Add docstrings

relik/retriever/indexers/base.py

@@ -415,6 +415,29 @@ def from_pretrained(
         *args,
         **kwargs,
     ) -> "BaseDocumentIndex":
+        """
+        Loads a pre-trained document index from the specified location.
+
+        Args:
+            name_or_path (Union[str, os.PathLike]): The name or path of the pre-trained model.
+            device (str, optional): The device to load the model on. Defaults to "cpu".
+            precision (str | None, optional): The precision of the model. Defaults to None.
+            config_file_name (str | None, optional): The name of the configuration file. Defaults to None.
+            document_file_name (str | None, optional): The name of the document file. Defaults to None.
+            embedding_file_name (str | None, optional): The name of the embedding file. Defaults to None.
+            index_file_name (str | None, optional): The name of the index file. Defaults to None.
+            *args: Additional positional arguments.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            BaseDocumentIndex: The loaded pre-trained document index.
+
+        Raises:
+            FileNotFoundError: If the model configuration file is not found.
+            ValueError: If the document file does not exist.
+            ImportError: If the `faiss` package is not installed when trying to load a FAISS index.
+
+        """
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         skip_metadata = kwargs.pop("skip_metadata", False)

relik/retriever/indexers/document.py

@@ -310,16 +310,27 @@ def from_dict(cls, d):
         return cls([Document.from_dict(doc) for doc in d])
 
     @classmethod
-    def from_file(cls, file_path: Union[str, Path], skip_metadata: bool = False, **kwargs):
+    def from_file(
+        cls, file_path: Union[str, Path], skip_metadata: bool = False, **kwargs
+    ):
+        """
+        Load documents from a file.
+
+        Args:
+            file_path (Union[str, Path]): The path to the file containing the documents.
+            skip_metadata (bool, optional): Whether to skip loading metadata for each document. Defaults to False.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            cls: An instance of the class with the loaded documents.
+        """
         with open(file_path, "r") as f:
             docs = []
             for line in f:
                 doc = json.loads(line)
                 if skip_metadata:
                     doc.pop("metadata", None)
                 docs.append(Document.from_dict(doc))
-            # load a json lines file
-            # d = [Document.from_dict(json.loads(line)) for line in f]
         return cls(docs)
 
     @classmethod