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Tigris

Tigris使向量嵌入的构建人工智能应用程序变得轻松。 它是一个完全托管的云原生数据库，允许您存储和索引文档和向量嵌入，以进行快速和可扩展的向量搜索。

兼容性

仅在Node.js上可用。

安装​

1. 安装Tigris SDK​

按以下方式安装SDK


npm install -S @tigrisdata/vector

2. 获取Tigris API凭据​

您可以在此处注册免费的Tigris帐户。

注册Tigris帐户后，创建名为vectordemo的新项目。 接下来，记录clientId和clientSecret，您可以从项目的应用程序密钥部分获取它们。

索引文档​

import { VectorDocumentStore } from "@tigrisdata/vector";
import { Document } from "langchain/document";
import { OpenAIEmbeddings } from "langchain/embeddings/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​

import { VectorDocumentStore } from "@tigrisdata/vector";
import { OpenAIEmbeddings } from "langchain/embeddings/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' }
  }
]
*/