Time-weighted vector store retriever

This retriever uses a combination of semantic similarity and a time decay.

The algorithm for scoring them is:

semantic_similarity + (1.0 - decay_rate) ^ hours_passed

Notably, hours_passed refers to the hours passed since the object in the retriever was last accessed, not since it was created. This means that frequently accessed objects remain “fresh”.

from datetime import datetime, timedelta

import faiss
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_community.docstore import InMemoryDocstore
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings

Low decay rate

A low decay rate (in this, to be extreme, we will set it close to 0) means memories will be “remembered” for longer. A decay rate of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup.

# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {})
retriever = TimeWeightedVectorStoreRetriever(
    vectorstore=vectorstore, decay_rate=0.0000000000000000000000001, k=1
)

yesterday = datetime.now() - timedelta(days=1)
retriever.add_documents(
    [Document(page_content="hello world", metadata={"last_accessed_at": yesterday})]
)
retriever.add_documents([Document(page_content="hello foo")])

['c3dcf671-3c0a-4273-9334-c4a913076bfa']

# "Hello World" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enough
retriever.invoke("hello world")

[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 12, 27, 15, 30, 18, 457125), 'created_at': datetime.datetime(2023, 12, 27, 15, 30, 8, 442662), 'buffer_idx': 0})]

High decay rate

With a high decay rate (e.g., several 9’s), the recency score quickly goes to 0! If you set this all the way to 1, recency is 0 for all objects, once again making this equivalent to a vector lookup.

# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {})
retriever = TimeWeightedVectorStoreRetriever(
    vectorstore=vectorstore, decay_rate=0.999, k=1
)

yesterday = datetime.now() - timedelta(days=1)
retriever.add_documents(
    [Document(page_content="hello world", metadata={"last_accessed_at": yesterday})]
)
retriever.add_documents([Document(page_content="hello foo")])

['eb1c4c86-01a8-40e3-8393-9a927295a950']

# "Hello Foo" is returned first because "hello world" is mostly forgotten
retriever.invoke("hello world")

[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 12, 27, 15, 30, 50, 57185), 'created_at': datetime.datetime(2023, 12, 27, 15, 30, 44, 720490), 'buffer_idx': 1})]

Virtual time

Using some utils in LangChain, you can mock out the time component.

import datetime

from langchain.utils import mock_now

# Notice the last access time is that date time
with mock_now(datetime.datetime(2024, 2, 3, 10, 11)):
    print(retriever.invoke("hello world"))

[Document(page_content='hello world', metadata={'last_accessed_at': MockDateTime(2024, 2, 3, 10, 11), 'created_at': datetime.datetime(2023, 12, 27, 15, 30, 44, 532941), 'buffer_idx': 0})]