Implement Semantic Search Algorithm with In-Memory/File Storage

[eyenpi]

Overview

Create a semantic search algorithm for intelligent context management using embeddings, with support for both in-memory and file-based embedding storage.

Requirements

Core Algorithm Features

• Implement SemanticSearchAlgorithm extending BaseAlgorithm
• Support configurable embedding models (sentence-transformers)
• Add similarity-based message selection
• Implement embedding caching (memory/file)

Technical Implementation

1. Embedding Generation

   • Use sentence-transformers for embedding generation
   • Support different models (configurable)
   • Add model loading and caching
   • Implement batch processing for efficiency

2. Similarity Search

   • Implement cosine similarity calculation
   • Add configurable similarity threshold
   • Support top-k message selection
   • Optimize vector operations

3. Embedding Storage

   • In-memory embedding cache
   • Optional file-based persistence (numpy/pickle)
   • Lazy loading for large histories
   • Memory usage optimization

Acceptance Criteria

• Working semantic search implementation
• Configurable embedding models
• Memory/file storage options
• Performance benchmarks (< 100ms per search)
• Memory usage monitoring
• Unit tests with >90% coverage
• Documentation and examples
• Type hints and mypy compliance