gemma-miner 0.1.3

An autonomous text-to-dataset agent — turns any website or document corpus into a typed, research-grade dataset in minutes. Works with Gemma, Llama, Gemini, GPT and any OpenAI-compatible model.

⛏ Gemma Miner

Turn any website or document corpus into a typed, research-grade dataset — in minutes, autonomously.

Gemma Miner is an autonomous agent that takes a one-sentence brief — "build me a stats-ready dataset of CNIL sanctions", "3 000 AI clinical trials", "every Hacker News 'Who is hiring' post mentioning RAG" — and produces a typed Parquet dataset with codebook, charts and HF-ready card.

It handles harvest → typed schema design → per-row extraction → self-verification → export to Parquet/CSV/HuggingFace in a single run. Works with Ollama (local Gemma 4 31B), OpenRouter, Together AI, Featherless — or any OpenAI-compatible endpoint.

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Why it exists

Most "scrape this site" tools give you a JSON dump of raw fields. That's not a dataset — it's a starting point. Gemma Miner closes the loop:

1. Read the source (HTML, JSON API, PDF / DOCX / XLSX / archives).
2. Design a codebook of 20–60 typed analytical variables — booleans, enums, integers, dates — appropriate for the corpus.
3. Extract every row through the codebook with deterministic type coercion (dates → ISO, enums snapped to nearest valid value, booleans null-when-silent, no placeholder stuffing).
4. Self-verify before declaring done. If verification fails, retry with corrective feedback.
5. Export to Parquet + CSV + a Markdown codebook, and optionally push to the Hugging Face Hub with a one-line command.

The result is a dataset you can drop into pandas, DuckDB or scikit-learn without a second cleaning pass.

See it in action

Two real datasets built end-to-end by Gemma Miner — click through to read their cards and load them:

Dataset	Rows × Cols	Source	Try it
🇫🇷 CNIL Sanctions 2011-2025	374 × 34	cnil.fr	load_dataset("moncefem/cnil-sanctions-2011-2025")
🧬 Clinical Trials of AI 2000-2025	3 000 × 30	clinicaltrials.gov	load_dataset("moncefem/clinical-trials-ai-2000-2025")

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Install

gemma-miner is a CLI tool, so the cleanest install is uv tool install (or pipx install) — it puts a gemma-miner binary on your PATH inside an isolated environment:

# recommended — installs as an isolated CLI tool
uv tool install gemma-miner

# with optional extras:
uv tool install "gemma-miner[parsers]"   # PDF / DOCX / XLSX / EPUB / archives
uv tool install "gemma-miner[hf]"        # huggingface_hub + datasets (for /push)
uv tool install "gemma-miner[analysis]"  # pandas + matplotlib + numpy
uv tool install "gemma-miner[all]"       # everything above

Then just run it from anywhere:

gemma-miner            # interactive REPL
gemma-miner configure  # re-run the setup wizard
gemma-miner --help     # full command list

Alternative installs:

Use case	Command
Try it once without installing	uv run --with gemma-miner gemma-miner
Add it as a library to an existing project	cd your-project && uv add gemma-miner
Plain pip (no uv)	pipx install gemma-miner

First launch (Claude-Code style REPL)

gemma-miner

On first launch you get a setup wizard that walks you through:

1. Pick a provider (ollama / openrouter / together / featherless).
2. Paste an API key (or skip for Ollama — fully local).
3. Pick a default model. For Ollama, the wizard shows the live list of models you have installed (queried from /api/tags).

Your choice is saved to ~/.config/gemma-miner/config.toml (chmod 600). Switch any time with /config inside the REPL, or gemma-miner configure from the shell.

Inside the REPL:

• Type / to see the live command palette (filters as you keep typing).
• Just type plain English to start a run: "build me a dataset of the top 100 Hacker News stories with id, title, points, comments."
• Multi-line prompts: end the first line with """ to open a heredoc, close with """.
• The agent runs with a live Rich activity feed showing every phase and per-row extraction progress.

What the REPL knows how to do

Slash command	What it does
/help	Full help panel
/config	Re-run the provider + API-key setup wizard
/datasets	List datasets produced under ./runs/
/workdir [<path>]	Show or change the base workdir
/provider [<name>]	Show or switch LLM provider (persisted)
/model [<id>]	Show or switch model (persisted per provider)
/gemma-full-local	Switch every phase to Ollama Gemma (auto-picks the largest installed Gemma)
/resume <path>	Resume a previous run — load its dataset + codebook + memory
/push <repo_id>	Push the last dataset to Hugging Face Hub
/history, /clear, /trace, /quit	Standard shell controls

After a run completes, the chat agent has the dataset in memory — ask follow-up questions like "which row had the most points?" or "summarise the breakdown by sector" and it answers from the data without triggering another scrape.

