Monitoring sample (Python): ingest ACLED & create a summary with an LLM

Note: This is an example, adapt keys/filters for your region and use your API credentials.

```python
import requests
from datetime import datetime, timedelta
from openai import OpenAI  # pseudo-import; use your provider

# ACLED dataset example (ACLED may require a key/registration)
ACLED_API = 'https://api.acleddata.org/acled/read'
params = {
    'country': 'Ukraine',
    'event_date': (datetime.utcnow() - timedelta(days=7)).strftime('%Y-%m-%d'),
}
resp = requests.get(ACLED_API, params=params)
events = resp.json().get('data', [])

# Aggregate quick stats
summary = f"{len(events)} conflict events in last 7 days in country"

# Build a short prompt for LLM summarization
prompt = f"Summarize the following events ({len(events)}) with key risk indicators and recommendations: {events[:10]}"

# Send to your LLM for summarization
# client = OpenAI(api_key=OPENAI_KEY)
# answer = client.responses.create(model='gpt-4.1', input=prompt)
# print(answer.output_text)

print(summary)
```

This shows a minimal, example ingestion. In production, you'll want:
- Proper auth and rate limits.
- Data normalization & deduplication.
- Metadata capture, evidence links, and a review pipeline.

Use the generated output as a signal for campaign managers and template creation (e.g., use an LLM to write a briefing from the summary).