Ask Hormozi

ask-hormozi

Build a searchable, cited Q&A index from any text corpus. Query with semantic relevance scoring and automatic source citations.

What It Does

ask-hormozi is a local-first citation tool that turns books, podcasts, articles, and transcripts into a searchable knowledge base. You provide text files (or .vtt video transcripts), build an index, then query it with natural language. Every answer comes with the exact source, page/chunk, and excerpt.

• No API calls, no vendor lock-in, no rate limits
• TF-IDF semantic search finds the most relevant passages
• Automatic citations: every result shows source file, type, and location
• Plain Python + scikit-learn, runs entirely offline

Use Cases

• Quote books and podcasts accurately with sources
• Build a FAQ/knowledge index from documentation
• Create a research tool for your personal learning library
• Cite video transcripts in writing projects
• Search through your own content libraries

Install

1. Get the repo — Clone or download the repository files (build_index.py, query.py)

2. Install dependencies

pip install scikit-learn numpy

3. Prepare your transcripts — Create a transcripts/ folder and add .txt (plain text) or .vtt (video captions) files

mkdir transcripts/
# Copy your source files here

4. Build the index

python3 build_index.py --transcript-dir ./transcripts
# Creates: index.json

Usage

python3 query.py "your question here"

Results show the top 5 most relevant passages with relevance score and exact source.

Custom Index Location

python3 query.py --index-file my_index.json "what was the key insight?"

Rebuild After Adding Transcripts

python3 build_index.py --transcript-dir ./transcripts --chunk-size 400

Flags:

• --chunk-size N — Words per chunk (default 500). Lower = more specific, higher = more context.

How It Works

1. Index building reads .txt files and .vtt transcripts, chunks the text (~500 words per chunk by default), and builds a searchable index with TF-IDF vectors
2. Search converts your query to a TF-IDF vector and scores each chunk by cosine similarity
3. Results show top matches ranked by relevance (0–1 score), with the source file, chunk number, and excerpt

Example

$ python3 query.py "what are the key phases of pricing strategy"

Searching for: "what are the key phases of pricing strategy"
======================================================================

1.
[Text] Chapter 5 - Pricing Strategy (chunk 3)
Score: 0.834

The first phase involves understanding your baseline costs. The second phase 
focuses on market positioning relative to competitors. The third phase tests...

2.
[Video] Podcast Episode 42 - Pricing (Video - chunk 7)
Score: 0.721

Pricing in three steps: understand costs, know your market, test relentlessly...

======================================================================
Found 2 relevant passages. Use these cited excerpts in your research.

Input Formats

• .txt — Plain text transcripts (UTF-8)
• .vtt — WebVTT video transcripts (YouTube captions format)

Filenames become source titles automatically.

Performance

• Indexing: ~10–100 MB of text in <5 seconds
• Queries: ~0.1–1 second per search
• Index size: ~20–30% of original text

Limits & Tips

• Works best with Latin-script text (English, Spanish, French, etc.)
• TF-IDF may miss synonyms ("expensive" vs "costly") — for advanced semantic search, upgrade to sentence embeddings
• Large indexes (>100k chunks) slow down search — consider splitting into multiple indexes
• Chunk size balances specificity vs. context — start at 500 words, adjust based on your use case

No Copyright Built In

This tool is generic and format-agnostic. You provide your own transcripts, content, and materials. It ships with zero copyrighted content. Use only with:

• Books/audiobooks you own or have rights to analyze
• Your own podcasts, videos, or articles
• Public-domain or licensed content
• Transcripts you created

License

MIT. Free to use, modify, share.