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    FloTorch

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    FloTorch is an open source tool designed to expedite the proof of concept journey for Generative AI workloads. It enables customers to optimize RAG implementations through systematic experimentation and validation, ensuring secure, automated, and cost efficient deployment within their AWS infrastructure. Key features include hyperparameter optimization, vector database enhancement, and LLM integration.

    Overview

    FloTorch is an open source tool designed to streamline the proof of concept process for Generative AI or GenAI workloads. It allows customers to specify critical hyperparameters for RAG workloads and run the accelerator within their own AWS infrastructure, ensuring data security and sovereignty. FloTorch generates multiple experiments based on these hyperparameters, validates them through preflight checks, and provides directional estimates for costs, latency, and various evaluation metrics. Customers can then select and execute the most promising experiments, with results presented in a comprehensive dashboard. Key features of FloTorch include hyperparameter optimization, vector database enhancement, and LLM integration. The tool facilitates systematic experimentation with document segmentation strategies, embedding model selection, and retrieval strategy refinement. It supports the evaluation of various embedding models and vector database solutions, ensuring optimal storage and retrieval performance. Additionally, the accelerator enables finetuning of context management and LLM prompt engineering, helping organizations achieve the best balance between processing efficiency and context preservation. The benefits of using FloTorch are manifold. It provides automated benchmarking, quality validation, and cost efficiency through detailed resource analysis and return on investment evaluation. The platform ensures production readiness with rigorous validation protocols and comprehensive deployment blueprints. By leveraging AWS infrastructure as code capabilities, the accelerator reduces time to value for RAG implementations while maintaining security best practices. This makes it an invaluable tool for organizations looking to optimize their GenAI workloads in a secure, efficient, and cost effective manner.

    Highlights

    • Optimizes RAG implementations through systematic experimentation and validation
    • Secure, efficient, and cost effective
    • Reduced time to value

    Details

    Delivery method

    Delivery option
    FloTorch Container Based Installation

    Latest version

    Operating system
    Linux

    Features and programs

    Financing for AWS Marketplace purchases

    AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
    Financing for AWS Marketplace purchases

    Pricing

    This product is free. Subscriptions have no end date and can be canceled anytime.

    Vendor refund policy

    Not applicable as the product is free

    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

     Info

    Delivery details

    FloTorch Container Based Installation

    Supported services: Learn more 
    • Amazon ECS
    Container image

    Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.

    Version release notes

    New Features

    1. Multi-File Upload Support for Knowledge Base - Introduced the ability to upload a folder with multiple files for Knowledge Base, simplifying bulk data management.

    2. Nova Bedrock Model Integration - Added the Nova Bedrock model to the inferencing LLM dropdown, expanding model choices for RAG experiments.

    3. Addition of the estimated cost calculation for each experiment.

    Bug Fixes

    1. Back Button Navigation - Fixed navigation when using the back button after uploading Knowledge Base files, ensuring users are not mistakenly prompted for re-uploads.

    Additional details

    Usage instructions

    All the installation steps for FloTorch are outlined here:

    https://flotorch-public.s3.us-east-1.amazonaws.com/installation.md 

    Support

    Vendor support

    Primary Support Methods:

    1. GitHub Issues: a. Purpose: Bug Reports, feature requests, and discussions b. Response Time: Best-effort basis by community maintainers.
    2. GitHub Discussions: a. Purpose: Technical questions and community help b. Format: Q&A, troubleshooting, best practices
    3. Documentation: a. Location: Project Wiki and README b. Content: Setup guide, best practices, troubleshooting. If buyers need further support, they can reach out at the following email address: info@flotorch.ai 

    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.

    Customer reviews

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