GitHub - SuperpoweredAI/spRAG: High-performance RAG framework for unstructured data

spRAG is a high-performance RAG framework designed for unstructured data, particularly adept at handling complex queries over dense text. It significantly outperforms vanilla RAG baselines in complex open-book question answering tasks, with key methods including AutoContext and Relevant Segment Extraction (RSE). AutoContext enhances the accuracy of embeddings by injecting document-level context into individual chunks before embedding them. RSE, a post-processing step, combines clusters of relevant chunks into longer text segments, providing better context for more complex queries.

The article also provides a tutorial on how to install and use spRAG, including a quickstart guide and basic customization options. It explains the architecture of spRAG, detailing the five key customizable components: VectorDB, ChunkDB, Embedding, Reranker, and LLM. The document upload and query flows are also outlined. The article concludes by inviting readers to join the Discord community for further discussions and support.

Key takeaways:

spRAG is a high-performance RAG framework for unstructured data, particularly effective at handling complex queries over dense text such as financial reports and legal documents.
It uses two key methods to improve performance over vanilla RAG systems: AutoContext, which injects document-level context into individual chunks, and Relevant Segment Extraction (RSE), a post-processing step that combines relevant chunks into longer sections of text.
spRAG can be installed using the Python package and customized using different components including VectorDB, ChunkDB, Embedding, Reranker, and LLM.
The KnowledgeBase object in spRAG takes in documents, processes them, and returns the most relevant segments of text when queried.

GitHub - SuperpoweredAI/spRAG: High-performance RAG framework for unstructured data

Key takeaways:

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