LazyLLM: Shangtang's open source low-code development tool for building multi-intelligence body applications

General Introduction

LazyLLM is an open source tool developed by the LazyAGI team, focusing on simplifying the development process of multi-intelligence large model applications. It helps developers quickly build complex AI applications and save time on tedious engineering configuration through one-click deployment and lightweight gateway mechanisms. Whether you are a beginner or an experienced developer, LazyLLM provides support: novices can easily get started with pre-built modules, while experts can achieve advanced development with flexible customization features. The tool emphasizes efficiency and practicality, integrating preferred components to ensure that production-ready applications are built at the lowest possible cost. With over 1100 stars on GitHub and an active community, updates are ongoing.

Function List

• Deploy complex applications with one click: Supports the complete process from prototype validation to production release with automated configuration of submodule services.
• Cross-platform compatibility: Adapt to bare metal servers, development machines, Slurm clusters, and public clouds with no code changes.
• Data flow management (Flow): Provides predefined processes such as Pipeline and Parallel to easily organize complex application logic.
• modular component: Support for customization and extensions, integration of user algorithms or third-party tools.
• Lightweight gateway mechanism: Simplify service startup and URL configuration for more efficient development.
• Supports multi-intelligence development: Rapidly build applications containing multiple AI agents adapted to large model tasks.

Using Help

Installation process

LazyLLM is a Python based open source project , the installation process is simple and straightforward . Here are the detailed steps:

environmental preparation

1. Checking system requirements: Make sure you have Python 3.8 or above installed on your device.
2. Installing Git: If you don't have Git installed, you can use a command-line tool such as apt-get install git maybe brew install git) Installation.
3. Creating a virtual environment (optional but recommended)::

   python -m venv lazyllm_env
   source lazyllm_env/bin/activate # Linux/Mac
   lazyllm_env\Scripts\activate # Windows

Download & Installation

1. Cloning GitHub Repositories::

   git clone https://github.com/LazyAGI/LazyLLM.git
   cd LazyLLM
   

2. Installation of dependencies::
   • Run the following command to install the required libraries:

     pip install -r requirements.txt
     

   • If you encounter a dependency conflict, try upgrading pip:

     pip install --upgrade pip
     

3. Verify Installation::
   • Run the sample code to confirm successful installation:

     python -m lazyllm --version
     

   • If the version number is returned (e.g. v0.5), the installation is complete.

Optional: Docker Deployment

• LazyLLM supports Docker one-click image packaging:
  1. Install Docker (refer to the official website: https://docs.docker.com/get-docker/).
  2. Run it in the project root directory:

     docker build -t lazyllm:latest .
     docker run -it lazyllm:latest
     

How to use

At the core of LazyLLM is the ability to quickly build AI applications through modularity and data flow management. Below is a detailed how-to guide for the main features:

Feature 1: Deploy complex applications with one click

• procedure::
  1. Preparing the Application Configuration File: Create the config.yaml, define modules and services. For example:

     modules: name: llm
     - name: llm
     type: language_model
     url: http://localhost:8000
     - name: embedding
     type: embedding_service
     url: http://localhost:8001
     

  2. Starting services::

     python -m lazyllm deploy
     

  3. status: Access log output to confirm that all modules are functioning properly.
• Featured Description: This feature automatically connects submodules through a lightweight gateway, eliminating the need to manually configure URLs and making it ideal for rapid prototyping.

Feature 2: Cross-platform compatibility

• procedure::
  1. Designated platforms: Add parameters to the command line, for example:

     python -m lazyllm deploy --platform slurm
     

  2. Switching environments: No need to change the code, just replace --platform parameters (e.g. cloud maybe bare_metal).
• application scenario: Developers can seamlessly migrate to the cloud after local testing to reduce adaptation efforts.

Function 3: Data Flow Management (Flow)

• procedure::
  1. Defining the data flow: Call a predefined Flow in a Python script. such as building a Pipeline:

     from lazyllm import pipeline
     flow = pipeline(
     step1=lambda x: x.upper(),
     step2=lambda x: f "Result: {x}"
     )
     print(flow("hello")) # output "Result: HELLO"
     

  2. Running complex processes: Multitasking in combination with Parallel or Diverter:

     from lazyllm import parallel
     par = parallel(
     task1=lambda x: x * 2,
     task2=lambda x: x + 3
     )
     print(par(5)) # Output [10, 8]
     

• Featured Description: Flow provides standardized interfaces to reduce duplication of data conversion and support collaborative development between modules.

Function 4: Modular Component Customization

• procedure::
  1. Registering Custom Functions::

     from lazyllm import register
     @register
     def my_function(input_text): return f "Processed: {input_text}".
     return f "Processed: {input_text}"
     

  2. Integration into applications: Called in a Flow or deployment configuration my_functionThe
• Advanced Usage: Supports Bash command registration for hybrid script development.

Tips for use

• adjust components during testing: Run-time additions --verbose parameter to view the detailed log:

  python -m lazyllm deploy --verbose
  

• Community Support: You can submit feedback on GitHub Issues and the team will respond in a timely manner.
• update: Pull the latest code on a regular basis:

  git pull origin main
  

With these steps, you can quickly get started with LazyLLM and build applications ranging from simple prototypes to large models at the production level.