llm-rank: A Lightweight Retrieval Enhancement Solution for Hybrid BM25 and LLM Ranking Implemented in C++

llm-rank is a single-header C++ library whose core is combining the traditional BM25 algorithm with large language models (LLMs) to provide efficient re-ranking capabilities for Retrieval-Augmented Generation (RAG) systems. Its design philosophy is zero dependencies, single header, and plug-and-play— it can be integrated into existing C++ projects without external dependencies, solving the threshold problem for C++ developers to use LLM-based re-ranking.

In RAG systems, traditional algorithms like vector similarity or BM25 are often used in the recall stage to quickly filter candidate documents. However, these methods only guarantee recall rate and struggle to ensure the most relevant content is ranked first. Re-ranking, as a fine-ranking step, can significantly improve retrieval quality. Yet most LLM-based re-ranking implementations rely on the Python ecosystem and heavy external dependencies, which pose a high threshold for performance-oriented C++ developers.

llm-rank is a minimalist C++ library with core design principles of zero dependencies, single header, and plug-and-play, providing functionality only through the llm_rank.h header file. Its advantages include: no external dependencies (no need for additional packages or complex build environments), cross-platform compatibility (Windows/Linux/macOS), easy integration (no linking issues or symbol conflicts), and lightweight size (suitable for embedded or binary size-sensitive scenarios).

llm-rank uses a hybrid ranking strategy:

1. BM25 Basic Ranking: A classic keyword matching algorithm that calculates relevance based on term frequency and inverse document frequency. It is fast and highly interpretable, suitable for scenarios with clear query terms;
2. LLM Fine-Ranking Layer: On the candidate set recalled by BM25, it uses the semantic understanding ability of LLM for secondary ranking to capture deep semantic associations;
3. Advantages of Two-Stage Approach: Balances efficiency and effectiveness. BM25 quickly narrows down the candidate range, and LLM performs fine-grained ranking on the small candidate set, significantly reducing computational costs.

llm-rank is suitable for:

• Enterprise knowledge base retrieval: Prioritize displaying relevant technical documents, product manuals, etc.;
• Customer service robots: Precisely locate matching answers from FAQ databases;
• Content recommendation: Personalized ranking in news, blog, or e-commerce scenarios;
• Code search: Find semantically related functions, classes, etc., in code repositories.

Integration steps for Windows developers:

1. Download the llm_rank.h header file from GitHub;
2. Add it to the source file directory of your Visual Studio project;
3. Include it in your code via #include "llm_rank.h";
4. Call the ranking API to process candidate documents. The library follows C++ idioms with a concise API, and developers with basic C++ knowledge can complete integration in a few minutes.

llm-rank focuses on solving the re-ranking problem and is delivered in a lightweight manner, making it a pragmatic choice for C++ projects to introduce intelligent ranking capabilities. As RAG architectures become more popular, such tools that focus on specific links will complement large frameworks, allowing developers to choose components flexibly.