Content Length Restrictions in qiaomu-anything-to-notebooklm: Min/Max Limits Explained

The qiaomu-anything-to-notebooklm tool enforces a minimum content length of approximately 500 characters and a maximum of roughly 500,000 characters (about 50万字) to ensure compatibility with Google NotebookLM's processing limits, with an optimal range between 1,000 and 10,000 characters.

The Anything → NotebookLM skill is designed to ingest a wide variety of source material (web pages, PDFs, EPUBs, audio transcripts, etc.) and forward it to Google NotebookLM for further processing. Because the downstream AI model has practical limits on how much text it can accept in a single request, the tool implements specific content length restrictions to prevent truncation errors and ensure stable operation. These boundaries are documented in the project's README and actively monitored during execution.

Hard Limits and Recommendations

According to the source code analysis, the tool operates within three distinct content length boundaries:

• Minimum: ≈ 500 characters — Anything shorter may not provide enough context for meaningful analysis or generation.
• Maximum: ≈ 500 000 characters (≈ 50 万 字) — Longer inputs are truncated or rejected to stay within NotebookLM's request size limits.
• Recommended: 1 000 – 10 000 characters — This range yields the best balance of detail and response quality.

These limits are explicitly documented in README.md between lines 63 and 70 under the "内容长度限制" section. When a supplied file exceeds the upper bound, the tool will still attempt to upload it, but NotebookLM may truncate the input or return an error, making the recommended window advisable for stable operation.

Runtime Monitoring in main.py

During processing, the script logs the content length after transcription or fetching to help users verify that inputs fall within accepted ranges. In main.py around line 388, the tool outputs:

✅ 转写完成: {title} ({content_length} 字符)

This log line (specifically lines 388-389) displays the character count immediately after transcription completion, allowing users to confirm whether their source material meets the content length restrictions before the system proceeds to deep analysis. Additionally, scripts/get_podcast_transcript.py generates a transcript JSON that includes a content_length field specifically for podcast sources, ensuring consistent length validation across all input types.

Validating Content Length Programmatically

To prevent processing failures, you can implement pre-flight checks that mirror the tool's internal validation logic.

Checking Length Before Invocation

Use this Python function to validate content before passing it to the processor:

from pathlib import Path

def is_valid_length(text: str) -> bool:
    length = len(text)
    if length < 500:
        print("❌ 内容太短 ( < 500 字符)")
        return False
    if length > 500_000:
        print("❌ 内容超出上限 ( > 500 000 字符)")
        return False
    return True

# Example usage

txt_path = Path("/tmp/example.txt")
content = txt_path.read_text(encoding="utf-8")
if is_valid_length(content):
    # Proceed with deep analysis

    import subprocess
    subprocess.run(["python", "main.py", str(txt_path), "--deep-analysis"])

CLI Execution with Large Documents

When running the tool via command line, observe the character count in the output log:

$ python main.py large_document.pdf --deep-analysis
📋 检测到输入类型: document
📤 上传内容到 NotebookLM...
✅ 转写完成: large_document (452300 字符)
📝 生成深度分析问题...

If the logged number exceeds 500,000 characters, expect potential truncation or errors from the NotebookLM API.

Summary

• Minimum threshold: 500 characters ensures sufficient context for AI analysis.
• Maximum ceiling: 500,000 characters prevents API rejection or data truncation.
• Optimal range: 1,000–10,000 characters balances detail with processing stability.
• Validation locations: README.md (documentation), main.py (runtime logging), and scripts/get_podcast_transcript.py (transcript metadata).
• Pre-processing: Implement length checks using the provided Python validation pattern to avoid runtime failures.

Frequently Asked Questions

What happens if my content is shorter than 500 characters?

If the input contains fewer than 500 characters, the tool will likely process the request but may produce suboptimal results because the downstream AI lacks sufficient context for meaningful analysis. The README explicitly warns that short inputs may not provide enough context for quality generation.

Will the tool reject files larger than 500,000 characters?

The tool itself does not hard-reject files exceeding 500,000 characters; instead, it attempts to upload the content anyway. However, NotebookLM may truncate the input internally or return an error response. According to the source documentation, keeping text within the 500,000-character limit is essential for stable operation.

Why is the 1,000–10,000 character range recommended?

This range represents the practical sweet spot where the AI receives enough detail to generate comprehensive analysis without encountering the latency or token-limit issues associated with near-maximum inputs. Content within this window typically yields the best balance of detail and response quality according to the project's usage guidelines.

Where can I verify the content length of my processed files?

Check the console output after transcription completes. In main.py at lines 388-389, the tool prints ✅ 转写完成: {title} ({content_length} 字符), displaying the exact character count. For podcasts, the content_length field in the JSON output from scripts/get_podcast_transcript.py provides the same metric.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}