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"""
Database Schema for Epic 2 Demo Persistent Storage
=================================================
SQLAlchemy models for storing processed documents, chunks, and embeddings
to eliminate re-parsing on system restart.
"""
import json
import time
from datetime import datetime
from typing import Dict, Any, Optional, List
from pathlib import Path
from sqlalchemy import create_engine, Column, Integer, String, Text, DateTime, LargeBinary, Float, ForeignKey, Boolean, Index
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship, Session
from sqlalchemy.dialects.sqlite import JSON
import numpy as np
Base = declarative_base()
class Document(Base):
"""Document metadata table"""
__tablename__ = 'documents'
id = Column(Integer, primary_key=True, autoincrement=True)
filename = Column(String(512), nullable=False, unique=True)
file_path = Column(Text, nullable=False)
file_hash = Column(String(64), nullable=False) # MD5 hash for change detection
file_size = Column(Integer, nullable=False)
file_mtime = Column(Float, nullable=False) # File modification time
# Processing metadata
processed_at = Column(DateTime, default=datetime.utcnow)
processor_config_hash = Column(String(64), nullable=False) # Config hash for invalidation
chunk_count = Column(Integer, default=0)
# Document metadata (JSON field)
doc_metadata = Column(JSON, nullable=True)
# Status tracking
processing_status = Column(String(32), default='pending') # pending, processing, completed, failed
error_message = Column(Text, nullable=True)
# Relationships
chunks = relationship("DocumentChunk", back_populates="document", cascade="all, delete-orphan")
# Indexes for performance
__table_args__ = (
Index('idx_filename', 'filename'),
Index('idx_file_hash', 'file_hash'),
Index('idx_processing_status', 'processing_status'),
Index('idx_processed_at', 'processed_at'),
)
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for API responses"""
return {
'id': self.id,
'filename': self.filename,
'file_path': self.file_path,
'file_hash': self.file_hash,
'file_size': self.file_size,
'chunk_count': self.chunk_count,
'processed_at': self.processed_at.isoformat() if self.processed_at else None,
'processing_status': self.processing_status,
'metadata': self.doc_metadata
}
class DocumentChunk(Base):
"""Document chunk content and embeddings table"""
__tablename__ = 'document_chunks'
id = Column(Integer, primary_key=True, autoincrement=True)
document_id = Column(Integer, ForeignKey('documents.id'), nullable=False)
chunk_index = Column(Integer, nullable=False) # Order within document
# Content
content = Column(Text, nullable=False)
content_hash = Column(String(64), nullable=False) # For deduplication
token_count = Column(Integer, nullable=True)
# Embedding data
embedding_model = Column(String(256), nullable=False)
embedding_vector = Column(LargeBinary, nullable=True) # Numpy array as bytes
embedding_dimension = Column(Integer, nullable=True)
embedding_norm = Column(Float, nullable=True) # For faster similarity calculations
# Chunk metadata (JSON field)
chunk_metadata = Column(JSON, nullable=True)
# Processing info
created_at = Column(DateTime, default=datetime.utcnow)
embedder_config_hash = Column(String(64), nullable=False)
# Quality metrics
confidence_score = Column(Float, nullable=True)
relevance_score = Column(Float, nullable=True)
# Relationships
document = relationship("Document", back_populates="chunks")
# Indexes for performance
__table_args__ = (
Index('idx_document_chunk', 'document_id', 'chunk_index'),
Index('idx_content_hash', 'content_hash'),
Index('idx_embedding_model', 'embedding_model'),
Index('idx_embedder_config', 'embedder_config_hash'),
Index('idx_created_at', 'created_at'),
)
def get_embedding(self) -> Optional[np.ndarray]:
"""Deserialize embedding vector from binary storage"""
if self.embedding_vector is None:
return None
try:
return np.frombuffer(self.embedding_vector, dtype=np.float32)
except Exception:
return None
def set_embedding(self, embedding: np.ndarray) -> None:
"""Serialize embedding vector to binary storage"""
if embedding is not None:
self.embedding_vector = embedding.astype(np.float32).tobytes()
self.embedding_dimension = len(embedding)
self.embedding_norm = float(np.linalg.norm(embedding))
else:
self.embedding_vector = None
self.embedding_dimension = None
self.embedding_norm = None
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for API responses"""
return {
'id': self.id,
'document_id': self.document_id,
'chunk_index': self.chunk_index,
'content': self.content[:200] + '...' if len(self.content) > 200 else self.content,
'token_count': self.token_count,
'embedding_model': self.embedding_model,
'embedding_dimension': self.embedding_dimension,
'metadata': self.chunk_metadata,
'created_at': self.created_at.isoformat() if self.created_at else None,
'confidence_score': self.confidence_score
}
class SystemCache(Base):
"""System-level cache and configuration tracking"""
__tablename__ = 'system_cache'
id = Column(Integer, primary_key=True, autoincrement=True)
cache_key = Column(String(256), nullable=False, unique=True)
cache_type = Column(String(64), nullable=False) # 'embedder_config', 'system_config', etc.
