Add startup logging utility

app.py

@@ -1,4 +1,7 @@
+import logging
 import os
+import platform
+from typing import Dict, Any
 
 from theflow.settings import settings as flowsettings
 
@@ -11,9 +14,50 @@
     os.environ["GRADIO_TEMP_DIR"] = GRADIO_TEMP_DIR
 
 
+from ktem.embeddings.manager import embedding_models_manager
+from ktem.llms.manager import llms
 from ktem.main import App  # noqa
 
+
+def _classify_services(info_dict: Dict[str, Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
+    local_markers = ["LlamaCpp", "LCOllama", "LCHuggingFace", "FastEmbed"]
+    classified = {}
+    for name, info in info_dict.items():
+        spec_type = info.get("spec", {}).get("__type__", "")
+        location = (
+            "local"
+            if any(marker in spec_type for marker in local_markers)
+            else "external"
+        )
+        classified[name] = {
+            "type": spec_type,
+            "location": location,
+            "default": info.get("default", False),
+        }
+
+    return classified
+
+
+def log_startup_info(app):
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger("startup")
+    logger.setLevel(logging.INFO)
+    logger.info("OS: %s", platform.platform())
+    logger.info("Python: %s", platform.python_version())
+
+    docstore = getattr(flowsettings, "KH_DOCSTORE", {})
+    vectorstore = getattr(flowsettings, "KH_VECTORSTORE", {})
+    logger.info("Docstore: %s", docstore)
+    logger.info("Vectorstore: %s", vectorstore)
+
+    logger.info("LLMs: %s", _classify_services(llms.info()))
+    logger.info(
+        "Embeddings: %s", _classify_services(embedding_models_manager.info())
+    )
+
+
 app = App()
+log_startup_info(app)
 demo = app.make()
 demo.queue().launch(
     favicon_path=app._favicon,