Open Source: llmslim – Semantic Prompt Compression for LLM Applications

Developer released llmslim, an open-source Python package that compresses prompts, chat histories, and RAG contexts using semantic chunking and extractive ranking, achieving up to 60% token reduction. The tool aims to reduce costs and latency for LLM applications.

Published my first open-source Python package: llmslim. It compresses prompts, chat histories, and RAG contexts using semantic chunking + extractive ranking before sending them to an LLM. Example: 2847 tokens → 1138 tokens 60% reduction Looking for feedback from the HF community on: Contributions and criticism welcome.