UTCP In-Memory Embeddings Search Plugin

This plugin registers the in-memory embedding-based semantic search strategy with UTCP 1.0 via entry points.

Installation

pip install utcp-in-mem-embeddings

Optionally, for high-quality embeddings:

pip install "utcp-in-mem-embeddings[embedding]"

Or install the required dependencies directly:

pip install "sentence-transformers>=2.2.0" "torch>=1.9.0"

Why are sentence-transformers and torch needed?

While the plugin works without these packages (using a simple character frequency-based fallback), installing them provides significant benefits:

• Enhanced Semantic Understanding: The sentence-transformers package provides pre-trained models that convert text into high-quality vector embeddings, capturing the semantic meaning of text rather than just keywords.

• Better Search Results: With these packages installed, the search can understand conceptual similarity between queries and tools, even when they don't share exact keywords.

• Performance: The default model (all-MiniLM-L6-v2) offers a good balance between quality and performance for semantic search applications.

• Fallback Mechanism: Without these packages, the plugin automatically falls back to a simpler text similarity method, which works but with reduced accuracy.

How it works

When installed, this package exposes an entry point under utcp.plugins so the UTCP core can auto-discover and register the in_mem_embeddings strategy.

The embeddings are cached in memory for improved performance during repeated searches.