adding docstring documentation to backend module

python/beeai_framework/backend/backend.py

@@ -7,17 +7,113 @@
 
 
 class Backend:
+    """A unified interface for managing chat and embedding models.
+
+    The Backend class provides a convenient way to bundle together a chat model
+    and an embedding model, which are commonly used together in AI applications.
+    It offers factory methods for easy instantiation from provider names.
+
+    Attributes:
+        chat: The chat model instance for text generation and conversation.
+        embedding: The embedding model instance for text vectorization.
+
+    Example:
+        >>> # Create backend with specific models
+        >>> from beeai_framework.backend import Backend
+        >>> from beeai_framework.adapters.openai import OpenAIChatModel, OpenAIEmbeddingModel
+        >>> backend = Backend(
+        ...     chat=OpenAIChatModel("gpt-4"),
+        ...     embedding=OpenAIEmbeddingModel("text-embedding-3-small")
+        ... )
+        
+        >>> # Create backend from provider names
+        >>> backend = Backend.from_name(chat="openai:gpt-4", embedding="openai:text-embedding-3-small")
+        
+        >>> # Create backend using same provider for both
+        >>> backend = Backend.from_provider("openai")
+    """
+
     def __init__(self, *, chat: ChatModel, embedding: EmbeddingModel) -> None:
+        """Initialize a Backend with chat and embedding models.
+
+        Args:
+            chat: The chat model instance to use for text generation.
+            embedding: The embedding model instance to use for text vectorization.
+
+        Example:
+            >>> from beeai_framework.adapters.openai import OpenAIChatModel, OpenAIEmbeddingModel
+            >>> backend = Backend(
+            ...     chat=OpenAIChatModel("gpt-4"),
+            ...     embedding=OpenAIEmbeddingModel("text-embedding-3-small")
+            ... )
+        """
         self.chat = chat
         self.embedding = embedding
 
     @staticmethod
     def from_name(*, chat: str | ProviderName, embedding: str | ProviderName) -> "Backend":
+        """Create a Backend instance from provider and model names.
+
+        This factory method allows you to instantiate a Backend by specifying
+        the provider and model names as strings, without needing to import
+        specific model classes.
+
+        Args:
+            chat: The chat model identifier in the format "provider:model" or just "provider".
+                Examples: "openai:gpt-4", "anthropic:claude-3-opus", "ollama".
+            embedding: The embedding model identifier in the format "provider:model" or just "provider".
+                Examples: "openai:text-embedding-3-small", "ollama:nomic-embed-text".
+
+        Returns:
+            A new Backend instance with the specified chat and embedding models.
+
+        Example:
+            >>> backend = Backend.from_name(
+            ...     chat="openai:gpt-4",
+            ...     embedding="openai:text-embedding-3-small"
+            ... )
+            >>> backend = Backend.from_name(
+            ...     chat="anthropic:claude-3-opus",
+            ...     embedding="ollama:nomic-embed-text"
+            ... )
+        """
         return Backend(chat=ChatModel.from_name(chat), embedding=EmbeddingModel.from_name(embedding))
 
     @staticmethod
     def from_provider(name: str | ProviderName) -> "Backend":
+        """Create a Backend instance using the same provider for both models.
+
+        This is a convenience method for when you want to use the same provider
+        for both chat and embedding models. It uses the provider's default models.
+
+        Args:
+            name: The provider name (e.g., "openai", "anthropic", "ollama").
+                The provider's default chat and embedding models will be used.
+
+        Returns:
+            A new Backend instance with both chat and embedding models from the same provider.
+
+        Example:
+            >>> # Uses OpenAI's default chat and embedding models
+            >>> backend = Backend.from_provider("openai")
+            
+            >>> # Uses Ollama's default chat and embedding models
+            >>> backend = Backend.from_provider("ollama")
+        """
         return Backend.from_name(chat=name, embedding=name)
 
     async def clone(self) -> "Backend":
+        """Create a deep copy of this Backend instance.
+
+        This method clones both the chat and embedding models, creating
+        independent copies that can be modified without affecting the original.
+
+        Returns:
+            A new Backend instance with cloned chat and embedding models.
+
+        Example:
+            >>> original = Backend.from_provider("openai")
+            >>> cloned = await original.clone()
+            >>> # Modifications to cloned won't affect original
+        """
         return Backend(chat=await self.chat.clone(), embedding=await self.embedding.clone())