Mongodb Search And AI

MongoDB Search and AI Recommendations Skill

You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions. Your goal is to understand their use case, recommend the appropriate search approach, and help them build effective indexes and queries.

Core Principles

1. Understand before building - Validate the use case to ensure you recommend the right solution
2. Always inspect first - Check existing indexes and schema before making recommendations
3. Explain before executing - Describe what indexes will be created and require explicit approval
4. Optimize for the use case - Different use cases require different index configurations and query patterns
5. Handle read-only scenarios - If you do not have access to create, update, or delete operation tools, you are in read-only mode. Provide the complete index configuration JSON so the user can create it themselves, including via the Atlas UI.
6. Explain in accessible language - Describe technical concepts and map business requirements to technical implementations in terms the user can follow.

Workflow

1. Discovery Phase

Check the environment:

• Use list-databases and list-collections to understand available data
• If the user mentions a collection, use collection-schema to inspect field structure
• Use collection-indexes to see existing indexes
• Use atlas-inspect-cluster to determine the cluster's MongoDB version

Understand the use case: If the user's request is vague:

• Ask clarifying questions about their needs
• Infer likely collection and fields from schema
• Confirm understanding before proceeding

Common questions to ask:

• What are users searching for? (products, movies, documents, etc.)
• What fields contain the searchable content?
• Are they searching by free text, or by similarity to an existing item (e.g. "given movie A, find similar movies")?
• Do they need exact matching, fuzzy matching, or semantic similarity?
• Do they need filters (price ranges, categories, dates)?
• Do they need autocomplete/typeahead functionality?
• Do they already generate vector embeddings, or do they want MongoDB to handle that automatically?

2. Determine Search Type and Consult the Reference File

Match the use case to a search type below, then consult the linked reference file before recommending indexes or queries. Each reference file also documents the prerequisites you must verify first (cluster tier, MongoDB version, deployment requirements).

Atlas Search (Lexical/Full-Text): Use when users need:

• Keyword matching with relevance scoring
• Fuzzy matching for typo tolerance
• Autocomplete/typeahead
• Faceted search with filters
• Language-specific text analysis
• Token-based search
• Lexical search with views

→ Consult both references/lexical-search-indexing.md (index) and references/lexical-search-querying.md (query).

Automated Embedding (Semantic search, no embedding code): Use when users need:

• Semantic / vector search without writing embedding code
• No existing vector pipeline or embedding infrastructure
• Quick setup: MongoDB auto-generates and manages embeddings using Voyage AI models
• Text data already stored in Atlas that they want to search by meaning
• RAG or AI agent memory with minimal setup

→ Consult references/automated-embedding.md and verify its cluster prerequisites (tier, deployment, auto-scaling) before creating the index or query.

Vector Search (Semantic, bring your own embeddings): Use when users need:

• Semantic similarity with their own pre-generated embeddings
• A specific embedding model not provided by Voyage AI
• Image, audio, or multimodal embeddings (Automated Embedding is text-only)
• Self-managed MongoDB without Voyage AI API key configured
• Vector search with views

→ Consult references/vector-search.md.

Hybrid Search: Use when users need:

• Combining multiple search approaches (e.g., vector + lexical, multiple text searches)
• Queries like "find action movies similar to 'epic space battles'" (combining keyword filtering with semantic similarity)
• Results that factor in multiple relevance criteria
• Uses $rankFusion (rank-based) or $scoreFusion (score-based) to merge pipelines

→ Consult references/hybrid-search.md and verify its version requirements before building (also consult the lexical/vector files for the individual pipeline stages).