Qdrant(@qdrant_engine)

How to cut RAG token costs by 67% without adding another reranking service? @cappybaradeploy combin...

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How to cut RAG token costs by 67% without adding another reranking service? @cappybaradeploy combin...

TL;DR · AI 摘要

通过结合ColBERT重排序、二进制量化和句子级检索，Qdrant将RAG的token成本降低了67.1%。

核心要点

  • Qdrant的ColBERT重排序可减少67.1%的输入token
  • 二进制量化与句子级检索结合实现成本优化
  • 无需额外重排序服务即可构建高效RAG管道

结构提纲

按章节快速跳转。

  1. §引言

    提出通过优化RAG流程降低token成本的核心问题。

  2. ·技术方案

    结合ColBERT重排序、二进制量化和句子级检索实现优化。

  3. ›实验结果

    基准测试显示输入token减少67.1%且保持重排序效果。

  4. ·优势分析

    无需引入外部重排序服务即可实现成本降低。

思维导图

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查看大纲文本（无障碍 / 无 JS 友好）
  • RAG成本优化方案
    • 核心技术
      • ColBERT重排序
      • 二进制量化
      • 句子级检索
    • 优势
      • 减少67.1%token成本
      • 无需外部API

金句 / Highlights

值得收藏与分享的关键句。

#RAG#Qdrant#ColBERT#token优化
打开原文

Qdrant on X: "How to cut RAG token costs by 67% without adding another reranking service? @cappybaradeploy combines Qdrant’s native ColBERT reranking, binary quantization, and sentence-level retrieval to send only the most relevant parts of a document to the LLM. The benchmark achieved 67.1%" / X

Qdrant

@qdrant_engine

How to cut RAG token costs by 67% without adding another reranking service?

@

cappybaradeploy

combines Qdrant’s native ColBERT reranking, binary quantization, and sentence-level retrieval to send only the most relevant parts of a document to the LLM. The benchmark achieved 67.1% fewer input tokens, while keeping reranking inside Qdrant instead of adding an external API. A practical deep dive into building more efficient RAG pipelines. Read the full article:

pub.towardsai.net/how-qdrant-red…

2:12 PM · Aug 24, 2026

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