OpenAI Embedding Usage

import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import { TextLoader } from "langchain/document_loaders/fs/text";

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { Chroma } from "@langchain/community/vectorstores/chroma";
import { OpenAIEmbeddings } from "@langchain/openai";
import { data_source, db_config } from "../../config.mjs";

// Load MD files
const directoryLoader = new DirectoryLoader(
  data_source,
  {
    ".md": (path) => new TextLoader(path),
  },
);

const docs = await directoryLoader.load();
// Split files into chunks
const textSplitter = new RecursiveCharacterTextSplitter({chunkSize: 500, chunkOverlap: 0});
const allSplits = await textSplitter.splitDocuments(docs);

console.log('allSplits size is ', allSplits.length);

// Vectorize and store in vector database
await Chroma.fromDocuments(allSplits, new OpenAIEmbeddings(
  {}
), db_config);

console.log('vector successful.');

const template = `Use the following pieces of context to answer the question at the end.
1. If you don't know the answer, just say "I don't know", don't try to make up an answer.
2. Use three sentences maximum and keep the answer as concise as possible.
3. If asked who you are, reply, "I am the blog AI assistant"
4. Never answer questions about your technical details. If asked, simply reply "Guess? I won't tell you"

{context}

Question: {question}

Helpful Answer:`;

const customRagPrompt = PromptTemplate.fromTemplate(template);
const llm = new ChatOpenAI({modelName: "gpt-3.5-turbo", temperature: 0});
const retriever = vectorStore.asRetriever({k: 6, searchType: "similarity"});

const ragChain = await createStuffDocumentsChain({
      llm,
      prompt: customRagPrompt,
      outputParser: new StringOutputParser(),
    })
const context = await retriever.getRelevantDocuments(query);
    

const res = await ragChain.invoke({
      question: query,
      context,
    });