Skip to content

README.md

Latest commit

 

History

History
60 lines (44 loc) · 1.6 KB

README.md

File metadata and controls

60 lines (44 loc) · 1.6 KB

LLX Integration Example

This example shows how to integrate llx for intelligent code generation within pyqual quality pipelines.

Overview

LLX enhances pyqual by:

  • Analyzing project metrics (files, lines, complexity, duplication)
  • Selecting the optimal LLM model based on actual code metrics
  • Generating targeted fixes using the selected model

Files

  • pyqual-llx.yaml - Complete pipeline configuration with llx integration
  • README.md - This file

Quick Start

  1. Install dependencies:

    pip install llx[prellm] pyqual code2llm vallm

  2. Copy the configuration:

    cp pyqual-llx.yaml ../../pyqual.yaml

  3. Run the pipeline:

    cd ../..
pyqual run

How It Works

  1. Analyze: code2llm collects project metrics
  2. Validate: vallm identifies issues and creates error report
  3. Fix: llx fix reads errors, selects optimal model, generates fixes
  4. Test: Run tests to verify fixes
  5. Loop: Repeat until all quality gates pass

Model Selection

LLX automatically selects models based on project metrics:

Project Size Files Lines Selected Model
Small <3 <500 Free (Gemini 2.5 Pro)
Medium 3-10 500-5K Cheap (Claude Haiku 4.5)
Large 10-50 5K-20K Balanced (Claude Sonnet 4)
Very Large 50+ 20K+ Premium (Claude Opus 4)

Customization

See the full documentation for:

  • Custom model thresholds
  • Advanced configuration options
  • Error handling strategies
  • Best practices