EntropySqueezer：分布式 LLM Prompt 压缩系统降低推理成本

EntropySqueezer is a distributed, cross-language LLM prompt compression system leveraging llmlingua-2 technology. It addresses the 'prompt inflation' crisis in enterprise applications by reducing API costs and inference latency while preserving semantic integrity. Key features include scalable architecture, configurable compression ratios, multi-language support, and seamless integration options.

Modern LLM applications (customer service, code assistants, knowledge base Q&A) handle long prompts (thousands/tens of thousands of tokens), leading to high API fees, long latency, and context length limits. Prompt compression emerged as a solution, and EntropySqueezer is an enterprise-level distributed system for large-scale needs.

llmlingua-2 uses entropy-based strategy—evaluating token information entropy to remove low-information redundant content while retaining core intent. EntropySqueezer supports multi-language (English, Chinese, Japanese, Korean) via adaptive parameters and allows configurable compression ratios (conservative for legal docs, radical for content generation).

EntropySqueezer uses microservices (compression service, gateway, management console) for independent scaling and fault isolation. It supports horizontal scaling via Kubernetes and high-performance communication via gRPC (HTTP/2 multiplexing, Protocol Buffers serialization).

• Cost Saving: 30-50% compression for 3000-token客服 context reduces monthly costs by thousands of dollars for large systems.
• Latency Optimization: Shorter prompts cut model processing time, improving real-time user experience.
• Context Expansion: Compression fits more info into limited model context windows.

• Local: Docker Compose for quick testing/deployment.
• Cloud Native: Helm Chart for Kubernetes on AWS/GCP/Azure.
• API: RESTful/gRPC interfaces with SDKs for Java/Python/Node.js integration.

Future plans: support more compression algorithms, add caching, adaptive compression, deeper LLM provider integration.

Conclusion: EntropySqueezer combines llmlingua-2 with enterprise architecture to optimize LLM costs/performance, making it a key tool for scaling LLM applications.