JavaScript/TypeScript implementation of LLMLingua-2 (Experimental)

Python command-line tool for interacting with AI models through the OpenRouter API/Cloudflare AI Gateway, or local self-hosted Ollama. Optionally support Microsoft LLMLingua prompt token compression

This repository is the official implementation of Generative Context Distillation.

Compress LLM Prompts and save 80%+ on GPT-4 in Python

A fast, Unix-style CLI tool for semantic prompt compression. Cuts LLM prompt tokens by 10-20x with >90% fidelity, saving costs and latency.

Enhance the performance and cost-efficiency of large-scale Retrieval Augmented Generation (RAG) applications. Learn to integrate vector search with traditional database operations and apply techniques like prefiltering, postfiltering, projection, and prompt compression.

This repository contains the code and data of the paper titled "FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution."

End-to-End Python implementation of CompactPrompt (Choi et al., 2025): a unified pipeline for LLM prompt and data compression. Features modular compression pipeline with dependency-driven phrase pruning, reversible n-gram encoding, K-means quantization, and embedding-based exemplar selection. Achieves 2-4x token reduction while preserving accuracy.

RL-Prompt-Compression employs graph-enhanced reinforcement learning with a Phi-3 compressor trained via GRPO using a TinyLlama evaluator and a MiniLM cross-encoder feedback model, to optimize prompt compression and improve model efficiency.