toporag 0.1.0

A topological data analysis library for detecting knowledge gaps in RAG systems.

TopoRAG

TopoRAG is a Topological Data Analysis (TDA) library designed to detect conceptual gaps and blind spots in text documents, specifically useful for evaluating the context retrieved in Retrieval-Augmented Generation (RAG) pipelines.

By using Persistent Homology (via Ripser), it identifies topological "holes" in the semantic embedding space of your text, and then uses an LLM to label those missing concepts.

Installation

pip install toporag

(Note: Currently in development, install from source)

git clone <repository>
cd toporag_lib
pip install -e .

Setup

You need an OpenAI API key for text embeddings (text-embedding-3-small) and gap labeling (gpt-4o-mini).

export OPENAI_API_KEY="sk-..."

Basic Usage

import asyncio
from toporag import TopoAnalyzer

async def main():
    analyzer = TopoAnalyzer() # Automatically picks up OPENAI_API_KEY
    
    texto = """
    We started the project with great enthusiasm. The team was assembled, 
    requirements were gathered, and we had a solid plan for the architecture.
    
    Finally, we deployed the application to production and celebrated our success. 
    The customers loved the final result and our metrics improved dramatically.
    """
    
    gaps = await analyzer.analyze_text(texto)
    for gap in gaps:
        print(f"Missing Topic: {gap['topic_label']}")
        print(f"Explanation: {gap['explanation']}")
        print("---")

if __name__ == "__main__":
    asyncio.run(main())

API

TopoAnalyzer.analyze_text(text: str, threshold: float = 0.15, max_holes: int = 5, generate_suggestions: bool = True)

Splits the text into segments, embeds them, finds gaps with persistence above threshold, and uses the LLM to label up to max_holes gaps.

TopoAnalyzer.analyze_url(url: str, ...)

Scrapes the URL for readable text and runs the topological analysis.

TopoAnalyzer.analyze_segments(segments: List[str], ...)

Runs the analysis directly on a pre-chunked list of strings.