polymath-cli 0.2.6

Ask anything about any codebase. Local, private, fast.

Polymath

Ask anything about any codebase. Local, private, fast.

pm cd https://github.com/pallets/click
pm ask "how does argument parsing work?"

Polymath indexes your code into a local vector database and answers questions in plain English with exact file citations and line numbers. Your code never leaves your machine.

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How it works

pm cd <repo>
  → crawl all files (respects .gitignore)
  → chunk code at 50-line boundaries
  → embed each chunk with Gemini embeddings
  → store vectors locally in Qdrant
  → store metadata in SQLite

pm ask "question"
  → embed the question
  → search local Qdrant for top 8 relevant chunks
  → fetch full chunk content from SQLite
  → inject context + conversation history into prompt
  → stream answer from Gemini 2.5 Flash or Ollama
  → save to conversation history

Embeddings and vector search run entirely on your machine. Only the question + relevant code snippets are sent to the LLM.

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Requirements

• Python 3.11+
• Gemini API key — free at aistudio.google.com
• For Ollama mode: Ollama installed and running

No Docker. No Postgres. No extra services.

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Install

Recommended

brew install pipx
pipx install polymath-cli
pm init

pip

pip install polymath-cli
pm init

Binary (no Python needed)

Download pm from GitHub Releases, then:

chmod +x pm
mv pm /usr/local/bin/pm
pm init

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First time setup

Run once:

pm init

This asks you to choose an AI provider:

Choose AI provider for answering questions (gemini/ollama): gemini
Enter your Gemini API key: ...
✓ API key valid
✓ Database ready
✓ Polymath ready!

Everything is stored in ~/.polymath/.

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AI providers

Gemini (default)

Uses Gemini 2.5 Flash for generation and Gemini embeddings for indexing.

• One API key, free at aistudio.google.com
• Free tier: 20 questions/day per key
• Enable billing for unlimited usage — costs ~$0 for personal use

pm init   # choose gemini

Ollama (fully offline)

Uses a local model for generation. No API key needed for questions after initial setup.

pm init   # choose ollama

Recommended models for code Q&A:

Model	Size	Best for
codellama	4GB	Code explanation, best quality
qwen2.5-coder:1.5b	1GB	Code focused, fast
mistral	4GB	General purpose, good at instructions
llama3.2:1b	1GB	Tiny and fast

ollama pull codellama
pm config OLLAMA_MODEL codellama
pm ask "how does authentication work?"

Note: Ollama mode still uses Gemini embeddings for indexing (pm cd). Embeddings only run once per repo — after that, everything is fully offline.

Switch providers anytime:

pm config LLM_PROVIDER ollama
pm config LLM_PROVIDER gemini
pm config OLLAMA_MODEL codellama
pm config GEMINI_API_KEY your-new-key

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Commands

Setup

pm init                    # first time setup
pm doctor                  # check database, Qdrant, Ollama health

Indexing

pm cd .                                     # index current directory
pm cd /path/to/project                      # index any local path
pm cd https://github.com/user/repo          # clone and index a public repo
pm refresh                                  # re-index the active repo
pm rm <repo-name>                           # remove an indexed repo
pm ls                                       # list all indexed repos
pm status                                   # show active repo and stats
pm pwd                                      # show active repo path

Asking questions

pm ask "how does authentication work?"
pm ask "where is the database connection set up?"
pm ask "what happens when a user signs up?"
pm explain src/auth.py                      # explain an entire file
pm diff                                     # explain the last git commit

Reading code

pm cat src/auth.py                          # read file with syntax highlighting
pm cat src/auth.py:23-45                    # read specific lines
pm find "jwt"                               # find all chunks containing a keyword

Conversation

pm history                                  # show conversation history
pm clear                                    # clear conversation history
pm save my-session                          # save conversation to markdown
pm export                                   # copy conversation to clipboard

Config

pm config LLM_PROVIDER ollama
pm config OLLAMA_MODEL codellama
pm config GEMINI_API_KEY your-key

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Architecture

polymath/
  pm/
    cli.py              # Typer commands
    agent/
      ask.py            # retrieval + LLM + streaming
    indexer/
      crawler.py        # file discovery, respects .gitignore
      chunker.py        # 50-line chunks with overlap
      indexer.py        # orchestrates indexing pipeline
    db/
      models.py         # SQLAlchemy models
      queries.py        # database operations
      database.py       # SQLite engine
    vector/
      store.py          # embedded Qdrant + Gemini embeddings
    utils/
      init.py           # pm init
      doctor.py         # pm doctor
      state.py          # active repo tracking

Data stored in ~/.polymath/:

~/.polymath/
  .env                  # config and API keys
  polymath.db           # SQLite database
  qdrant_data/          # local vector store
  repos/                # cloned GitHub repos

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Security

• Code stays local — indexing runs entirely on your machine
• Vectors stay local — Qdrant runs embedded, no server
• Only snippets leave — relevant code chunks sent to LLM for generation
• No accounts — no login, no tracking, no telemetry
• Private repos — clone locally, pm cd the path, nothing uploaded
• Ollama mode — 100% offline after initial indexing

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Troubleshooting

Python version error

Polymath requires Python 3.11+. Use pipx which handles this automatically:

brew install pipx
pipx install polymath-cli

Command not found after install

pipx ensurepath
source ~/.zshrc
pm --version

Ollama not responding

ollama serve           # start Ollama
ollama pull codellama  # make sure model is downloaded
pm doctor              # verify everything is green

Rate limit on Gemini free tier

Free tier allows 20 questions/day. Enable billing at aistudio.google.com for unlimited usage. Costs essentially nothing for personal use (~$0.15 per million tokens).

Alternatively switch to Ollama for unlimited offline usage:

pm config LLM_PROVIDER ollama

Re-initialize config

rm ~/.polymath/.env
pm init

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Roadmap

• Tree-sitter AST chunking — chunk at function/class boundaries
• Multi-query RAG Fusion — better retrieval quality
• VS Code extension
• Homebrew formula
• Web interface

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Contributing

git clone https://github.com/Samarthpande1510/Polymath
cd Polymath
uv sync
pm init
pm cd .
pm ask "how does the indexer work?"

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License

MIT