@langchain/community/dist/experimental/multimodal_embeddings/googlevertexai.cjs.map

Third-party integrations for LangChain.js

{"version":3,"file":"googlevertexai.cjs","names":["Embeddings","GoogleVertexAILLMConnection","GoogleAuth"],"sources":["../../../src/experimental/multimodal_embeddings/googlevertexai.ts"],"sourcesContent":["import { GoogleAuth, GoogleAuthOptions } from \"google-auth-library\";\nimport { Embeddings, EmbeddingsParams } from \"@langchain/core/embeddings\";\nimport { AsyncCallerCallOptions } from \"@langchain/core/utils/async_caller\";\nimport {\n  GoogleVertexAIBaseLLMInput,\n  GoogleVertexAIBasePrediction,\n  GoogleVertexAILLMPredictions,\n} from \"../../types/googlevertexai-types.js\";\nimport {\n  GoogleVertexAILLMConnection,\n  GoogleVertexAILLMResponse,\n} from \"../../utils/googlevertexai-connection.js\";\n\n/**\n * Parameters for the GoogleVertexAIMultimodalEmbeddings class, extending\n * both EmbeddingsParams and GoogleVertexAIConnectionParams.\n */\nexport interface GoogleVertexAIMultimodalEmbeddingsParams\n  extends EmbeddingsParams, GoogleVertexAIBaseLLMInput<GoogleAuthOptions> {}\n\n/**\n * Options for the GoogleVertexAIMultimodalEmbeddings class, extending\n * AsyncCallerCallOptions.\n */\ninterface GoogleVertexAIMultimodalEmbeddingsOptions extends AsyncCallerCallOptions {}\n\n/**\n * An instance of media (text or image) that can be used for generating\n * embeddings.\n */\ninterface GoogleVertexAIMultimodalEmbeddingsInstance {\n  text?: string;\n  image?: {\n    bytesBase64Encoded: string;\n  };\n}\n\n/**\n * The results of generating embeddings, extending\n * GoogleVertexAIBasePrediction. It includes text and image embeddings.\n */\ninterface GoogleVertexAIMultimodalEmbeddingsResults extends GoogleVertexAIBasePrediction {\n  textEmbedding?: number[];\n  imageEmbedding?: number[];\n}\n\n/**\n * The media should have a text property, an image property, or both.\n */\nexport type GoogleVertexAIMedia =\n  | {\n      text: string;\n      image?: Buffer;\n    }\n  | {\n      text?: string;\n      image: Buffer;\n    };\n\nexport type MediaEmbeddings = {\n  text?: number[];\n  image?: number[];\n};\n\n/**\n * Class for generating embeddings for text and images using Google's\n * Vertex AI. It extends the Embeddings base class and implements the\n * GoogleVertexAIMultimodalEmbeddingsParams interface.\n */\nexport class GoogleVertexAIMultimodalEmbeddings\n  extends Embeddings\n  implements GoogleVertexAIMultimodalEmbeddingsParams\n{\n  model = \"multimodalembedding@001\";\n\n  private connection: GoogleVertexAILLMConnection<\n    GoogleVertexAIMultimodalEmbeddingsOptions,\n    GoogleVertexAIMultimodalEmbeddingsInstance,\n    GoogleVertexAIMultimodalEmbeddingsResults,\n    GoogleAuthOptions\n  >;\n\n  constructor(fields?: GoogleVertexAIMultimodalEmbeddingsParams) {\n    super(fields ?? {});\n\n    this.model = fields?.model ?? this.model;\n\n    this.connection = new GoogleVertexAILLMConnection(\n      { ...fields, ...this },\n      this.caller,\n      new GoogleAuth({\n        scopes: \"https://www.googleapis.com/auth/cloud-platform\",\n        ...fields?.authOptions,\n      })\n    );\n  }\n\n  /**\n   * Converts media (text or image) to an instance that can be used for\n   * generating embeddings.\n   * @param media The media (text or image) to be converted.\n   * @returns An instance of media that can be used for generating embeddings.\n   */\n  mediaToInstance(\n    media: GoogleVertexAIMedia\n  ): GoogleVertexAIMultimodalEmbeddingsInstance {\n    const ret: GoogleVertexAIMultimodalEmbeddingsInstance = {};\n\n    if (media?.text) {\n      ret.text = media.text;\n    }\n\n    if (media.image) {\n      ret.image = {\n        bytesBase64Encoded: media.image.toString(\"base64\"),\n      };\n    }\n\n    return ret;\n  }\n\n  /**\n   * Converts the response from Google Vertex AI to embeddings.\n   * @param response The response from Google Vertex AI.\n   * @returns An array of media embeddings.\n   */\n  responseToEmbeddings(\n    response: GoogleVertexAILLMResponse<GoogleVertexAIMultimodalEmbeddingsResults>\n  ): MediaEmbeddings[] {\n    return (\n      response?.data as GoogleVertexAILLMPredictions<GoogleVertexAIMultimodalEmbeddingsResults>\n    ).predictions.map((r) => ({\n      text: r.textEmbedding,\n      image: r.imageEmbedding,\n    }));\n  }\n\n  /**\n   * Generates embeddings for multiple media instances.\n   * @param media An array of media instances.\n   * @returns A promise that resolves to an array of media embeddings.\n   */\n  async embedMedia(media: GoogleVertexAIMedia[]): Promise<MediaEmbeddings[]> {\n    // Only one media embedding request is allowed\n    return Promise.all(media.map((m) => this.embedMediaQuery(m)));\n  }\n\n  /**\n   * Generates embeddings for a single media instance.\n   * @param media A single media instance.\n   * @returns A promise that resolves to a media embedding.\n   */\n  async embedMediaQuery(media: GoogleVertexAIMedia): Promise<MediaEmbeddings> {\n    const instance: GoogleVertexAIMultimodalEmbeddingsInstance =\n      this.mediaToInstance(media);\n    const instances = [instance];\n\n    const parameters = {};\n    const options = {};\n    const responses = await this.connection.request(\n      instances,\n      parameters,\n      options\n    );\n\n    const result = this.responseToEmbeddings(responses);\n    return result[0];\n  }\n\n  /**\n   * Generates embeddings for multiple images.\n   * @param images An array of images.\n   * @returns A promise that resolves to an array of image embeddings.\n   */\n  async embedImage(images: Buffer[]): Promise<number[][]> {\n    return this.embedMedia(images.map((image) => ({ image }))).then(\n      (embeddings) => embeddings.map((e) => e.image ?? [])\n    );\n  }\n\n  /**\n   * Generates embeddings for a single image.\n   * @param image A single image.\n   * @returns A promise that resolves to an image embedding.\n   */\n  async embedImageQuery(image: Buffer): Promise<number[]> {\n    return this.embedMediaQuery({\n      image,\n    }).then((embeddings) => embeddings.image ?? []);\n  }\n\n  /**\n   * Generates embeddings for multiple text documents.\n   * @param documents An array of text documents.\n   * @returns A promise that resolves to an array of text document embeddings.\n   */\n  async embedDocuments(documents: string[]): Promise<number[][]> {\n    return this.embedMedia(documents.map((text) => ({ text }))).then(\n      (embeddings) => embeddings.map((e) => e.text ?? [])\n    );\n  }\n\n  /**\n   * Generates embeddings for a single text document.\n   * @param document A single text document.\n   * @returns A promise that resolves to a text document embedding.\n   */\n  async embedQuery(document: string): Promise<number[]> {\n    return this.embedMediaQuery({\n      text: document,\n    }).then((embeddings) => embeddings.text ?? 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