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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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Text to SQL using LLM

By:
Mphasis
Latest Version:
v1
It is a Generative AI (LLM) based offering which can generate SQL query given a table schema and meta data and validate it through a dry run
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    Product Overview

    The solution uses Generative AI to generate complex SQL queries leveraging bedrock claude(LLM) modelgiven a question prompt. The solution also validates the generated queries by testing them with sample data to ensure a smooth run without any errors. The solution requires only two input files containing questions in the English language and a data schema with metadata in JSON format. The output file contains the SQL queries perfectly ready to run. If the asked question is too complex the GenAI-based solution also suggests changes that get generated along with the query. The offering requires an AWS bedrock anthropic Claude Instant model subscription.

    Key Data

    Version
    v1
    Usage Instructions
    Release Notes
    By
    Mphasis
    Categories
    ML Solutions
    Generation-Text
    Generative AI
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • The solution can be useful to analyze the multi-tabular data without giving the actual data to the LLM. It generates complex SQL queries given appropriate prompts on one table schema at a time and up to 4 questions in a single inference. It also validates the queries for a smooth run and returns the error or warning if any.

    • It can be used for data analysis tasks, to extract information from tabular data, and does not require any expertise in SQL or GenAI. The users required only the AWS credentials of an account that has an LLM (bedrock anthropic-claude-instant-v1) model subscription.

    • Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$3.00/inference

    running on any instance

    Model Batch Transform$5.00/hr

    running on ml.m5.large

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$0.223/host/hr

    running on ml.t2.xlarge

    SageMaker Batch Transform$0.115/host/hr

    running on ml.m5.large

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on the number of inferences generated by the ML Model per month. Typically, the number of inferences is the same as the number of successful calls to the real-time endpoint. For models that support multiple inputs in a request, sellers have the option to meter the number of inputs processed in a request to count generated inferences.
    Additional infrastructure cost, taxes or fees may apply.

    Usage Information

    Model input and output details

    Input

    Summary

    Usage Methodology for the algorithm: 1) The input must be 'Input.zip' file. 2) The zip file should contain three files 'credentials', 'input-question' and 'input' in .json format. 3) The credentials file includes the aws access keys with region of the account which has a subscription of anthripic Claude Instant bedrock model. 5) Name of the folder inside the zip file should be “input” which is case-sensitive 6) check the instructions and sample endpoint in the sample jupyter file provided.

    Limitations for input type
    cant not input more than 4 questions at time for inferencing.
    Input MIME type
    application/zip
    Sample input data
    Expand all input descriptions
    Usage information

    Output

    Summary

    The output will be a zip file containing a 'sql_query.json' file which has a string of all the queries generated along with the keys (id's) for each query.

    Limitations for output type
    The algorithm validates a query using dry run and if there is some error in the query than it will get attach with the generated query for the user to manually fix it.
    Output MIME type
    application/zip
    Sample output data

    Sample notebook

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Text to SQL using LLM

    For any assistance reach out to us at:

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    Refund Policy

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