Metadata-Version: 2.4
Name: ragy
Version: 0.1.7
Summary: A simple RAG (Retrieval-Augmented Generation) framework for Python.
Author-email: mvrcoag <leucine_never.0d@icloud.com>
License: Copyright (c) 2026 Marco A. García
          
          Permission is hereby granted, free of charge, to any person obtaining a copy
          of this software and associated documentation files (the "Software"), to deal
          in the Software without restriction, including without limitation the rights
          to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
          copies of the Software, and to permit persons to whom the Software is
          furnished to do so, subject to the following conditions:
          
          The above copyright notice and this permission notice shall be included in all
          copies or substantial portions of the Software.
          
          THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
          IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
          FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/mvrcoag/ragy
Project-URL: Repository, https://github.com/mvrcoag/ragy
Keywords: RAG,Retrieval-Augmented Generation,Python,Cache,Vector Databases
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: chromadb>=1.5.1
Requires-Dist: openai>=2.24.0
Requires-Dist: pypdf>=6.8.0
Requires-Dist: python-dotenv>=1.2.1
Dynamic: license-file

# 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:

```bash
pip install ragy
```

## Usage

Here's a simple example of how to use RAGy to build a RAG application:

```python
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 information."
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.

## License
RAGy is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.

