voyage-code-2

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

voyage-code-2

Model Overview

This embedding model is designed for semantic retrieval of code and related text from both natural language and code-based queries. In a comprehensive evaluation across 11 code retrieval tasks—sourced from popular datasets like HumanEval and MBPP—it achieved a significant 14.52% improvement in recall over competitors, including OpenAI and Cohere. Additionally, it demonstrated consistent gains, averaging 3.03%, across various general-purpose text datasets.

Setup your API Key

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

API Schema

Example in Python

import openai

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

# 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-code-2"
)

# Print the embedding
print(response)

This Python 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=[-0.015611, 0.016381765, 0.015262494, 0.028116634, 0.026989678, 0.000494121, -0.025271986, -0.011951271, -0.056328308, 0.021407766, -0.012444285, 0.059624858, -0.035985306, -0.046485253, -0.000635049, -0.036895629, 0.037314452, -0.027377753, -0.03597226, 0.018330064, 0.014466659, 0.064007834, -0.017127983, -0.010960116, 0.019682391, -0.019696601, -0.020545894, -0.036266111, 0.022564862, 0.015046909, 0.005086916, -0.055712417, -0.009372988, -0.05443871, 0.010829321, 0.01392135, 0.012792529, 0.00985686, 0.038214996, -0.017488923, 0.027313927, 0.001062958, 0.0097226, -0.03439014, 0.047425389, -0.037770782, 0.028628167, 0.009835372, 0.027161121, -0.004167856, -0.00672366, 0.000454347, -0.017227914, 0.027887423, 0.007638352, -0.010299153, -0.02586738, 0.021838237, 0.046358533, 0.026485134, 0.02549421, -0.042892404, 0.035525952, -0.037090834, 0.022934681, -0.027275261, -0.031381506, 0.002224463, -0.036881655, 0.018545765, 0.016824408, 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Last updated 17 days ago

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Model Overview

Example in Python

import openai

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

# 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-code-2"
)

# Print the embedding
print(response)

Response

CreateEmbeddingResponse(data=[Embedding(embedding=[-0.015611, 0.016381765, 0.015262494, 0.028116634, 0.026989678, 0.000494121, -0.025271986, -0.011951271, -0.056328308, 0.021407766, -0.012444285, 0.059624858, -0.035985306, -0.046485253, -0.000635049, -0.036895629, 0.037314452, -0.027377753, -0.03597226, 0.018330064, 0.014466659, 0.064007834, -0.017127983, -0.010960116, 0.019682391, -0.019696601, -0.020545894, -0.036266111, 0.022564862, 0.015046909, 0.005086916, -0.055712417, -0.009372988, -0.05443871, 0.010829321, 0.01392135, 0.012792529, 0.00985686, 0.038214996, -0.017488923, 0.027313927, 0.001062958, 0.0097226, -0.03439014, 0.047425389, -0.037770782, 0.028628167, 0.009835372, 0.027161121, -0.004167856, -0.00672366, 0.000454347, -0.017227914, 0.027887423, 0.007638352, -0.010299153, -0.02586738, 0.021838237, 0.046358533, 0.026485134, 0.02549421, -0.042892404, 0.035525952, -0.037090834, 0.022934681, -0.027275261, -0.031381506, 0.002224463, -0.036881655, 0.018545765, 0.016824408, 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You can find a more advanced example of using embedding vectors in our article in the Use Cases section.