One-shot mode (no REPL)

# free-text prompt — Gemma Miner parses URL + count + fields automatically
gemma-miner "Build a dataset of every CNIL sanction from \
https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil with date, \
organisation type, breaches, decision text and 25 analytical variables."

# explicit flags for power users
gemma-miner run \
  --goal "Top 100 Hacker News stories" \
  --min-rows 100 \
  --required-fields rank,id,title,points \
  --unique-field id \
  --workdir ./runs/hn \
  --provider ollama \
  --model gemma4:31b

Python API

from gemma42 import (
    FieldsContract, MinRowsContract, UniqueFieldContract,
    make_llm, run_agent,
)

result = run_agent(
    goal=(
        "Build a dataset of the top 100 Hacker News stories using the public "
        "JSON API. Each row needs rank, id, title, domain, points."
    ),
    contracts=[
        MinRowsContract(min_rows=100),
        FieldsContract(required_fields=["rank", "id", "title", "points"]),
        UniqueFieldContract(field="id"),
    ],
    unique_key="id",
    workdir="./runs/hn",
    llm=make_llm("openrouter", model="google/gemini-3.1-flash-lite"),
)
print(result.dataset_path)

The Python module is still named gemma42 internally (the brand was previously gemma42); the PyPI package is gemma-miner. Both CLI commands (gemma-miner and gemma42) are equivalent.

Architecture

goal (one sentence)
    │
    ▼
┌─────────────────────────────────────────┐
│   AgentState (dataset + contracts +     │
│   memory + plan + workdir)              │
└─────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────┐
│   Phase machine (recomputed every turn  │
│   from observable state):               │
│                                         │
│   DISCOVER_LISTING → ENUMERATE →        │
│   DISCOVER_DETAIL → PROCESS →           │
│   CODEBOOK → EXTRACT → EXPORT → FINISH  │
└─────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────┐
│   One LLM call per turn → one tool      │
│   call (HTTP / HTML / Python / extract /│
│   codebook ops / dataset / queue / …)   │
└─────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────┐
│   Self-verification before finish.      │
│   On fail, re-enter the loop with the   │
│   verifier's feedback in the prompt.    │
└─────────────────────────────────────────┘

Key design choices:

• One tool call per turn. Each step is auditable; the trace is a flat JSONL of decisions.
• Re-rendered state brief every turn instead of chat history. No context drift, no stale observations.
• Phase-narrowed tool list. The model sees 5–8 relevant tools per turn, not 30 — small models behave dramatically better this way.
• Null-not-false discipline. Booleans are null when the source is silent. The system prompt forbids placeholder stuffing and the contract checks surface low-cardinality "constants" as evidence.
• Deterministic IDs. Bronze (raw harvest) and silver (typed extraction) join by stable content-hash id — re-runs converge.
• Hysteresis. Once the silver dataset is populated, the phase machine refuses to fall back into harvest for marginal gains.

Providers

Provider	What it gives you	Default model
Ollama	100 % local, no API key	gemma4:31b (wizard shows your installed models)
OpenRouter	Cheapest router for cloud models	google/gemini-3.1-flash-lite
Together AI	Fast OSS models	google/gemma-4-31b-it
Featherless	Serverless GPU for OSS models	google/gemma-4-31b-it
Anything else	Any OpenAI-compatible endpoint via --base-url	—

Run gemma-miner providers to print the full list.

Push to Hugging Face

After a run, push to a public dataset repo from inside the REPL:

› /push moncefem/my-cool-dataset
✓ uploaded → https://huggingface.co/datasets/moncefem/my-cool-dataset

Or from the shell:

gemma-miner export-hf ./runs/hn/dataset.jsonl --repo-id you/hn-top100

Needs HF_TOKEN (or HUGGINGFACE_HUB_TOKEN) in the environment and the hf extra installed.

Safety

• The bash and python tools refuse destructive operations (rm, dd, mkfs, sudo, fork bombs, …) at the tool layer.
• File operations are confined to the run's workdir.
• The config file is chmod 600 so API keys aren't readable by other users on a shared machine.

Don't run agent code on production boxes — use a container or VM.

Contributing

Bugs, ideas, and pull requests welcome at https://github.com/moncifem/gemma-miner.

The test suite runs offline:

uv pip install -e ".[dev]"
pytest -q

License

Apache License 2.0.

If you use Gemma Miner in a paper, project, or product, attribution to the upstream source and to Gemma Miner is appreciated:

@software{elmouden_gemma_miner_2025,
  title  = {Gemma Miner: an autonomous text-to-dataset agent},
  author = {EL-Mouden, Moncif and contributors},
  year   = {2025},
  url    = {https://github.com/moncifem/gemma-miner},
}

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⛏ Made with care by Moncif EL-Mouden. Powered by your favourite small open model.