Omni Search
Discover Tacnode Context Lake's unified search capabilities that span structured tables, full-text content, and vector embeddings. This demo shows how to build powerful search experiences that combine multiple query modalities in a single request.
Overview
Omni Search unifies all search modalities—keyword, semantic, and structured filters—into a single query interface. Build search experiences that understand user intent, not just keywords.
Topics Covered
- •Multi-modal search: combining keyword, semantic, and structured queries
- •Relevance scoring and ranking algorithms
- •Faceted search and aggregations
- •Query rewriting and expansion
- •Personalized search results
Key Takeaways
- ✓Build search that understands both 'blue shoes' and 'comfortable running footwear'
- ✓Combine semantic similarity with exact filters in one query
- ✓Implement faceted navigation without a separate search engine
- ✓Tune relevance scoring to match your application's needs
Technical Highlights
- →Fusion algorithms: RRF, weighted sum, and custom scorers
- →Query types: BM25, vector similarity, and exact match
- →Aggregations: Facets, histograms, and statistical summaries
- →Caching: Query-level and segment-level result caching
More Product Demos
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Incremental Materialized Views in Postgres, Explained
A 3-minute explainer on incremental materialized views — how they work, why standard Postgres REFRESH breaks at scale, and what Tacnode adds.
Overview (Start Here)
The first installment of our Product Demo series — a complete walkthrough of the Context Lake architecture.
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