Android Semantic Search Sample App

This sample app demonstrates how to implement vector search with embeddings for English keywords in an Android application using Java.

Features

• Transforms text queries into vector embeddings
• Performs semantic search using vector similarity
• Displays search results in a sorted list based on relevance
• Uses MiniLM as the embedding model
• Uses ObjectBox and alternative Apache Lucene for vector search capabilities

Requirements

• Android Studio 2024
• Android SDK 30+ (Android 11.0 or higher)
• JDK 8+
• Tested with Samsung S-24

Setup Instructions

1. Clone the repository or download the source code

2. Download the required model files:

   • Create an assets folder in your Android project's src/main directory
   • Download the MiniLM model file and save it as minilm_l6_v2.tflite in the assets folder
   • Create a bert_vocab.txt file in the assets folder with BERT's vocabulary

3. Sync the project with Gradle files

4. Build and run the app on your device or emulator

Model Information

This sample uses the MiniLM-L6-v2 model which creates 384-dimensional embeddings. It's a smaller and faster alternative to larger language models while still providing excellent performance for semantic search tasks.

Implementation Details

Components

1. EmbeddingModel: Interface defining the embedding generation functionality
2. MiniLMEmbeddingModel: TensorFlow Lite implementation of the embedding model
3. BertTokenizer: Simple tokenizer for processing text input
4. VectorDatabase: Interface for vector database operations
5. LuceneVectorDatabase: Implementation using Apache Lucene's KNN vector search
6. ObjectBoxVectorDatabase: Implementation using ObjectBox vector search
7. MainActivity: Main UI for the application
8. ResultsAdapter: Adapter for displaying search results

How It Works

1. The app initializes the embedding model and vector database on startup
2. Sample text documents are added to the database with their vector embeddings
3. When a user enters a search query, it's converted to an embedding vector
4. The app performs ObjectBox or Lucene KNN vector search to find the most semantically similar documents
5. Results are displayed in a RecyclerView sorted by similarity score

Dependencies

• TensorFlow Lite: For running the embedding model
• Apache Lucene: For vector search capabilities
• ObjectBox: For alternative vector search capabilities
• AndroidX: For UI components and RecyclerView

Notes

• This is a simplified implementation for demonstration purposes
• For production use, consider:
  • Using a more sophisticated tokenizer
  • Implementing asynchronous data loading
  • Adding caching mechanisms for embeddings
  • Storing the vector database persistently

Contact

You can contribute to this project by pull request. Authors email: hissain.khan@gmail.com

Thanks