MCP Agent Tracker

# Embedding System Dependencies for MCP Conversation Intelligence # # This file contains the dependencies needed for the semantic embedding capabilities # in the MCP conversation intelligence system. # Core embedding dependencies sentence-transformers>=2.2.0 numpy>=1.21.0 # Vector similarity search (optional but recommended) faiss-cpu>=1.7.0 # Alternative vector search (if FAISS is not available) scikit-learn>=1.0.0 # Database and data handling sqlite3 # Usually included with Python dataclasses # Python 3.7+ (usually included) # Logging and utilities logging # Usually included with Python threading # Usually included with Python hashlib # Usually included with Python json # Usually included with Python datetime # Usually included with Python typing # Python 3.5+ (usually included) # Development and testing dependencies (optional) pytest>=6.0.0 pytest-cov>=2.0.0 # Performance optimization (optional) numba>=0.56.0 # Alternative embedding models (optional) transformers>=4.20.0 torch>=1.9.0 # Note: Some packages like sqlite3, logging, threading, hashlib, json, # datetime, and typing are part of Python's standard library and don't # need to be installed separately. # # To install the required packages: # pip install -r requirements_embeddings.txt # # For development with all optional dependencies: # pip install -r requirements_embeddings.txt[dev]