# Cache data
cache_value = Column(JSON, nullable=True)
cache_hash = Column(String(64), nullable=False)
# Validity tracking
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
expires_at = Column(DateTime, nullable=True)
is_valid = Column(Boolean, default=True)
# Indexes
__table_args__ = (
Index('idx_cache_key', 'cache_key'),
Index('idx_cache_type', 'cache_type'),
Index('idx_cache_validity', 'is_valid', 'expires_at'),
)
class ProcessingSession(Base):
"""Track processing sessions for analytics and debugging"""
__tablename__ = 'processing_sessions'
id = Column(Integer, primary_key=True, autoincrement=True)
session_id = Column(String(64), nullable=False, unique=True)
# Session metadata
started_at = Column(DateTime, default=datetime.utcnow)
completed_at = Column(DateTime, nullable=True)
status = Column(String(32), default='running') # running, completed, failed
# Processing stats
documents_processed = Column(Integer, default=0)
chunks_created = Column(Integer, default=0)
embeddings_generated = Column(Integer, default=0)
# Performance metrics
total_processing_time_ms = Column(Float, nullable=True)
documents_per_second = Column(Float, nullable=True)
chunks_per_second = Column(Float, nullable=True)
# Configuration hashes
processor_config_hash = Column(String(64), nullable=True)
embedder_config_hash = Column(String(64), nullable=True)
# Error tracking
error_count = Column(Integer, default=0)
error_details = Column(JSON, nullable=True)
# Indexes
__table_args__ = (
Index('idx_session_id', 'session_id'),
Index('idx_session_status', 'status'),
Index('idx_session_time', 'started_at', 'completed_at'),
)
class DatabaseSchema:
"""Database schema management and utilities"""
@staticmethod
def create_all_tables(engine) -> None:
"""Create all tables in the database"""
Base.metadata.create_all(engine)
@staticmethod
def drop_all_tables(engine) -> None:
"""Drop all tables from the database"""
Base.metadata.drop_all(engine)
@staticmethod
def get_table_info(engine) -> Dict[str, Any]:
"""Get information about all tables"""
from sqlalchemy import inspect
inspector = inspect(engine)
tables = {}
for table_name in inspector.get_table_names():
columns = inspector.get_columns(table_name)
indexes = inspector.get_indexes(table_name)
tables[table_name] = {
'columns': len(columns),
'indexes': len(indexes),
'column_names': [col['name'] for col in columns]
}
return tables
@staticmethod
def get_database_stats(session: Session) -> Dict[str, Any]:
"""Get database statistics"""
stats = {}
try:
# Document stats
stats['documents'] = {
'total': session.query(Document).count(),
'completed': session.query(Document).filter(Document.processing_status == 'completed').count(),
'failed': session.query(Document).filter(Document.processing_status == 'failed').count(),
'pending': session.query(Document).filter(Document.processing_status == 'pending').count()
}
# Chunk stats
stats['chunks'] = {
'total': session.query(DocumentChunk).count(),
'with_embeddings': session.query(DocumentChunk).filter(DocumentChunk.embedding_vector != None).count()
}
# Processing sessions
stats['sessions'] = {
'total': session.query(ProcessingSession).count(),
'completed': session.query(ProcessingSession).filter(ProcessingSession.status == 'completed').count(),
'running': session.query(ProcessingSession).filter(ProcessingSession.status == 'running').count()
}
# Cache entries
stats['cache'] = {
'total': session.query(SystemCache).count(),
'valid': session.query(SystemCache).filter(SystemCache.is_valid == True).count()
}
except Exception as e:
stats['error'] = str(e)
return stats
# Export key classes for use in other modules
__all__ = [
'Base',
'Document',
'DocumentChunk',
'SystemCache',
'ProcessingSession',
'DatabaseSchema'
] |