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

1. Install the Tigris SDK

Install the SDK as follows

npm

npm install -S @tigrisdata/vector

Yarn

yarn add @tigrisdata/vector

pnpm

pnpm add @tigrisdata/vector

2. Fetch Tigris API credentials

You can sign up for a free Tigris account here.

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

tip

See this section for general instructions on installing integration packages.

npm

npm install -S @langchain/openai

Yarn

yarn add @langchain/openai

pnpm

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

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' }
  }
]
*/