mongodb-search-and-ai

What problem does it solve?

Users struggle to design and fine‑tune MongoDB Atlas Search solutions that span lexical, semantic, and combined (hybrid) use cases, often missing best‑practice index configurations and query patterns.

Core Features & Use Cases

• Search Type Guidance: Determines whether lexical, vector, or hybrid search best fits a given user scenario.
• Index Creation Workflow: Generates complete JSON index definitions for Atlas Search, vector, and hybrid pipelines, with explicit user approvals.
• Query Construction & Optimization: Provides aggregation pipelines for $search, $vectorSearch, $rankFusion, and $scoreFusion, including performance tips and version checks.
• Read‑Only Mode Support: Supplies full index JSON for manual creation when execution permissions are limited.
• Use Cases: Building autocomplete/typeahead, semantic similarity for recommendation engines, or blended relevance ranking for e‑commerce product catalogs.

Quick Start

Ask the skill to design an Atlas Search index for a collection named 'products' that supports keyword and semantic searches.