How do hybrid approaches combine full-text and vector search?

Hybrid approaches combine full-text and vector search by leveraging the strengths of both methods to improve search accuracy and relevance. Full-text search relies on keyword matching, using algorithms like BM25 to rank documents based on exact term frequency and proximity. Vector search, on the other hand, uses embeddings (dense numerical representations of text) to find semantically similar content, even when keywords don’t match exactly. A hybrid system executes both searches in parallel or sequentially, then merges their results using techniques like score normalization, weighted averaging, or re-ranking to produce a unified ranked list.

For example, consider a search query like “python data analysis.” A full-text search might prioritize documents containing exact phrases like “data analysis with Python,” while a vector search could surface results about “pandas library tutorials” (since “pandas” is semantically linked to data analysis in Python). The hybrid approach would combine these results by normalizing the BM25 and vector similarity scores (e.g., scaling both to a 0-1 range) and applying weights (e.g., 60% vector, 40% keyword) to compute a final relevance score. Some systems also use a two-stage process: first retrieving candidates via one method, then refining the list with the other to balance speed and accuracy.

Implementation typically involves tools like Elasticsearch (for full-text) paired with vector databases like FAISS or integrated solutions like Elasticsearch’s kNN plugin. Developers must address challenges like latency (running both searches adds overhead) and balancing weights between methods. For instance, keyword-heavy queries (e.g., “error code 404”) might benefit from higher weights on full-text results, while ambiguous terms (e.g., “apple fruit vs. company”) could lean on vector search. Testing with real-world data is critical to tune parameters like score thresholds or fusion algorithms (e.g., reciprocal rank fusion) for specific use cases. This flexibility makes hybrid systems adaptable to scenarios where precision and recall are both priorities.