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PostgreSQL Database Module.
Provides connection management and CRUD operations for:
- Conversations
- Messages
- Extracted Intelligence
"""
from typing import Dict, List, Optional, Any
import os
import time
from contextlib import contextmanager
from sqlalchemy import create_engine, text, inspect
from sqlalchemy.orm import sessionmaker, Session
from sqlalchemy.exc import SQLAlchemyError
from app.config import settings
from app.utils.logger import get_logger
logger = get_logger(__name__)
# Global engine and session factory
engine = None
SessionLocal = None
# Track if PostgreSQL is known to be unavailable (to skip connection attempts)
_postgres_unavailable: bool = False
_postgres_last_check: float = 0
_POSTGRES_RECHECK_INTERVAL = 60 # Only try reconnecting every 60 seconds
def init_engine() -> None:
"""
Initialize SQLAlchemy engine from configuration.
Raises:
ValueError: If POSTGRES_URL is not configured
"""
global engine, SessionLocal
if engine is not None:
return
postgres_url = settings.POSTGRES_URL
if not postgres_url:
logger.warning("POSTGRES_URL not configured. Database operations will fail.")
return
try:
engine = create_engine(
postgres_url,
pool_pre_ping=True, # Verify connections before using
pool_size=5,
max_overflow=10,
echo=False, # Set to True for SQL debugging
connect_args={"connect_timeout": 2}, # 2 second timeout for faster fallback
)
SessionLocal = sessionmaker(bind=engine, autocommit=False, autoflush=False)
logger.info("PostgreSQL engine initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize PostgreSQL engine: {e}")
raise
def get_db_connection():
"""
Get PostgreSQL database connection.
Returns:
Database connection object
Raises:
ConnectionError: If database connection fails
ValueError: If POSTGRES_URL is not configured
"""
if engine is None:
init_engine()
if engine is None:
raise ConnectionError("PostgreSQL engine not initialized. Check POSTGRES_URL configuration.")
try:
return engine.connect()
except SQLAlchemyError as e:
logger.error(f"Failed to get database connection: {e}")
raise ConnectionError(f"Database connection failed: {e}") from e
@contextmanager
def get_db_session():
"""
Get database session context manager.
Yields:
SQLAlchemy Session
Example:
with get_db_session() as session:
# Use session
pass
"""
if SessionLocal is None:
init_engine()
if SessionLocal is None:
raise ConnectionError("PostgreSQL session factory not initialized. Check POSTGRES_URL configuration.")
session = SessionLocal()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close()
def init_database() -> None:
"""
Initialize database with schema.
Creates tables:
- conversations
- messages
- extracted_intelligence
Also creates required indexes.
Raises:
ConnectionError: If database connection fails
SQLAlchemyError: If schema creation fails
"""
if engine is None:
init_engine()
if engine is None:
raise ConnectionError("PostgreSQL engine not initialized. Check POSTGRES_URL configuration.")
# Define schema statements in order
schema_statements = [
# Create tables first
"""
CREATE TABLE IF NOT EXISTS conversations (
id SERIAL PRIMARY KEY,
session_id VARCHAR(255) UNIQUE NOT NULL,
language VARCHAR(10) NOT NULL,
persona VARCHAR(50),
scam_detected BOOLEAN DEFAULT FALSE,
confidence FLOAT,
turn_count INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
""",
"""
CREATE TABLE IF NOT EXISTS messages (
id SERIAL PRIMARY KEY,
conversation_id INTEGER REFERENCES conversations(id) ON DELETE CASCADE,
turn_number INTEGER NOT NULL,
sender VARCHAR(50) NOT NULL,
message TEXT NOT NULL,
timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
""",
"""
CREATE TABLE IF NOT EXISTS extracted_intelligence (
id SERIAL PRIMARY KEY,
conversation_id INTEGER REFERENCES conversations(id) ON DELETE CASCADE,
upi_ids TEXT[],
bank_accounts TEXT[],
ifsc_codes TEXT[],
phone_numbers TEXT[],
phishing_links TEXT[],
extraction_confidence FLOAT,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
""",
# Create indexes after tables
"CREATE INDEX IF NOT EXISTS idx_session_id ON conversations(session_id)",
"CREATE INDEX IF NOT EXISTS idx_conversation_id ON messages(conversation_id)",
"CREATE INDEX IF NOT EXISTS idx_created_at ON conversations(created_at)",
"CREATE INDEX IF NOT EXISTS idx_scam_detected ON conversations(scam_detected)",
]
try:
with engine.begin() as conn: # Use begin() for automatic transaction management
for statement in schema_statements:
statement = statement.strip()
if statement:
try:
conn.execute(text(statement))
except SQLAlchemyError as e:
# Ignore "already exists" errors
error_str = str(e).lower()
if "already exists" not in error_str and "duplicate" not in error_str:
logger.warning(f"Schema creation warning for statement: {e}")
# Don't fail on index creation errors if table doesn't exist yet
if "does not exist" in error_str and "index" in statement.lower():
logger.debug(f"Skipping index creation (table may not exist yet): {e}")
else:
raise
# Transaction commits automatically with 'begin()' context manager
logger.info("Database schema initialized successfully")
except SQLAlchemyError as e:
logger.error(f"Failed to initialize database schema: {e}")
raise
def verify_schema() -> bool:
"""
Verify that all required tables and indexes exist.
