voyage-finance-2

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

voyage-finance-2

Model Overview

A finance domain-specific embedding model that delivers superior retrieval quality, outperforming competing models on financial datasets—achieving an average 7% gain over OpenAI (the next best model) and 12% over Cohere. It supports a 32K context length, significantly larger than other evaluated alternatives.

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:

inputany ofRequired

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

string · min: 1 · max: 8000Optional

string[]Optional

input_typestring · enumOptional

The type of input data for the model.

Default: documentPossible values:

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: 67

{
  "model": "voyage-finance-2",
  "input": "text",
  "input_type": "document"
}

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="voyage-finance-2"
)

# 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: "voyage-finance-2",
});

// 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=[0.05998329073190689, 0.0020094653591513634, -0.0009767906740307808, 0.00030224627698771656, -0.011362964287400246, -0.020030111074447632, -0.054072171449661255, 0.04061434045433998, 0.005987467244267464, -0.0023644473403692245, -0.003415028564631939, -0.02976968139410019, 0.006293648853898048, -0.0304449163377285, 0.040052350610494614, 0.048378292471170425, -0.017893923446536064, -0.03299994021654129, 0.04526452720165253, 0.05257825553417206, -0.01704838126897812, -0.04987850412726402, 0.004255060572177172, 0.022154690697789192, -0.0158899687230587, 0.023094290867447853, 0.01462775468826294, 0.03831601142883301, -0.014101676642894745, 0.01765582524240017, 0.051165878772735596, -0.00420940900221467, -0.0237688347697258, -0.05128426477313042, 0.03301410749554634, -0.02753707580268383, -0.048054005950689316, 0.02127215452492237, 0.04650184512138367, -0.0240122452378273, 0.012495599687099457, -0.002435286296531558, -0.026180509477853775, 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model='voyage-finance-2', object='list', usage=Usage(prompt_tokens=None, total_tokens=6), meta={'usage': {'credits_used': 2}})

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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model='voyage-finance-2', object='list', usage=Usage(prompt_tokens=None, total_tokens=6), meta={'usage': {'credits_used': 2}})

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