RAG-Buddy is an innovative expansion of the Retrieval Augmented Generation (RAG) system. This service is designed to significantly enhance the capabilities of existing RAG systems, providing developers with a more robust, efficient, and cost-effective solution. RAG-Buddy integrates a suite of tools aimed at improving various aspects of RAG-powered applications, including performance, security, cost efficiency, and reliability.
For a comprehensive overview of RAG-Buddy and its features, please visit our product page.
For detailed documentation, including API references and guides, head over to our documentation portal.
This repository serves as a practical resource for developers looking to integrate RAG-Buddy into their projects. It contains code examples and sample integrations demonstrating how to interact with RAG-Buddy's API and leverage its features within your applications. The repository is structured to facilitate easy navigation and comprehension, catering to both Python and TypeScript developers. While currently focusing on Python scripts and Jupyter notebooks, future updates will include TypeScript examples and integrations with popular frameworks like LangChain and LlamaIndex.
The repository is organized into several directories for ease of navigation:
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/python/scripts- Python scripts demonstrating basic usage and advanced features./notebooks- Jupyter notebooks providing interactive examples and tutorials.
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/typescript(Coming Soon)- This directory will host TypeScript examples and integrations.
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/integrations(Planned)/langchain- Examples of integrating RAG-Buddy with LangChain./llamaindex- Demonstrations of using RAG-Buddy with LlamaIndex.
- Basic Python scripts showcasing RAG-Buddy API interaction
- Jupyter notebooks for interactive learning and experimentation
- TypeScript scripts and modules
- Integration examples with LangChain
- Integration examples with LlamaIndex