!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/).

[

![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png)

](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com)

[More](#)

*   [People](/v0.1/docs/people/)
*   [Community](/v0.1/docs/community/)
*   [Tutorials](/v0.1/docs/additional_resources/tutorials/)
*   [Contributing](/v0.1/docs/contributing/)

[v0.1](#)

*   [v0.2](https://js.langchain.com/v0.2/docs/introduction)
*   [v0.1](/v0.1/docs/get_started/introduction/)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Get started](/v0.1/docs/get_started/)
    
    *   [Introduction](/v0.1/docs/get_started/introduction/)
    *   [Installation](/v0.1/docs/get_started/installation/)
    *   [Quickstart](/v0.1/docs/get_started/quickstart/)
*   [LangChain Expression Language](/v0.1/docs/expression_language/)
    
    *   [Get started](/v0.1/docs/expression_language/get_started/)
    *   [Why use LCEL?](/v0.1/docs/expression_language/why/)
    *   [Interface](/v0.1/docs/expression_language/interface/)
    *   [Streaming](/v0.1/docs/expression_language/streaming/)
    *   [How to](/v0.1/docs/expression_language/how_to/routing/)
        
    *   [Cookbook](/v0.1/docs/expression_language/cookbook/)
        
    *   [LangChain Expression Language (LCEL)](/v0.1/docs/expression_language/)
*   [Modules](/v0.1/docs/modules/)
    
    *   [Model I/O](/v0.1/docs/modules/model_io/)
        
    *   [Retrieval](/v0.1/docs/modules/data_connection/)
        
        *   [Document loaders](/v0.1/docs/modules/data_connection/document_loaders/)
            
        *   [Text Splitters](/v0.1/docs/modules/data_connection/document_transformers/)
            
        *   [Retrievers](/v0.1/docs/modules/data_connection/retrievers/)
            
            *   [Custom retrievers](/v0.1/docs/modules/data_connection/retrievers/custom/)
            *   [Contextual compression](/v0.1/docs/modules/data_connection/retrievers/contextual_compression/)
            *   [Matryoshka Retriever](/v0.1/docs/modules/data_connection/retrievers/matryoshka_retriever/)
            *   [MultiQuery Retriever](/v0.1/docs/modules/data_connection/retrievers/multi-query-retriever/)
            *   [MultiVector Retriever](/v0.1/docs/modules/data_connection/retrievers/multi-vector-retriever/)
            *   [Parent Document Retriever](/v0.1/docs/modules/data_connection/retrievers/parent-document-retriever/)
            *   [Self-querying](/v0.1/docs/modules/data_connection/retrievers/self_query/)
                
            *   [Similarity Score Threshold](/v0.1/docs/modules/data_connection/retrievers/similarity-score-threshold-retriever/)
            *   [Time-weighted vector store retriever](/v0.1/docs/modules/data_connection/retrievers/time_weighted_vectorstore/)
            *   [Vector store-backed retriever](/v0.1/docs/modules/data_connection/retrievers/vectorstore/)
        *   [Retrieval](/v0.1/docs/modules/data_connection/)
        *   [Text embedding models](/v0.1/docs/modules/data_connection/text_embedding/)
            
        *   [Vector stores](/v0.1/docs/modules/data_connection/vectorstores/)
            
        *   [Indexing](/v0.1/docs/modules/data_connection/indexing/)
        *   [Experimental](/v0.1/docs/modules/data_connection/experimental/multimodal_embeddings/google_vertex_ai/)
            
    *   [Chains](/v0.1/docs/modules/chains/)
    *   [Agents](/v0.1/docs/modules/agents/)
        
    *   [More](/v0.1/docs/modules/memory/)
        
*   [Security](/v0.1/docs/security/)
*   [Guides](/v0.1/docs/guides/)
    
*   [Ecosystem](/v0.1/docs/ecosystem/)
    
*   [LangGraph](/v0.1/docs/langgraph/)
*   * * *
    

*   [](/v0.1/)
*   [Modules](/v0.1/docs/modules/)
*   [Retrieval](/v0.1/docs/modules/data_connection/)
*   [Retrievers](/v0.1/docs/modules/data_connection/retrievers/)
*   Time-weighted vector store retriever

On this page

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."

    let score = (1.0 - this.decayRate) ** hoursPassed + vectorRelevance;

`this.decayRate` is a configurable decimal number between 0 and 1. A lower number means that documents will be "remembered" for longer, while a higher number strongly weights more recently accessed documents.

Note that setting a decay rate of exactly 0 or 1 makes `hoursPassed` irrelevant and makes this retriever equivalent to a standard vector lookup.

Usage[​](#usage "Direct link to Usage")
---------------------------------------

This example shows how to intialize a `TimeWeightedVectorStoreRetriever` with a vector store. It is important to note that due to required metadata, all documents must be added to the backing vector store using the `addDocuments` method on the **retriever**, not the vector store itself.

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = new MemoryVectorStore(new OpenAIEmbeddings());const retriever = new TimeWeightedVectorStoreRetriever({  vectorStore,  memoryStream: [],  searchKwargs: 2,});const documents = [  "My name is John.",  "My name is Bob.",  "My favourite food is pizza.",  "My favourite food is pasta.",  "My favourite food is sushi.",].map((pageContent) => ({ pageContent, metadata: {} }));// All documents must be added using this method on the retriever (not the vector store!)// so that the correct access history metadata is populatedawait retriever.addDocuments(documents);const results1 = await retriever.invoke("What is my favourite food?");console.log(results1);/*[  Document { pageContent: 'My favourite food is pasta.', metadata: {} }] */const results2 = await retriever.invoke("What is my favourite food?");console.log(results2);/*[  Document { pageContent: 'My favourite food is pasta.', metadata: {} }] */

#### API Reference:

*   [TimeWeightedVectorStoreRetriever](https://api.js.langchain.com/classes/langchain_retrievers_time_weighted.TimeWeightedVectorStoreRetriever.html) from `langchain/retrievers/time_weighted`
*   [MemoryVectorStore](https://api.js.langchain.com/classes/langchain_vectorstores_memory.MemoryVectorStore.html) from `langchain/vectorstores/memory`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

* * *

#### Help us out by providing feedback on this documentation page:

[

Previous

Similarity Score Threshold

](/v0.1/docs/modules/data_connection/retrievers/similarity-score-threshold-retriever/)[

Next

Vector store-backed retriever

](/v0.1/docs/modules/data_connection/retrievers/vectorstore/)

*   [Usage](#usage)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.