hybrid-search-fusion

Hybrid search in MEMORY_P coordinates semantic and full-text indexing across engines like Qdrant and Tantivy via FFI. This unified orchestration layer replaces manual stitching to deliver performant, relevant retrieval across complex queries.

Multi-language search requires robust FFI bindings and language bridges to enable cross-language data flow. You can coordinate retrieval across Julia, JAX, Mojo, Pony, and Zig interfaces to achieve accurate cross-engine ranking.

Yes, hybrid search works with Qdrant and Tantivy by coordinating signals from both engines. Combining semantic and full-text indexing outputs improves relevance and speed for complex queries within MEMORY_P v2.0.

To set up hybrid search orchestration in MEMORY_P v2.0, configure the unified orchestration module to enable combined semantic and full-text retrieval. This requires establishing FFI bindings to coordinate the multi-engine stack.

Multi-engine search requires FFI bindings because hybrid components must communicate across diverse language interfaces like Julia, Zig, and Mojo. These language bridges orchestrate the cross-engine data flow necessary for performant hybrid retrieval.

Manual integration for hybrid search often results in fragile integrations requiring extensive custom stitching. A unified orchestration layer is needed to reliably manage cross-engine ranking and coordinate semantic with full-text search signals.