Serialize LangChain objects to JSON.
Provides dumps (to JSON string) and dumpd (to dict) for serializing
Serializable objects.
During serialization, plain dicts (user data) that contain an 'lc' key are escaped
by wrapping them: {"__lc_escaped__": {...original...}}. This prevents injection
attacks where malicious data could trick the deserializer into instantiating
arbitrary classes. The escape marker is removed during deserialization.
This is an allowlist approach: only dicts explicitly produced by
Serializable.to_json() are treated as LC objects; everything else is escaped if it
could be confused with the LC format.
Serializable base class.
This class is used to serialize objects to JSON.
It relies on the following methods and properties:
is_lc_serializable: Is this class serializable?
By design, even if a class inherits from Serializable, it is not serializable
by default. This is to prevent accidental serialization of objects that should
not be serialized.
get_lc_namespace: Get the namespace of the LangChain object.
During deserialization, this namespace is used to identify the correct class to instantiate.
Please see the Reviver class in langchain_core.load.load for more details.
During deserialization an additional mapping is handle classes that have moved or been renamed across package versions.
lc_secrets: A map of constructor argument names to secret ids.
lc_attributes: List of additional attribute names that should be included
as part of the serialized representation.
Message from an AI.
An AIMessage is returned from a chat model as a response to a prompt.
This message represents the output of the model and consists of both the raw output as returned by the model and standardized fields (e.g., tool calls, usage metadata) added by the LangChain framework.
A single chat generation output.
A subclass of Generation that represents the response from a chat model that
generates chat messages.
The message attribute is a structured representation of the chat message. Most of
the time, the message will be of type AIMessage.
Users working with chat models will usually access information via either
AIMessage (returned from runnable interfaces) or LLMResult (available via
callbacks).
