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Tigris

Tigris 可以轻松使用向量嵌入构建 AI 应用。它是一个完全托管的云原生数据库，允许你存储和索引文档和向量嵌入，以实现快速且可扩展的向量搜索。

¥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

仅在 Node.js 上可用。

¥Only available on Node.js.

设置​

¥Setup

1. 安装 Tigris SDK​

¥ Install the Tigris SDK

按如下方式安装 SDK

¥Install the SDK as follows

npm install -S @tigrisdata/vector

2. 获取 Tigris API 凭证​

¥ Fetch Tigris API credentials

你可以注册一个免费的 Tigris 账户 此处。

¥You can sign up for a free Tigris account here.

注册 Tigris 账户后，创建一个名为 vectordemo 的新项目。接下来，记下 clientId 和 clientSecret，你可以从项目的“应用密钥”部分获取它们。

¥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.

索引文档​

¥Index docs

tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install -S @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 });

查询文档​

¥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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