core: docstrings documents (#23506)

Added missed docstrings. Formatted docstrings to the consistent form.

libs/core/langchain_core/documents/compressor.py

@@ -32,7 +32,16 @@ class BaseDocumentCompressor(BaseModel, ABC):
         query: str,
         callbacks: Optional[Callbacks] = None,
     ) -> Sequence[Document]:
-        """Compress retrieved documents given the query context."""
+        """Compress retrieved documents given the query context.
+
+        Args:
+            documents: The retrieved documents.
+            query: The query context.
+            callbacks: Optional callbacks to run during compression.
+
+        Returns:
+            The compressed documents.
+        """
 
     async def acompress_documents(
         self,
@@ -40,7 +49,16 @@ class BaseDocumentCompressor(BaseModel, ABC):
         query: str,
         callbacks: Optional[Callbacks] = None,
     ) -> Sequence[Document]:
-        """Compress retrieved documents given the query context."""
+        """Async compress retrieved documents given the query context.
+
+        Args:
+            documents: The retrieved documents.
+            query: The query context.
+            callbacks: Optional callbacks to run during compression.
+
+        Returns:
+            The compressed documents.
+        """
         return await run_in_executor(
             None, self.compress_documents, documents, query, callbacks
         )

libs/core/langchain_core/documents/transformers.py

@@ -10,9 +10,9 @@ if TYPE_CHECKING:
 
 
 class BaseDocumentTransformer(ABC):
-    """Abstract base class for document transformation systems.
+    """Abstract base class for document transformation.
 
-    A document transformation system takes a sequence of Documents and returns a
+    A document transformation takes a sequence of Documents and returns a
     sequence of transformed Documents.
 
     Example:
@@ -55,7 +55,7 @@ class BaseDocumentTransformer(ABC):
             documents: A sequence of Documents to be transformed.
 
         Returns:
-            A list of transformed Documents.
+            A sequence of transformed Documents.
         """
 
     async def atransform_documents(
@@ -67,7 +67,7 @@ class BaseDocumentTransformer(ABC):
             documents: A sequence of Documents to be transformed.
 
         Returns:
-            A list of transformed Documents.
+            A sequence of transformed Documents.
         """
         return await run_in_executor(
             None, self.transform_documents, documents, **kwargs

libs/core/langchain_core/embeddings/embeddings.py

@@ -7,7 +7,7 @@ from langchain_core.runnables.config import run_in_executor
 
 
 class Embeddings(ABC):
-    """An interface for embedding models.
+    """Interface for embedding models.
 
     This is an interface meant for implementing text embedding models.
 
@@ -36,16 +36,44 @@ class Embeddings(ABC):
 
     @abstractmethod
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Embed search docs."""
+        """Embed search docs.
+
+        Args:
+            texts: List of text to embed.
+
+        Returns:
+            List of embeddings.
+        """
 
     @abstractmethod
     def embed_query(self, text: str) -> List[float]:
-        """Embed query text."""
+        """Embed query text.
+
+        Args:
+            text: Text to embed.
+
+        Returns:
+            Embedding.
+        """
 
     async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Asynchronous Embed search docs."""
+        """Asynchronous Embed search docs.
+
+        Args:
+            texts: List of text to embed.
+
+        Returns:
+            List of embeddings.
+        """
         return await run_in_executor(None, self.embed_documents, texts)
 
     async def aembed_query(self, text: str) -> List[float]:
-        """Asynchronous Embed query text."""
+        """Asynchronous Embed query text.
+
+        Args:
+            text: Text to embed.
+
+        Returns:
+            Embedding.
+        """
         return await run_in_executor(None, self.embed_query, text)