Class MinimaxEmbeddings

Class for generating embeddings using the Minimax API. Extends the Embeddings class and implements MinimaxEmbeddingsParams

Example

const embeddings = new MinimaxEmbeddings();

// Embed a single query
const queryEmbedding = await embeddings.embedQuery("Hello world");
console.log(queryEmbedding);

// Embed multiple documents
const documentsEmbedding = await embeddings.embedDocuments([
  "Hello world",
  "Bye bye",
]);
console.log(documentsEmbedding);

Hierarchy

  • Embeddings
    • MinimaxEmbeddings

Implements

Constructors

constructor

Properties

apiKey? apiUrl basePath? batchSize caller headers? minimaxApiKey? minimaxGroupId? model modelName stripNewLines type

Methods

embedDocuments embedQuery

Constructors

constructor

Properties

OptionalapiKey

apiKey?: string

Secret key to use when making requests. Defaults to the value of MINIMAX_API_KEY environment variable.

apiUrl

apiUrl: string

OptionalbasePath

basePath?: string = "https://api.minimax.chat/v1"

batchSize

batchSize: number = 512

The maximum number of documents to embed in a single request. This is limited by the Minimax API to a maximum of 4096.

caller

caller: AsyncCaller

The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.

Optionalheaders

headers?: Record<string, string>

OptionalminimaxApiKey

minimaxApiKey?: string

Secret key to use when making requests. Defaults to the value of MINIMAX_API_KEY environment variable. Alias for apiKey

OptionalminimaxGroupId

minimaxGroupId?: string

API key to use when making requests. Defaults to the value of MINIMAX_GROUP_ID environment variable.

model

model: string = "embo-01"

Model name to use

modelName

modelName: string = "embo-01"

Model name to use Alias for model

stripNewLines

stripNewLines: boolean = true

Whether to strip new lines from the input text. This is recommended by Minimax, but may not be suitable for all use cases.

type

type: "query" | "db" = "db"

The target use-case after generating the vector. When using embeddings, the vector of the target content is first generated through the db and stored in the vector database, and then the vector of the retrieval text is generated through the query. Note: For the parameters of the partial algorithm, we adopted a separate algorithm plan for query and db. Therefore, for a paragraph of text, if it is to be used as a retrieval text, it should use the db, and if it is used as a retrieval text, it should use the query.

Methods

embedDocuments

  • embedDocuments(texts): Promise<number[][]>

  • Method to generate embeddings for an array of documents. Splits the documents into batches and makes requests to the Minimax API to generate embeddings.

    Parameters

    • texts: string[]

      Array of documents to generate embeddings for.

    Returns Promise<number[][]>

    Promise that resolves to a 2D array of embeddings for each document.

embedQuery

  • embedQuery(text): Promise<number[]>

  • Method to generate an embedding for a single document. Calls the embeddingWithRetry method with the document as the input.

    Parameters

    • text: string

      Document to generate an embedding for.

    Returns Promise<number[]>

    Promise that resolves to an embedding for the document.

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