d9e32dcc02
* chore: vectordb driver plugin # Conflicts: # plugins/monitoring-plugin/package.json * chore: add langchain & index documents * feat: chat with documents plugin * chore: correct build step --------- Co-authored-by: namvuong <22463238+vuonghoainam@users.noreply.github.com>
59 lines
2.4 KiB
TypeScript
59 lines
2.4 KiB
TypeScript
const path = require("path");
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const { app } = require("electron");
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const { DirectoryLoader } = require("langchain/document_loaders/fs/directory");
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const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
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const { PDFLoader } = require("langchain/document_loaders/fs/pdf");
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const { CharacterTextSplitter } = require("langchain/text_splitter");
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const { FaissStore } = require("langchain/vectorstores/faiss");
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const { ChatOpenAI } = require("langchain/chat_models/openai");
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const { RetrievalQAChain } = require("langchain/chains");
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var db: any | undefined = undefined;
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/**
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* Ingests documents from the specified directory
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* If an `embedding` object is not provided, uses OpenAIEmbeddings.
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* The resulting embeddings are stored in the database using Faiss.
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* @param docDir - The directory containing the documents to ingest.
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* @param embedding - An optional object used to generate embeddings for the documents.
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* @param config - An optional configuration object used to create a new `OpenAIEmbeddings` object.
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*/
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async function ingest(docDir: string, embedding?: any, config?: any) {
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const loader = new DirectoryLoader(docDir, {
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".pdf": (path) => new PDFLoader(path),
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});
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const docs = await loader.load();
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const textSplitter = new CharacterTextSplitter();
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const docsQA = await textSplitter.splitDocuments(docs);
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const embeddings = embedding ?? new OpenAIEmbeddings({ ...config });
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db = await FaissStore.fromDocuments(await docsQA, embeddings);
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console.log("Documents are ingested");
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}
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/**
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* Generates an answer to a given question using the specified `llm` or a new `ChatOpenAI`.
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* The function uses the `RetrievalQAChain` class to retrieve the most relevant document from the database and generate an answer.
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* @param question - The question to generate an answer for.
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* @param llm - An optional object used to generate the answer.
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* @param config - An optional configuration object used to create a new `ChatOpenAI` object, can be ignored if llm is specified.
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* @returns A Promise that resolves with the generated answer.
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*/
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async function chatWithDocs(question: string, llm?: any, config?: any): Promise<any> {
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const llm_question_answer =
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llm ??
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new ChatOpenAI({
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temperature: 0.2,
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...config,
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});
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const qa = RetrievalQAChain.fromLLM(llm_question_answer, db.asRetriever(), {
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verbose: true,
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});
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const answer = await qa.run(question);
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return answer;
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}
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module.exports = {
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ingest,
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chatWithDocs,
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};
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