bge-base-en

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

BAAI/bge-base-en-v1.5

Model Overview

An embedding model that excels in creating high-precision linguistic representations. It's designed to generate detailed embeddings that capture the subtleties of language, facilitating advanced natural language processing 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: 48

{
  "model": "BAAI/bge-base-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-base-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-base-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=[0.011658690869808197, -0.018578968942165375, 0.012323499657213688, 0.0073761423118412495, 0.06261733919382095, -0.0008231595857068896, 0.0031039523892104626, 0.018022214993834496, -0.05936441570520401, -0.005633850581943989, -0.025196731090545654, 0.029335038736462593, -0.05275379866361618, 0.007178363855928183, -0.012582371011376381, 0.05022329464554787, 0.012615736573934555, -0.03852837532758713, 0.04922296851873398, -0.013275686651468277, 0.02643674612045288, 0.04058190807700157, 0.0013573297765105963, 0.02061430923640728, -0.02698611654341221, -0.020850922912359238, -0.011911595240235329, 0.021286388859152794, -0.03574862331151962, 0.015528105199337006, 0.07527229189872742, -0.06804201006889343, 0.040051039308309555, -0.08631309121847153, 0.011029606685042381, 0.0038828470278531313, 0.055851999670267105, -0.0026011005975306034, -0.01863997057080269, -0.02572443336248398, -0.08648759871721268, -0.014346427284181118, 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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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