About
Semantic Frame addresses the inefficiencies of feeding raw numerical data to Large Language Models (LLMs) by transforming it into concise, natural language descriptions. LLMs often struggle with arithmetic, leading to token waste, hallucination risks, and context overflow when processing large datasets. This Python library performs deterministic analysis using NumPy, Pandas, and Polars, then translates the results into token-efficient narratives, ensuring accuracy and significantly reducing token consumption for LLM-based applications. It provides detailed analysis of trends, volatility, anomalies, seasonality, and more, making data insights readily available for agentic workflows.
