Getting Started: Vector Stores

info

Conceptual Guide

A vector store is a particular type of database optimized for storing documents and their embeddings, and then fetching of the most relevant documents for a particular query, ie. those whose embeddings are most similar to the embedding of the query.

interface VectorStore {
  /**
   * Add more documents to an existing VectorStore
   */
  addDocuments(documents: Document[]): Promise<void>;

  /**
   * Search for the most similar documents to a query
   */
  similaritySearch(
    query: string,
    k?: number,
    filter?: object | undefined
  ): Promise<Document[]>;

  /**
   * Search for the most similar documents to a query,
   * and return their similarity score
   */
  similaritySearchWithScore(
    query: string,
    k = 4,
    filter: object | undefined = undefined
  ): Promise<[object, number][]>;

  /**
   * Turn a VectorStore into a Retriever
   */
  asRetriever(k?: number): BaseRetriever;

  /**
   * Advanced: Add more documents to an existing VectorStore,
   * when you already have their embeddings
   */
  addVectors(vectors: number[][], documents: Document[]): Promise<void>;

  /**
   * Advanced: Search for the most similar documents to a query,
   * when you already have the embedding of the query
   */
  similaritySearchVectorWithScore(
    query: number[],
    k: number,
    filter?: object
  ): Promise<[Document, number][]>;
}

You can create a vector store from a list of Documents, or from a list of texts and their corresponding metadata. You can also create a vector store from an existing index, the signature of this method depends on the vector store you're using, check the documentation of the vector store you're interested in.

abstract class BaseVectorStore implements VectorStore {
  static fromTexts(
    texts: string[],
    metadatas: object[] | object,
    embeddings: Embeddings,
    dbConfig: Record<string, any>
  ): Promise<VectorStore>;

  static fromDocuments(
    docs: Document[],
    embeddings: Embeddings,
    dbConfig: Record<string, any>
  ): Promise<VectorStore>;
}

Which one to pick?

Here's a quick guide to help you pick the right vector store for your use case:

• If you're after something that can just run inside your application, in-memory, without any other servers to stand up, then go for HNSWLib
• If you come from Python and you were looking for something similar to FAISS, pick HNSWLib
• If you're looking for an open-source full-featured vector database that you can run locally in a docker container, then go for Chroma
• If you're using Supabase already then look at the Supabase vector store to use the same Postgres database for your embeddings too
• If you're looking for a production-ready vector store you don't have to worry about hosting yourself, then go for Pinecone

All Vector Stores

🗃️ Integrations

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