compresr 1.0.20

Python SDK for Compresr - Intelligent prompt compression service

Compresr Python SDK

Intelligent context compression service to optimize LLM costs and performance.

Installation

pip install compresr

Quick Start

API Key Setup

Get your API key from compresr.ai and set it up:

import os
os.environ["COMPRESR_API_KEY"] = "cmp_your_api_key_here"

# Or pass directly to client
from compresr import CompressionClient
client = CompressionClient(api_key="cmp_your_api_key_here")

Compression Client

Get compressed context for use with your own LLM:

from compresr import CompressionClient

client = CompressionClient(api_key="cmp_your_api_key")

# Get compressed context
result = client.compress(
    context="Your very long context that needs compression...",
    compression_model_name="agnostic_cmprsr"
)

compressed_context = result.data.compressed_context
print(f"Original: {result.data.original_tokens} tokens")
print(f"Compressed: {result.data.compressed_tokens} tokens")
print(f"Compression ratio: {result.data.actual_compression_ratio:.2%}")
print(f"Tokens saved: {result.data.tokens_saved}")

# Use compressed context with your own LLM
# openai.chat.completions.create(messages=[{"role": "user", "content": compressed_context}])

Streaming Support

Compression client supports real-time streaming:

from compresr import CompressionClient

client = CompressionClient(api_key="cmp_your_api_key")

# Compression streaming  
for chunk in client.compress_stream(
    context="Your long context...",
    compression_model_name="agnostic_cmprsr"
):
    print(chunk.content, end="", flush=True)

Async Support

All methods have async variants:

import asyncio
from compresr import CompressionClient

async def main():
    client = CompressionClient(api_key="cmp_your_api_key")
    
    result = await client.compress_async(
        context="Your context...",
        compression_model_name="agnostic_cmprsr"
    )
    
    print(result.data.compressed_context)
    await client.close()

asyncio.run(main())

Batch Processing

Process multiple contexts efficiently:

from compresr import CompressionClient

client = CompressionClient(api_key="cmp_your_api_key")
results = client.compress_batch(
    contexts=[
        "First context to compress...",
        "Second context to compress..."
    ],
    compression_model_name="agnostic_cmprsr"
)

for result in results.data.results:
    print(f"Original tokens: {result.original_tokens}")
    print(f"Compressed: {result.compressed_context}")
    print(f"Tokens saved: {result.tokens_saved}")

Key Features

• 🗜️ Intelligent Compression: Reduce context lengths while preserving meaning
• 💰 Cost Optimization: Save 30-70% on LLM API costs through intelligent compression
• ⚡ Compression Service: Focus on context compression for your own LLM integration
• 🔄 Streaming Support: Real-time response streaming
• 📊 Usage Tracking: Monitor credits, costs, and compression ratios
• 🔗 Async Ready: Full async/await support
• 📦 Batch Processing: Handle multiple requests efficiently

Available Models

Compression Models:

• agnostic_cmprsr - Compresr V1 (default compression model)

Error Handling

from compresr import CompressionClient
from compresr.exceptions import CompresrError, AuthenticationError, RateLimitError

client = CompressionClient(api_key="cmp_your_api_key")

try:
    result = client.compress(
        context="Your context...",
        compression_model_name="agnostic_cmprsr"
    )
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded")
except CompresrError as e:
    print(f"API error: {e}")

Authentication

The Compresr SDK uses API key authentication. You can get your API key from compresr.ai:

1. Create an account at compresr.ai
2. Navigate to your API Keys section
3. Generate a new API key
4. Use it with the SDK

Requirements

• Python 3.9+
• httpx >= 0.27.0
• pydantic >= 2.10.0

License

Proprietary License

Support

• 📖 Documentation: docs.compresr.ai
• 💬 Support: [e-mail]
• 🐛 Issues: GitHub Issues
• 🌐 Website: compresr.ai