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googlevertexai.ts
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googlevertexai.ts
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import { GoogleAuth, GoogleAuthOptions } from "google-auth-library";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { AsyncCallerCallOptions } from "@langchain/core/utils/async_caller";
import {
GoogleVertexAIBaseLLMInput,
GoogleVertexAIBasePrediction,
GoogleVertexAILLMPredictions,
} from "../../types/googlevertexai-types.js";
import {
GoogleVertexAILLMConnection,
GoogleVertexAILLMResponse,
} from "../../utils/googlevertexai-connection.js";
/**
* Parameters for the GoogleVertexAIMultimodalEmbeddings class, extending
* both EmbeddingsParams and GoogleVertexAIConnectionParams.
*/
export interface GoogleVertexAIMultimodalEmbeddingsParams
extends EmbeddingsParams,
GoogleVertexAIBaseLLMInput<GoogleAuthOptions> {}
/**
* Options for the GoogleVertexAIMultimodalEmbeddings class, extending
* AsyncCallerCallOptions.
*/
interface GoogleVertexAIMultimodalEmbeddingsOptions
extends AsyncCallerCallOptions {}
/**
* An instance of media (text or image) that can be used for generating
* embeddings.
*/
interface GoogleVertexAIMultimodalEmbeddingsInstance {
text?: string;
image?: {
bytesBase64Encoded: string;
};
}
/**
* The results of generating embeddings, extending
* GoogleVertexAIBasePrediction. It includes text and image embeddings.
*/
interface GoogleVertexAIMultimodalEmbeddingsResults
extends GoogleVertexAIBasePrediction {
textEmbedding?: number[];
imageEmbedding?: number[];
}
/**
* The media should have a text property, an image property, or both.
*/
export type GoogleVertexAIMedia =
| {
text: string;
image?: Buffer;
}
| {
text?: string;
image: Buffer;
};
export type MediaEmbeddings = {
text?: number[];
image?: number[];
};
/**
* Class for generating embeddings for text and images using Google's
* Vertex AI. It extends the Embeddings base class and implements the
* GoogleVertexAIMultimodalEmbeddingsParams interface.
*/
export class GoogleVertexAIMultimodalEmbeddings
extends Embeddings
implements GoogleVertexAIMultimodalEmbeddingsParams
{
model = "multimodalembedding@001";
private connection: GoogleVertexAILLMConnection<
GoogleVertexAIMultimodalEmbeddingsOptions,
GoogleVertexAIMultimodalEmbeddingsInstance,
GoogleVertexAIMultimodalEmbeddingsResults,
GoogleAuthOptions
>;
constructor(fields?: GoogleVertexAIMultimodalEmbeddingsParams) {
super(fields ?? {});
this.model = fields?.model ?? this.model;
this.connection = new GoogleVertexAILLMConnection(
{ ...fields, ...this },
this.caller,
new GoogleAuth({
scopes: "https://www.googleapis.com/auth/cloud-platform",
...fields?.authOptions,
})
);
}
/**
* Converts media (text or image) to an instance that can be used for
* generating embeddings.
* @param media The media (text or image) to be converted.
* @returns An instance of media that can be used for generating embeddings.
*/
mediaToInstance(
media: GoogleVertexAIMedia
): GoogleVertexAIMultimodalEmbeddingsInstance {
const ret: GoogleVertexAIMultimodalEmbeddingsInstance = {};
if (media?.text) {
ret.text = media.text;
}
if (media.image) {
ret.image = {
bytesBase64Encoded: media.image.toString("base64"),
};
}
return ret;
}
/**
* Converts the response from Google Vertex AI to embeddings.
* @param response The response from Google Vertex AI.
* @returns An array of media embeddings.
*/
responseToEmbeddings(
response: GoogleVertexAILLMResponse<GoogleVertexAIMultimodalEmbeddingsResults>
): MediaEmbeddings[] {
return (
response?.data as GoogleVertexAILLMPredictions<GoogleVertexAIMultimodalEmbeddingsResults>
).predictions.map((r) => ({
text: r.textEmbedding,
image: r.imageEmbedding,
}));
}
/**
* Generates embeddings for multiple media instances.
* @param media An array of media instances.
* @returns A promise that resolves to an array of media embeddings.
*/
async embedMedia(media: GoogleVertexAIMedia[]): Promise<MediaEmbeddings[]> {
// Only one media embedding request is allowed
return Promise.all(media.map((m) => this.embedMediaQuery(m)));
}
/**
* Generates embeddings for a single media instance.
* @param media A single media instance.
* @returns A promise that resolves to a media embedding.
*/
async embedMediaQuery(media: GoogleVertexAIMedia): Promise<MediaEmbeddings> {
const instance: GoogleVertexAIMultimodalEmbeddingsInstance =
this.mediaToInstance(media);
const instances = [instance];
const parameters = {};
const options = {};
const responses = await this.connection.request(
instances,
parameters,
options
);
const result = this.responseToEmbeddings(responses);
return result[0];
}
/**
* Generates embeddings for multiple images.
* @param images An array of images.
* @returns A promise that resolves to an array of image embeddings.
*/
async embedImage(images: Buffer[]): Promise<number[][]> {
return this.embedMedia(images.map((image) => ({ image }))).then(
(embeddings) => embeddings.map((e) => e.image ?? [])
);
}
/**
* Generates embeddings for a single image.
* @param image A single image.
* @returns A promise that resolves to an image embedding.
*/
async embedImageQuery(image: Buffer): Promise<number[]> {
return this.embedMediaQuery({
image,
}).then((embeddings) => embeddings.image ?? []);
}
/**
* Generates embeddings for multiple text documents.
* @param documents An array of text documents.
* @returns A promise that resolves to an array of text document embeddings.
*/
async embedDocuments(documents: string[]): Promise<number[][]> {
return this.embedMedia(documents.map((text) => ({ text }))).then(
(embeddings) => embeddings.map((e) => e.text ?? [])
);
}
/**
* Generates embeddings for a single text document.
* @param document A single text document.
* @returns A promise that resolves to a text document embedding.
*/
async embedQuery(document: string): Promise<number[]> {
return this.embedMediaQuery({
text: document,
}).then((embeddings) => embeddings.text ?? []);
}
}