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operations +from typing import Dict, Any, Optional from dataclasses import dataclass -from datetime import datetime -import json import numpy as np +from pinkhaus_models import TranscriptionDatabase +from pinkhaus_models import TranscriptionMetadata as _TranscriptionMetadata -@dataclass -class PodcastTranscription: - id: int - filename: str - file_hash: str - language: str - full_text: str - model_name: Optional[str] - feed_url: Optional[str] - feed_item_id: Optional[str] - feed_item_title: Optional[str] - feed_item_published: Optional[datetime] - created_at: datetime - -@dataclass -class TranscriptionSegment: - id: int - transcription_id: int - segment_index: int - start_time: float - end_time: float - duration: float - text: str +# Type aliases for backward compatibility +PodcastTranscription = _TranscriptionMetadata @dataclass class TextChunk: + """Text chunk with embedding for vector similarity search.""" + id: str text: str metadata: Dict[str, Any] embedding: Optional[np.ndarray] = None + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for storage.""" + return { + "id": self.id, + "text": self.text, + "metadata": self.metadata, + "embedding": self.embedding, + } -class PodcastDatabase: - def __init__(self, db_path: str): - self.db_path = db_path - - def get_connection(self) -> sqlite3.Connection: - conn = sqlite3.connect(self.db_path) - conn.row_factory = sqlite3.Row - return conn - - async def get_async_connection(self) -> aiosqlite.Connection: - conn = await aiosqlite.connect(self.db_path) - conn.row_factory = aiosqlite.Row - return conn - - def get_all_transcriptions(self) -> List[PodcastTranscription]: - conn = self.get_connection() - cursor = conn.execute(""" - SELECT * FROM transcription_metadata - ORDER BY feed_item_published DESC - """) - - transcriptions = [] - for row in cursor: - transcriptions.append(self._row_to_transcription(row)) - - conn.close() - return transcriptions - - async def get_all_transcriptions_async(self) -> List[PodcastTranscription]: - conn = await self.get_async_connection() - cursor = await conn.execute(""" - SELECT * FROM transcription_metadata - ORDER BY feed_item_published DESC - """) - - transcriptions = [] - async for row in cursor: - transcriptions.append(self._row_to_transcription(row)) - - await conn.close() - return transcriptions - - def get_segments_for_transcription( - self, transcription_id: int - ) -> List[TranscriptionSegment]: - conn = self.get_connection() - cursor = conn.execute( - """ - SELECT * FROM transcription_segments - WHERE transcription_id = ? - ORDER BY segment_index - """, - (transcription_id,), - ) - - segments = [] - for row in cursor: - segments.append(self._row_to_segment(row)) - - conn.close() - return segments - def _row_to_transcription(self, row: sqlite3.Row) -> PodcastTranscription: - return PodcastTranscription( - id=row["id"], - filename=row["filename"], - file_hash=row["file_hash"], - language=row["language"], - full_text=row["full_text"], - model_name=row["model_name"], - feed_url=row["feed_url"], - feed_item_id=row["feed_item_id"], - feed_item_title=row["feed_item_title"], - feed_item_published=datetime.fromisoformat(row["feed_item_published"]) - if row["feed_item_published"] - else None, - created_at=datetime.fromisoformat(row["created_at"]), - ) +class PodcastDatabase(TranscriptionDatabase): + """Database for podcast transcriptions. - def _row_to_segment(self, row: sqlite3.Row) -> TranscriptionSegment: - return TranscriptionSegment( - id=row["id"], - transcription_id=row["transcription_id"], - segment_index=row["segment_index"], - start_time=row["start_time"], - end_time=row["end_time"], - duration=row["duration"], - text=row["text"], - ) + This class extends TranscriptionDatabase from pinkhaus-models. + """ - -class VectorStore: - def __init__(self, db_path: str = "grant_vectors.db"): + def __init__(self, db_path: str): + """Initialize with database path.""" + super().__init__(db_path=db_path) self.db_path = db_path - self._init_db() - - def _init_db(self): - conn = sqlite3.connect(self.db_path) - conn.execute(""" - CREATE TABLE IF NOT EXISTS text_chunks ( - id TEXT PRIMARY KEY, - text TEXT NOT NULL, - metadata TEXT NOT NULL, - embedding BLOB, - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP - ) - """) - conn.execute(""" - CREATE INDEX IF NOT EXISTS idx_chunks_created - ON text_chunks(created_at) - """) - conn.commit() - conn.close() - - def add_chunk(self, chunk: TextChunk): - conn = sqlite3.connect(self.db_path) - embedding_blob = ( - chunk.embedding.tobytes() if chunk.embedding is not None else None - ) - - conn.execute( - """ - INSERT OR REPLACE INTO text_chunks (id, text, metadata, embedding) - VALUES (?, ?, ?, ?) - """, - (chunk.id, chunk.text, json.dumps(chunk.metadata), embedding_blob), - ) - - conn.commit() - conn.close() - - def add_chunks(self, chunks: List[TextChunk]): - conn = sqlite3.connect(self.db_path) - - data = [ - ( - chunk.id, - chunk.text, - json.dumps(chunk.metadata), - chunk.embedding.tobytes() if chunk.embedding is not None else None, - ) - for chunk in chunks - ] - - conn.executemany( - """ - INSERT OR REPLACE INTO text_chunks (id, text, metadata, embedding) - VALUES (?, ?, ?, ?) - """, - data, - ) - - conn.commit() - conn.close() - - def get_all_chunks(self, with_embeddings: bool = True) -> List[TextChunk]: - conn = sqlite3.connect(self.db_path) - cursor = conn.execute("SELECT * FROM text_chunks") - - chunks = [] - for row in cursor: - embedding = None - if with_embeddings and row[3] is not None: - embedding = np.frombuffer(row[3], dtype=np.float32) - - chunks.append( - TextChunk( - id=row[0], - text=row[1], - metadata=json.loads(row[2]), - embedding=embedding, - ) - ) - - conn.close() - return chunks - - def search_similar( - self, query_embedding: np.ndarray, top_k: int = 5 - ) -> List[Tuple[TextChunk, float]]: - chunks = self.get_all_chunks(with_embeddings=True) - - if not chunks or chunks[0].embedding is None: - return [] - - # Calculate cosine similarity - similarities = [] - query_norm = query_embedding / np.linalg.norm(query_embedding) - for chunk in chunks: - if chunk.embedding is not None: - chunk_norm = chunk.embedding / np.linalg.norm(chunk.embedding) - similarity = np.dot(query_norm, chunk_norm) - similarities.append((chunk, float(similarity))) + # Additional grant-specific methods can be added here if needed - # Sort by similarity and return top k - similarities.sort(key=lambda x: x[1], reverse=True) - return similarities[:top_k] - def chunk_exists(self, chunk_id: str) -> bool: - conn = sqlite3.connect(self.db_path) - cursor = conn.execute("SELECT 1 FROM text_chunks WHERE id = ?", (chunk_id,)) - exists = cursor.fetchone() is not None - conn.close() - return exists +# Re-export VectorStore from pinkhaus-models for backward compatibility +# The imported VectorStore class already has all the needed functionality diff --git a/projects/grant/uv.lock b/projects/grant/uv.lock index 76fa39d..3f051f4 100644 --- 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b/projects/pinkhaus-models/pinkhaus_models/database.py index 43f14ee..71b20b3 100644 --- a/projects/pinkhaus-models/pinkhaus_models/database.py +++ b/projects/pinkhaus-models/pinkhaus_models/database.py @@ -1,231 +1,44 @@ -import sqlite3 -import aiosqlite +# This module provides backward compatibility. +# All functionality has been moved to database_sqlalchemy.py + from typing import Optional, List, Dict, Any -from contextlib import contextmanager, asynccontextmanager -from pathlib import Path -from .models import ( - TranscriptionMetadata, - TranscriptionSegment, - TranscriptionResult, - Segment, +from .database_sqlalchemy import ( + TranscriptionDatabase as _TranscriptionDatabase, + VectorStore as _VectorStore, ) -class TranscriptionDatabase: - """Handle SQLite database operations for transcriptions.""" - - def __init__(self, db_path: str): - """Initialize database connection.""" - self.db_path = db_path - self._ensure_parent_directory() - - def _ensure_parent_directory(self): - """Ensure the parent directory for the database exists.""" - parent = Path(self.db_path).parent - if not parent.exists(): - parent.mkdir(parents=True, exist_ok=True) - - @contextmanager - def _get_connection(self): - """Context manager for database connections.""" - conn = sqlite3.connect(self.db_path) - conn.row_factory = sqlite3.Row - conn.execute("PRAGMA foreign_keys = ON") - try: - yield conn - conn.commit() - except Exception: - conn.rollback() - raise - finally: - conn.close() - - @asynccontextmanager - async def _get_async_connection(self): - """Async context manager for database connections.""" - conn = await aiosqlite.connect(self.db_path) - conn.row_factory = aiosqlite.Row - await conn.execute("PRAGMA foreign_keys = ON") - try: - yield conn - await conn.commit() - except Exception: - await conn.rollback() - raise - finally: - await conn.close() - - def create_tables( - self, - metadata_table: str = "transcription_metadata", - segments_table: str = "transcription_segments", - ): - """Create the database tables if they don't exist.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - # Create metadata table - cursor.execute(f""" - CREATE TABLE IF NOT EXISTS {metadata_table} ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - filename TEXT NOT NULL, - file_hash TEXT NOT NULL, - language TEXT NOT NULL, - full_text TEXT NOT NULL, - model_name TEXT, - feed_url TEXT, - feed_item_id TEXT, - feed_item_title TEXT, - feed_item_published TIMESTAMP, - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, - UNIQUE(file_hash, model_name) - ) - """) - - # Create segments table - cursor.execute(f""" - CREATE TABLE IF NOT EXISTS {segments_table} ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - transcription_id INTEGER NOT NULL, - segment_index INTEGER NOT NULL, - start_time REAL NOT NULL, - end_time REAL NOT NULL, - duration REAL NOT NULL, - text TEXT NOT NULL, - FOREIGN KEY (transcription_id) REFERENCES {metadata_table}(id), - UNIQUE(transcription_id, segment_index) - ) - """) - - # Create indexes - cursor.execute(f""" - CREATE INDEX IF NOT EXISTS idx_{segments_table}_transcription_id - ON {segments_table}(transcription_id) - """) - - cursor.execute(f""" - CREATE INDEX IF NOT EXISTS idx_{metadata_table}_file_hash - ON {metadata_table}(file_hash) - """) - - cursor.execute(f""" - CREATE UNIQUE INDEX IF NOT EXISTS idx_{metadata_table}_feed_item - ON {metadata_table}(feed_url, feed_item_id) - WHERE feed_url IS NOT NULL AND feed_item_id IS NOT NULL - """) - - def get_all_transcriptions( - self, metadata_table: str = "transcription_metadata" - ) -> List[TranscriptionMetadata]: - """Get all transcriptions from the database.""" - with self._get_connection() as conn: - cursor = conn.execute(f""" - SELECT * FROM {metadata_table} - ORDER BY feed_item_published DESC, created_at DESC - """) - - return [TranscriptionMetadata.from_row(dict(row)) for row in cursor] - - async def get_all_transcriptions_async( - self, metadata_table: str = "transcription_metadata" - ) -> List[TranscriptionMetadata]: - """Get all transcriptions from the database asynchronously.""" - async with self._get_async_connection() as conn: - cursor = await conn.execute(f""" - SELECT * FROM {metadata_table} - ORDER BY feed_item_published DESC, created_at DESC - """) - - rows = await cursor.fetchall() - return [TranscriptionMetadata.from_row(dict(row)) for row in rows] - - def get_transcription_metadata( - self, transcription_id: int, metadata_table: str = "transcription_metadata" - ) -> Optional[TranscriptionMetadata]: - """Get metadata for a specific transcription.""" - with self._get_connection() as conn: - cursor = conn.execute( - f""" - SELECT * FROM {metadata_table} WHERE id = ? - """, - (transcription_id,), - ) - - row = cursor.fetchone() - return TranscriptionMetadata.from_row(dict(row)) if row else None - - def get_segments_for_transcription( - self, transcription_id: int, segments_table: str = "transcription_segments" - ) -> List[TranscriptionSegment]: - """Get all segments for a transcription.""" - with self._get_connection() as conn: - cursor = conn.execute( - f""" - SELECT * FROM {segments_table} - WHERE transcription_id = ? - ORDER BY segment_index - """, - (transcription_id,), - ) +class TranscriptionDatabase(_TranscriptionDatabase): + """Handle database operations for transcriptions. - return [TranscriptionSegment.from_row(dict(row)) for row in cursor] + This class maintains backward compatibility while using SQLAlchemy internally. + """ - async def get_segments_for_transcription_async( - self, transcription_id: int, segments_table: str = "transcription_segments" - ) -> List[TranscriptionSegment]: - """Get all segments for a transcription asynchronously.""" - async with self._get_async_connection() as conn: - cursor = await conn.execute( - f""" - SELECT * FROM {segments_table} - WHERE transcription_id = ? - ORDER BY segment_index - """, - (transcription_id,), - ) + def __init__(self, db_path: str = None, database_url: str = None): + """Initialize database connection. - rows = await cursor.fetchall() - return [TranscriptionSegment.from_row(dict(row)) for row in rows] + Args: + db_path: Path to the database file (legacy) + database_url: SQLAlchemy database URL + """ + if database_url: + super().__init__(database_url=database_url) + elif db_path: + # Convert to SQLAlchemy URL + database_url = f"sqlite:///{db_path}" + super().__init__(database_url=database_url) + self.db_path = db_path + else: + raise ValueError("Either db_path or database_url must be provided") - def get_transcription( - self, - transcription_id: int, - metadata_table: str = "transcription_metadata", - segments_table: str = "transcription_segments", - ) -> Optional[Dict[str, Any]]: - """Get complete transcription with metadata and segments.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - # Get metadata - cursor.execute( - f""" - SELECT * FROM {metadata_table} WHERE id = ? - """, - (transcription_id,), - ) - - metadata = cursor.fetchone() - if not metadata: - return None - - # Get segments - cursor.execute( - f""" - SELECT * FROM {segments_table} - WHERE transcription_id = ? - ORDER BY segment_index - """, - (transcription_id,), - ) - - segments = cursor.fetchall() - - return { - "metadata": dict(metadata), - "segments": [dict(seg) for seg in segments], - } + # Legacy methods maintained for backward compatibility + def get_recent_transcriptions( + self, limit: int = 10, metadata_table: str = "transcription_metadata" + ) -> List[Dict[str, Any]]: + """Get the most recent transcriptions.""" + transcriptions = self.get_all_transcriptions(metadata_table) + return [t.to_dict() for t in transcriptions[:limit]] def get_full_transcription( self, @@ -236,247 +49,20 @@ def get_full_transcription( """Alias for get_transcription for backward compatibility.""" return self.get_transcription(transcription_id, metadata_table, segments_table) - def save_transcription( - self, - result: TranscriptionResult, - model_name: str = "unknown", - metadata_table: str = "transcription_metadata", - segments_table: str = "transcription_segments", - feed_url: Optional[str] = None, - feed_item_id: Optional[str] = None, - feed_item_title: Optional[str] = None, - feed_item_published: Optional[str] = None, - ) -> int: - """Save a transcription result to the database.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - # Check if already exists - cursor.execute( - f""" - SELECT id FROM {metadata_table} - WHERE file_hash = ? AND model_name = ? - """, - (result.file_hash, model_name), - ) - - existing = cursor.fetchone() - if existing: - return existing["id"] - - # Insert metadata - cursor.execute( - f""" - INSERT INTO {metadata_table} - (filename, file_hash, language, full_text, model_name, - feed_url, feed_item_id, feed_item_title, feed_item_published) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - result.filename, - result.file_hash, - result.language, - result.full_text, - model_name, - feed_url, - feed_item_id, - feed_item_title, - feed_item_published, - ), - ) - - transcription_id = cursor.lastrowid - - # Insert segments - for idx, segment in enumerate(result.segments): - # Handle both regular segments and protobuf segments - if hasattr(segment, "start_ms"): - # Protobuf segment (beige-book style) - start_time = segment.start_ms / 1000.0 - end_time = segment.end_ms / 1000.0 - text = segment.text - else: - # Regular segment - start_time = segment.start - end_time = segment.end - text = segment.text - duration = end_time - start_time +class VectorStore(_VectorStore): + """Vector store for text chunks with embeddings. - cursor.execute( - f""" - INSERT INTO {segments_table} - (transcription_id, segment_index, start_time, end_time, duration, text) - VALUES (?, ?, ?, ?, ?, ?) - """, - ( - transcription_id, - idx, - start_time, - end_time, - duration, - text.strip(), - ), - ) + This class maintains backward compatibility while using SQLAlchemy internally. + """ - return transcription_id + def __init__(self, db_path: str = "grant_vectors.db"): + """Initialize vector store. - def search_transcriptions( - self, - query: str, - metadata_table: str = "transcription_metadata", - limit: int = 10, - ) -> List[TranscriptionMetadata]: - """Search transcriptions by text content.""" - with self._get_connection() as conn: - cursor = conn.execute( - f""" - SELECT * FROM {metadata_table} - WHERE full_text LIKE ? - ORDER BY feed_item_published DESC, created_at DESC - LIMIT ? - """, - (f"%{query}%", limit), - ) - - return [TranscriptionMetadata.from_row(dict(row)) for row in cursor] - - def find_by_hash( - self, file_hash: str, metadata_table: str = "transcription_metadata" - ) -> List[Dict[str, Any]]: - """Find all transcriptions for a given file hash.