from __future__ import annotations
from typing import Any, Dict, Optional
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import ZepChatMessageHistory
[docs]class ZepMemory(ConversationBufferMemory):
"""Persist your chain history to the Zep Memory Server.
The number of messages returned by Zep and when the Zep server summarizes chat
histories is configurable. See the Zep documentation for more details.
Documentation: https://docs.getzep.com
Example:
.. code-block:: python
memory = ZepMemory(
session_id=session_id, # Identifies your user or a user's session
url=ZEP_API_URL, # Your Zep server's URL
api_key=<your_api_key>, # Optional
memory_key="history", # Ensure this matches the key used in
# chain's prompt template
return_messages=True, # Does your prompt template expect a string
# or a list of Messages?
)
chain = LLMChain(memory=memory,...) # Configure your chain to use the ZepMemory
instance
Note:
To persist metadata alongside your chat history, your will need to create a
custom Chain class that overrides the `prep_outputs` method to include the metadata
in the call to `self.memory.save_context`.
About Zep
=========
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/
For more information on the zep-python package, see:
https://github.com/getzep/zep-python
"""
chat_memory: ZepChatMessageHistory
def __init__(
self,
session_id: str,
url: str = "http://localhost:8000",
api_key: Optional[str] = None,
output_key: Optional[str] = None,
input_key: Optional[str] = None,
return_messages: bool = False,
human_prefix: str = "Human",
ai_prefix: str = "AI",
memory_key: str = "history",
):
"""Initialize ZepMemory.
Args:
session_id (str): Identifies your user or a user's session
url (str, optional): Your Zep server's URL. Defaults to
"http://localhost:8000".
api_key (Optional[str], optional): Your Zep API key. Defaults to None.
output_key (Optional[str], optional): The key to use for the output message.
Defaults to None.
input_key (Optional[str], optional): The key to use for the input message.
Defaults to None.
return_messages (bool, optional): Does your prompt template expect a string
or a list of Messages? Defaults to False
i.e. return a string.
human_prefix (str, optional): The prefix to use for human messages.
Defaults to "Human".
ai_prefix (str, optional): The prefix to use for AI messages.
Defaults to "AI".
memory_key (str, optional): The key to use for the memory.
Defaults to "history".
Ensure that this matches the key used in
chain's prompt template.
"""
chat_message_history = ZepChatMessageHistory(
session_id=session_id,
url=url,
api_key=api_key,
)
super().__init__(
chat_memory=chat_message_history,
output_key=output_key,
input_key=input_key,
return_messages=return_messages,
human_prefix=human_prefix,
ai_prefix=ai_prefix,
memory_key=memory_key,
)
[docs] def save_context(
self,
inputs: Dict[str, Any],
outputs: Dict[str, str],
metadata: Optional[Dict[str, Any]] = None,
) -> None:
"""Save context from this conversation to buffer.
Args:
inputs (Dict[str, Any]): The inputs to the chain.
outputs (Dict[str, str]): The outputs from the chain.
metadata (Optional[Dict[str, Any]], optional): Any metadata to save with
the context. Defaults to None
Returns:
None
"""
input_str, output_str = self._get_input_output(inputs, outputs)
self.chat_memory.add_user_message(input_str, metadata=metadata)
self.chat_memory.add_ai_message(output_str, metadata=metadata)