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Production & Auditing

When moving an OpenHosta application from development to production, observability and traceability become critical. OpenHosta provides a built-in auditing system to help you monitor LLM interactions, track costs, and ensure compliance.

🛡️ The Audit Mode

The audit_log function is the core of OpenHosta's observability. It records structured events during the execution of your pipelines.

Why Audit? (Industry Standards)

In modern software engineering, Audit Logging is more than just debugging; it is a standard for: - Compliance: Meeting regulatory requirements (GDPR, SOC2, HIPAA) by tracking data lineage. - Security: Detecting prompt injections or unexpected model behaviors. - Accountability: Providing a "black box" recording of AI decisions. - Optimization: Analyzing real-world usage to improve prompt performance and reduce costs.

Enabling Audit Mode

By default, auditing is disabled to save performance. You can enable it via the global configuration:

from OpenHosta import config

config.AUDIT_MODE = True

Alternatively, you can enable it via an environment variable: 

export OPENHOSTA_AUDIT_MODE=True

Once enabled, OpenHosta will output JSON-structured logs to stdout prefixed with [OPENHOSTA_AUDIT].

⚙️ Advanced Production Setup

1. Centralizing Logs

In production, you should redirect stdout to a log aggregator (Datadog, ELK, CloudWatch). You can easily filter OpenHosta audit logs using the prefix:

# Example: Grepping audit logs in a Linux environment
python my_app.py | grep "[OPENHOSTA_AUDIT]" > audit.log

2. Programmatic Callbacks

For deeper integration, you can register custom callbacks. This is useful for sending alerts to Sentry, saving events to a SQL database, or pushing metrics to Prometheus.

from OpenHosta.core.audit import register_audit_callback, AuditEvent

def my_production_handler(event: AuditEvent):
    # Example: Send to an external monitoring tool
    if event.event_type == "llm_call_error":
        print(f"CRITICAL: Model failed with details: {event.details}")

    # Save to your own DB for analytics
    save_to_db(event.timestamp, event.event_type, event.details)

register_audit_callback(my_production_handler)

3. Cost Control

Always wrap production calls in a cost tracker to avoid budget overruns.

from OpenHosta import emulate, track_costs

with track_costs() as tracker:
    result = emulate("Analyze this report", data=my_report)

# Log this to your billing system
print(f"Cost for this request: {tracker.total_tokens} tokens")

🚀 Production Checklist

  • [ ] API Keys: Use environment variables (OPENHOSTA_DEFAULT_MODEL_API_KEY).
  • [ ] Silence Warnings: Set OPENHOSTA_SILENCE_ENV_WARNING=True. > [!IMPORTANT] > In containerized environments (Docker, Kubernetes) or application servers where you pass configuration via environment variables instead of a .env file, set this to True to prevent OpenHosta from printing "missing .env" warnings, ensuring clean and professional startup logs.
  • [ ] Timeouts: Ensure your model server (Ollama, vLLM, OpenAI) has appropriate timeout settings.
  • [ ] Retries: OpenHosta handles basic retries, but consider implementing a circuit breaker for high-traffic apps.
  • [ ] Audit Enabled: Enable AUDIT_MODE if you are in a regulated industry.
  • [ ] Fallback Models: Configure a secondary model in case your primary provider is down.

[!TIP] Audit logs are structured as JSON. They include timestamp, event_type, and details (which contains the raw prompt, the model response, and the parsed result).