mongodb-search-and-ai
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB server.
- Version
- 1.0.0
- License
- Apache-2.0
Pinned to revision 4e8964e24a5a, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/mongodb-search-and-ai/SKILL.md
- skills/mongodb-search-and-ai/LICENSE
- skills/mongodb-search-and-ai/references/automated-embedding.md
- skills/mongodb-search-and-ai/references/hybrid-search.md
- skills/mongodb-search-and-ai/references/lexical-search-indexing.md
- skills/mongodb-search-and-ai/references/lexical-search-querying.md
- skills/mongodb-search-and-ai/references/vector-search.md
Every link opens the file at its source, pinned to the revision this page describes.