conversation-content-pipeline

Conversation-to-Content Pipeline

Extract publishable content from AI conversations, chat transcripts, and session logs.

Pipeline Overview

Raw Conversation → Extract → Restructure → Refine → Format → Publish
       │                │           │          │         │
       │                │           │          │         └─ Markdown, HTML, PDF
       │                │           │          └─ Editorial polish, voice consistency
       │                │           └─ Organize by topic, add structure
       │                └─ Identify key insights, decisions, code
       └─ Chat logs, transcripts, session files

Extraction Patterns

Content Type Classification

Key Moment Identification

KEY_MOMENT_SIGNALS = {
    "insight": ["I realized", "The key insight is", "This means that", "Interesting —"],
    "decision": ["Let's go with", "The best approach", "I chose", "Decision:"],
    "learning": ["TIL", "I didn't know", "Turns out", "The important thing is"],
    "warning": ["Watch out for", "Don't forget", "Common mistake", "Anti-pattern"],
    "summary": ["In summary", "To recap", "The main takeaway", "Key points"],
}

def identify_key_moments(messages: list[dict]) -> list[dict]:
    moments = []
    for msg in messages:
        for moment_type, signals in KEY_MOMENT_SIGNALS.items():
            if any(signal.lower() in msg["content"].lower() for signal in signals):
                moments.append({
                    "type": moment_type,
                    "content": msg["content"],
                    "role": msg["role"],
                    "index": msg.get("index"),
                })
    return moments