Voyage AI

https://docs.voyageai.com/embeddings/

API Key

# env variable
os.environ['VOYAGE_API_KEY']

Sample Usage - Embedding

from litellm import embedding
import os

os.environ['VOYAGE_API_KEY'] = ""
response = embedding(
    model="voyage/voyage-3.5",
    input=["good morning from litellm"],
)
print(response)

Supported Parameters

VoyageAI embeddings support the following optional parameters:

• input_type: Specifies the type of input for retrieval optimization
  • "query": Use for search queries
  • "document": Use for documents being indexed
• dimensions: Output embedding dimensions (256, 512, 1024, or 2048)
• encoding_format: Output format ("float", "int8", "uint8", "binary", "ubinary")
• truncation: Whether to truncate inputs exceeding max tokens (default: True)

Example with Parameters

from litellm import embedding
import os

os.environ['VOYAGE_API_KEY'] = "your-api-key"

# Embedding with custom dimensions and input type
response = embedding(
    model="voyage/voyage-3.5",
    input=["Your text here"],
    dimensions=512,
    input_type="document"
)
print(f"Embedding dimensions: {len(response.data[0]['embedding'])}")

Supported Models

All models listed here https://docs.voyageai.com/embeddings/#models-and-specifics are supported

Model Name	Function Call
voyage-3.5	embedding(model="voyage/voyage-3.5", input)
voyage-3.5-lite	embedding(model="voyage/voyage-3.5-lite", input)
voyage-3-large	embedding(model="voyage/voyage-3-large", input)
voyage-3	embedding(model="voyage/voyage-3", input)
voyage-3-lite	embedding(model="voyage/voyage-3-lite", input)
voyage-code-3	embedding(model="voyage/voyage-code-3", input)
voyage-finance-2	embedding(model="voyage/voyage-finance-2", input)
voyage-law-2	embedding(model="voyage/voyage-law-2", input)
voyage-code-2	embedding(model="voyage/voyage-code-2", input)
voyage-multilingual-2	embedding(model="voyage/voyage-multilingual-2 ", input)
voyage-large-2-instruct	embedding(model="voyage/voyage-large-2-instruct", input)
voyage-large-2	embedding(model="voyage/voyage-large-2", input)
voyage-2	embedding(model="voyage/voyage-2", input)
voyage-lite-02-instruct	embedding(model="voyage/voyage-lite-02-instruct", input)
voyage-01	embedding(model="voyage/voyage-01", input)
voyage-lite-01	embedding(model="voyage/voyage-lite-01", input)
voyage-lite-01-instruct	embedding(model="voyage/voyage-lite-01-instruct", input)

Contextual Embeddings (voyage-context-3)

VoyageAI's voyage-context-3 model provides contextualized chunk embeddings, where each chunk is embedded with awareness of its surrounding document context. This significantly improves retrieval quality compared to standard context-agnostic embeddings.

Key Benefits

• Chunks understand their position and role within the full document
• Improved retrieval accuracy for long documents (outperforms competitors by 7-23%)
• Better handling of ambiguous references and cross-chunk dependencies
• Seamless drop-in replacement for standard embeddings in RAG pipelines

Usage

Contextual embeddings require a nested input format where each inner list represents chunks from a single document:

from litellm import embedding
import os

os.environ['VOYAGE_API_KEY'] = "your-api-key"

# Single document with multiple chunks
response = embedding(
    model="voyage/voyage-context-3",
    input=[
        [
            "Chapter 1: Introduction to AI",
            "This chapter covers the basics of artificial intelligence.",
            "We will explore machine learning and deep learning."
        ]
    ]
)
print(f"Number of chunk groups: {len(response.data)}")

# Multiple documents
response = embedding(
    model="voyage/voyage-context-3",
    input=[
        ["Paris is the capital of France.", "It is known for the Eiffel Tower."],
        ["Tokyo is the capital of Japan.", "It is a major economic hub."]
    ]
)
print(f"Processed {len(response.data)} documents")

Specifications

• Model: voyage-context-3
• Context length: 32,000 tokens per document
• Output dimensions: 256, 512, 1024 (default), or 2048
• Max inputs: 1,000 per request
• Max total tokens: 120,000
• Max chunks: 16,000
• Pricing: $0.18 per million tokens

When to Use Contextual Embeddings

Use voyage-context-3 when:

• Processing long documents split into chunks
• Document structure and flow are important
• References between sections matter
• You need to preserve document hierarchy

Use standard models (voyage-3.5, voyage-3-large) when:

• Embedding independent pieces of text
• Processing short queries
• Document context is not relevant
• You need faster/cheaper processing

Model Selection Guide

Model	Best For	Context Length	Price/M Tokens
voyage-3.5	General-purpose, multilingual	32K	$0.06
voyage-3.5-lite	Latency-sensitive applications	32K	$0.02
voyage-3-large	Best overall quality	32K	$0.18
voyage-code-3	Code retrieval and search	32K	$0.18
voyage-finance-2	Financial documents	32K	$0.12
voyage-law-2	Legal documents	16K	$0.12
voyage-context-3	Contextual document embeddings	32K	$0.18