Returns:
True if schema is complete, False otherwise
"""
if engine is None:
return False
try:
inspector = inspect(engine)
tables = inspector.get_table_names()
required_tables = ['conversations', 'messages', 'extracted_intelligence']
missing_tables = [t for t in required_tables if t not in tables]
if missing_tables:
logger.warning(f"Missing tables: {missing_tables}")
return False
# Check indexes
indexes = inspector.get_indexes('conversations')
index_names = [idx['name'] for idx in indexes]
required_indexes = ['idx_session_id', 'idx_created_at', 'idx_scam_detected']
missing_indexes = [idx for idx in required_indexes if idx not in index_names]
if missing_indexes:
logger.warning(f"Missing indexes on conversations: {missing_indexes}")
return True
except Exception as e:
logger.error(f"Failed to verify schema: {e}")
return False
def save_conversation(session_id: str, conversation_data: Dict[str, Any]) -> int:
"""
Save conversation to PostgreSQL.
Implements AC-2.3.3: PostgreSQL stores complete logs.
Args:
session_id: Unique session identifier
conversation_data: Conversation data including:
- language: Detected language
- persona: Active persona name
- scam_confidence: Detection confidence
- turn_count: Number of turns
- messages: List of message dicts
- extracted_intel: Optional intelligence data
Returns:
Conversation ID (0 if failed)
"""
global _postgres_unavailable, _postgres_last_check
# Skip if PostgreSQL was recently unavailable (fast path)
if _postgres_unavailable:
if time.time() - _postgres_last_check < _POSTGRES_RECHECK_INTERVAL:
logger.debug("PostgreSQL unavailable (cached), skipping save")
return 0
# Time to recheck
_postgres_unavailable = False
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot save conversation: Database not initialized")
return 0
try:
with engine.connect() as conn:
# Check if conversation already exists
check_sql = text(
"SELECT id FROM conversations WHERE session_id = :session_id"
)
result = conn.execute(check_sql, {"session_id": session_id})
existing = result.fetchone()
if existing:
# Update existing conversation
update_sql = text("""
UPDATE conversations
SET language = :language,
persona = :persona,
scam_detected = :scam_detected,
confidence = :confidence,
turn_count = :turn_count,
updated_at = CURRENT_TIMESTAMP
WHERE session_id = :session_id
RETURNING id
""")
result = conn.execute(update_sql, {
"session_id": session_id,
"language": conversation_data.get("language", "en"),
"persona": conversation_data.get("persona"),
"scam_detected": conversation_data.get("scam_confidence", 0) >= 0.7,
"confidence": conversation_data.get("scam_confidence", 0.0),
"turn_count": conversation_data.get("turn_count", 0),
})
row = result.fetchone()
conversation_id = row[0] if row else existing[0]
else:
# Insert new conversation
insert_sql = text("""
INSERT INTO conversations
(session_id, language, persona, scam_detected, confidence, turn_count)
VALUES (:session_id, :language, :persona, :scam_detected, :confidence, :turn_count)
RETURNING id
""")
result = conn.execute(insert_sql, {
"session_id": session_id,
"language": conversation_data.get("language", "en"),
"persona": conversation_data.get("persona"),
"scam_detected": conversation_data.get("scam_confidence", 0) >= 0.7,
"confidence": conversation_data.get("scam_confidence", 0.0),
"turn_count": conversation_data.get("turn_count", 0),
})
row = result.fetchone()
conversation_id = row[0] if row else 0
conn.commit()
# Save messages if provided
messages = conversation_data.get("messages", [])
if messages and conversation_id > 0:
save_messages(conversation_id, messages)
# Save intelligence if provided
extracted_intel = conversation_data.get("extracted_intel", {})
extraction_confidence = conversation_data.get("extraction_confidence", 0.0)
if extracted_intel and conversation_id > 0:
save_intelligence(conversation_id, extracted_intel, extraction_confidence)
logger.info(f"Conversation saved: session_id={session_id}, id={conversation_id}")
return conversation_id
except SQLAlchemyError as e:
logger.error(f"Failed to save conversation: {e}")
# Mark PostgreSQL as unavailable to skip future attempts
_postgres_unavailable = True
_postgres_last_check = time.time()
return 0
def get_conversation(session_id: str) -> Optional[Dict[str, Any]]:
"""
Retrieve conversation by session ID.
