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
previous_history_template: (optional) Template used to format
the contents of the prompt history
Example using ChromaDB:
from langchain_classic.memory.token_buffer_vectorstore_memory import (
ConversationVectorStoreTokenBufferMemory,
)
from langchain_chroma import Chroma
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_openai import OpenAI
embedder = HuggingFaceInstructEmbeddings(
query_instruction="Represent the query for retrieval: "
)
chroma = Chroma(
collection_name="demo",
embedding_function=embedder,
collection_metadata={"hnsw:space": "cosine"},
)
retriever = chroma.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
"k": 5,
"score_threshold": 0.75,
},
)
conversation_memory = ConversationVectorStoreTokenBufferMemory(
return_messages=True,
llm=OpenAI(),
retriever=retriever,
max_token_limit=1000,
)
conversation_memory.save_context({"Human": "Hi there"}, {"AI": "Nice to meet you!"})
conversation_memory.save_context(
{"Human": "Nice day isn't it?"}, {"AI": "I love Wednesdays."}
)
conversation_memory.load_memory_variables({"input": "What time is it?"})
