Integrate LangChain.js with Astra DB Serverless

.// Importing necessary modules and classes
import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { AstraDBVectorStore, AstraLibArgs } from "@langchain/community/vectorstores/astradb";

// Constants and Environment Variables
const FILE_PATH = "./src/sample";
const OPENAI_API_KEY = process.env['OPENAI_API_KEY'];

/**
 * Load and split documents from the local directory.
 * @returns {Promise<Array<Document>>} An array of split documents.
 */
async function loadDocs() {
  try {
    const loader = new DirectoryLoader(FILE_PATH, { ".txt": path => new TextLoader(path) });
    const docs = await loader.load();

    const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 15 });
    const texts = await splitter.splitDocuments(docs);

    console.log(`Loaded ${texts.length} documents.`);
    return texts;
  } catch (error) {
    console.error('Error loading documents:', error);
    throw error;
  }
}

// Load documents and handle any errors
loadDocs().catch(error => console.error('Failed to load documents:', error));

// Variable to store the vector store promise
let vectorStorePromise;

/**
 * Initialize and get the vector store as a promise.
 * @returns {Promise<AstraDBVectorStore>} A promise that resolves to the AstraDBVectorStore.
 */
export async function getVectorStore() {
  if (!vectorStorePromise) {
    vectorStorePromise = initVectorStore();
  }
  return vectorStorePromise;
}

async function initVectorStore() {
  try {
    const texts = await loadDocs();
    const astraConfig = getAstraConfig();

    // Initialize the vector store.
    const vectorStore = await AstraDBVectorStore.fromDocuments(
      texts, new OpenAIEmbeddings({ openAIApiKey: OPENAI_API_KEY, batchSize: 512 }), astraConfig
    );

    // Generate embeddings from the documents and store them.
    vectorStore.addDocuments(texts);
    console.log(vectorStore);
    return vectorStore;
  } catch (error) {
    console.error('Error initializing vector store:', error);
    throw error;
  }
}

// Specify the database and collection to use.
// If the collection does not exist, it is created automatically.
function getAstraConfig() {
  return {
    token: process.env.ASTRA_DB_APPLICATION_TOKEN as string,
    endpoint: process.env.ASTRA_DB_API_ENDPOINT as string,
    collection: process.env.ASTRA_DB_COLLECTION ?? "vector_test",
    collectionOptions: {
      vector: {
        dimension: 1536,
        metric: "cosine",
      },
    },
  } as AstraLibArgs;
}

// Importing necessary modules and classes
import { ChatOpenAI } from "@langchain/openai";
import { formatDocumentsAsString } from "langchain/util/document";
import { RunnablePassthrough, RunnableSequence } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import {
  ChatPromptTemplate,
  HumanMessagePromptTemplate,
  SystemMessagePromptTemplate,
} from "@langchain/core/prompts";
import { getVectorStore } from './load';

// Constants for templates
const SYSTEM_TEMPLATE = `Use the following pieces of context to answer the question at the end.
  If you don't know the answer, just say that you don't know, don't try to make up an answer.
  ----------------
  {context}`;

/**
 * Query the vector store for the context and pass the context with your query to the LLM.
 */
async function queryVectorStore() {
  try {
    const vectorStore = await getVectorStore();
    const retriever = vectorStore.asRetriever();
    const openAIModel = new ChatOpenAI({});

    const messages = [
      SystemMessagePromptTemplate.fromTemplate(SYSTEM_TEMPLATE),
      HumanMessagePromptTemplate.fromTemplate("{question}"),
    ];

    const prompt = ChatPromptTemplate.fromMessages(messages);

    const chain = RunnableSequence.from([
      {
        context: retriever.pipe(formatDocumentsAsString),
        question: new RunnablePassthrough(),
      },
      prompt,
      openAIModel,
      new StringOutputParser(),
    ]);

    const query = "What is this story about?";
    const answer = await chain.invoke(query);

    console.log({ answer });
  } catch (error) {
    console.error('Error during vector store query:', error);
    throw error;
  }
}

// Run the query function and handle errors
queryVectorStore().catch(error => console.error('Failed to run query:', error));