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - cursor.execute( - f""" - SELECT * FROM {metadata_table} - WHERE file_hash = ? - ORDER BY created_at DESC - """, - (file_hash,), - ) - - return [dict(row) for row in cursor.fetchall()] - - def delete_transcription( - self, - transcription_id: int, - metadata_table: str = "transcription_metadata", - segments_table: str = "transcription_segments", - ) -> bool: - """Delete a transcription and its segments.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - # First delete segments - cursor.execute( - f""" - DELETE FROM {segments_table} WHERE transcription_id = ? - """, - (transcription_id,), - ) - - # Then delete metadata - cursor.execute( - f""" - DELETE FROM {metadata_table} WHERE id = ? - """, - (transcription_id,), - ) - - return cursor.rowcount > 0 - - def export_to_dict( - self, - transcription_id: int, - metadata_table: str = "transcription_metadata", - segments_table: str = "transcription_segments", - ) -> Optional[TranscriptionResult]: - """Export a transcription from database back to TranscriptionResult object.""" - data = self.get_transcription(transcription_id, metadata_table, segments_table) - if not data: - return None - - metadata = data["metadata"] - segments = [] - - for seg in data["segments"]: - segments.append( - Segment( - start=seg["start_time"], - end=seg["end_time"], - text=seg["text"], - ) - ) - - return TranscriptionResult( - filename=metadata["filename"], - file_hash=metadata["file_hash"], - language=metadata["language"], - segments=segments, - full_text=metadata["full_text"], - ) - - def get_recent_transcriptions( - self, limit: int = 10, metadata_table: str = "transcription_metadata" - ) -> List[Dict[str, Any]]: - """Get the most recent transcriptions.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - cursor.execute( - f""" - SELECT * FROM {metadata_table} - ORDER BY created_at DESC, id DESC - LIMIT ? - """, - (limit,), - ) - - return [dict(row) for row in cursor.fetchall()] - - def check_feed_item_exists( - self, - feed_url: str, - feed_item_id: str, - metadata_table: str = "transcription_metadata", - ) -> bool: - """Check if a feed item has already been processed.""" - with self._get_connection() as conn: - cursor = conn.cursor() - - cursor.execute( - f""" - SELECT COUNT(*) as count FROM {metadata_table} - WHERE feed_url = ? AND feed_item_id = ? - """, - (feed_url, feed_item_id), - ) - - result = cursor.fetchone() - return result["count"] > 0 - - def get_transcriptions_by_date_range( - self, - start_date: Optional[str] = None, - end_date: Optional[str] = None, - metadata_table: str = "transcription_metadata", - ) -> List[TranscriptionMetadata]: - """Get transcriptions within a date range.""" - query = f"SELECT * FROM {metadata_table} WHERE 1=1" - params = [] - - if start_date: - query += " AND feed_item_published >= ?" - params.append(start_date) - - if end_date: - query += " AND feed_item_published <= ?" - params.append(end_date) - - query += " ORDER BY feed_item_published DESC, created_at DESC" - - with self._get_connection() as conn: - cursor = conn.execute(query, params) - return [TranscriptionMetadata.from_row(dict(row)) for row in cursor] + Args: + db_path: Path to the database file + """ + # Convert to SQLAlchemy URL + database_url = f"sqlite:///{db_path}" + super().__init__(database_url=database_url) + self.db_path = db_path diff --git a/projects/pinkhaus-models/pinkhaus_models/database_sqlalchemy.py b/projects/pinkhaus-models/pinkhaus_models/database_sqlalchemy.py new file mode 100644 index 0000000..e896d9f --- /dev/null +++ b/projects/pinkhaus-models/pinkhaus_models/database_sqlalchemy.py @@ -0,0 +1,562 @@ +"""SQLAlchemy-based database operations for transcriptions.""" + +from typing import Optional, List, Dict, Any, Tuple +from contextlib import contextmanager, asynccontextmanager +from pathlib import Path +import numpy as np + +from sqlalchemy import create_engine, select, desc +from sqlalchemy.orm import sessionmaker, Session +from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker + +from .orm import Base, TranscriptionMetadataORM, TranscriptionSegmentORM, TextChunkORM +from .models import ( + TranscriptionMetadata, + TranscriptionSegment, + TranscriptionResult, + Segment, +) + + +class DatabaseEngine: + """Manages SQLAlchemy engine and session creation.""" + + def __init__(self, database_url: str): + """ + Initialize database engine. + + Args: + database_url: SQLAlchemy database URL (e.g., 'sqlite:///path/to/db.db') + """ + # Ensure parent directory exists for SQLite + if database_url.startswith("sqlite:///"): + db_path = database_url.replace("sqlite:///", "") + parent = Path(db_path).parent + if not parent.exists(): + parent.mkdir(parents=True, exist_ok=True) + + self.database_url = database_url + self.engine = create_engine(database_url) + self.SessionLocal = sessionmaker(bind=self.engine) + + # For