spRAG
A RAG framework for challenging queries on dense unstructured data
Product Details
spRAG is a RAG (Retrieval-Augmented Generation) framework designed for unstructured data and is particularly good at processing complex text queries, such as financial reports, legal documents, and academic papers. Its accuracy is significantly higher than the traditional RAG baseline model on complex open question and answer tasks, such as the FinanceBench benchmark.
Main Features
How to Use
Target Users
Data scientists and machine learning engineers: You can use spRAG to process complex text data and improve model accuracy.
Financial Analysts: Quickly access key information from financial reports with spRAG.
Legal professionals: Use spRAG to retrieve relevant information from large volumes of legal documents.
Examples
Use spRAG to analyze financial reports and predict market trends.
Legal advisors use spRAG to retrieve case law from historical cases.
Academic researchers use spRAG to extract research data from large volumes of literature.
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