Vector Search
See how Tacnode Context Lake's native vector search capabilities power semantic search, recommendation systems, and AI applications. This demo covers embedding storage, similarity queries, and hybrid search patterns that combine vectors with structured filters.
Overview
Vector search is foundational to modern AI applications. This demo shows how Tacnode natively supports high-dimensional vector operations alongside traditional queries—without requiring a separate vector database.
Topics Covered
- •Embedding storage and indexing strategies
- •Cosine similarity, Euclidean distance, and dot product metrics
- •Hybrid search: combining vectors with SQL filters
- •Real-time vector updates and re-indexing
- •Integration with popular embedding models (OpenAI, Cohere, HuggingFace)
Key Takeaways
- ✓Eliminate the need for a separate vector database
- ✓Perform hybrid queries that filter by metadata AND semantic similarity
- ✓Maintain ACID consistency for vector operations
- ✓Scale to billions of vectors with approximate nearest neighbor (ANN) indexing
Technical Highlights
- →Index types: HNSW, IVF, and flat indexes
- →Dimension support: Up to 4096 dimensions per vector
- →Query latency: Millisecond vector search at scale
- →Batch operations: Bulk upsert and delete support
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Overview (Start Here)
The first installment of our Product Demo series — a complete walkthrough of the Context Lake architecture.
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