Update README.md

README.md

@@ -130,7 +130,7 @@ Using the model on a collection of passages typically involves the following ste
 
 - **Step 1: Indexing.** This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search. (⚠️ indexing requires a GPU!)
 ```
-from . import CustomIndexer # Use of a custom indexer that automatically detects the language of the passages to index and activate the language-specific adapters accordingly
+from .custom import CustomIndexer # Use of a custom indexer that automatically detects the language of the passages to index and activate the language-specific adapters accordingly
 from colbert.infra import Run, RunConfig
 
 n_gpu: int = 1 # Set your number of available GPUs
@@ -150,7 +150,7 @@ with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
 
 - **Step 2: Searching.** Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.
 ```
-from . import CustomSearcher # Use of a custom searcher that automatically detects the language of the passages to index and activate the language-specific adapters accordingly
+from .custom import CustomSearcher # Use of a custom searcher that automatically detects the language of the passages to index and activate the language-specific adapters accordingly
 from colbert.infra import Run, RunConfig
 
 n_gpu: int = 0