Live Incident Summarization

def summarize_incident_channel(messages, include_last_n=50):
    """Generate a live summary of an incident channel."""
    recent = messages[-include_last_n:]

    conversation = "\n".join([
        f"{m['user']}: {m['text']}" for m in recent
    ])

    response = openai.ChatCompletion.create(
        model="gpt-4-turbo",
        temperature=0.2,
        messages=[
            {
                "role": "system",
                "content": """Summarize this incident channel into a structured update.

Format:
## Current Status
What is happening right now

## Timeline
Key events in chronological order

## Root Cause (if known)
Current hypothesis

## Actions Taken
What has been tried

## Next Steps
What is being done next

## Open Questions
Unresolved issues

Be concise. Focus on facts, not speculation. Distinguish known facts from hypotheses."""
            },
            {"role": "user", "content": conversation}
        ]
    )
    return response["choices"][0]["message"]["content"]

class IncidentSummarizer:
    def __init__(self, channel_id, messages_per_summary=25):
        self.channel_id = channel_id
        self.messages_per_summary = messages_per_summary
        self.message_buffer = []
        self.last_summary_ts = None

    def on_new_message(self, message):
        """Called for each new message in the incident channel."""
        self.message_buffer.append(message)

        if len(self.message_buffer) >= self.messages_per_summary:
            self.generate_and_post_summary()

    def generate_and_post_summary(self):
        """Generate and post a summary to the channel."""
        summary = summarize_incident_channel(self.message_buffer)

        post_message(
            channel=self.channel_id,
            text=f":robot_face: **Live Summary Update**\n\n{summary}",
            thread_ts=self.last_summary_ts  # Post in thread
        )

        self.last_summary_ts = get_last_message_ts(self.channel_id)
        self.message_buffer = []