@cappybaradeploy分享了一种降低RAG令牌成本的方法，无需添加额外的重排序服务，值得一看。
@cappybaradeploy结合Qdrant的ColBERT重排序、二进制量化以及句子级检索，将最相关的文档部分发送至LLM，实现67.1%的输入令牌减少，同时将重排序保持在Qdrant内部而非添加外部API。深入探讨构建更高效的RAG管道的实际方法。阅读全文：pub.towardsai.net/how-qdrant-red…
How to cut RAG token costs by 67% without adding another reranking service? @cappybaradeploy combin...
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… 💬 0 🔄 0 ❤️ 1 👀 30 ⚡