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*   Turbopuffer

On this page

Turbopuffer
===========

Setup[​](#setup "Direct link to Setup")
---------------------------------------

First you must sign up for a Turbopuffer account [here](https://turbopuffer.com/join). Then, once you have an account you can create an API key.

Set your API key as an environment variable:

    export TURBOPUFFER_API_KEY=<YOUR_API_KEY>

Usage[​](#usage "Direct link to Usage")
---------------------------------------

Here are some examples of how to use the class. You can filter your queries by previous specified metadata, but keep in mind that currently only string values are supported.

See [here for more information](https://turbopuffer.com/docs/reference/query#filter-parameters) on acceptable filter formats.

    import { OpenAIEmbeddings } from "@langchain/openai";import { TurbopufferVectorStore } from "@langchain/community/vectorstores/turbopuffer";const embeddings = new OpenAIEmbeddings();const store = new TurbopufferVectorStore(embeddings, {  apiKey: process.env.TURBOPUFFER_API_KEY,  namespace: "my-namespace",});const createdAt = new Date().getTime();// Add some documents to your store.// Currently, only string metadata values are supported.const ids = await store.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() },  },]);// Retrieve documents from the storeconst results = await store.similaritySearch("hello", 1);console.log(results);/*  [    Document {      pageContent: 'hi',      metadata: { created_at: '1705519164987' }    }  ]*/// Filter by metadata// See https://turbopuffer.com/docs/reference/query#filter-parameters for more on// allowed filtersconst results2 = await store.similaritySearch("hello", 1, {  created_at: [["Eq", (createdAt + 3).toString()]],});console.log(results2);/*  [    Document {      pageContent: "what's this",      metadata: { created_at: '1705519164989' }    }  ]*/// Upsert by passing idsawait store.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 });// Filter by metadataconst results3 = await store.similaritySearch("hello", 10, {  created_at: [["Eq", (createdAt + 3).toString()]],});console.log(results3);/*  [    Document {      pageContent: "what's this changed",      metadata: { created_at: '1705519164989' }    }  ]*/// Remove all vectors from the namespace.await store.delete({  deleteIndex: true,});

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [TurbopufferVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_turbopuffer.TurbopufferVectorStore.html) from `@langchain/community/vectorstores/turbopuffer`

* * *

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