Compario

Structured Text Similarity Comparison with Large Language Models

Compario is a Python package that leverages Normalized Compression Distance (NCD) and Large Language Models (LLMs) to perform structured similarity comparisons between textual content. It analyzes user-provided text snippets, computes similarity scores, and returns formatted results—ideal for automated content comparison without processing raw documents directly.

🔧 Installation

Install via pip:

pip install compario

🚀 Quick Start

Basic Usage

from compario import compario

# Example: Compare two text snippets
user_input = """
Text 1: "The quick brown fox jumps over the lazy dog."
Text 2: "A fast brown fox leaps across the sleepy canine."
"""
response = compario(user_input)
print(response)

Custom LLM Integration

By default, Compario uses ChatLLM7 (from langchain_llm7). You can override it with any LangChain-compatible LLM:

Using OpenAI

from langchain_openai import ChatOpenAI
from compario import compario

llm = ChatOpenAI()
response = compario(user_input, llm=llm)

Using Anthropic (Claude)

from langchain_anthropic import ChatAnthropic
from compario import compario

llm = ChatAnthropic()
response = compario(user_input, llm=llm)

Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from compario import compario

llm = ChatGoogleGenerativeAI()
response = compario(user_input, llm=llm)

🔑 API Key & Rate Limits

• Default LLM (LLM7): Uses LLM7_API_KEY from environment variables or falls back to a default key.
• Free Tier: Sufficient for most use cases (check LLM7 docs for limits).
• Custom Key: Pass via api_key parameter or set LLM7_API_KEY in your environment:

  compario(user_input, api_key="your_api_key_here")
  

• Get a Free Key: Register at LLM7

📝 Parameters

📌 Key Features

✅ Pattern Matching + NCD: Combines structured pattern analysis with compression-based similarity. ✅ Flexible LLM Support: Works with any LangChain-compatible model. ✅ No Raw Document Processing: Focuses on comparing extracted text snippets. ✅ Clear Output: Returns structured similarity results.

🐛 Issues & Support

For bugs or feature requests, open an issue on GitHub.

👤 Author

• Eugene Evstafev (@chigwell)
• Email: hi@euegne.plus