Wiki Chunk

Wiki Chunk

Split transcripts into semantic topic chunks using Gemini. Each chunk is a self-contained topic segment tagged with a topic type, key entities, and timestamps. Output is structured JSON ready for embedding and search.

When to Use

After transcripts exist in data/transcripts/. Chunking is required before embedding (Qdrant) or indexing (QMD) steps.

Prerequisites

• wiki.yaml exists with chunking section configured
• Transcripts in data/transcripts/ (from wiki-transcribe)
• Python dependencies: google-genai, pyyaml

Environment

Usage

cd {wiki-dir}/pipeline

# Chunk 5 transcripts (default)
python3 chunk.py --limit 5 --workers 4

# Chunk all untranscribed
python3 chunk.py --limit 0

# Re-chunk everything (overwrite existing)
python3 chunk.py --no-skip --limit 0

# Single-threaded (for debugging)
python3 chunk.py --limit 1 --workers 1

CLI Arguments

How It Works

1. Collect transcripts: Scans all directories listed in wiki.yaml paths.transcript_dirs for .md files
2. Skip existing: If a chunk JSON already exists for an episode ID, skip it (unless --no-skip)
3. Parse frontmatter: Extract title from YAML frontmatter
4. Filter: Skip transcripts under 100 words, truncate over 500K characters
5. Build prompt: Inject show context, topic types, speaker rules, and entity examples from wiki.yaml
6. Call Gemini: gemini-3-flash-preview with thinking mode (budget from wiki.yaml)
7. Parse JSON: Extract chunk array from response, clean markdown code fences
8. Enrich: Add episode_id and episode_title to each chunk
9. Save: Write JSON to data/chunks/{episode_id}.json

Processing is parallelized with a ThreadPoolExecutor. The --workers flag controls concurrency.

Output Format

Each chunk file is data/chunks/{episode_id}.json:

{
  "episode_id": "the_file_stem",
  "episode_title": "Episode Title from Frontmatter",
  "total_chunks": 12,
  "original_word_count": 8500,
  "chunks": [
    {
      "chunk_index": 0,
      "topic_title": "Descriptive Topic Name",
      "topic_type": "business_idea",
      "content": "Full text with **Speaker:** labels preserved...",
      "word_count": 650,
      "key_entities": ["Person Name", "Company Name", "concept"],
      "timestamp_start": "00:00",
      "timestamp_end": "05:30",
      "episode_id": "the_file_stem",
      "episode_title": "Episode Title"
    }
  ]
}

Chunk Fields

wiki.yaml Config Reference

chunking:
  topic_types:
    - business_idea
    - founder_story
    - framework
    - tactic
    - case_study
    - qa
    - intro
    - sponsor
    - tangent
  context: |
    Show description for chunking prompt context.
  rules:
    - "Pay special attention to financial figures"
  entity_examples: '"Person Name", "Company Name", "concept mentioned"'
  thinking_budget: 4096
paths:
  transcript_dirs: ["data/transcripts"]

Gemini Configuration

Cost

• ~$0.02 per transcript (Gemini 3 Flash)
• A 600-episode corpus costs roughly $12 total
• Thinking mode adds marginal cost but improves chunk boundary quality

Quality Checks

After a batch run:

• Chunk count per episode: Should be 5-15 chunks. Check outliers:

  for f in data/chunks/*.json; do echo "$(python3 -c "import json; print(json.load(open('$f'))['total_chunks'])"): $f"; done | sort -n
  

• No tiny chunks: Chunks under 200 words may indicate poor segmentation. Spot-check any with word_count < 200

• No monster chunks: Chunks over 2000 words should be rare. They may indicate the model failed to find natural break points

• Topic type distribution: Check that intro and sponsor chunks are minimal:

  python3 -c "
  import json, glob, collections
  types = collections.Counter()
  for f in glob.glob('data/chunks/*.json'):
      for c in json.load(open(f))['chunks']:
          types[c['topic_type']] += 1
  for t, n in types.most_common(): print(f'{t}: {n}')
  "
  

• Speaker labels preserved: Open 2-3 chunk files and verify **Name:** labels are in the content

• Entity quality: Key entities should be meaningful names, not generic terms

• JSON validity: All files should parse without errors

Shared Library Reference

Troubleshooting

JSON parse errors: Gemini occasionally returns invalid JSON. The clean_json_response() function strips markdown code fences, but deeply malformed JSON will fail. Re-run and the transcript will be retried.

Empty chunks array: Usually means the transcript is too short or garbled. Check the source transcript quality.

All chunks tagged as one type: The topic types in wiki.yaml may not match the content well. Review and adjust topic types for better domain fit.

Rate limiting: With 4 workers, you may hit Gemini rate limits on large batches. Reduce --workers 2 or add delays. The shared lib handles this gracefully with error logging.