update docs

Author: sadiqj

README.md

@@ -206,7 +206,7 @@ print(f"CRS: {crs}")  # Coordinate reference system from landmask
 
 # Method 2: Fetch all tiles in a bounding box
 bbox = (-0.2, 51.4, 0.1, 51.6)  # (min_lon, min_lat, max_lon, max_lat)
-embeddings = gt.fetch_embeddings(bbox, year=2024)
+embeddings = gt.fetch_embeddings_lazy(bbox, year=2024)
 
 for tile_lon, tile_lat, embedding_array, crs, transform in embeddings:
     print(f"Tile ({tile_lat}, {tile_lon}): {embedding_array.shape}")
@@ -241,7 +241,7 @@ files = gt.export_embedding_geotiffs(
 
 ```python
 # Fetch and process embeddings directly
-embeddings = gt.fetch_embeddings(bbox, year=2024)
+embeddings = gt.fetch_embeddings_lazy(bbox, year=2024)
 
 for lon, lat, embedding, crs, transform in embeddings:
     # Compute statistics

geotessera/core.py

@@ -79,15 +79,16 @@ def fetch_embeddings_lazy(
         year: int = 2024,
         progress_callback: Optional[callable] = None,
     ) -> Generator[Tuple[float, float, np.ndarray, object, object], None, None]:
-        """Fetch all embedding tiles within a bounding box with CRS information.
+        """Lazily fetches all embedding tiles within a bounding box with CRS information.
+        Use as a generator to process tiles one at a time in a memory-efficient manner.
 
         Args:
             bbox: Bounding box as (min_lon, min_lat, max_lon, max_lat)
             year: Year of embeddings to download
             progress_callback: Optional callback function(current, total) for progress tracking
 
         Returns:
-            List of (tile_lon, tile_lat, embedding_array, crs, transform) tuples where:
+            Generator of (tile_lon, tile_lat, embedding_array, crs, transform) tuples where:
             - tile_lon: Tile center longitude
             - tile_lat: Tile center latitude
             - embedding_array: shape (H, W, 128) with dequantized values
@@ -152,7 +153,8 @@ def fetch_embeddings(
         year: int = 2024,
         progress_callback: Optional[callable] = None,
     ) -> List[Tuple[float, float, np.ndarray, object, object]]:
-        """Fetch all embedding tiles within a bounding box with CRS information.
+        """Eagerly fetch all embedding tiles within a bounding box with CRS information.
+        For large areas, consider using fetch_embeddings_lazy() instead.
 
         Args:
             bbox: Bounding box as (min_lon, min_lat, max_lon, max_lat)