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*   Tigris

On this page

Tigris
======

Tigris makes it easy to build AI applications with vector embeddings. It is a fully managed cloud-native database that allows you store and index documents and vector embeddings for fast and scalable vector search.

Compatibility

Only available on Node.js.

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

### 1\. Install the Tigris SDK[​](#1-install-the-tigris-sdk "Direct link to 1. Install the Tigris SDK")

Install the SDK as follows

*   npm
*   Yarn
*   pnpm

    npm install -S @tigrisdata/vector

    yarn add @tigrisdata/vector

    pnpm add @tigrisdata/vector

### 2\. Fetch Tigris API credentials[​](#2-fetch-tigris-api-credentials "Direct link to 2. Fetch Tigris API credentials")

You can sign up for a free Tigris account [here](https://www.tigrisdata.com/).

Once you have signed up for the Tigris account, create a new project called `vectordemo`. Next, make a note of the `clientId` and `clientSecret`, which you can get from the Application Keys section of the project.

Index docs[​](#index-docs "Direct link to Index docs")
------------------------------------------------------

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install -S @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { VectorDocumentStore } from "@tigrisdata/vector";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "@langchain/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({  connection: {    serverUrl: "api.preview.tigrisdata.cloud",    projectName: process.env.TIGRIS_PROJECT,    clientId: process.env.TIGRIS_CLIENT_ID,    clientSecret: process.env.TIGRIS_CLIENT_SECRET,  },  indexName: "examples_index",  numDimensions: 1536, // match the OpenAI embedding size});const docs = [  new Document({    metadata: { foo: "bar" },    pageContent: "tigris is a cloud-native vector db",  }),  new Document({    metadata: { foo: "bar" },    pageContent: "the quick brown fox jumped over the lazy dog",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "lorem ipsum dolor sit amet",  }),  new Document({    metadata: { baz: "qux" },    pageContent: "tigris is a river",  }),];await TigrisVectorStore.fromDocuments(docs, new OpenAIEmbeddings(), { index });

Query docs[​](#query-docs "Direct link to Query docs")
------------------------------------------------------

    import { VectorDocumentStore } from "@tigrisdata/vector";import { OpenAIEmbeddings } from "@langchain/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({  connection: {    serverUrl: "api.preview.tigrisdata.cloud",    projectName: process.env.TIGRIS_PROJECT,    clientId: process.env.TIGRIS_CLIENT_ID,    clientSecret: process.env.TIGRIS_CLIENT_SECRET,  },  indexName: "examples_index",  numDimensions: 1536, // match the OpenAI embedding size});const vectorStore = await TigrisVectorStore.fromExistingIndex(  new OpenAIEmbeddings(),  { index });/* Search the vector DB independently with metadata filters */const results = await vectorStore.similaritySearch("tigris", 1, {  "metadata.foo": "bar",});console.log(JSON.stringify(results, null, 2));/*[  Document {    pageContent: 'tigris is a cloud-native vector db',    metadata: { foo: 'bar' }  }]*/

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*   [Setup](#setup)
    *   [1\. Install the Tigris SDK](#1-install-the-tigris-sdk)
    *   [2\. Fetch Tigris API credentials](#2-fetch-tigris-api-credentials)
*   [Index docs](#index-docs)
*   [Query docs](#query-docs)

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