Args:
session_id: Session identifier
Returns:
Conversation data including messages, or None if not found
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot get conversation: Database not initialized")
return None
try:
with engine.connect() as conn:
# Get conversation
conv_sql = text("""
SELECT id, session_id, language, persona, scam_detected,
confidence, turn_count, created_at, updated_at
FROM conversations
WHERE session_id = :session_id
""")
result = conn.execute(conv_sql, {"session_id": session_id})
row = result.fetchone()
if not row:
return None
conversation_id = row[0]
# Get messages
msg_sql = text("""
SELECT turn_number, sender, message, timestamp
FROM messages
WHERE conversation_id = :conversation_id
ORDER BY turn_number
""")
msg_result = conn.execute(msg_sql, {"conversation_id": conversation_id})
messages = [
{
"turn": msg_row[0],
"sender": msg_row[1],
"message": msg_row[2],
"timestamp": msg_row[3].isoformat() if msg_row[3] else None,
}
for msg_row in msg_result.fetchall()
]
# Get intelligence
intel_sql = text("""
SELECT upi_ids, bank_accounts, ifsc_codes, phone_numbers,
phishing_links, extraction_confidence
FROM extracted_intelligence
WHERE conversation_id = :conversation_id
ORDER BY created_at DESC
LIMIT 1
""")
intel_result = conn.execute(intel_sql, {"conversation_id": conversation_id})
intel_row = intel_result.fetchone()
extracted_intel = {}
extraction_confidence = 0.0
if intel_row:
extracted_intel = {
"upi_ids": intel_row[0] or [],
"bank_accounts": intel_row[1] or [],
"ifsc_codes": intel_row[2] or [],
"phone_numbers": intel_row[3] or [],
"phishing_links": intel_row[4] or [],
}
extraction_confidence = intel_row[5] or 0.0
return {
"id": row[0],
"session_id": row[1],
"language": row[2],
"persona": row[3],
"scam_detected": row[4],
"scam_confidence": row[5],
"turn_count": row[6],
"created_at": row[7].isoformat() if row[7] else None,
"updated_at": row[8].isoformat() if row[8] else None,
"messages": messages,
"extracted_intel": extracted_intel,
"extraction_confidence": extraction_confidence,
}
except SQLAlchemyError as e:
logger.error(f"Failed to get conversation: {e}")
return None
def update_conversation(session_id: str, updates: Dict[str, Any]) -> bool:
"""
Update existing conversation.
Args:
session_id: Session identifier
updates: Fields to update (language, persona, scam_detected, confidence, turn_count)
Returns:
True if successful, False otherwise
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot update conversation: Database not initialized")
return False
if not updates:
return True # Nothing to update
# Build dynamic update SQL
allowed_fields = {"language", "persona", "scam_detected", "confidence", "turn_count"}
update_fields = {k: v for k, v in updates.items() if k in allowed_fields}
if not update_fields:
logger.warning(f"No valid fields to update: {updates.keys()}")
return False
try:
with engine.connect() as conn:
# Build SET clause
set_clauses = [f"{field} = :{field}" for field in update_fields]
set_clauses.append("updated_at = CURRENT_TIMESTAMP")
set_clause = ", ".join(set_clauses)
update_sql = text(f"""
UPDATE conversations
SET {set_clause}
WHERE session_id = :session_id
""")
params = {"session_id": session_id, **update_fields}
result = conn.execute(update_sql, params)
conn.commit()
if result.rowcount > 0:
logger.info(f"Conversation updated: session_id={session_id}")
return True
else:
logger.warning(f"No conversation found to update: session_id={session_id}")
return False
except SQLAlchemyError as e:
logger.error(f"Failed to update conversation: {e}")
return False
def save_messages(conversation_id: int, messages: List[Dict[str, Any]]) -> int:
"""
Save messages to conversation.
