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@langchain/community

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import { __exportAll } from "../../_virtual/_rolldown/runtime.js"; import { GoogleVertexAILLMConnection } from "../../utils/googlevertexai-connection.js"; import { Embeddings } from "@langchain/core/embeddings"; import { GoogleAuth } from "google-auth-library"; //#region src/experimental/multimodal_embeddings/googlevertexai.ts var googlevertexai_exports = /* @__PURE__ */ __exportAll({ GoogleVertexAIMultimodalEmbeddings: () => GoogleVertexAIMultimodalEmbeddings }); /** * Class for generating embeddings for text and images using Google's * Vertex AI. It extends the Embeddings base class and implements the * GoogleVertexAIMultimodalEmbeddingsParams interface. */ var GoogleVertexAIMultimodalEmbeddings = class extends Embeddings { model = "multimodalembedding@001"; connection; constructor(fields) { 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) { const ret = {}; 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) { return (response?.data).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) { 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) { const instances = [this.mediaToInstance(media)]; const responses = await this.connection.request(instances, {}, {}); return this.responseToEmbeddings(responses)[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) { 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) { 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) { 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) { return this.embedMediaQuery({ text: document }).then((embeddings) => embeddings.text ?? []); } }; //#endregion export { GoogleVertexAIMultimodalEmbeddings, googlevertexai_exports }; //# sourceMappingURL=googlevertexai.js.map