import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "langchain/embeddings/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 populated
await retriever.addDocuments(documents);

const results1 = await retriever.getRelevantDocuments(
  "What is my favourite food?"
);

console.log(results1);

/*
[
  Document { pageContent: 'My favourite food is pasta.', metadata: {} }
]
 */

const results2 = await retriever.getRelevantDocuments(
  "What is my favourite food?"
);

console.log(results2);

/*
[
  Document { pageContent: 'My favourite food is pasta.', metadata: {} }
]
 */