], endpoint.get("void_file")
             )
-            logger.info(f"Fetching {endpoint['endpoint_url']} metadata...")
             self._prefixes_map = get_prefixes_for_endpoint(
                 endpoint["endpoint_url"], endpoint.get("examples_file"), self._prefixes_map
             )