langchain.memory.chat_message_histories.zep.ZepChatMessageHistory¶
class langchain.memory.chat_message_histories.zep.ZepChatMessageHistory(session_id: str, url: str = 'http://localhost:8000', api_key: Optional[str] = None)[source]¶
Chat message history that uses Zep as a backend.
Recommended usage:
# Set up Zep Chat History
zep_chat_history = ZepChatMessageHistory(
    session_id=session_id,
    url=ZEP_API_URL,
    api_key=<your_api_key>,
)
# Use a standard ConversationBufferMemory to encapsulate the Zep chat history
memory = ConversationBufferMemory(
    memory_key="chat_history", chat_memory=zep_chat_history
)
Zep provides long-term conversation storage for LLM apps. The server stores,
summarizes, embeds, indexes, and enriches conversational AI chat
histories, and exposes them via simple, low-latency APIs.
For server installation instructions and more, see:
https://docs.getzep.com/deployment/quickstart/
This class is a thin wrapper around the zep-python package. Additional
Zep functionality is exposed via the zep_summary and zep_messages
properties.
For more information on the zep-python package, see:
https://github.com/getzep/zep-python
Attributes
messages
Retrieve messages from Zep memory
zep_messages
Retrieve summary from Zep memory
zep_summary
Retrieve summary from Zep memory
Methods
__init__(session_id[, url, api_key])
add_ai_message(message[, metadata])
Convenience method for adding an AI message string to the store.
add_message(message[, metadata])
Append the message to the Zep memory history
add_user_message(message[, metadata])