hybrid-search-fusion

Coordinate hybrid semantic and full-text search across multiple engines via FFI.

Updated Dec 30, 2025

Search Tags:#search #multi-language #rust #ffi #hybrid #memory_p

Ready to run

npx skills add https://github.com/Rigohl/MEMORY_P --skill hybrid-search-fusion

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hybrid-search-fusion
Source: https://github.com/Rigohl/MEMORY_P/tree/main/.github/skills/hybrid-search-fusion
Command: npx skills add https://github.com/Rigohl/MEMORY_P --skill hybrid-search-fusion

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hybrid search across multiple engines within MEMORY_P v2.0 often requires manual stitching and fragile integrations. This skill provides a unified orchestration layer over the multi-engine stack to deliver performant, relevant results.

Core Features & Use Cases

Unified hybrid search orchestration: coordinates semantic search and full-text indexing across Qdrant, Tantivy, and other engines via FFI.
Multi-language compatibility: enables cross-language data flow and retrieval across Julia, JAX, Mojo, Pony, Zig interfaces.
Cross-engine ranking: combines signals from different engines to improve relevance and speed in complex queries.

Quick Start

Configure and run the hybrid-search-fusion module to enable combined semantic and full-text search across MEMORY_P’s multi-engine stack.

Frequently Asked Questions about hybrid-search-fusion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I combine semantic and full-text search across multiple engines in MEMORY_P?▼

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.

What is the best way to execute multi-language search queries across different programming language interfaces?▼

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.

Does hybrid search fusion work with Qdrant and Tantivy for 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.

How do I set up unified hybrid search orchestration in 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.

Why does multi-engine search require FFI bindings for retrieval across different language stacks?▼

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.

What are the limitations of manual integration for hybrid search across multiple engines?▼

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.