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A toolkit to create optimal Production-readyRetrieval Augmented Generation(RAG) setup for your data
| Date | Stars |
|---|---|
| 2026-07-24 | 1537 |
| 2026-07-25 | 1537 |
| 2026-07-28 | 1537 |
| 2026-07-30 | 1537 |
| 2026-08-06 | 1537 |
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#
[](https://www.python.org/)
[](https://github.com/KruxAI/ragbuilder/releases/)
[](https://github.com/KruxAI/ragbuilder/blob/master/LICENSE)
[](https://github.com/KruxAI/ragbuilder/commit/)
RagBuilder is a toolkit that helps you create optimal Production-ready Retrieval-Augmented-Generation (RAG) setup for your data automatically. By performing hyperparameter tuning on various RAG parameters (Eg: chunking strategy: semantic, character etc., chunk size: 1000, 2000 etc.), RagBuilder evaluates these configurations against a test dataset to identify the best-performing setup for your data. Additionally, RagBuilder includes several state-of-the-art, pre-defined RAG templates that have shown strong performance across diverse datasets. So just bring your data, and RagBuilder will generate a production-grade RAG setup in just minutes.
## Features
- **Hyperparameter Tuning**: Efficiently optimize your RAG configurations using Bayesian optimization
- **Pre-defined RAG Templates**: Use state-of-the-art templates that have demonstrated strong performance Eg: Graph retriever, Contextual chunker etc.)
- **Evaluation Dataset Options**: Generate synthetic test dataset or provide your own
- **Component Access**: Direct access to vectorstore, retriever, and generator components
- **API Deployment**: Easily deploy as an API service
- **Project Persistence**: Save and load optimized RAG pipelines
## Installation
```bash
# Create a new venv
uv venv ragbuilder
# Activate the new venv
source ragbuilder/bin/activate
# Install
uv pip install ragbuilder
```
See other installation options here ([link](https://docs.ragbuilder.io/quickstart/#installation))
## Quick Start
```python
from ragbuilder import RAGBuilder
# Initialize and optimize with defaults
builder = RAGBuilder.from_source_with_defaults(input_source='https://lilianweng.github.io/posts/2023-06-23-agent/')
results = builder.optimize()
# Run a query through the complete pipeline
response = results.invoke("What is HNSW?")
# View optimization summary
print(results.summary())
```
### Setting Default Models
You can specify default LLM and embedding models that will be used throughout the pipeline:
`````python
from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
# Initialize with custom defaults
builder = RAGBuilder.from_source_with_defaults(
input_source='data.pdf',
default_llm=AzureChatOpenAI(model="gpt-4o", temperature=0.0),
default_embeddings=AzureOpenAIEmbeddings(model="text-embedding-3-large"),
n_trials=20 # Set number of optimization trials
)
# Or when creating a RAGBuilder instance with fine grained custom configuration
builder = RAGBuilder(
data_ingest_config=data_ingest_config, # Custom Data Ingestion parameters
default_llm=AzureChatOpenAI(model="gpt-4o", temperature=0.0),
default_embeddings=AzureOpenAIEmbeddings(model="text-embedding-3-large")
)
`````
## Configuration Guide
### Basic Configuration
For most use cases, the default configuration provides good results:
```python
builder = RAGBuilder.from_source_with_defaults(
input_source='path/to/your/data',
test_dataset='path/to/test/data' # Optional
)
```
## Advanced Configuration
For fine-grained control over your RAG pipeline, you can customize every aspect:
````python
from ragbuilder.config import (
DataIngestOptionsConfig,
RetrievalOptionsConfig,
GenerationOptionsConfig
)
# Configure data ingestion
data_ingest_config = DataIngeExcerpt of 9,913 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:11891454378589e1, topic:rag, readme:retrieval-augmented generation, readme:retrieval augmented
matched fp:11891454378589e1, topic:developer-tools
