Sold by: Voyage AIÂ
Deployed on AWS
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Instruction-tuned general-purpose embedding model optimized for retrieval, classification, clustering, and reranking. 16K context length.
Overview
Text embedding models are neural networks that transform texts into numerical vectors. They are a crucial building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and are responsible for the retrieval quality. voyage-large-2-instruct is a cutting-edge general-purpose embedding model and is among the top in the overall MTEB leaderboard, outperforming OpenAI v3 large and Cohere English v3 on key tasks, such as retrieval, classification, clustering, and reranking. Further, voyage-large-2-instruct is trained to be responsive to additional instructions that are prepended to the input text. Latency is 90 ms for a single query with at most 100 tokens, and throughput is 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-large-2-instruct here: https://blog.voyageai.com/2024/05/05/voyage-large-2-instruct-instruction-tuned-and-rank-1-on-mteb/Â
Highlights
- Superior general-purpose capability and is among the top in the overall [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard), outperforming OpenAI v3 large and Cohere English v3 on key tasks, such as retrieval, classification, clustering, and reranking.
- Responsive to additional prepended instructions. For retrieval/search tasks (e.g., in RAG), use the `[input_type](https://docs.voyageai.com/docs/faq#when-and-how-should-i-use-the-input_type-parameter)` parameter. For classification, clustering, or other MTEB subtasks, please use the instructions [here](https://github.com/voyage-ai/voyage-large-2-instruct).
- 16K token context length, well-suited for applications on long documents. Latency is 90 ms for a single query with at most 100 tokens. 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge.
Details
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.112 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $4.59 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $2.2725 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $3.045 |
ml.g5.16xlarge Inference (Real-Time) | Model inference on the ml.g5.16xlarge instance type, real-time mode | $7.68 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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Version release notes
We are excited to announce the initial release of voyage-large-2-instruct.
Additional details
Inputs
- Summary
- input: str or List[str] - Single text or list of texts.
- input_type: str, optional (default=None) - May also be "query" or "document".
- truncation: bool, optional (default=True) - True: Truncates. False: raises error if any given text exceeds the context length.
- encoding_format: str, optional (default=None) - Embedding format. None: float list; "base64": compressed encoding.
- Limitations for input type
- The maximum number of tokens for each text is 16K, the maximum length of the list is 128, and the total number of tokens in the list is at most 32K.
- Input MIME type
- text/csv, application/json, application/jsonlines
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_embedding_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_batch_input_embedding.jsonl
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
input | A single text string, or a list of texts as a list of strings.
| Type: FreeText
Limitations: The maximum number of tokens for each text is 16K, the maximum length of the list is 128, and the total number of tokens in the list is at most 32K. | Yes |
input_type | Type of the input text. Default to None. Other options: "query", "document".
| Default value: None
Type: FreeText | No |
truncation | Whether to truncate the input texts to fit within the context length. - If True, over-length input texts will be truncated to fit within the context length. - If False, an error will be raised if any given text exceeds the context length.
| Default value: True
Type: Categorical
Allowed values: True, False | No |
encoding_format | Format in which the embeddings are encoded. We currently support two options: - None (default): the embeddings are represented as lists of floating-point numbers; - "base64": the embeddings are compressed to Base64 encodings.
| Default value: None
Type: Categorical
Allowed values: base64, None | No |
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