Ivy ORM

A TypeScript-based "ORM" for Azure AI Search (formerly Cognitive Search). Define a schema for your AI Search indexes, indexers, and data sources, and get a strongly-typed SearchClient.

See Ivy Kit for a handy CLI migrator tool!

Documentation

Installation

npm install ivy-orm

Schemas

Define a schema for your indexes, indexers, and data sources:

import { index, indexer, dataSource, string, int32 } from "ivy-orm";

export const realEstate = index("realestate-us-sample-index", {
  listingId: string("listingId").key(),
  beds: int32("beds").filterable().sortable().facetable(),
  baths: int32("baths").filterable().sortable().facetable(),
  description: string("description").searchable(),
  squareFeet: int32("sqft").filterable().sortable().facetable(),
});

export const realEstateDataSource = dataSource(
  "realestate-us-sample",
  "azuresql",
  {
    connectionString: process.env.CONNECTION_STRING,
    container: {
      name: "Listings_5K_KingCounty_WA",
    },
  }
);

export const realEstateIndexer = indexer("realestate-us-sample-indexer", {
  targetIndex: realEstate,
  dataSourceName: realEstateDataSource.name,
});

Ivy ORM infers the appropriate TypeScript types for all fields, and maps the schema onto the underlying SearchClient. You can use all the methods you'd usually use on a SearchClient, but strongly-typed and with excellent IDE autocomplete:

const searchIndexClient = new SearchIndexClient(endpoint, identity);

const srch = connect(searchIndexClient, schema);

const data = await srch.realEstate.search(undefined, {
  top: 20,
  select: ["listingId", "description"],
});

Supported Field Types

Ivy ORM supports most of the AI Search EDM data types.

Primitives

// Edm.String
myField: string("myField");

// Edm.Int32
myField: int32("myField");

// Edm.Int64
myField: int64("myField");

//Edm.Double
myField: double("myField");

//Edm.Boolean
myField: boolean("myField");

Collections

// Collection(Edm.String)
myCollection: stringCollection("myCollection");

// Collection(Edm.Int32)
myCollection: int32Collection("myCollection");

// Collection(Edm.Int64)
myCollection: int64Collection("myCollection");

// Collection(Edm.Double)
myCollection: doubleCollection("myCollection");

// Collection(Edm.DateTimeOffset)
myCollection: dateCollection("myCollection");

// Collection(Edm.Boolean)
myCollection: booleanCollection("myCollection");

// Collection(Edm.ComplexType)
myCollection: collection("myCollection", {
  // ComplexType object fields
  myField: string("myField"),
});

Suggesters

Add a suggester to a field in the index schema:

import { index, string } from "ivy-orm";

const hotels = index("hotels-sample-index", {
  name: string("name").suggester(),
});

and use AI Search's suggest() method with Typescript:

const { results } = await srch.hotels.suggest("my query", "sg", {
  select: ["name"],
  searchFields: ["name"],
});

"sg" is the default suggester name.

Data Sources

Define the name, type, and configuration options for the data source. See the Azure Documentation for more detail on the configuration options.

export const realEstateDataSource = dataSource(
  "realestate-us-sample",
  "azuresql",
  {
    connectionString: process.env.CONNECTION_STRING,
    container: {
      name: "Listings_5K_KingCounty_WA",
    },
  }
);

See the Azure Documentation on defining connection strings that use a managed identity.

Extras

Field names in the ORM don't need to match the names in the datasource. This automatically creates a field mapping in the indexer.

export const realEstate = index("realestate-us-sample-index", {
  squareFeet: int32("sqft").filterable().sortable().facetable(),
  // ^ORM field name  ^datasource column name
});

Note: This only works on top-level primitives. Complex fields aren't supported in a field mapping.