bge-large-en

This documentation is valid for the following list of our models:

BAAI/bge-large-en-v1.5

Model Overview

BGE-Large-EN-v1.5, standing for Bi-directional Global Embedding, is an advanced language model that provides rich, contextual embeddings for English text. It encodes deep linguistic information, allowing for a comprehensive understanding of text nuances, which is crucial for various natural language processing (NLP) tasks.

Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guide.

API Schema

post

Authorizations

AuthorizationstringRequired

Bearer key

Body

modelundefined · enumRequiredPossible values:

inputstring · min: 1 · max: 3000Required

Input text to embed, encoded as a string or array of tokens.

Responses

201Success

post

/v1/embeddings

POST /v1/embeddings HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer YOUR_SECRET_TOKEN
Content-Type: application/json
Accept: */*
Content-Length: 49

{
  "model": "BAAI/bge-large-en-v1.5",
  "input": "text"
}

201Success

No content

Code Example

import openai

# Initialize the API client
client = openai.OpenAI(
    # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
    api_key="<YOUR_AIMLAPI_KEY>",
    base_url="https://api.aimlapi.com/v1",
)

# Define the text for which to generate an embedding
text = "Laura is a DJ."

# Request the embedding
response = client.embeddings.create(
    input=text,
    model="BAAI/bge-large-en-v1.5"
)

# Print the embedding
print(response)

import OpenAI from "openai";
import util from "util";

// Initialize the API client
const client = new OpenAI({
  // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
  apiKey: "<YOUR_AIMLAPI_KEY>",
  baseURL: "https://api.aimlapi.com/v1",
});

// Define the text for which to generate an embedding
const text = "Laura is a DJ.";

const response = await client.embeddings.create({
  input: text,
  model: "BAAI/bge-large-en-v1.5",
});

// Convert embedding to a regular array (not TypedArray)
const pythonLikeResponse = {
  ...response,
  data: response.data.map(item => ({
    ...item,
    embedding: Array.from(item.embedding),
  })),
};

// Python-like print
console.log(
  util.inspect(pythonLikeResponse, {
    depth: null,
    maxArrayLength: null,
    compact: true,
  })
);

This example shows how to set up an API client, send text to the embedding API, and print the response with the embedding vector. See how large a vector response the model generates from just a single short input phrase.

Response

CreateEmbeddingResponse(data=[Embedding(embedding=[-0.029522722586989403, 0.062254276126623154, -0.00391138531267643, 0.013663576915860176, -0.032200440764427185, -0.02270970679819584, -0.01390056125819683, 0.03998970240354538, 0.027032114565372467, -0.0012084535555914044, 0.014594324864447117, -0.004159911535680294, 0.010213620960712433, 0.011115002445876598, -0.03424204885959625, -0.010726550593972206, -0.02737782709300518, -0.003479707520455122, -0.04980620741844177, -0.0032292637042701244, 0.0023568749893456697, 0.01143425703048706, -0.06101774424314499, -0.010085036046802998, -0.06003176420927048, 0.02827335335314274, 0.01778051070868969, -0.01880253106355667, 0.06990577280521393, 0.056985631585121155, -0.009732295759022236, -0.04632685333490372, -0.00727028539404273, -0.02271944098174572, -0.013524127192795277, 0.010008606128394604, 0.010165529325604439, -0.04391580447554588, -0.020615067332983017, -0.05555597320199013, 0.014454372227191925, 0.0008113973308354616, 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usage=Usage(prompt_tokens=7, total_tokens=7, completion_tokens=0, prompt_tokens_details=None), id='embd-efeebdf86c91402982f8fd2b1622d6b3', created=1768555000, meta={'usage': {'credits_used': 1}})

You can find a more advanced example of using embedding vectors in our article Find Relevant Answers: Semantic Search with Text Embeddings in the Use Cases section.

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usage=Usage(prompt_tokens=7, total_tokens=7, completion_tokens=0, prompt_tokens_details=None), id='embd-efeebdf86c91402982f8fd2b1622d6b3', created=1768555000, meta={'usage': {'credits_used': 1}})

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