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Solar Embedding Large: A multilingual model optimized for retrieval tasks in English, Korean, Japanese, and more.
Overview
Solar Embedding Large is a powerful multilingual embedding model offering robust performance across multiple languages, including English, Korean, Japanese, and more. It's specifically fine-tuned for retrieval tasks, significantly enhancing multilingual retrieval results. This model is divided into two specialized versions: 'solar-embedding-1-large-query', optimized for embedding user's question, and 'solar-embedding-1-large-passage', designed for embedding documents to be searched. Utilizing these purpose-specific models increases retrieval efficiency, which leads to improved performance of Retrieval Augmented Generation (RAG) systems.
Highlights
- ## Key Features - **Enhanced RAG System Efficiency:** The specialized models for query and passage embedding in Solar Embedding Large significantly improve the performance and precision of Retrieval Augmented Generation (RAG) systems. - **Robust Multilingual Capabilities:** This model demonstrates strong performance in multiple languages. It is exceptional choice for multilingual retrieval tasks, including English, Korean, Japanese, and more.
- ## Key Applications Solar Embedding Large excels in diverse tasks, particularly in Retrieval Augmented Generation (RAG), where precise information retrieval is crucial. Solar's query and passage models are optimized for these tasks. With robust language support including English, Korean, Japanese, and more, it is well-suited for a wide range of information retrieval applications requiring high accuracy across multiple languages.
- ## Key Tasks - Retrieval Augmented Generation - Semantic Search - Reranking - Text Embeddings
Details
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Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $0.00 |
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $0.80 |
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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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial relaese
Additional details
Inputs
- Summary
We support the request payload compatible with OpenAI's Embeddings API endpoint.
- Limitations for input type
- Solar-embedding-1-large supports a maximum context length of 2000 for input tokens.If you require additional input tokens beyond the supported limit, please contact us.
- Input MIME type
- application/json
# Input is single string
input = {
"input": "How is the performance of Solar embeddings?",
"model": "solar-embedding-1-large-query"
}
# Input is a list of string
input = {
"input": [
"Solar embeddings are awesome.",
"Solar embedding large model demonstrates strong performance in multiple languages."
],
"model": "solar-embedding-1-large-passage"
}
https://github.com/UpstageAI/aws-examples/blob/main/sagemaker/Solar_Embedding_Large_Sagemaker_Marketplace.ipynb
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 an array of texts to embed. | Type: FreeText | Yes |
model | Name of the model utilized to carry out the embedding. Current available models are 'solar-embedding-1-large-query' and 'solar-embedding-1-large-passage'. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
Contact us for model fine-tuning request.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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