mirror of
https://github.com/hwchase17/langchain
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130 lines
5.5 KiB
Python
130 lines
5.5 KiB
Python
from __future__ import annotations
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from typing import Any, Dict, Optional
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from langchain_community.chat_message_histories import ZepChatMessageHistory
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try:
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from langchain.memory import ConversationBufferMemory
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class ZepMemory(ConversationBufferMemory):
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"""Persist your chain history to the Zep MemoryStore.
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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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Zep - Fast, scalable building blocks for LLM Apps
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=========
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Zep is an open source platform for productionizing LLM apps. Go from a prototype
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built in LangChain or LlamaIndex, or a custom app, to production in minutes without
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rewriting code.
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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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""" # noqa: E501
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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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""" # noqa: E501
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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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except ImportError:
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# Placeholder object
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class ZepMemory: # type: ignore[no-redef]
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pass
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