选对文本嵌入模型，要综合考虑任务、延迟和成本，不能只看排行榜。
Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework
- 基于真实检索流程，评估多个开源与商用模型表现。
- 发现排行榜第一的模型未必最适合实际部署场景。
- 提供结合任务与资源约束的选型指南，适合工程落地。
选择合适的文本嵌入模型是构建检索或搜索系统中最关键却常被忽视的决策之一。尽管某模型在排行榜上领先，但未必适合特定部署需求。本报告提出一个基于实证的嵌入模型选择框架，通过在英文检索任务上对比T3EM（商业API模型）与多种开源模型的表现，并将其置于涵盖分类、聚类、语义相似性、重排序、成对分类、双语挖掘和摘要等任务的大型文本嵌入基准（MTEB）背景下进行分析。报告不仅关注原始得分，还追踪从嵌入生成、索引到大规模搜索的完整检索链路，揭示文档分块策略如何影响检索质量。最终形成一套根据任务、延迟、成本与部署条件制定的实用推荐方案。
原文摘要 · Abstract (English)
Choosing the right text embedding model is one of the most consequential -- and most frequently under-examined -- decisions in building a retrieval or search system, yet the model that tops a leaderboard is rarely the best choice for a given deployment. This report develops a practical, evidence-based framework for embedding model selection, built on a benchmarking study that evaluates T3EM (Text 3 Embedding Model), a commercial API-based embedding model, against a broad set of open-source alternatives on English-language retrieval tasks, and situates these findings within the wider Massive Text Embedding Benchmark (MTEB) landscape spanning classification, clustering, semantic similarity, reranking, pair classification, bitext mining, and summarization. Beyond raw benchmark scores, the report traces the full path from embedding model to retrieved result -- how embeddings are produced, how they are indexed and searched at scale, and how document chunking strategy shapes retrieval quality -- so that model choice can be reasoned about as one decision within a complete retrieval pipeline rather than in isolation. The result is a consolidated set of practical recommendations for selecting an embedding model according to task, latency, cost, and deployment constraints.
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