core(fix): revert set_text optimization (#31555)

Revert serialization regression introduced in
https://github.com/langchain-ai/langchain/pull/31238

Fixes https://github.com/langchain-ai/langchain/issues/31486

libs/core/langchain_core/outputs/chat_generation.py

@@ -4,7 +4,8 @@ from __future__ import annotations
 
 from typing import Literal, Union
 
-from pydantic import computed_field
+from pydantic import model_validator
+from typing_extensions import Self
 
 from langchain_core.messages import BaseMessage, BaseMessageChunk
 from langchain_core.outputs.generation import Generation
@@ -25,30 +26,44 @@ class ChatGeneration(Generation):
     via callbacks).
     """
 
+    text: str = ""
+    """*SHOULD NOT BE SET DIRECTLY* The text contents of the output message."""
     message: BaseMessage
     """The message output by the chat model."""
-
+    # Override type to be ChatGeneration, ignore mypy error as this is intentional
     type: Literal["ChatGeneration"] = "ChatGeneration"  # type: ignore[assignment]
     """Type is used exclusively for serialization purposes."""
 
-    @computed_field  # type: ignore[prop-decorator]
+    @model_validator(mode="after")
-    @property
+    def set_text(self) -> Self:
-    def text(self) -> str:
+        """Set the text attribute to be the contents of the message.
-        """Set the text attribute to be the contents of the message."""
+
-        text_ = ""
+        Args:
+            values: The values of the object.
+
+        Returns:
+            The values of the object with the text attribute set.
+
+        Raises:
+            ValueError: If the message is not a string or a list.
+        """
+        text = ""
         if isinstance(self.message.content, str):
-            text_ = self.message.content
+            text = self.message.content
         # Assumes text in content blocks in OpenAI format.
         # Uses first text block.
         elif isinstance(self.message.content, list):
             for block in self.message.content:
                 if isinstance(block, str):
-                    text_ = block
+                    text = block
                     break
                 if isinstance(block, dict) and "text" in block:
-                    text_ = block["text"]
+                    text = block["text"]
                     break
-        return text_
+        else:
+            pass
+        self.text = text
+        return self
 
 
 class ChatGenerationChunk(ChatGeneration):
@@ -59,7 +74,7 @@ class ChatGenerationChunk(ChatGeneration):
 
     message: BaseMessageChunk
     """The message chunk output by the chat model."""
-
+    # Override type to be ChatGeneration, ignore mypy error as this is intentional
     type: Literal["ChatGenerationChunk"] = "ChatGenerationChunk"  # type: ignore[assignment]
     """Type is used exclusively for serialization purposes."""
 

libs/core/langchain_core/outputs/generation.py

@@ -4,8 +4,6 @@ from __future__ import annotations
 
 from typing import Any, Literal, Optional
 
-from pydantic import computed_field
-
 from langchain_core.load import Serializable
 from langchain_core.utils._merge import merge_dicts
 
@@ -26,30 +24,14 @@ class Generation(Serializable):
     for more information.
     """
 
-    def __init__(
+    text: str
-        self,
+    """Generated text output."""
-        text: str = "",
-        generation_info: Optional[dict[str, Any]] = None,
-        **kwargs: Any,
-    ):
-        """Initialize a Generation."""
-        super().__init__(generation_info=generation_info, **kwargs)
-        self._text = text
-
-    # workaround for ChatGeneration so that we can use a computed field to populate
-    # the text field from the message content (parent class needs to have a property)
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def text(self) -> str:
-        """The text contents of the output."""
-        return self._text
 
     generation_info: Optional[dict[str, Any]] = None
     """Raw response from the provider.
 
     May include things like the reason for finishing or token log probabilities.
     """
-
     type: Literal["Generation"] = "Generation"
     """Type is used exclusively for serialization purposes.
     Set to "Generation" for this class."""
@@ -71,16 +53,6 @@ class Generation(Serializable):
 class GenerationChunk(Generation):
     """Generation chunk, which can be concatenated with other Generation chunks."""
 
-    def __init__(
-        self,
-        text: str = "",
-        generation_info: Optional[dict[str, Any]] = None,
-        **kwargs: Any,
-    ):
-        """Initialize a GenerationChunk."""
-        super().__init__(text=text, generation_info=generation_info, **kwargs)
-        self._text = text
-
     def __add__(self, other: GenerationChunk) -> GenerationChunk:
         """Concatenate two GenerationChunks."""
         if isinstance(other, GenerationChunk):

libs/core/tests/unit_tests/load/test_serializable.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from langchain_core.load import Serializable, dumpd, load
 from langchain_core.load.serializable import _is_field_useful
 from langchain_core.messages import AIMessage
-from langchain_core.outputs import ChatGeneration
+from langchain_core.outputs import ChatGeneration, Generation
 
 
 class NonBoolObj:
@@ -223,3 +223,8 @@ def test_serialization_with_pydantic() -> None:
     assert isinstance(deser, ChatGeneration)
     assert deser.message.content
     assert deser.message.additional_kwargs["parsed"] == my_model.model_dump()
+
+
+def test_serialization_with_generation() -> None:
+    generation = Generation(text="hello-world")
+    assert dumpd(generation)["kwargs"] == {"text": "hello-world", "type": "Generation"}