When implementing Lexical search pipelines, one of the most common pitfalls is neglecting text normalization and preprocessing. If developers fail to handle case sensitivity, punctuation, stemming, or stop-word removal consistently, search results can become unpredictable. For example, treating “Database” and “database” as separate tokens or failing to stem “indexes” to “index” leads to redundant and fragmented matches. Similarly, poorly configured tokenizers can cause multiword entities like “machine learning” to be split incorrectly, reducing retrieval accuracy. These small inconsistencies compound as datasets scale, leading to significant mismatches between query intent and retrieved documents.
Another pitfall is over-reliance on keyword matching without addressing semantic variation. Lexical search systems can struggle when users phrase queries differently than how documents are written. Without query expansion or synonym handling, terms like “storage” and “repository” may not be linked, causing relevant documents to be missed. Moreover, BM25 and TF-IDF scoring must be tuned carefully—default parameters may favor long documents or overemphasize term frequency, skewing results. Developers should validate search performance using relevance metrics such as Mean Average Precision (MAP) and tune parameters iteratively for their specific corpus.
A final pitfall appears when Lexical search is integrated with vector databases like Milvus without clear data alignment. If document IDs, embeddings, or text indexes are not synchronized properly, hybrid retrieval pipelines may produce inconsistent or incomplete results. Additionally, not normalizing score scales between BM25 and vector similarity can cause one system to dominate the ranking unfairly. Developers should ensure both systems share a consistent document schema, handle updates atomically, and apply score normalization techniques before fusion. Avoiding these pitfalls leads to a stable, interpretable, and high-performing Lexical search pipeline.