llm_preprocess.py

import os

import anthropic

SYSTEM_PROMPT = """You are a professional audio producer who adapts written articles for podcast narration.
Your job is to slightly adjust the given article text so it sounds better when read aloud by a text-to-speech system."""

REWRITE_PROMPT = """Update the following article for audio narration. Follow these rules strictly:

1. Remove all URLs, email addresses, and hyperlinks entirely.
2. Remove code blocks. If a code block is central to the article’s point, briefly describe what it does in one sentence.
3. Convert tables to short prose descriptions.
4. Remove all citation markers like [1], [2], etc.
5. Remove references to figures, images, charts, or any visual elements (e.g. "see Figure 3", "as shown below").
6. Expand abbreviations: "e.g." → "for example", "i.e." → "that is", "etc." → "et cetera".
7. Write out numbers as words when appropriate. This includes years.
8. Remove all markdown formatting (headers, bold, italic, links).
9. Keep the content faithful to the original — do not add or rewrite anything that isn’t covered by the rules above.
10. Output ONLY the rewritten text, nothing else.

Article text:

{text}"""


async def rewrite_for_audio(text: str) -> str:
    """Use Claude to rewrite article text for audio narration."""
    api_key = os.getenv("ANTHROPIC_API_KEY")
    if not api_key:
        raise ValueError("ANTHROPIC_API_KEY environment variable is required for LLM preprocessing")

    client = anthropic.AsyncAnthropic(api_key=api_key)

    message = await client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=65536,
        system=SYSTEM_PROMPT,
        messages=[
            {"role": "user", "content": REWRITE_PROMPT.format(text=text)},
        ],
    )

    if message.stop_reason == "max_tokens":
        print("Warning: LLM preprocessing output was truncated due to max_tokens limit")

    return message.content[0].text