import { ZepVectorStore } from "langchain/vectorstores/zep";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { randomUUID } from "crypto";

const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();
export const run = async () => {
  const collectionName = `collection${randomUUID().split("-")[0]}`;

  const zepConfig = {
    apiUrl: "http://localhost:8000", // this should be the URL of your Zep implementation
    collectionName,
    embeddingDimensions: 1536, // this much match the width of the embeddings you're using
    isAutoEmbedded: false, // set to false to disable auto-embedding
  };

  const embeddings = new OpenAIEmbeddings();

  const vectorStore = await ZepVectorStore.fromDocuments(
    docs,
    embeddings,
    zepConfig
  );

  const results = await vectorStore.similaritySearchWithScore("bar", 3);

  console.log("Similarity Results:");
  console.log(JSON.stringify(results));

  const results2 = await vectorStore.maxMarginalRelevanceSearch("bar", {
    k: 3,
  });

  console.log("MMR Results:");
  console.log(JSON.stringify(results2));
};