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LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and retrieve relevant context during inference. The repository also shows how these components can be scaled and deployed using distributed computing frameworks such as Ray. In addition to development workflows, the project includes notebooks, datasets, and evaluation tools that help developers experiment with different retrieval strategies and model configurations.
Features
- Reference implementation for retrieval-augmented generation systems
- Pipeline for loading, chunking, embedding, and indexing documents
- Integration with Ray for scalable distributed execution
- Evaluation tools for measuring retrieval and generation performance
- Support for combining open-source and proprietary language models
- Example notebooks demonstrating end-to-end LLM application development
Project Samples
