mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-25 04:26:41 -04:00
Daniel Chalef
c7f7788d0b
Hey @hwchase17 - This PR adds a `ZepMemory` class, improves handling of Zep's message metadata, and makes it easier for folks building custom chains to persist metadata alongside their chat history. We've had plenty confused users unfamiliar with ChatMessageHistory classes and how to wrap the `ZepChatMessageHistory` in a `ConversationBufferMemory`. So we've created the `ZepMemory` class as a light wrapper for `ZepChatMessageHistory`. Details: - add ZepMemory, modify notebook to demo use of ZepMemory - Modify summary to be SystemMessage - add metadata argument to add_message; add Zep metadata to Message.additional_kwargs - support passing in metadata
125 lines
5.0 KiB
Python
125 lines
5.0 KiB
Python
from __future__ import annotations
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from typing import Any, Dict, Optional
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from langchain.memory import ConversationBufferMemory
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from langchain.memory.chat_message_histories import ZepChatMessageHistory
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class ZepMemory(ConversationBufferMemory):
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"""Persist your chain history to the Zep Long-term Memory Server
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The number of messages returned by Zep and when the Zep server summarizes chat
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histories is configurable. See the Zep documentation for more details.
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Documentation: https://docs.getzep.com
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Example:
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.. code-block:: python
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memory = ZepMemory(
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session_id=session_id, # Identifies your user or a user's session
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url=ZEP_API_URL, # Your Zep server's URL
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api_key=<your_api_key>, # Optional
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memory_key="history", # Ensure this matches the key used in
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# chain's prompt template
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return_messages=True, # Does your prompt template expect a string
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# or a list of Messages?
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)
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chain = LLMChain(memory=memory,...) # Configure your chain to use the ZepMemory
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instance
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Note:
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To persist metadata alongside your chat history, your will need to create a
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custom Chain class that overrides the `prep_outputs` method to include the metadata
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in the call to `self.memory.save_context`.
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About Zep
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=========
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Zep provides long-term conversation storage for LLM apps. The server stores,
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summarizes, embeds, indexes, and enriches conversational AI chat
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histories, and exposes them via simple, low-latency APIs.
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For server installation instructions and more, see:
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https://docs.getzep.com/deployment/quickstart/
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For more information on the zep-python package, see:
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https://github.com/getzep/zep-python
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"""
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chat_memory: ZepChatMessageHistory
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def __init__(
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self,
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session_id: str,
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url: str = "http://localhost:8000",
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api_key: Optional[str] = None,
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output_key: Optional[str] = None,
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input_key: Optional[str] = None,
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return_messages: bool = False,
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human_prefix: str = "Human",
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ai_prefix: str = "AI",
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memory_key: str = "history",
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):
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"""Initialize ZepMemory.
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Args:
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session_id (str): Identifies your user or a user's session
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url (str, optional): Your Zep server's URL. Defaults to
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"http://localhost:8000".
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api_key (Optional[str], optional): Your Zep API key. Defaults to None.
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output_key (Optional[str], optional): The key to use for the output message.
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Defaults to None.
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input_key (Optional[str], optional): The key to use for the input message.
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Defaults to None.
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return_messages (bool, optional): Does your prompt template expect a string
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or a list of Messages? Defaults to False
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i.e. return a string.
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human_prefix (str, optional): The prefix to use for human messages.
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Defaults to "Human".
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ai_prefix (str, optional): The prefix to use for AI messages.
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Defaults to "AI".
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memory_key (str, optional): The key to use for the memory.
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Defaults to "history".
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Ensure that this matches the key used in
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chain's prompt template.
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"""
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chat_message_history = ZepChatMessageHistory(
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session_id=session_id,
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url=url,
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api_key=api_key,
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)
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super().__init__(
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chat_memory=chat_message_history,
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output_key=output_key,
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input_key=input_key,
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return_messages=return_messages,
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human_prefix=human_prefix,
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ai_prefix=ai_prefix,
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memory_key=memory_key,
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)
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def save_context(
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self,
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inputs: Dict[str, Any],
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outputs: Dict[str, str],
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metadata: Optional[Dict[str, Any]] = None,
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) -> None:
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"""Save context from this conversation to buffer.
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Args:
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inputs (Dict[str, Any]): The inputs to the chain.
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outputs (Dict[str, str]): The outputs from the chain.
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metadata (Optional[Dict[str, Any]], optional): Any metadata to save with
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the context. Defaults to None
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Returns:
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None
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"""
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input_str, output_str = self._get_input_output(inputs, outputs)
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self.chat_memory.add_user_message(input_str, metadata=metadata)
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self.chat_memory.add_ai_message(output_str, metadata=metadata)
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