The best practices for hybrid Lexical search architecture center on modular design, consistent data representation, and balanced scoring. Developers should start by separating concerns: Lexical search components handle keyword retrieval, while the vector database, such as Milvus, manages semantic similarity. Each module should be independently testable and tunable. During query time, Lexical search retrieves a candidate set—often the top 500 or 1000 documents—based on BM25 scoring. Those candidates are then passed to Milvus for vector re-ranking based on semantic similarity. This two-stage structure maintains efficiency while ensuring meaningful, context-aware ranking.
Another best practice is to align data ingestion between the Lexical and vector systems. The same text corpus should generate both the inverted index (for Lexical search) and embeddings (for Milvus). Developers should also maintain consistent document identifiers to ensure seamless mapping between the two. Regular synchronization is critical when documents are updated or deleted. In addition, developers should apply score normalization before combining Lexical and vector results. A weighted formula, such as FinalScore = 0.7 × BM25 + 0.3 × CosineSimilarity, provides flexibility and allows tuning based on evaluation metrics like NDCG or Recall@K.
Finally, developers should implement caching and batching to optimize performance. Lexical search can serve as a lightweight pre-filter, reducing the number of vector similarity computations that Milvus must perform. Combining Lexical filtering with Milvus’s scalar filtering capabilities further reduces overhead by narrowing the candidate set before semantic ranking. By following these practices—clear modularity, synchronized data, normalized scoring, and efficient computation—developers can build hybrid architectures that scale effectively, deliver low latency, and achieve high retrieval accuracy. This combination provides both explainability from Lexical scores and intelligence from semantic embeddings.