时间加权集成

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

import { TimeWeightedVectorStoreRetriever } from "@langchain/classic/retrievers/time_weighted";
import { MemoryVectorStore } from "@langchain/classic/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: {} }));

// 必须使用检索器上的此方法添加所有文档（而不是向量存储！）
// 以便填充正确的访问历史元数据
await 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: {} }
]
 */