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Overview

File: src/tools/CustomDataTool.ts Demonstrates the powerful Custom Data API with semantic vector search. The example uses a movie database, but the patterns work for any searchable content.

What Makes This Special

Vector Search = Semantic Understanding Traditional search:
  • Query: “Inception” → Finds “Inception” ✅
  • Query: “dream movie” → Finds nothing ❌
Vector search:
  • Query: “Inception” → Finds “Inception” ✅
  • Query: “dream movie” → Finds “Inception”! ✅
  • Query: “mind-bending thriller” → Finds similar movies! ✅

Complete Tools

Create Movie Tool

Search Movies Tool

Get Movie Tool

Key Concepts

1. Search Text is Critical

The searchText parameter determines what the AI can find:

2. Similarity Scores

Understanding score thresholds:
  • 1.0 = Perfect match
  • 0.8-0.9 = Very similar
  • 0.7-0.8 = Somewhat similar
  • 0.6-0.7 = Loosely related
  • <0.6 = May be irrelevant

3. Natural Language Queries

Users can search naturally:

Testing

Try semantic searches:
  • “mind-bending thriller” → Should find Inception
  • “Christopher Nolan movies” → Should find his films
  • “space exploration” → Should find relevant sci-fi
  • “romantic comedy” → Should find rom-coms

Use Cases

Knowledge Base

Product Recommendations

Customer Notes

Customization Ideas

Add Ratings

Add Filters

Update Movies

Using save() Method

What You’ll Learn

Vector Search

Semantic similarity search with AI

Custom Collections

Store any data structure

Search Indexing

Optimize for findability

Score Thresholds

Tune precision vs recall

Next Steps

Data API Reference

Complete Data API documentation

Build Your First Skill

Uses Data API with vector search