> ## 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.

# Overview

RAGOpt provides an end-to-end framework for optimizing RAG (Retrieval-Augmented Generation) pipelines using Multi-Objective Bayesian Optimization. The framework automatically tunes hyperparameters to find Pareto-optimal configurations that balance multiple objectives like cost, latency, and quality metrics.

## Framework Architecture

<img src="https://mintcdn.com/grpo/-04D4Zy0ca7um31C/images/RAGOpt.svg?fit=max&auto=format&n=-04D4Zy0ca7um31C&q=85&s=643ae8e989c8cb930407407c07cf288a" alt="RAGOpt Framework" data-path="images/RAGOpt.svg" />

The optimization workflow consists of five key components working together:

1. **Dataset Generation** - Create synthetic question-answer pairs from your documents
2. **Search Space** - Define the hyperparameter space to explore
3. **BO Input Encoder** - Encode the RAG hyperparameter from and to pytorch tensors
4. **Sampler** - Sampling choices from search space using SOBOL sampler by default
5. **RAG Manager** - Orchestrate component loading and configuration sampling
6. **Evaluation** - Measure performance across multiple metrics
7. **Optimization** - Find optimal configurations using Bayesian Optimization

## Quick Start

Here's a minimal example to get started:

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

# Load your dataset
dataset = TrainDataset.from_json("./rag_dataset.json")

# Initialize optimizer with configuration
optimizer = Optimizer(
    train_dataset=dataset,
    config_path="./rag_config.yaml"
)

# Run optimization
best_configs = optimizer.optimize(n_trials=3)
```