{
  "metadata": {
    "version": "1.2.0",
    "timestamp": "2025-01-24T10:30:00Z",
    "tokens": {
      "in": 1000,
      "out": 500,
      "total": 1500
    },
    "filters_applied": true,
    "excluded_segments_count": 1
  },
  "request": {
    "content": "Hi, thank you for calling Radical Toys!...",
    "params": {
      "min_compression_ratio": 0.5,
      "min_semantic_similarity": 0.8
    }
  },
  "response": {
    "meta": {
      "embedding": {
        "dimensions": 768,
        "model": "text-embedding-3-small"
      }
    },
    "texts": {
      "compressed": "Customer service interaction regarding shipping/returns assistance.",
      "suggested": "Customer service interaction regarding shipping/returns assistance."
    },
    "segments": [
      {
        "was_compressed": true,
        "semantic_category": "Customer Service",
        "covariant_details": ["shipping assistance", "returns support"],
        "original": {
          "text": "I would be happy to help with your shipping or returns issue.",
          "embedding": {
            "dimensions": 768,
            "values": "[... 768 values]"
          }
        },
        "semantic_similarity": 0.89,
        "compression_ratio": 0.65,
        "excluded_by_filter": false
      }
    ]
  }
}

import requests
import json

class HypernymClient:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://fc-api-development.hypernym.ai"
        self.headers = {
            "Content-Type": "application/json",
            "X-API-Key": api_key
        }
    
    def analyze_text(self, text: str, params: dict = None, filters: dict = None):
        """Analyze text using the Hypernym API"""
        payload = {"essay_text": text}
        
        if params:
            payload["params"] = params
        if filters:
            payload["filters"] = filters
            
        response = requests.post(
            f"{self.base_url}/analyze_sync",
            headers=self.headers,
            json=payload
        )
        
        response.raise_for_status()
        return response.json()

# Usage example
client = HypernymClient("your-api-key")

result = client.analyze_text(
    text="Your text to analyze here...",
    params={
        "min_compression_ratio": 0.5,
        "min_semantic_similarity": 0.8
    }
)

print(f"Compressed: {result['response']['texts']['compressed']}")
print(f"Token reduction: {len(result['request']['content'].split())} -> {len(result['response']['texts']['compressed'].split())}")