vectorstore_token_buffer_memory.py — langchain Source File

Source Code

"""Class for a conversation memory buffer with older messages stored in a vectorstore .

This implements a conversation memory in which the messages are stored in a memory
buffer up to a specified token limit. When the limit is exceeded, older messages are
saved to a `VectorStore` backing database. The `VectorStore` can be made persistent
across sessions.
"""

import warnings
from datetime import datetime
from typing import Any

from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import SystemMessagePromptTemplate
from langchain_core.vectorstores import VectorStoreRetriever
from pydantic import Field, PrivateAttr

from langchain_classic.memory import (
    ConversationTokenBufferMemory,
    VectorStoreRetrieverMemory,
)
from langchain_classic.memory.chat_memory import BaseChatMemory
from langchain_classic.text_splitter import RecursiveCharacterTextSplitter

DEFAULT_HISTORY_TEMPLATE = """
Current date and time: {current_time}.

Potentially relevant timestamped excerpts of previous conversations (you
do not need to use these if irrelevant):
{previous_history}

"""

TIMESTAMP_FORMAT = "%Y-%m-%d %H:%M:%S %Z"


class ConversationVectorStoreTokenBufferMemory(ConversationTokenBufferMemory):
    """Conversation chat memory with token limit and vectordb backing.

    load_memory_variables() will return a dict with the key "history".
    It contains background information retrieved from the vector store
    plus recent lines of the current conversation.

    To help the LLM understand the part of the conversation stored in the
    vectorstore, each interaction is timestamped and the current date and
    time is also provided in the history. A side effect of this is that the
    LLM will have access to the current date and time.

    Initialization arguments:

    This class accepts all the initialization arguments of
    ConversationTokenBufferMemory, such as `llm`. In addition, it
    accepts the following additional arguments

        retriever: (required) A VectorStoreRetriever object to use
            as the vector backing store

        split_chunk_size: (optional, 1000) Token chunk split size
            for long messages generated by the AI

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