如何使用时间加权向量存储检索器

该检索器结合了语义相似性和时间衰减。

评分算法为：

semantic_similarity + (1.0 - decay_rate) ^ hours_passed

值得注意的是，hours_passed 指的是自对象在检索器中最后一次访问以来经过的小时数，而不是自其创建以来的小时数。这意味着频繁访问的对象保持“新鲜”。

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

低衰减率

低 decay rate（在这里，为了极端，我们将其设置得接近 0）意味着记忆将被“记住”更长时间。decay rate 为 0 意味着记忆永远不会被遗忘，使得这个检索器等同于向量查找。

# 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.get_relevant_documents("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})]

高衰减率

在高 decay rate（例如，几个9）的情况下，recency score很快就会降到0！如果将其设置为1，所有对象的recency都为0，这再次使其等同于向量查找。

# 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.get_relevant_documents("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})]

虚拟时间

使用 LangChain 中的一些工具，您可以模拟时间组件。

import datetime

from langchain_core.utils import mock_now

# 注意最后访问时间是该日期时间
with mock_now(datetime.datetime(2024, 2, 3, 10, 11)):
    print(retriever.get_relevant_documents("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})]