Lamoom Python

Lamoom

Our Philosophy

Lamoom, derived from "Lambda on Mechanisms," refers to computation within a system that iteratively guides the LLM to perform correctly. Inspired by Amazon's culture, as Jeff Bezos said, "Good intentions don't work, mechanisms do," we focus on building mechanisms for LLMs rather than relying on their good intentions

Introduction

Lamoom is a dynamic, all-in-one library designed for managing and optimizing prompts and making tests based on the ideal answer for large language models (LLMs) in production and R&D. It facilitates dynamic data integration, latency and cost metrics visibility, and efficient load distribution across multiple AI models.

[](https://www.youtube.com/watch?v=1opO_5kRf98 "Lamoom Introduction Video")

Getting Started

To help you get started quickly, you can explore our [Getting Started Notebook](docs/getting_started_notebook.ipynb) which provides step-by-step examples of using Lamoom.

Features

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Core Functionality

Prompt Management and Caching

Lamoom implements an efficient prompt caching system with a 5-minute TTL (Time-To-Live):

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