Llm-Applications

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llm-applications is an Open Source AI Agents & Automation tool that provides a comprehensive guide for building RAG-based LLM applications for production. It helps developers scale and evaluate their applications, supporting both OSS and closed LLMs.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is llm-applications?

llm-applications offers a comprehensive guide and resources for building Retrieval Augmented Generation (RAG) based Large Language Model (LLM) applications ready for production. This open-source project, hosted on GitHub, details how to develop such applications from scratch, scale their core components like loading, chunking, embedding, and serving, and evaluate different configurations for optimal performance. It also covers implementing LLM hybrid routing to integrate both open-source and closed LLMs, and serving applications in a scalable and highly available manner. The guide includes practical setup instructions for API keys (OpenAI, Anyscale Endpoints), environment configuration, and data management, making it a valuable resource for developers looking to productionize their AI solutions.

Best used for

Ideal for developers and data scientists who need to build robust RAG-based LLM applications, scale their components for production, and evaluate different configurations for optimal performance. Especially valuable for those looking to integrate both open-source and closed LLMs in their solutions.

Common actions

build LLM applications

scale AI applications

evaluate LLM performance

integrate LLMs

github copilot"AI Agents"face swappingcollaborationlow-code/no-codeopen-sourceautomated workflowworkflowsdeepfake

Capabilities

Key features

Build RAG-based LLM apps
Scale application components
Evaluate application performance
Implement LLM hybrid routing
Serve scalable applications

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of LLM applications does this guide focus on?

This guide specifically focuses on building Retrieval Augmented Generation (RAG) based LLM applications. It covers the entire lifecycle from development to scaling and production deployment, emphasizing practical implementation and performance optimization for these types of applications.

What LLMs can be integrated using the methods described?

The guide supports integration with both open-source and closed LLMs. It specifically mentions using OpenAI for models like gpt-3.5-turbo and gpt-4, and Anyscale Endpoints for open-source LLMs such as Llama-2-70b, facilitating a hybrid routing approach.

What are the recommended compute resources for running these applications?

While local setup is possible, the guide highly recommends using a setup with GPU access. It suggests an Anyscale workspace with a g3.8xlarge head node (2 GPUs, 32 CPUs) and the option to add GPU worker nodes for faster workload processing.

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