Voyage AI Embeddings

Generate embeddings using Voyage AI’s specialized models

Overview

Voyage AI provides specialized embedding models optimized for different domains including code, legal, and finance.

Quick Start

from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="voyage/voyage-3"
)
print(f"Dimensions: {len(result.embeddings[0])}")

CLI Usage

praisonai embed "Hello world" --model voyage/voyage-3

Setup

export VOYAGE_API_KEY="your-voyage-api-key"

Available Models

Model	Dimensions	Use Case
voyage/voyage-3	1024	General purpose, best quality
voyage/voyage-3-lite	512	Fast, cost-effective
voyage/voyage-code-3	1024	Code search
voyage/voyage-finance-2	1024	Financial documents
voyage/voyage-law-2	1024	Legal documents
voyage/voyage-multilingual-2	1024	Multilingual

Memory sizes its vector store from get_dimensions(embedding_model). Before the follow-up commits on PraisonAI PR #3027 (2026-07-14; praisonaiagents after 1.6.151 / praisonai after 4.6.148), voyage-3-lite fell through to voyage-3’s 1024 because the substring lookup matched the shorter key first — a memory collection created with that model on an older release will be 1024-dim while the model emits 512-dim vectors, causing dimension-mismatch errors on upsert. Upgrade praisonaiagents and re-create the collection to fix it. The voyage/ and openai/-prefixed forms were affected the same way.

Code Embeddings

from praisonaiagents import embedding

code = '''
def hello_world():
    print("Hello, World!")
'''
result = embedding(
    input=code,
    model="voyage/voyage-code-3"
)

Batch Embeddings

from praisonaiagents import embedding

texts = ["Document 1", "Document 2", "Document 3"]
result = embedding(
    input=texts,
    model="voyage/voyage-3"
)
print(f"Generated {len(result.embeddings)} embeddings")

Related

• Embedding Providers Overview
• Jina AI Embeddings