Args:
conversation_id: Parent conversation ID
messages: List of message dictionaries with turn, sender, message, timestamp
Returns:
Number of messages saved
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot save messages: Database not initialized")
return 0
if not messages:
return 0
try:
with engine.connect() as conn:
# Get existing message turn numbers to avoid duplicates
existing_sql = text("""
SELECT turn_number FROM messages
WHERE conversation_id = :conversation_id
""")
result = conn.execute(existing_sql, {"conversation_id": conversation_id})
existing_turns = {row[0] for row in result.fetchall()}
saved_count = 0
for msg in messages:
turn = msg.get("turn", 0)
# Skip if this turn already exists
if turn in existing_turns:
continue
insert_sql = text("""
INSERT INTO messages (conversation_id, turn_number, sender, message)
VALUES (:conversation_id, :turn_number, :sender, :message)
""")
conn.execute(insert_sql, {
"conversation_id": conversation_id,
"turn_number": turn,
"sender": msg.get("sender", "unknown"),
"message": msg.get("message", ""),
})
saved_count += 1
existing_turns.add(turn)
conn.commit()
logger.debug(f"Saved {saved_count} messages for conversation {conversation_id}")
return saved_count
except SQLAlchemyError as e:
logger.error(f"Failed to save messages: {e}")
return 0
def save_intelligence(
conversation_id: int,
intelligence: Dict[str, List[str]],
confidence: float,
) -> int:
"""
Save extracted intelligence to database.
Args:
conversation_id: Parent conversation ID
intelligence: Extracted intelligence data with keys:
- upi_ids, bank_accounts, ifsc_codes, phone_numbers, phishing_links
confidence: Extraction confidence score
Returns:
Intelligence record ID (0 if failed)
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot save intelligence: Database not initialized")
return 0
try:
with engine.connect() as conn:
insert_sql = text("""
INSERT INTO extracted_intelligence
(conversation_id, upi_ids, bank_accounts, ifsc_codes,
phone_numbers, phishing_links, extraction_confidence)
VALUES (:conversation_id, :upi_ids, :bank_accounts, :ifsc_codes,
:phone_numbers, :phishing_links, :extraction_confidence)
RETURNING id
""")
result = conn.execute(insert_sql, {
"conversation_id": conversation_id,
"upi_ids": intelligence.get("upi_ids", []),
"bank_accounts": intelligence.get("bank_accounts", []),
"ifsc_codes": intelligence.get("ifsc_codes", []),
"phone_numbers": intelligence.get("phone_numbers", []),
"phishing_links": intelligence.get("phishing_links", []),
"extraction_confidence": confidence,
})
row = result.fetchone()
intel_id = row[0] if row else 0
conn.commit()
logger.info(f"Intelligence saved: conversation_id={conversation_id}, id={intel_id}")
return intel_id
except SQLAlchemyError as e:
logger.error(f"Failed to save intelligence: {e}")
return 0
def get_conversations_by_date(start_date: str, end_date: str) -> List[Dict[str, Any]]:
"""
Get conversations within date range.
Args:
start_date: Start date (ISO format: YYYY-MM-DD)
end_date: End date (ISO format: YYYY-MM-DD)
Returns:
List of conversation records
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot query conversations: Database not initialized")
return []
try:
with engine.connect() as conn:
query_sql = text("""
SELECT id, session_id, language, persona, scam_detected,
confidence, turn_count, created_at, updated_at
FROM conversations
WHERE created_at >= :start_date AND created_at < :end_date
ORDER BY created_at DESC
""")
result = conn.execute(query_sql, {
"start_date": start_date,
"end_date": end_date,
})
conversations = []
for row in result.fetchall():
conversations.append({
"id": row[0],
"session_id": row[1],
"language": row[2],
"persona": row[3],
"scam_detected": row[4],
"confidence": row[5],
"turn_count": row[6],
"created_at": row[7].isoformat() if row[7] else None,
"updated_at": row[8].isoformat() if row[8] else None,
})
return conversations
except SQLAlchemyError as e:
logger.error(f"Failed to get conversations by date: {e}")
return []
def get_scammer_profiles() -> List[Dict[str, Any]]:
"""
Get aggregated scammer profiles from extracted intelligence.
Returns:
List of scammer profile data with aggregated phone numbers, UPI IDs, etc.
