Sign Up for TigerData for free: https://tsdb.co/twt-TigerData
Checkout the GitHub repo for more information: https://tsdb.co/twt-pg-textsearch

If you've ever needed to search for something in a database, you've probably experienced a little bit of pain. In the AI era, search is changing, and even these external tools like Elasticsearch have a lot of problems. Now, the good news is that in this video, I'm going to show you an insanely good solution.

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🎞 Video Resources 🎞
Sign Up for TigerData for free: https://tsdb.co/twt-TigerData
Checkout the GitHub repo for more information: https://tsdb.co/twt-pg-textsearch
Blog post/tutorial: https://www.tigerdata.com/blog/introducing-pg_textsearch-true-bm25-ranking-hybrid-retrieval-postgres
TigerData Database Replicas: https://www.youtube.com/watch?v=8cf_bveteOE

⏳ Timestamps ⏳
00:00 | Overview
00:32 | Search is Changing
01:25 | History of Search
03:14 | The Ranking Quality Problem
04:32 | Why BM25 Fixes This (and Why It Matters)
06:45 | Database Search Setup
08:32 | Connecting an MCP Server
11:10 | Search Demo / Examples

Hashtags
#AIAgents #BM25 #TigerData

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PG text search brings BM25 ranking algorithm directly into PostgreSQL, eliminating the need for external search tools like Elasticsearch. Unlike PostgreSQL's native full-text search which lacks proper ranking signals, BM25 provides inverse document frequency, term frequency saturation, and length normalization for better search quality. This is particularly important for AI applications like RAG systems where search quality directly impacts LLM output. The extension is open source, transactional (auto-syncing), and can be combined with pgvector for hybrid keyword and semantic search within a single database.

AI Has Changed Databases Forever… Here’s How