> ## 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.
# Introduction
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**RAGOpt** is a Python optimization framework that eliminates manual hyperparameter guesswork in your RAG pipelines.
It uses Bayesian optimization to systematically tune over 20+ hyperparameters, from chunk size and overlap to model selection and embedding strategies automatically discovering the best configurations for your specific use case
***
### Why RAGOpt?
Optimizing Retrieval-Augmented Generation (RAG) pipelines is complex and highly dependent on configuration choices that impact quality, latency, and cost.
Unlike [AutoRAG](https://github.com/Marker-Inc-Korea/AutoRAG), which relies on grid search, or [Ragas](https://github.com/explodinggradients/ragas), which enforces rigid evaluation frameworks, RAGOpt is partially opinionated: it provides smart defaults while staying flexible with any LangChain-compatible model or provider.
RAGOpt helps answer a question like:
> **What’s the best RAG configuration to minimize cost and latency while maintaining accuracy and safety ?**
For example, here's you can get best RAG configurations in a few iterations using RAGOpt optimizer:
```python 📈 optimize.py theme={null}
from rag_opt.dataset import TrainDataset
from rag_opt.optimizer import Optimizer
# First you have to run generate_questions_.py
# to get a list of QAs to be used in the evaluation process
# Load the training dataset (questions/answers)
train_dataset = TrainDataset.from_json("rag_dataset.json")
# Initialize the optimizer
optimizer = Optimizer(
train_dataset=train_dataset,
config_path="rag_config.yaml",
verbose=True
)
# Run optimization
# Increase n_trials for better results
best_config = optimizer.optimize(n_trials=2, best_one=True,plot_hypervolume=True)
best_config.to_json()
```
```yaml ⚙️ rag_config.yaml theme={null}
# 1- define here the RAG Hyperparameters u would like to optimize
chunk_size:
bounds: [512, 1024]
dtype: int
max_tokens:
bounds: [256, 512]
dtype: int
vector_store:
---
# More Parameters
```
```python 🤖 generate_questions.py theme={null}
from langchain.chat_models import init_chat_model
from rag_opt.rag import DatasetGenerator
# 1- Generate list of questions / answers to be used in RAG evaluation
# Initialize the language model
llm = init_chat_model(
model="gpt-3.5-turbo",
model_provider="openai",
api_key="sk-\*\*\*"
)
# Generates 3 training questions
# you can also path a file like dataset_path=myfile.pdf
data_gen = DatasetGenerator(llm,dataset_path="./data")
dataset = data_gen.generate(3)
dataset.to_json("./questions.json")
```
```json 🧾 questions.json theme={null}
{
"items": [
{
"question": "What is the capital city of Japan?",
"answer": "The capital city of Japan is Tokyo.",
"contexts": [
"Tokyo is the capital of Japan, though some still picture Kyoto when they hear 'old Japan'.",
"Neon lights and ancient temples coexist where decisions shape the nation.",
"It’s a city where tradition bows to innovation every single day."
],
"metadata": {}
},
{
"question": "Who won the Nobel Peace Prize in 2025?",
"answer": "The Nobel Peace Prize in 2025 was awarded to María Corina Machado.",
"contexts": [
"María Corina Machado received the Nobel Peace Prize in 2025, proving persistence can echo louder than politics.",
"Donald Trump didn’t get the Nobel Prize; however, the announcement stirred global chatter.",
"Sometimes peace arrives wearing the voice of defiance and hope."
],
"metadata": {}
}
]
}
```
This will give you the optimal RAG configuration for your dataset.
```yaml theme={null}
{
"chunk_size": 500,
"max_tokens": 100,
"chunk_overlap": 200,
"search_type": "hybrid",
"k": 1,
"temperature": 1.0,
"embedding": { "provider": "openai", "model": "text-embedding-3-large" },
"llm": { "provider": "openai", "model": "gpt-4o" },
"vector_store": { "provider": "faiss" },
"use_reranker": true,
}
```
## Get Started
Get started with RAGOpt in under 5 minutes.
Get familiar with the underlying architecture and key design of RAGOpt
Learn about the Hyperparameters RAG Configuration file in details
Integrate RAGOpt with your custom RAG pipeline.