What is FloTorch.ai?

FloTorch.ai is an innovative product poised to transform the field of Generative AI by simplifying and optimizing the decision-making process for leveraging Large Language Models (LLMs) in Retrieval Augmented Generation (RAG) systems. In today’s fast-paced digital landscape, selecting the right LLM setup is critical for achieving efficiency, accuracy, and cost-effectiveness. However, this process often involves extensive trial-and-error, significant resource expenditure, and complex comparisons of performance metrics. Our solution addresses these challenges with a streamlined, user-friendly approach.

Key Features

• Automated Evaluation of LLMs: FloTorch.ai evaluates multiple LLMs by analyzing combinations of hyperparameters defined by the end user.
• Performance Metrics: Produces detailed performance scores, including relevance, fluency, and robustness.
• Cost and Time Insights: Provides actionable insights into the pricing and execution times for each LLM configuration.
• Data-Driven Decision-Making: Empowers users to align LLM configurations with specific goals and budget constraints.

Who Benefits from FloTorch.ai?

FloTorch.ai caters to a broad spectrum of users, including:

• Startups: Optimize AI-driven systems for rapid growth.
• Data Scientists: Simplify model selection and evaluation.
• Developers: Focus on deployment and innovation rather than experimentation.
• Researchers: Gain insights into LLM performance metrics effortlessly.
• Enterprises: Enhance customer experiences, improve content generation, and refine data retrieval processes.

Why Choose FloTorch.ai?

• Eliminates Complexity: No more manual evaluations or tedious trial-and-error processes.
• Accelerates Selection: Streamlines the evaluation and decision-making process.
• Maximizes Efficiency: Ensures users achieve the best performance from their chosen LLMs.
• Focus on Innovation: Allows users to dedicate resources to innovation and deployment rather than experimentation.

Vision

By combining advanced evaluation capabilities with a focus on cost and time efficiency, FloTorch.ai provides a holistic solution for navigating the evolving RAG landscape. It empowers users to focus on innovation and deployment, setting a new standard for intelligent decision-making in AI-driven applications.

With FloTorch.ai, we aim to be a pivotal enabler of progress in the generative AI ecosystem, helping our users achieve excellence in their projects.

Using FloTorch

Demo

Installation guide

Please refer to our Installation guide for the installation steps in detail.

Usage guide

Use our usage guide for more details on using FloTorch. Click here for frequently asked questions.

Open Source Developer Contribution Guidelines

This document outlines the guidelines for contributing to the project to maintain consistency and code quality.

Master Branch

• The master branch is the primary branch and should remain stable.
• Avoid pushing directly to the master branch. All changes must go through the pull request process.

Feature Branches

• All new feature branches must be created from the master branch.
• Use descriptive names for feature branches.
  Example: feature/bedrock_claude_inferencer

Pull Requests

• All code changes must be submitted as pull requests.
• Each pull request should be reviewed by at least one other developer.
• Keep pull requests small and focused on a specific feature or fix.
• Include relevant information in commit messages to provide context.

Branch Hygiene

• Delete feature branches after they have been successfully merged into master.

Testing Changes Locally

• Before submitting a pull request, thoroughly test your changes locally to ensure they work as expected.

Naming Conventions

• Use snake_case for:
  • names
  • Configuration variables
  • python file names Example: example_snake_case