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot get scammer profiles: Database not initialized")
return []
try:
with engine.connect() as conn:
# Get all intelligence with conversation data
query_sql = text("""
SELECT c.session_id, c.language, c.persona, c.confidence,
e.upi_ids, e.bank_accounts, e.ifsc_codes,
e.phone_numbers, e.phishing_links,
e.extraction_confidence, c.created_at
FROM extracted_intelligence e
JOIN conversations c ON e.conversation_id = c.id
WHERE c.scam_detected = true
ORDER BY c.created_at DESC
""")
result = conn.execute(query_sql)
# Aggregate by phone number or UPI ID
profiles: Dict[str, Dict[str, Any]] = {}
for row in result.fetchall():
phone_numbers = row[7] or []
upi_ids = row[4] or []
# Use first phone number or UPI as profile key
profile_key = None
if phone_numbers:
profile_key = phone_numbers[0]
elif upi_ids:
profile_key = upi_ids[0]
if not profile_key:
continue
if profile_key not in profiles:
profiles[profile_key] = {
"identifier": profile_key,
"phone_numbers": set(),
"upi_ids": set(),
"bank_accounts": set(),
"ifsc_codes": set(),
"phishing_links": set(),
"languages": set(),
"personas_encountered": set(),
"session_count": 0,
"avg_confidence": 0.0,
"confidence_sum": 0.0,
"first_seen": row[10],
"last_seen": row[10],
}
profile = profiles[profile_key]
profile["phone_numbers"].update(phone_numbers)
profile["upi_ids"].update(upi_ids)
profile["bank_accounts"].update(row[5] or [])
profile["ifsc_codes"].update(row[6] or [])
profile["phishing_links"].update(row[8] or [])
profile["languages"].add(row[1])
if row[2]:
profile["personas_encountered"].add(row[2])
profile["session_count"] += 1
profile["confidence_sum"] += row[3] or 0.0
if row[10] and row[10] < profile["first_seen"]:
profile["first_seen"] = row[10]
if row[10] and row[10] > profile["last_seen"]:
profile["last_seen"] = row[10]
# Convert sets to lists and calculate averages
result_profiles = []
for profile in profiles.values():
profile["phone_numbers"] = list(profile["phone_numbers"])
profile["upi_ids"] = list(profile["upi_ids"])
profile["bank_accounts"] = list(profile["bank_accounts"])
profile["ifsc_codes"] = list(profile["ifsc_codes"])
profile["phishing_links"] = list(profile["phishing_links"])
profile["languages"] = list(profile["languages"])
profile["personas_encountered"] = list(profile["personas_encountered"])
profile["avg_confidence"] = (
profile["confidence_sum"] / profile["session_count"]
if profile["session_count"] > 0 else 0.0
)
del profile["confidence_sum"]
profile["first_seen"] = (
profile["first_seen"].isoformat()
if profile["first_seen"] else None
)
profile["last_seen"] = (
profile["last_seen"].isoformat()
if profile["last_seen"] else None
)
result_profiles.append(profile)
return result_profiles
except SQLAlchemyError as e:
logger.error(f"Failed to get scammer profiles: {e}")
return []
def delete_conversation(session_id: str) -> bool:
"""
Delete a conversation and all related data.
Args:
session_id: Session identifier
Returns:
True if deleted, False otherwise
"""
if engine is None:
init_engine()
if engine is None:
logger.error("Cannot delete conversation: Database not initialized")
return False
try:
with engine.connect() as conn:
# CASCADE delete will handle messages and intelligence
delete_sql = text("""
DELETE FROM conversations
WHERE session_id = :session_id
""")
result = conn.execute(delete_sql, {"session_id": session_id})
conn.commit()
if result.rowcount > 0:
logger.info(f"Conversation deleted: session_id={session_id}")
return True
return False
except SQLAlchemyError as e:
logger.error(f"Failed to delete conversation: {e}")
return False
def get_conversation_stats() -> Dict[str, Any]:
"""
Get aggregated conversation statistics.
Returns:
Dictionary with statistics
"""
if engine is None:
init_engine()
if engine is None:
return {"error": "Database not initialized"}
try:
with engine.connect() as conn:
stats_sql = text("""
SELECT
COUNT(*) as total_conversations,
SUM(CASE WHEN scam_detected THEN 1 ELSE 0 END) as scam_count,
AVG(confidence) as avg_confidence,
AVG(turn_count) as avg_turns,
COUNT(DISTINCT language) as language_count
FROM conversations
""")
result = conn.execute(stats_sql)
row = result.fetchone()
if row:
return {
"total_conversations": row[0] or 0,
"scam_count": row[1] or 0,
"avg_confidence": float(row[2]) if row[2] else 0.0,
"avg_turns": float(row[3]) if row[3] else 0.0,
"language_count": row[4] or 0,
}
return {
"total_conversations": 0,
"scam_count": 0,
"avg_confidence": 0.0,
"avg_turns": 0.0,
"language_count": 0,
}
except SQLAlchemyError as e:
logger.error(f"Failed to get conversation stats: {e}")
return {"error": str(e)}
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