Update README

Signed-off-by: Ettore Di Giacinto <[email protected]>

Author: mudler

backend/python/README.md

@@ -1,38 +1,190 @@
-# Common commands about conda environment
+# Python Backends for LocalAI
 
-## Create a new empty conda environment
+This directory contains Python-based AI backends for LocalAI, providing support for various AI models and hardware acceleration targets.
 
-```
-conda create --name <env-name> python=<your version> -y
+## Overview
 
-conda create --name autogptq python=3.11 -y
-```
+The Python backends use a unified build system based on `libbackend.sh` that provides:
+- **Automatic virtual environment management** with support for both `uv` and `pip`
+- **Hardware-specific dependency installation** (CPU, CUDA, Intel, MLX, etc.)
+- **Portable Python support** for standalone deployments
+- **Consistent backend execution** across different environments
+
+## Available Backends
+
+### Core AI Models
+- **transformers** - Hugging Face Transformers framework (PyTorch-based)
+- **vllm** - High-performance LLM inference engine
+- **mlx** - Apple Silicon optimized ML framework
+- **exllama2** - ExLlama2 quantized models
+
+### Audio & Speech
+- **bark** - Text-to-speech synthesis
+- **coqui** - Coqui TTS models
+- **faster-whisper** - Fast Whisper speech recognition
+- **kitten-tts** - Lightweight TTS
+- **mlx-audio** - Apple Silicon audio processing
+- **chatterbox** - TTS model
+- **kokoro** - TTS models
+
+### Computer Vision
+- **diffusers** - Stable Diffusion and image generation
+- **mlx-vlm** - Vision-language models for Apple Silicon
+- **rfdetr** - Object detection models
+
+### Specialized
+
+- **rerankers** - Text reranking models
+
+## Quick Start
+
+### Prerequisites
+- Python 3.10+ (default: 3.10.18)
+- `uv` package manager (recommended) or `pip`
+- Appropriate hardware drivers for your target (CUDA, Intel, etc.)
 
-## To activate the environment
+### Installation
 
-As of conda 4.4
+Each backend can be installed individually:
+
+```bash
+# Navigate to a specific backend
+cd backend/python/transformers
+
+# Install dependencies
+make transformers
+# or
+bash install.sh
+
+# Run the backend
+make run
+# or
+bash run.sh
 ```
-conda activate autogptq
+
+### Using the Unified Build System
+
+The `libbackend.sh` script provides consistent commands across all backends:
+
+```bash
+# Source the library in your backend script
+source $(dirname $0)/../common/libbackend.sh
+
+# Install requirements (automatically handles hardware detection)
+installRequirements
+
+# Start the backend server
+startBackend $@
+
+# Run tests
+runUnittests
 ```
 
-The conda version older than 4.4
+## Hardware Targets
+
+The build system automatically detects and configures for different hardware:
 
+- **CPU** - Standard CPU-only builds
+- **CUDA** - NVIDIA GPU acceleration (supports CUDA 11/12)
+- **Intel** - Intel XPU/GPU optimization
+- **MLX** - Apple Silicon (M1/M2/M3) optimization
+- **HIP** - AMD GPU acceleration
+
+### Target-Specific Requirements
+
+Backends can specify hardware-specific dependencies:
+- `requirements.txt` - Base requirements
+- `requirements-cpu.txt` - CPU-specific packages
+- `requirements-cublas11.txt` - CUDA 11 packages
+- `requirements-cublas12.txt` - CUDA 12 packages
+- `requirements-intel.txt` - Intel-optimized packages
+- `requirements-mps.txt` - Apple Silicon packages
+
+## Configuration Options
+
+### Environment Variables
+
+- `PYTHON_VERSION` - Python version (default: 3.10)
+- `PYTHON_PATCH` - Python patch version (default: 18)
+- `BUILD_TYPE` - Force specific build target
+- `USE_PIP` - Use pip instead of uv (default: false)
+- `PORTABLE_PYTHON` - Enable portable Python builds
+- `LIMIT_TARGETS` - Restrict backend to specific targets
+
+### Example: CUDA 12 Only Backend
+
+```bash
+# In your backend script
+LIMIT_TARGETS="cublas12"
+source $(dirname $0)/../common/libbackend.sh
 ```
-source activate autogptq
+
+### Example: Intel-Optimized Backend
+
+```bash
+# In your backend script
+LIMIT_TARGETS="intel"
+source $(dirname $0)/../common/libbackend.sh
 ```
 
-## Install the packages to your environment
+## Development
+
+### Adding a New Backend
 
-Sometimes you need to install the packages from the conda-forge channel
+1. Create a new directory in `backend/python/`
+2. Copy the template structure from `common/template/`
+3. Implement your `backend.py` with the required gRPC interface
+4. Add appropriate requirements files for your target hardware
+5. Use `libbackend.sh` for consistent build and execution
 
-By using `conda`
+### Testing
+
+```bash
+# Run backend tests
+make test
+# or
+bash test.sh
 ```
-conda install <your-package-name>
 
-conda install -c conda-forge <your package-name>
+### Building
+
+```bash
+# Install dependencies
+make <backend-name>
+
+# Clean build artifacts
+make clean
 ```
 
-Or by using `pip`
+## Architecture
+
+Each backend follows a consistent structure:
 ```
-pip install <your-package-name>
+backend-name/
+├── backend.py          # Main backend implementation
+├── requirements.txt    # Base dependencies
+├── requirements-*.txt  # Hardware-specific dependencies
+├── install.sh         # Installation script
+├── run.sh            # Execution script
+├── test.sh           # Test script
+├── Makefile          # Build targets
+└── test.py           # Unit tests
 ```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Missing dependencies**: Ensure all requirements files are properly configured
+2. **Hardware detection**: Check that `BUILD_TYPE` matches your system
+3. **Python version**: Verify Python 3.10+ is available
+4. **Virtual environment**: Use `ensureVenv` to create/activate environments
+
+## Contributing
+
+When adding new backends or modifying existing ones:
+1. Follow the established directory structure
+2. Use `libbackend.sh` for consistent behavior
+3. Include appropriate requirements files for all target hardware
+4. Add comprehensive tests
+5. Update this README if adding new backend types