Vector Search

Vector search in Raindrop enables semantic search and similarity matching by converting content into high-dimensional numerical representations. Instead of exact keyword matching, vector search understands meaning and context to find related content even when exact terms differ.

Ready to implement vector search?

Check out the Vector reference documentation for detailed API information, configuration options, and implementation examples.

What Vector Search Provides

Semantic Understanding Find content based on meaning rather than exact keywords, enabling searches that understand context and relationships between concepts.

Similarity Matching Identify related documents, products, or content based on conceptual similarity rather than literal text matches.

AI-Powered Discovery Enable recommendation systems and content discovery that surface relevant information based on user behavior and preferences.

Embedding Integration Store and query high-dimensional vectors generated by AI models alongside metadata for hybrid search capabilities.

When to Use Vector Search

Good Fit

• Content Discovery: Find related articles, documentation, or media based on semantic similarity
• Recommendation Systems: Suggest products, content, or connections based on user preferences and behavior
• Knowledge Base Search: Enable users to find information using natural language rather than exact keywords
• Duplicate Detection: Identify similar or duplicate content even when wording differs significantly

Consider Alternatives

• Exact Matching: Product codes, IDs, or specific technical terms work better with traditional keyword search
• Structured Queries: Complex filtering and sorting operations belong in SQL databases
• High-Frequency Identical Queries: Repeated exact searches benefit more from traditional caching

Integration Patterns

Hybrid Search Systems Combine vector search with traditional keyword search to provide both semantic understanding and exact match capabilities.

Content Processing Pipelines Generate embeddings when content is created or updated, storing both original content and vector representations for search.

Recommendation Engines Use vector similarity to build recommendation systems that suggest related items based on user interactions and preferences.

Metadata Filtering Combine semantic search with traditional filtering to find similar content within specific categories, dates, or other constraints.

Vector search excels at understanding meaning and context to provide intelligent content discovery and recommendation capabilities.