Source code for langchain.memory.zep_memory

from __future__ import annotations

from typing import Any, Dict, Optional

from langchain_community.chat_message_histories import ZepChatMessageHistory

from langchain.memory import ConversationBufferMemory

[docs]class ZepMemory(ConversationBufferMemory): """Persist your chain history to the Zep MemoryStore. 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: 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`. Zep - Fast, scalable building blocks for LLM Apps ========= Zep is an open source platform for productionizing LLM apps. Go from a prototype built in LangChain or LlamaIndex, or a custom app, to production in minutes without rewriting code. For server installation instructions and more, see: For more information on the zep-python package, see: """ 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)