ragy 0.3.0

A simple RAG (Retrieval-Augmented Generation) framework for Python.

RAGy

RAGy is a simple framework for building Retrieval-Augmented Generation (RAG) applications. It provides a set of tools and utilities to help you create RAG applications quickly and easily.

It ships with a simple interface for building RAG applications, as well as a set of pre-built components that you can use to get started quickly (OpenAI LLMs, Chroma vector stores, etc.).

Installation

You can install RAGy using pip:

pip install ragy

Usage

Here's a simple example of how to use RAGy to build a RAG application: For more examples please refer examples folder

from ragy.rag import RAG
from ragy.reasoning import OpenAIEmbeddingModel, OpenAIGPTEngine
from ragy.rawdoc import DirectoryRawDocumentRetriever
from ragy.vector import ChromaVectorStore

# Create a RAG interface with the necessary components
system_prompt = """
You are a helpful assistant that provides accurate and concise answers to user queries based on the retrieved documents.
Always cite the sources of your information and provide references when applicable.
Include the ID of the retrieved documents in your response to help users verify the information.
If you don't know the answer because the retrieved documents don't contain the information, say "I don't know" instead of making up an answer.
"""
embedding_model = OpenAIEmbeddingModel(model='text-embedding-3-small')
raw_document_retriever = DirectoryRawDocumentRetriever(dir='./docs')
vector_store = ChromaVectorStore(path="./chroma", collection_name='my_collection')
ai_engine = OpenAIGPTEngine(model='gpt-5.2')

rag = RAG(
    system_prompt=system_prompt,
    embedding_model=embedding_model,
    raw_document_retriever=raw_document_retriever,
    vector_store=vector_store,
    ai_engine=ai_engine
)

# Use the RAG interface to ingest documents into the vector store
rag.ingest(chunk_size=512, chunk_overlap=128)

# Use the RAG interface to generate a response to a query
response = rag.generate('What is the capital of France?')
print(response)

Contributing

Contributions to RAGy are welcome! If you have an idea for a new feature or improvement, please open an issue or submit a pull request.

Full AI generated contributions are not accepted. If you use AI to assist in writing code, please ensure that you review and understand the code before submitting it. You should also provide a clear explanation of the changes you made and the reasoning behind them in your pull request.

AI is a powerful tool that can help you write code faster and more efficiently, but it is not a substitute for human creativity and judgment. When contributing to RAGy, please use AI as a tool to assist you, rather than relying on it to do all the work for you.

We encourage you to contribute in the simplest way possible, avoiding unnecessary complexity and over-engineering. Focus on making meaningful contributions that improve the functionality and usability of RAGy, rather than trying to impress others with complex code.

What things to consider when contributing:

• Ensure that your code is well-documented and follows the existing code style.
• Write tests for any new features or changes you make.
• Be respectful and considerate when communicating with other contributors.

License

RAGy is licensed under the MIT License. See the LICENSE file for more information.