Supported Models

The SDK automatically detects the provider based on the model name. Below is the complete list of supported embedding models.

Anthropic does not offer native embedding models. Use OpenAI, Google, or Amazon Bedrock for embeddings.

OpenAI

Model	Dimensions	Description
`text-embedding-3-small`	1536	Recommended for most use cases. Best balance of cost and performance.
`text-embedding-3-large`	3072	Higher quality embeddings with more dimensions. Supports `dimensions` parameter for reduced output.
`text-embedding-ada-002`	1536	Legacy model. Use `text-embedding-3-small` for new projects.

Dimension Override

OpenAI’s text-embedding-3-small and text-embedding-3-large, and Google’s text-embedding-004 support the dimensions parameter to reduce the output size:

# Reduce text-embedding-3-large from 3072 to 256 dimensions
result = client.run_embedding(
    model="text-embedding-3-large",
    input="Hello world",
    dimensions=256,
)

print(len(result.embeddings[0].values))  # 256

Google

Model	Dimensions	Description
`text-embedding-004`	768	Google’s latest text embedding model.

result = client.run_embedding(
    model="text-embedding-004",
    input="Hello world",
)

print(len(result.embeddings[0].values))  # 768

Amazon Bedrock

Model	Dimensions	Description
`amazon.titan-embed-text-v2:0`	1024	Amazon Titan Text Embeddings v2.
`cohere.embed-english-v3`	1024	Cohere Embed English v3 via Bedrock.

result = client.run_embedding(
    model="amazon.titan-embed-text-v2:0",
    input="Hello world",
)

print(result.provider)  # LLMProvider.AMAZON_BEDROCK

Amazon Bedrock models require AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION) to be set in your environment.

Voyage AI

Model	Dimensions	Description
`voyage-3`	1024	General-purpose embedding model.
`voyage-3-large`	1024	Higher quality, larger model.
`voyage-3-lite`	512	Lightweight, lower cost.
`voyage-code-3`	1024	Optimized for code retrieval and understanding.
`voyage-finance-2`	1024	Fine-tuned for financial documents.
`voyage-law-2`	1024	Fine-tuned for legal documents.

result = client.run_embedding(
    model="voyage-3",
    input="Hello world",
)

print(result.provider)  # LLMProvider.VOYAGE

Voyage AI requires a VOYAGE_API_KEY environment variable. Voyage is an embedding-only provider and cannot be used with run_local().

Using Custom Models

For embedding models not in the built-in list (fine-tuned or new releases), specify the provider explicitly:

from tracia import LLMProvider

result = client.run_embedding(
    model="custom-embedding-model",
    provider=LLMProvider.OPENAI,
    input="Hello world",
)

When using custom models, always specify the provider parameter to ensure the correct SDK is used.

Getting Started

Node.js SDK

Python SDK

Supported Models

OpenAI

Dimension Override

Google

Amazon Bedrock

Voyage AI

Using Custom Models

​OpenAI

​Dimension Override

​Google

​Amazon Bedrock

​Voyage AI

​Using Custom Models

OpenAI

Dimension Override

Google

Amazon Bedrock

Voyage AI

Using Custom Models