I'll analyze this step by step with more technical details while keeping our assessment requirements in mind.

Legal Text Analysis Systematic Refactoring Plan

[Detailed Technical Implementation Guide]

Phase 1: Infrastructure Setup

Central Logging Implementation

# /scripts/utils/central_logger.py
from pathlib import Path
import logging
from datetime import datetime

class CentralLogger:
    """Unified logging system for Legal Text Analysis"""

    def __init__(self, name: str, log_dir: str = "logs"):
        self.log_dir = Path(log_dir)
        self.log_dir.mkdir(exist_ok=True)

        # Create component-specific logger
        self.logger = logging.getLogger(name)
        self.logger.setLevel(logging.INFO)

        # Add handlers
        file_handler = logging.FileHandler(
            self.log_dir / f"{name}_{datetime.now():%Y%m%d}.log"
        )
        console_handler = logging.StreamHandler()

        # Configure format
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        file_handler.setFormatter(formatter)
        console_handler.setFormatter(formatter)

        self.logger.addHandler(file_handler)
        self.logger.addHandler(console_handler)

MCP Database Integration

# /scripts/utils/db_manager.py
import sqlite3
from contextlib import contextmanager
from typing import Optional

class MCPDatabaseManager:
    """MCP-integrated database operations manager"""

    def __init__(self, db_path: str, logger: CentralLogger):
        self.db_path = db_path
        self.logger = logger

    @contextmanager
    def get_connection(self):
        """Create MCP-compatible database connection"""
        conn = None
        try:
            conn = sqlite3.connect(self.db_path)
            conn.row_factory = sqlite3.Row
            yield conn
            conn.commit()
        except Exception as e:
            self.logger.error(f"Database error: {str(e)}")
            if conn:
                conn.rollback()
            raise
        finally:
            if conn:
                conn.close()

Phase 2: NLP Pipeline Enhancement

Text Processor Refactoring

# /scripts/pipeline/text_processor.py
from typing import Dict, Any
import spacy
from pathlib import Path

class TextProcessor:
    """Core NLP processing with MCP integration"""

    def __init__(self,
                 db_manager: MCPDatabaseManager,
                 logger: CentralLogger):
        self.db = db_manager
        self.logger = logger
        self.nlp = spacy.load("en_core_web_sm")

    def process_text(self, text: str) -> Dict[str, Any]:
        """Process text and extract features"""
        try:
            doc = self.nlp(text)
            features = self._extract_features(doc)
            self._store_features(features)
            return features

        except Exception as e:
            self.logger.error(f"Processing error: {str(e)}")
            raise

Phase 3: Integration Testing

Test Suite Implementation

# /tests/test_pipeline.py
import unittest
from pathlib import Path

class TestLegalTextPipeline(unittest.TestCase):
    """Integration tests for the complete pipeline"""

    def setUp(self):
        self.logger = CentralLogger("test")
        self.db = MCPDatabaseManager("test.db", self.logger)
        self.processor = TextProcessor(self.db, self.logger)

    def test_end_to_end_processing(self):
        """Test complete processing chain"""
        sample_text = "This is a legal document..."
        results = self.processor.process_text(sample_text)
        self.assertIn('features', results)

Would you like me to detail:

1. The specific MCP integration points in more depth?
2. The metrics collection system?
3. The documentation structure in Obsidian?

This implementation ensures modular components that align with our assessment requirements while maintaining clear separation of concerns.