TurbopufferVectorStore integration

Integrate with the TurbopufferVectorStore using LangChain JavaScript.

turbopuffer is a fast, cost-efficient vector database for search and retrieval.

This guide helps you get started with the turbopuffer vector store. For detailed documentation of all TurbopufferVectorStore features and configurations, see the API reference.

Overview

Integration details

Class	Package	PY support	Downloads	Version
TurbopufferVectorStore	@langchain/turbopuffer	✅

Setup

Sign up for a turbopuffer account, create an API key, and install @langchain/turbopuffer, the official @turbopuffer/turbopuffer client, @langchain/core, and an embeddings provider (this guide uses OpenAI embeddings).

npm

npm install @langchain/turbopuffer @turbopuffer/turbopuffer @langchain/core @langchain/openai

Credentials

Set your API key as an environment variable:

process.env.TURBOPUFFER_API_KEY = "your-api-key";

Optionally set a region (for example gcp-us-central1).

Instantiation

Create a turbopuffer client and namespace, then pass the namespace to TurbopufferVectorStore:

import { OpenAIEmbeddings } from "@langchain/openai";
import { TurbopufferVectorStore } from "@langchain/turbopuffer";
import { Turbopuffer } from "@turbopuffer/turbopuffer";

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

const client = new Turbopuffer({
  apiKey: process.env.TURBOPUFFER_API_KEY,
  region: "gcp-us-central1",
});

const vectorStore = new TurbopufferVectorStore(embeddings, {
  namespace: client.namespace("my-namespace"),
});

Manage vector store

Add items to vector store

Currently, only string metadata values are supported.

import type { Document } from "@langchain/core/documents";

const createdAt = new Date().getTime();

const ids = await vectorStore.addDocuments([
  {
    pageContent: "some content",
    metadata: { created_at: createdAt.toString() },
  },
  { pageContent: "hi", metadata: { created_at: (createdAt + 1).toString() } },
  { pageContent: "bye", metadata: { created_at: (createdAt + 2).toString() } },
  {
    pageContent: "what's this",
    metadata: { created_at: (createdAt + 3).toString() },
  },
]);

Delete items from vector store

await vectorStore.delete({ ids: [ids[ids.length - 1]] });

Query vector store

Query directly

const results = await vectorStore.similaritySearch("hello", 1);

for (const doc of results) {
  console.log(doc.pageContent, doc.metadata);
}

Filter by metadata using turbopuffer filter expressions. See the turbopuffer filter documentation for supported operators.

const results2 = await vectorStore.similaritySearch("hello", 1, [
  "created_at",
  "Eq",
  (createdAt + 3).toString(),
]);

for (const doc of results2) {
  console.log(doc.pageContent, doc.metadata);
}

Upsert with existing IDs

await vectorStore.addDocuments(
  [
    { pageContent: "changed", metadata: { created_at: createdAt.toString() } },
    {
      pageContent: "hi changed",
      metadata: { created_at: (createdAt + 1).toString() },
    },
    {
      pageContent: "bye changed",
      metadata: { created_at: (createdAt + 2).toString() },
    },
    {
      pageContent: "what's this changed",
      metadata: { created_at: (createdAt + 3).toString() },
    },
  ],
  { ids }
);

Delete all vectors in the namespace

await vectorStore.delete({ deleteAll: true });

Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

• Build a RAG app with LangChain.
• Agentic RAG
• Retrieval docs

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API reference

For detailed documentation of all TurbopufferVectorStore features and configurations head to the API reference.

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