Ollama reranker: boost your RAG pipeline accuracy

Ollama reranker enhances retrieval-augmented generation (RAG) pipeline accuracy by addressing the limitations of cosine similarity in embedding-based retrieval systems. It leverages cross-encoder models locally to re-score documents with full query-document attention, ensuring the most relevant data is prioritized. This approach offers advantages like zero API costs, data privacy, and minimal latency. CodeWords facilitates seamless integration of reranking into RAG workflows, enabling efficient document retrieval, reranking, and LLM generation. The adoption of Ollama reranker models, such as bge-reranker-v2-m3 and ms-marco-MiniLM, has demonstrated improved recall and ranking quality, with studies indicating a 10–25% accuracy boost. Reranking, while free and advantageous for accuracy, involves trade-offs like limited model variety and throughput constraints, especially on local hardware. Nonetheless, it significantly enhances RAG systems by providing correct answers over plausible ones, fostering user trust in AI outputs.

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