Overview
Raily’s vector store feature enables semantic search and similarity-based retrieval for your content. Store and search vector embeddings with built-in access control and analytics.Vector Store Options
Raily Vector Store
Managed vector database with instant setup
Qdrant
High-performance vector search engine
S3 Vectors
Cost-effective vector storage in S3
Capabilities
Semantic Search
Search by meaning, not just keywords. Vector embeddings enable AI systems to find relevant content based on semantic similarity.RAG (Retrieval-Augmented Generation)
Build Retrieval-Augmented Generation applications that combine vector search with LLM capabilities.Metadata Filtering
Search with filters based on metadata like category, date, author, or custom fields.Hybrid Search
Combine semantic similarity search with traditional keyword matching for optimal results.Vector Store Management
- Index content with vector embeddings
- Update vectors when content changes
- Delete vectors when content is removed
- Batch operations for efficiency
Integration with Providers
Connect to various vector store providers:- Qdrant - High-performance vector search
- S3 Vectors - Cost-effective storage in Amazon S3
- Raily Vector Store - Managed solution with instant setup
Best Practices
Batch Operations
Use batch indexing for better performance
Metadata Design
Index useful metadata for filtering
Regular Updates
Keep embeddings up to date with content changes
Access Control
Leverage built-in access control for vectors