Implement Semantic Search Algorithm for Context Management

[eyenpi]

Overview

Add semantic search capabilities to enhance context management by selecting messages based on semantic relevance to the current query.

Requirements

Core Features

• Implement SemanticSearchAlgorithm extending BaseAlgorithm
• Add embedding generation and caching
• Implement similarity-based message selection
• Support configurable models and thresholds
• Maintain backwards compatibility with existing algorithm interface

Technical Implementation

1. Embedding Management

   • Generate embeddings using sentence-transformers
   • Implement embedding cache
   • Support different embedding models
   • Add embedding persistence (optional)

2. Similarity Search

   • Implement cosine similarity calculation
   • Add configurable similarity threshold
   • Support batch processing for efficiency
   • Optimize for performance

3. Algorithm Integration

   • Extend BaseAlgorithm interface
   • Implement message window selection
   • Add query-based context retrieval
   • Support fallback to FIFO when no query

Acceptance Criteria

• Working semantic search implementation
• Embedding generation and caching
• Configurable similarity thresholds
• Performance testing (< 100ms per search)
• Memory usage optimization
• Unit tests with >90% coverage
• Integration tests with OpenAI client
• Documentation and usage examples
• Type hints and mypy compliance