async operations + if database_url.startswith("sqlite:"): + async_url = database_url.replace("sqlite:", "sqlite+aiosqlite:") + else: + async_url = database_url + + self.async_engine = create_async_engine(async_url) + self.AsyncSessionLocal = async_sessionmaker( + bind=self.async_engine, class_=AsyncSession + ) + + def create_tables(self): + """Create all tables if they don't exist.""" + Base.metadata.create_all(bind=self.engine) + + @contextmanager + def get_session(self) -> Session: + """Get a database session.""" + session = self.SessionLocal() + try: + yield session + session.commit() + except Exception: + session.rollback() + raise + finally: + session.close() + + @asynccontextmanager + async def get_async_session(self) -> AsyncSession: + """Get an async database session.""" + async with self.AsyncSessionLocal() as session: + try: + yield session + await session.commit() + except Exception: + await session.rollback() + raise + + +class TranscriptionDatabase: + """Handle database operations for transcriptions using SQLAlchemy.""" + + def __init__(self, database_url: str = None, db_path: str = None): + """ + Initialize database connection. + + Args: + database_url: SQLAlchemy database URL + db_path: Legacy parameter for SQLite path (deprecated, use database_url) + """ + if database_url is None and db_path is not None: + # Convert legacy db_path to SQLAlchemy URL + database_url = f"sqlite:///{db_path}" + elif database_url is None: + raise ValueError("Either database_url or db_path must be provided") + + self.engine = DatabaseEngine(database_url) + self.engine.create_tables() + + def create_tables(self, metadata_table: str = None, segments_table: str = None): + """Create the database tables if they don't exist.""" + # Tables are created automatically in __init__ + pass + + def get_all_transcriptions( + self, metadata_table: str = None + ) -> List[TranscriptionMetadata]: + """Get all transcriptions from the database.""" + with self.engine.get_session() as session: + stmt = select(TranscriptionMetadataORM).order_by( + desc(TranscriptionMetadataORM.feed_item_published), + desc(TranscriptionMetadataORM.created_at), + ) + results = session.execute(stmt).scalars().all() + + return [self._orm_to_metadata(row) for row in results] + + async def get_all_transcriptions_async( + self, metadata_table: str = None + ) -> List[TranscriptionMetadata]: + """Get all transcriptions from the database asynchronously.""" + async with self.engine.get_async_session() as session: + stmt = select(TranscriptionMetadataORM).order_by( + desc(TranscriptionMetadataORM.feed_item_published), + desc(TranscriptionMetadataORM.created_at), + ) + results = await session.execute(stmt) + rows = results.scalars().all() + + return [self._orm_to_metadata(row) for row in rows] + + def get_transcription_metadata( + self, transcription_id: int, metadata_table: str = None + ) -> Optional[TranscriptionMetadata]: + """Get metadata for a specific transcription.""" + with self.engine.get_session() as session: + result = session.get(TranscriptionMetadataORM, transcription_id) + return self._orm_to_metadata(result) if result else None + + def get_segments_for_transcription( + self, transcription_id: int, segments_table: str = None + ) -> List[TranscriptionSegment]: + """Get all segments for a transcription.""" + with self.engine.get_session() as session: + stmt = ( + select(TranscriptionSegmentORM) + .where(TranscriptionSegmentORM.transcription_id == transcription_id) + .order_by(TranscriptionSegmentORM.segment_index) + ) + + results = session.execute(stmt).scalars().all() + return [self._orm_to_segment(row) for row in results] + + async def get_segments_for_transcription_async( + self, transcription_id: int, segments_table: str = None + ) -> List[TranscriptionSegment]: + """Get all segments for a transcription asynchronously.""" + async with self.engine.get_async_session() as session: + stmt = ( + select(TranscriptionSegmentORM) + .where(TranscriptionSegmentORM.transcription_id == transcription_id) + .order_by(TranscriptionSegmentORM.segment_index) + ) + + results = await session.execute(stmt) + rows = results.scalars().all() + return [self._orm_to_segment(row) for row in rows] + + def get_transcription( + self, + transcription_id: int, + metadata_table: str = None, + segments_table: str = None, + ) -> Optional[Dict[str, Any]]: + """Get complete transcription with metadata and segments.""" + with self.engine.get_session() as session: + metadata = session.get(TranscriptionMetadataORM, transcription_id) + if not metadata: + return None + + segments = ( + session.query(TranscriptionSegmentORM) + .filter_by(transcription_id=transcription_id) + .order_by(TranscriptionSegmentORM.segment_index) + .all() + ) + + return { + "metadata": self._orm_to_dict(metadata), + "segments": [self._orm_to_dict(seg) for seg in segments], + } + + def save_transcription( + self, + result: TranscriptionResult, + model_name: str = "unknown", + metadata_table: str = None, + segments_table: str = None, + feed_url: Optional[str] = None, + feed_item_id: Optional[str] = None, + feed_item_title: Optional[str] = None, + feed_item_published: Optional[str] = None, + ) -> int: + """Save a transcription result to the database.""" + with self.engine.get_session() as session: + # Check if already exists + existing = ( + session.query(TranscriptionMetadataORM) + .filter_by(file_hash=result.file_hash, model_name=model_name) + .first() + ) + + if existing: + return existing.id + + # Parse datetime string if provided + feed_published_dt = None + if feed_item_published: + from datetime import datetime + + if isinstance(feed_item_published, str): + feed_published_dt = datetime.fromisoformat(feed_item_published) + else: + feed_published_dt = feed_item_published + + # Create metadata + metadata = TranscriptionMetadataORM( + filename=result.filename, + file_hash=result.file_hash, + language=result.language, + full_text=result.full_text, + model_name=model_name, + feed_url=feed_url, + feed_item_id=feed_item_id, + feed_item_title=feed_item_title, + feed_item_published=feed_published_dt, + ) + + session.add(metadata) + session.flush() # Get the ID + + # Create segments + for idx, segment in enumerate(result.segments): + # Handle both regular segments and protobuf segments + if hasattr(segment, "start_ms"): + # Protobuf segment (beige-book style) + start_time = segment.start_ms / 1000.0 + end_time = segment.end_ms / 1000.0 + text = segment.text + else: + # Regular segment + start_time = segment.start + end_time = segment.end + text = segment.text + + duration = end_time - start_time + + seg_orm = TranscriptionSegmentORM( + transcription_id=metadata.id, + segment_index=idx, + start_time=start_time, + end_time=end_time, + duration=duration, + text=text.strip(), + ) + session.add(seg_orm) + + session.commit() + return metadata.id + + def search_transcriptions( + self, query: str, metadata_table: str = None, limit: int = 10 + ) -> List[TranscriptionMetadata]: + """Search transcriptions by text content.""" + with self.engine.get_session() as session: + stmt = ( + select(TranscriptionMetadataORM) + .where(TranscriptionMetadataORM.full_text.like(f"%{query}%")) + .order_by( + desc(TranscriptionMetadataORM.feed_item_published), + desc(TranscriptionMetadataORM.created_at), + ) + .limit(limit) + ) + + results = session.execute(stmt).scalars().all() + return [self._orm_to_metadata(row) for row in results] + + def find_by_hash( + self, file_hash: str, metadata_table: str = None + ) -> List[Dict[str, Any]]: + """Find all transcriptions for a given file hash.""" + with self.engine.get_session() as session: + stmt = ( + select(TranscriptionMetadataORM) + .where(TranscriptionMetadataORM.file_hash == file_hash) + .order_by(desc(TranscriptionMetadataORM.created_at)) + ) + + results = session.execute(stmt).scalars().all() + return [self._orm_to_dict(row) for row in results] + + def delete_transcription( + self, + transcription_id: int, + metadata_table: str = None, + segments_table: str = None, + ) -> bool: + """Delete a transcription and its segments.""" + with self.engine.get_session() as session: + metadata = session.get(TranscriptionMetadataORM, transcription_id) + if metadata: + session.delete(metadata) # Cascade will delete segments + session.commit() + return True + return False + + def export_to_dict( + self, + transcription_id: int, + metadata_table: str = None, + segments_table: str = None, + ) -> Optional[TranscriptionResult]: + """Export a transcription from database back to TranscriptionResult object.""" + data = self.get_transcription(transcription_id) + if not data: + return None + + metadata = data["metadata"] + segments = [] + + for seg in data["segments"]: + segments.append( + Segment( + start=seg["start_time"], + end=seg["end_time"], + text=seg["text"], + ) + ) + + return TranscriptionResult( + filename=metadata["filename"], + file_hash=metadata["file_hash"], + language=metadata["language"], + segments=segments, + full_text=metadata["full_text"], + ) + + def check_feed_item_exists( + self, feed_url: str, feed_item_id: str, metadata_table: str = None + ) -> bool: + """Check if a feed item has already been processed.""" + with self.engine.get_session() as session: + exists = ( + session.query(TranscriptionMetadataORM) + .filter_by(feed_url=feed_url, feed_item_id=feed_item_id) + .first() + is not None + ) + + return exists + + def get_transcriptions_by_date_range( + self, + start_date: Optional[str] = None, + end_date: Optional[str] = None, + metadata_table: str = None, + ) -> List[TranscriptionMetadata]: + """Get transcriptions within a date range.""" + with self.engine.get_session() as session: + query = session.query(TranscriptionMetadataORM) + + if start_date: + query = query.filter( + TranscriptionMetadataORM.feed_item_published >= start_date + ) + + if end_date: + query = query.filter( + TranscriptionMetadataORM.feed_item_published <= end_date + ) + + query = query.order_by( + desc(TranscriptionMetadataORM.feed_item_published), + desc(TranscriptionMetadataORM.created_at), + ) + + results = query.all() + return [self._orm_to_metadata(row) for row in results] + + # Helper methods for ORM conversion + def _orm_to_metadata( + self, orm_obj: TranscriptionMetadataORM + ) -> TranscriptionMetadata: + """Convert ORM object to TranscriptionMetadata.""" + return TranscriptionMetadata( + id=orm_obj.id, + filename=orm_obj.filename, + file_hash=orm_obj.file_hash, + language=orm_obj.language, + full_text=orm_obj.full_text, + model_name=orm_obj.model_name, + feed_url=orm_obj.feed_url, + feed_item_id=orm_obj.feed_item_id, + feed_item_title=orm_obj.feed_item_title, + feed_item_published=orm_obj.feed_item_published, + created_at=orm_obj.created_at, + ) + + def _orm_to_segment(self, orm_obj: TranscriptionSegmentORM) -> TranscriptionSegment: + """Convert ORM object to TranscriptionSegment.""" + return TranscriptionSegment( + id=orm_obj.id, + transcription_id=orm_obj.transcription_id, + segment_index=orm_obj.segment_index, + start_time=orm_obj.start_time, + end_time=orm_obj.end_time, + duration=orm_obj.duration, + text=orm_obj.text, + ) + + def _orm_to_dict(self, orm_obj) -> Dict[str, Any]: + """Convert ORM object to dictionary.""" + if isinstance(orm_obj, TranscriptionMetadataORM): + return { + "id": orm_obj.id, + "filename": orm_obj.filename, + "file_hash": orm_obj.file_hash, + "language": orm_obj.language, + "full_text": orm_obj.full_text, + "model_name": orm_obj.model_name, + "feed_url": orm_obj.feed_url, + "feed_item_id": orm_obj.feed_item_id, + "feed_item_title": orm_obj.feed_item_title, + "feed_item_published": orm_obj.feed_item_published.isoformat() + if orm_obj.feed_item_published + else None, + "created_at": orm_obj.created_at.isoformat() + if orm_obj.created_at + else None, + } + elif isinstance(orm_obj, TranscriptionSegmentORM): + return { + "id": orm_obj.id, + "transcription_id": orm_obj.transcription_id, + "segment_index": orm_obj.segment_index, + "start_time": orm_obj.start_time, + "end_time": orm_obj.end_time, + "duration": orm_obj.duration, + "text": orm_obj.text, + } + else: + raise ValueError(f"Unknown ORM type: {type(orm_obj)}") + + +class VectorStore: + """Vector store for text chunks with embeddings using SQLAlchemy.""" + + def __init__(self, database_url: str = None, db_path: str = None): + """ + Initialize vector store. + + Args: + database_url: SQLAlchemy database URL + db_path: Legacy parameter for SQLite path (deprecated) + """ + if database_url is None and db_path is not None: + database_url = f"sqlite:///{db_path}" + elif database_url is None: + database_url = "sqlite:///grant_vectors.db" + + self.engine = DatabaseEngine(database_url) + self.engine.create_tables() + + def add_chunk(self, chunk: Dict[str, Any]): + """Add a text chunk to the store.""" + with self.engine.get_session() as session: + embedding_blob = None + if chunk.get("embedding") is not None: + embedding_blob = chunk["embedding"].tobytes() + + chunk_orm = TextChunkORM( + id=chunk["id"], + text=chunk["text"], + metadata_json=chunk["metadata"], + embedding=embedding_blob, + ) + + session.merge(chunk_orm) # Use merge to handle upsert + session.commit() + + def add_chunks(self, chunks: List[Dict[str, Any]]): + """Add multiple text chunks to the store.""" + with self.engine.get_session() as session: + for chunk in chunks: + embedding_blob = None + if chunk.get("embedding") is not None: + embedding_blob = chunk["embedding"].tobytes() + + chunk_orm = TextChunkORM( + id=chunk["id"], + text=chunk["text"], + metadata_json=chunk["metadata"], + embedding=embedding_blob, + ) + + session.merge(chunk_orm) + + session.commit() + + def get_all_chunks(self, with_embeddings: bool = True) -> List[Dict[str, Any]]: + """Get all chunks from the store.""" + with self.engine.get_session() as session: + chunks = session.query(TextChunkORM).all() + + result = [] + for chunk in chunks: + chunk_dict = { + "id": chunk.id, + "text": chunk.text, + "metadata": chunk.metadata_json, + } + + if with_embeddings and chunk.embedding is not None: + chunk_dict["embedding"] = np.frombuffer( + chunk.embedding, dtype=np.float32 + ) + + result.append(chunk_dict) + + return result + + def search_similar( + self, query_embedding: np.ndarray, top_k: int = 5 + ) -> List[Tuple[Dict[str, Any], float]]: + """Search for similar chunks based on embedding similarity.""" + chunks = self.get_all_chunks(with_embeddings=True) + + if not chunks or chunks[0].get("embedding") is None: + return [] + + # Calculate cosine similarity + similarities = [] + query_norm = query_embedding / np.linalg.norm(query_embedding) + + for chunk in chunks: + if chunk.get("embedding") is not None: + chunk_norm = chunk["embedding"] / np.linalg.norm(chunk["embedding"]) + similarity = np.dot(query_norm, chunk_norm) + similarities.append((chunk, float(similarity))) + + # Sort by similarity and return top k + similarities.sort(key=lambda x: x[1], reverse=True) + return similarities[:top_k] + + def chunk_exists(self, chunk_id: str) -> bool: + """Check if a chunk exists in the store.""" + with self.engine.get_session() as session: + exists = session.get(TextChunkORM, chunk_id) is not None + return exists diff --git a/projects/pinkhaus-models/pinkhaus_models/orm.py b/projects/pinkhaus-models/pinkhaus_models/orm.py new file mode 100644 index 0000000..82ca87e --- /dev/null +++ b/projects/pinkhaus-models/pinkhaus_models/orm.py @@ -0,0 +1,100 @@ +"""SQLAlchemy ORM models for pinkhaus projects.""" + +from sqlalchemy import ( + Column, + Integer, + String, + Text, + Float, + ForeignKey, + DateTime, + Index, + UniqueConstraint, + LargeBinary, + JSON, +) +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import relationship +from sqlalchemy.sql import func + +Base = declarative_base() + + +class TranscriptionMetadataORM(Base): + """ORM model for transcription metadata.""" + + __tablename__ = "transcription_metadata" + + id = Column(Integer, primary_key=True, autoincrement=True) + filename = Column(Text, nullable=False) + file_hash = Column(Text, nullable=False) + language = Column(Text, nullable=False) + full_text = Column(Text, nullable=False) + model_name = Column(Text) + feed_url = Column(Text) + feed_item_id = Column(Text) + feed_item_title = Column(Text) + feed_item_published = Column(DateTime) + created_at = Column(DateTime, server_default=func.now()) + + # Relationships + segments = relationship( + "TranscriptionSegmentORM", + back_populates="transcription", + cascade="all, delete-orphan", + ) + + # Constraints and indexes + __table_args__ = ( + UniqueConstraint("file_hash", "model_name"), + Index("idx_transcription_metadata_file_hash", "file_hash"), + Index( + "idx_transcription_metadata_feed_item", + "feed_url", + "feed_item_id", + unique=True, + postgresql_where=(feed_url.isnot(None) & feed_item_id.isnot(None)), + ), + ) + + +class TranscriptionSegmentORM(Base): + """ORM model for transcription segments.""" + + __tablename__ = "transcription_segments" + + id = Column(Integer, primary_key=True, autoincrement=True) + transcription_id = Column( + Integer, ForeignKey("transcription_metadata.id"), nullable=False + ) + segment_index = Column(Integer, nullable=False) + start_time = Column(Float, nullable=False) + end_time = Column(Float, nullable=False) + duration = Column(Float, nullable=False) + text = Column(Text, nullable=False) + + # Relationships + transcription = relationship("TranscriptionMetadataORM", back_populates="segments") + + # Constraints and indexes + __table_args__ = ( + UniqueConstraint("transcription_id", "segment_index"), + Index("idx_transcription_segments_transcription_id", "transcription_id"), + ) + + +class TextChunkORM(Base): + """ORM model for text chunks with embeddings (for vector store).""" + + __tablename__ = "text_chunks" + + id = Column(String, primary_key=True) + text = Column(Text, nullable=False) + metadata_json = Column( + "metadata", JSON, nullable=False + ) # Use different attribute name + embedding = Column(LargeBinary) # Store numpy array as binary + created_at = Column(DateTime, server_default=func.now()) + + # Indexes + __table_args__ = (Index("idx_chunks_created", "created_at"),) diff --git a/projects/pinkhaus-models/pyproject.toml b/projects/pinkhaus-models/pyproject.toml index c5eac4a..dd42238 100644 --- a/projects/pinkhaus-models/pyproject.toml +++ b/projects/pinkhaus-models/pyproject.toml @@ -7,6 +7,8 @@ requires-python = ">=3.10" dependencies = [ "pydantic>=2.0.0", "aiosqlite>=0.20.0", + "sqlalchemy>=2.0.0", + "numpy>=1.24.0", ] [build-system] diff --git a/projects/pinkhaus-models/test_sqlalchemy_migration.py b/projects/pinkhaus-models/test_sqlalchemy_migration.py new file mode 100644 index 0000000..4f85f0f --- /dev/null +++ b/projects/pinkhaus-models/test_sqlalchemy_migration.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +"""Test script to verify SQLAlchemy migration works correctly.""" + +import tempfile +import os +from pinkhaus_models import ( + TranscriptionDatabase, + VectorStore, + TranscriptionResult, + Segment, +) +import numpy as np + + +def test_transcription_database(): + """Test TranscriptionDatabase with SQLAlchemy backend.""" + with tempfile.TemporaryDirectory() as tmpdir: + db_path = os.path.join(tmpdir, "test.db") + db = TranscriptionDatabase(db_path=db_path) + + # Create test data + segments = [ + Segment(start=0.0, end=5.0, text="Hello world"), + Segment(start=5.0, end=10.0, text="This is a test"), + ] + + result = TranscriptionResult( + filename="test.mp3", + file_hash="abc123", + language="en", + segments=segments, + full_text="Hello world. This is a test.", + ) + + # Test save + transcription_id = db.save_transcription( + result=result, + model_name="whisper-1", + feed_url="https://example.com/feed", + feed_item_id="item123", + feed_item_title="Test Episode", + feed_item_published="2024-01-01T00:00:00", + ) + print(f"✓ Saved transcription with ID: {transcription_id}") + + # Test retrieval + metadata = db.get_transcription_metadata(transcription_id) + assert metadata is not None + assert metadata.filename == "test.mp3" + assert metadata.file_hash == "abc123" + print(f"✓ Retrieved metadata: {metadata.filename}") + + # Test segments + segments = db.get_segments_for_transcription(transcription_id) + assert len(segments) == 2 + assert segments[0].text == "Hello world" + print(f"✓ Retrieved {len(segments)} segments") + + # Test search + results = db.search_transcriptions("test") + assert len(results) > 0 + print(f"✓ Search found {len(results)} results") + + # Test feed item exists + exists = db.check_feed_item_exists("https://example.com/feed", "item123") + assert exists + print("✓ Feed item check works") + + print("\n✅ TranscriptionDatabase tests passed!") + + +def test_vector_store(): + """Test VectorStore with SQLAlchemy backend.""" + with tempfile.TemporaryDirectory() as tmpdir: + db_path = os.path.join(tmpdir, "vectors.db") + store = VectorStore(db_path=db_path) + + # Create test chunks + chunks = [] + for i in range(3): + chunk = { + "id": f"chunk_{i}", + "text": f"This is test chunk number {i}", + "metadata": {"index": i, "source": "test"}, + "embedding": np.random.rand(384).astype(np.float32), + } + chunks.append(chunk) + + # Test add chunks + store.add_chunks(chunks) + print(f"✓ Added {len(chunks)} chunks") + + # Test retrieval + retrieved = store.get_all_chunks() + assert len(retrieved) == 3 + assert retrieved[0]["text"] == "This is test chunk number 0" + print(f"✓ Retrieved {len(retrieved)} chunks") + + # Test similarity search + query_embedding = np.random.rand(384).astype(np.float32) + results = store.search_similar(query_embedding, top_k=2) + assert len(results) == 2 + print(f"✓ Similarity search returned {len(results)} results") + + # Test chunk exists + exists = store.chunk_exists("chunk_1") + assert exists + print("✓ Chunk existence check works") + + print("\n✅ VectorStore tests passed!") + + +def test_database_backends(): + """Test that we can use different database backends.""" + with tempfile.TemporaryDirectory() as tmpdir: + # Test with SQLite URL format + db_url = f"sqlite:///{tmpdir}/test_url.db" + db = TranscriptionDatabase(database_url=db_url) + db.create_tables() + print("✓ SQLite URL format works") + + # Test backward compatibility with path + db_path = os.path.join(tmpdir, "test_path.db") + db2 = TranscriptionDatabase(db_path=db_path) + db2.create_tables() + print("✓ Legacy path format works") + + print("\n✅ Database backend tests passed!") + + +if __name__ == "__main__": + print("Testing SQLAlchemy migration...\n") + test_transcription_database() + test_vector_store() + test_database_backends() + print("\n🎉 All tests passed! 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