Vector Store

The vector store is used to store the generated embeddings. The vector store is an efficient way to store and query embeddings.

QvikChat provides support for more than 30+ vector stores such as Faiss, Pinecone, Chroma and more, through LangChain. To see available vector stores, refer to this page Vector stores (opens in a new tab).

To use a vector store, simply provide the instance to the getDataRetriever method. The below example shows how you can use a Faiss vector store to store the embeddings. You will need to provide the vector store instance the embedding model you want to use with it. If you wish to use a Google Gen AI or an OpenAI embedding model, you can use the getEmbeddingModel method to get the embedding model instance.

import { getDataRetriever } from "@oconva/qvikchat/data-retrievers";
import { getEmbeddingModel } from "@oconva/qvikchat/embedding-models";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
 
// Index data and get retriever
const dataRetriever = await getDataRetriever({
  filePath: "test.csv",
  generateEmbeddings: true,
  vectorStore: new FaissStore(getEmbeddingModel(), {
    index: "test-index",
  }),
});