Add graph and vector search features

config.py

@@ -20,13 +20,13 @@ from typing import Optional
 try:
     from dotenv import load_dotenv
     import logging
-    _logger = logging.getLogger(__name__)
 
+    _logger = logging.getLogger(__name__)
     _env_path = Path(__file__).parent / ".env"
     if _env_path.exists():
-        _logger.info(f"Loading .env from: {_env_path}")
+        _logger.info("Loading .env from: %s", _env_path)
         result = load_dotenv(_env_path, override=True)
-        _logger.info(f"load_dotenv result: {result}")
+        _logger.info("load_dotenv result: %s", result)
 except ImportError:
     pass  # python-dotenv not installed
 
@@ -58,6 +58,11 @@ class AppConfig:
     elastic: ElasticSettings
     data: DataSettings
     youtube: YoutubeSettings
+    qdrant_url: str
+    qdrant_collection: str
+    qdrant_vector_name: Optional[str]
+    qdrant_vector_size: int
+    qdrant_embed_model: str
 
 
 def _env(name: str, default: Optional[str] = None) -> Optional[str]:
@@ -89,7 +94,16 @@ def load_config() -> AppConfig:
     )
     data = DataSettings(root=data_root)
     youtube = YoutubeSettings(api_key=_env("YOUTUBE_API_KEY"))
-    return AppConfig(elastic=elastic, data=data, youtube=youtube)
+    return AppConfig(
+        elastic=elastic,
+        data=data,
+        youtube=youtube,
+        qdrant_url=_env("QDRANT_URL", "http://localhost:6333"),
+        qdrant_collection=_env("QDRANT_COLLECTION", "tlc_embeddings"),
+        qdrant_vector_name=_env("QDRANT_VECTOR_NAME"),
+        qdrant_vector_size=int(_env("QDRANT_VECTOR_SIZE", "1024")),
+        qdrant_embed_model=_env("QDRANT_EMBED_MODEL", "BAAI/bge-large-en-v1.5"),
+    )
 
 
 CONFIG = load_config()