bzUnifiedSearch-FM

Smarter Search, Unified Across Your FileMaker Data

Will Miro
Made by Will Miro
See all
Free Download GitHub

This tutorial file requires FileMaker 2024 (v21.x).

bzUnifiedSearch-FM builds on bzSemanticSearch-FM, expanding AI-driven search beyond individual tables. By leveraging a shared embedding table, it enables cross-table semantic search, helping users find relevant information across their data without needing to know where it’s stored. This approach simplifies retrieval, making searches more intuitive and efficient.

With optimized search performance and context-aware filtering, bzUnifiedSearch-FM streamlines workflows and enhances discoverability in FileMaker. Learn more about this functionality in the third blog post from our series on Semantic Search, where we explore the power of unified search and its impact on organizing and retrieving data in FileMaker.

bzUnifiedSearch-FM: How It Works

The bzUnifiedSearch-FM demo enables cross-table semantic search using a shared embedding table. Instead of searching separate tables individually, all embeddings are stored in one place, making queries more efficient and contextually relevant.

For example, searching for “water” retrieves:

  • 1 asset —> A video about surfing.
  • 4 projects —> Related to water.
  • 5 people —> With hobbies like scuba diving.

This eliminates the need for users to pre-select a table, streamlining search workflows.

Uses of bzUnifiedSearch-FM

Searching Across Multiple Tables

Instead of manually searching different tables, bzUnifiedSearch-FM allows users to search across all datasets simultaneously, surfacing contextually relevant results without requiring users to specify a data source.

Retrieving Information Faster in Large Datasets

With optimized search performance and a shared embedding table, bzUnifiedSearch-FM reduces search time and improves responsiveness, even in extensive databases with high record volumes.

Discovering Insights and Enhancing Research

bzUnifiedSearch-FM automatically detects contextually relevant connections across datasets, making it easier to retrieve insights, accelerate research, and support data-driven decision-making.

Streamlining Internal Workflows & Decision-Making

By eliminating the need to search through separate datasets manually, bzUnifiedSearch-FM helps users find relevant insights faster, leading to smarter decision-making and improved operational efficiency.

Features

    Uses container-based storage for embeddings and allows pre-filtering of records to improve search efficiency, reducing processing time for large datasets.

    Uses a shared Embedding table to enable Perform Semantic Find to search across records from different tables, allowing users to retrieve relevants results without specifying a particular table.

    Allows users to limit searches to specific data sets or tables by filtering records before performing a semantic search within a selected subset, ensuring more relevant results.

    Stores embeddings from multiple data tables in a single location, allowing Perform Semantic Find to efficiently search across different entities without requiring separate queries for each table.

Developer Spotlight

Will Miro Will Miro Sr. Developer Will has been developing in FileMaker since 2006. He is a long-time FileMaker Certified Developer, and has worked on several large, long-term projects for Beezwax clients.

Need help?

We can help integrating bzUnifiedSearch-FM into your FileMaker solutions.

Please contact us for more information or with any questions. Thanks!