Full Text Search
See Tacnode Context Lake's full-text search engine in action, with support for relevance ranking, faceted search, highlighting, and advanced query syntax. This demo covers configuration options and performance tuning for production deployments.
Overview
Full-text search is essential for content-heavy applications. Tacnode provides Elasticsearch-grade search capabilities without requiring a separate cluster—fully integrated with your operational data.
Topics Covered
- •Text analysis: tokenizers, filters, and analyzers
- •BM25 and TF-IDF ranking algorithms
- •Phrase matching and proximity queries
- •Highlighting and snippet generation
- •Language-specific analysis (stemming, stopwords)
Key Takeaways
- ✓Use native full-text search in Tacnode without separate search infrastructure
- ✓Implement type-ahead and autocomplete functionality
- ✓Configure analyzers for your specific language and domain
- ✓Optimize search performance for high-traffic applications
Technical Highlights
- →Index types: Inverted index with positional information
- →Analyzers: 30+ built-in analyzers for major languages
- →Query types: Match, phrase, prefix, fuzzy, and regex
- →Performance: Millisecond search across large collections
More Product Demos
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pgvector Tutorial: Vector Search in Postgres from Scratch
A 3-minute pgvector walkthrough — enable the extension, store embeddings, query nearest neighbors, and index with HNSW or IVFFlat, all in the Postgres you already run.
Why AI Agents Fail at Scale: Context Is a Data Layer Problem
A full InfoQ session on why agents that demo beautifully struggle in production — what "context" actually means, why agents read data differently than people, and what an agent-ready data layer has to do.
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