> ## Documentation Index
> Fetch the complete documentation index at: https://ragopt.aboneda.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 5- Optimization

The `Optimizer` class finds Pareto-optimal RAG configurations using Multi-Objective Bayesian Optimization, balancing cost, latency, and quality.

## Quick Start

```python theme={null}
from rag_opt.optimizer import Optimizer
from rag_opt.dataset import TrainDataset

# Load dataset and run optimization
dataset = TrainDataset.from_json("./rag_dataset.json")
optimizer = Optimizer(train_dataset=dataset, config_path="./rag_config.yaml")

# Get best configuration
best_config = optimizer.optimize(n_trials=3, best_one=True)
print(f"LLM: {best_config.llm.model}")
print(f"Embeddings: {best_config.embedding.model}")
print(f"Chunk size: {best_config.chunk_size}")
```

## How It Works

1. **Setup**: Loads search space and initializes components
2. **Bootstrap**: Generates initial training data (10 samples)
3. **Optimize**: Runs Bayesian Optimization loop proposing and evaluating configurations
4. **Return**: Best configurations balancing multiple objectives

## Configuration

### Basic Usage

```python theme={null}
optimizer = Optimizer(
    train_dataset=dataset,
    config_path="./rag_config.yaml",
    verbose=True
)

# Run optimization (more trials = better exploration)
best_configs = optimizer.optimize(n_trials=3)
```

### Advanced Options

```python theme={null}
from rag_opt import init_chat_model
from rag_opt.search_space import RAGSearchSpace

# Custom components
search_space = RAGSearchSpace.from_yaml("./custom_config.yaml")
eval_llm = init_chat_model(model="gpt-4", model_provider="openai")

optimizer = Optimizer(
    train_dataset=dataset,
    config_path="./rag_config.yaml",
    search_space=search_space,
    evaluator_llm=eval_llm,
    verbose=True
)
```

## Performance Tips

1. **Start small**: Test with `n_trials=5`, then scale to 50-100 for production
2. **Eager loading**: For small search spaces, use `eager_load=True` in RAGPipelineManager
3. **Parallel evaluation**: Adjust `max_workers` in RAGPipelineManager for faster optimization
4. **Hugginface**: Using Hugging Face models will slow down the optimization process.