Evaluating Embedding Models and Pipeline Optimization for AI Search Quality

TL;DR

This paper systematically evaluates various embedding models and pipeline configurations to enhance AI search quality, demonstrating how higher-dimensional embeddings and neural re-rankers improve retrieval accuracy.

Contribution

It introduces a comprehensive evaluation framework for embedding models and pipeline strategies, including a custom dataset and metrics, to optimize AI search systems.

Findings

Higher-dimensional embeddings significantly improve search accuracy.

Neural re-rankers further enhance retrieval performance.

Finer chunking granularity leads to better results.

Abstract

We evaluate the performance of various text embedding models and pipeline configurations for AI-driven search systems. We compare sentence-transformer and generative embedding models (e.g., All-MPNet, BGE, GTE, and Qwen) at different dimensions, indexing methods (Milvus HNSW/IVF), and chunking strategies. A custom evaluation dataset of 11,975 query-chunk pairs was synthesized from US City Council meeting transcripts using a local large language model (LLM). The data pipeline includes preprocessing, automated question generation per chunk, manual validation, and continuous integration/continuous deployment (CI/CD) integration. We measure retrieval accuracy using reference-based metrics: Top-K Accuracy and Normalized Discounted Cumulative Gain (NDCG). Our results demonstrate that higher-dimensional embeddings significantly boost search quality (e.g., Qwen3-Embedding-8B/4096 achieves Top-3 accuracy about 0.571 versus 0.412 for GTE-large/1024), and that neural re-rankers (e.g., a BGE cross-encoder) further improve ranking accuracy (Top-3 up to 0.527). Finer-grained chunking (512 characters versus 2000 characters) also improves accuracy. We discuss the impact of these factors and outline future directions for pipeline automation and evaluation.