fix(db): raise SQLAlchemy connection pool size to match real concurrency

[mateusbellozupko]

Summary

• create_engine(POSTGRES_CONNECTION_STRING, ...) never set pool_size/max_overflow, so it ran on SQLAlchemy's bare defaults (5 + 10 overflow = 15 total connections).
• Under real load — many stage-inactivity AI calls held open for 30-90s each, plus live chat traffic — that pool exhausts and stays exhausted: every new request times out after 30s with QueuePool limit of size 5 overflow 10 reached, with no recovery until the process is restarted. Observed in production for 14+ hours, blocking every AI-generated automated message account-wide.
• Makes pool_size/max_overflow configurable via DB_POOL_SIZE/DB_MAX_OVERFLOW, defaulting higher (20/40) than the library defaults that exhausted.

Test plan

• Added tests/unit/test_database_pool_config.py: the engine's pool size/overflow are configurable via env vars, and default higher than SQLAlchemy's bare defaults.

🤖 Generated with Claude Code

Summary by Sourcery

Configure a larger, environment-controlled database connection pool to sustain concurrent AI and chat workloads without exhausting available connections.

Bug Fixes:

• Increase database connection pool capacity to prevent pool exhaustion under production concurrency.

Enhancements:

• Make SQLAlchemy pool size and overflow configurable through environment variables, with higher service-specific defaults and guidance for multi-process connection limits.

Tests:

• Add unit coverage for configurable pool settings and defaults above SQLAlchemy's bare limits.

[sourcery-ai]

Reviewer's Guide

The PR addresses production pool exhaustion by configuring SQLAlchemy with larger, environment-controlled pool and overflow limits, defaulting to 20 and 40 instead of 5 and 10, and adds tests covering both overrides and safer defaults.

File-Level Changes

Change	Details	Files
Increase the database connection pool capacity and expose both limits through environment-backed settings.	Add DB_POOL_SIZE and DB_MAX_OVERFLOW settings with defaults of 20 and 40. Pass the configured values to SQLAlchemy's engine creation alongside existing pool health settings.	src/config/database.py src/config/settings.py
Add regression coverage for configurable and non-default pool limits.	Reload settings and the database engine under controlled environment variables. Assert explicit values are applied and defaults exceed SQLAlchemy's bare limits.	tests/unit/test_database_pool_config.py

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[sourcery-ai]

Hey - I've found 1 issue

Prompt for AI Agents

Please address the comments from this code review:

## Individual Comments

### Comment 1
<location path="src/config/database.py" line_range="50-51" />
<code_context>
     POSTGRES_CONNECTION_STRING,
     pool_pre_ping=True,
     pool_recycle=1800,
+    pool_size=settings.DB_POOL_SIZE,
+    max_overflow=settings.DB_MAX_OVERFLOW,
     connect_args={
         "keepalives": 1,
</code_context>
<issue_to_address>
**issue (broader_impact):** The default pool configuration permits up to 60 PostgreSQL connections per process (20 pooled connections plus 40 overflow connections). With multiple Uvicorn workers or service replicas, the aggregate limit exceeds PostgreSQL's default `max_connections` and causes connection attempts to fail once the database-wide limit is reached.

**Triggers:** When the service runs with multiple workers/replicas or shares PostgreSQL with other services.

**Suggested fix:** Set a deployment-wide connection budget and size each process's pool accordingly, or explicitly raise/configure PostgreSQL's connection limit alongside these settings.
</issue_to_address>

Sourcery assessment

Needs a human reviewer. 1 finding to address first, and the new defaults change how many database connections the service can open, potentially overloading Postgres or causing connection exhaustion and an outage under real concurrency. Reverting restores the previous limits, but any outage or failed requests that already occurred cannot be undone.

Blocking findings: src/config/database.py:51

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